Object Detection Evaluation 2012


The object detection and object orientation estimation benchmark consists of 7481 training images and 7518 test images, comprising a total of 80.256 labeled objects. All images are color and saved as png. For evaluation, we compute precision-recall curves for object detection and orientation-similarity-recall curves for joint object detection and orientation estimation. In the latter case not only the object 2D bounding box has to be located correctly, but also the orientation estimate in bird's eye view is evaluated. To rank the methods we compute average precision and average orientation similiarity. We require that all methods use the same parameter set for all test pairs. Our development kit provides details about the data format as well as MATLAB / C++ utility functions for reading and writing the label files.

We evaluate object detection performance using the PASCAL criteria and object detection and orientation estimation performance using the measure discussed in our CVPR 2012 publication. For cars we require an overlap of 70%, while for pedestrians and cyclists we require an overlap of 50% for a detection. Detections in don't care areas or detections which are smaller than the minimum size do not count as false positive. Difficulties are defined as follows:

  • Easy: Min. bounding box height: 40 Px, Max. occlusion level: Fully visible, Max. truncation: 15 %
  • Moderate: Min. bounding box height: 25 Px, Max. occlusion level: Partly occluded, Max. truncation: 30 %
  • Hard: Min. bounding box height: 25 Px, Max. occlusion level: Difficult to see, Max. truncation: 50 %

All methods are ranked based on the moderately difficult results. Note that for the hard evaluation ~2 % of the provided bounding boxes have not been recognized by humans, thereby upper bounding recall at 98 %. Hence, the hard evaluation is only given for reference.
Note 1: On 25.04.2017, we have fixed a bug in the object detection evaluation script. As of now, the submitted detections are filtered based on the min. bounding box height for the respective category which we have been done before only for the ground truth detections, thus leading to false positives for the category "Easy" when bounding boxes of height 25-39 Px were submitted (and to false positives for all categories if bounding boxes smaller than 25 Px were submitted). We like to thank Amy Wu, Matt Wilder, Pekka Jänis and Philippe Vandermersch for their feedback. The last leaderboards right before the changes can be found here!

Note 2: On 08.10.2019, we have followed the suggestions of the Mapillary team in their paper Disentangling Monocular 3D Object Detection and use 40 recall positions instead of the 11 recall positions proposed in the original Pascal VOC benchmark. This results in a more fair comparison of the results, please check their paper. The last leaderboards right before this change can be found here: Object Detection Evaluation, 3D Object Detection Evaluation, Bird's Eye View Evaluation.
Important Policy Update: As more and more non-published work and re-implementations of existing work is submitted to KITTI, we have established a new policy: from now on, only submissions with significant novelty that are leading to a peer-reviewed paper in a conference or journal are allowed. Minor modifications of existing algorithms or student research projects are not allowed. Such work must be evaluated on a split of the training set. To ensure that our policy is adopted, new users must detail their status, describe their work and specify the targeted venue during registration. Furthermore, we will regularly delete all entries that are 6 months old but are still anonymous or do not have a paper associated with them. For conferences, 6 month is enough to determine if a paper has been accepted and to add the bibliography information. For longer review cycles, you need to resubmit your results.
Additional information used by the methods
  • Stereo: Method uses left and right (stereo) images
  • Flow: Method uses optical flow (2 temporally adjacent images)
  • Multiview: Method uses more than 2 temporally adjacent images
  • Laser Points: Method uses point clouds from Velodyne laser scanner
  • Additional training data: Use of additional data sources for training (see details)

Car


Method Setting Code Moderate Easy Hard Runtime Environment
1 MB3D 97.87 % 98.77 % 93.04 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
2 LVP(84.92) 97.84 % 98.70 % 93.07 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
3 MM-UniTOD 97.69 % 98.78 % 94.62 % 0.04 s 1 core @ 2.5 Ghz (Python)
4 HPC-Net 97.59 % 98.61 % 93.01 % 0.18 s 1 core @ 2.5 Ghz (C/C++)
5 UDeerPEP code 97.57 % 98.42 % 95.08 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Z. Dong, H. Ji, X. Huang, W. Zhang, X. Zhan and J. Chen: PeP: a Point enhanced Painting method for unified point cloud tasks. 2023.
6 VirConv-S code 97.27 % 98.00 % 94.53 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, C. Wen, S. Shi and C. Wang: Virtual Sparse Convolution for Multimodal 3D Object Detection. CVPR 2023.
7 GraR-VoI code 96.38 % 96.81 % 91.20 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph. ECCV 2022.
8 VirConv-T code 96.38 % 98.93 % 93.56 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, C. Wen, S. Shi and C. Wang: Virtual Sparse Convolution for Multimodal 3D Object Detection. CVPR 2023.
9 DAF-SSD 96.28 % 96.95 % 88.75 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
10 LFT 96.27 % 99.29 % 88.94 % 0.1s 1 core @ 2.5 Ghz (C/C++)
11 GraR-Po code 96.18 % 96.84 % 91.11 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph. ECCV 2022.
12 SFD code 96.17 % 98.97 % 91.13 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
X. Wu, L. Peng, H. Yang, L. Xie, C. Huang, C. Deng, H. Liu and D. Cai: Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completion. CVPR 2022.
13 MLF-DET 96.17 % 96.89 % 88.90 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
Z. Lin, Y. Shen, S. Zhou, S. Chen and N. Zheng: MLF-DET: Multi-Level Fusion for Cross- Modal 3D Object Detection. International Conference on Artificial Neural Networks 2023.
14 VPFNet code 96.15 % 96.64 % 91.14 % 0.06 s 2 cores @ 2.5 Ghz (Python)
H. Zhu, J. Deng, Y. Zhang, J. Ji, Q. Mao, H. Li and Y. Zhang: VPFNet: Improving 3D Object Detection with Virtual Point based LiDAR and Stereo Data Fusion. IEEE Transactions on Multimedia 2022.
15 HDet3D 96.12 % 96.69 % 91.01 % 0.07 s >8 cores @ 2.5 Ghz (Python)
16 CLOCs code 96.07 % 96.77 % 91.11 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection . 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
17 ACFNet 96.06 % 96.68 % 93.36 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
Y. Tian, X. Zhang, X. Wang, J. Xu, J. Wang, R. Ai, W. Gu and W. Ding: ACF-Net: Asymmetric Cascade Fusion for 3D Detection With LiDAR Point Clouds and Images. IEEE Transactions on Intelligent Vehicles 2023.
18 PVFusion code 96.06 % 98.68 % 91.08 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
19 RDIoU code 96.05 % 98.79 % 91.03 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Sheng, S. Cai, N. Zhao, B. Deng, J. Huang, X. Hua, M. Zhao and G. Lee: Rethinking IoU-based Optimization for Single- stage 3D Object Detection. ECCV 2022.
20 GraR-Vo code 96.05 % 96.67 % 93.01 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph. ECCV 2022.
21 TED code 96.03 % 96.64 % 93.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, C. Wen, W. Li, R. Yang and C. Wang: Transformation-Equivariant 3D Object Detection for Autonomous Driving. AAAI 2023.
22 CLOCs_PVCas code 95.96 % 96.76 % 91.08 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection . 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
23 PVT-SSD 95.90 % 96.75 % 90.69 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Yang, W. Wang, M. Chen, B. Lin, T. He, H. Chen, X. He and W. Ouyang: PVT-SSD: Single-Stage 3D Object Detector with Point-Voxel Transformer. CVPR 2023.
24 GraR-Pi code 95.89 % 98.59 % 92.85 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph. ECCV 2022.
25 PIPC-3Ddet code 95.86 % 96.80 % 90.92 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
26 DiffCandiDet 95.85 % 96.59 % 93.03 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
27 OcTr 95.84 % 96.48 % 90.99 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
C. Zhou, Y. Zhang, J. Chen and D. Huang: OcTr: Octree-based Transformer for 3D Object Detection. CVPR 2023.
28 CDF 95.83 % 96.22 % 90.77 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
29 VPA 95.82 % 96.71 % 90.95 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
30 3D Dual-Fusion code 95.82 % 96.54 % 93.11 % 0.1 s 1 core @ 2.5 Ghz (Python)
Y. Kim, K. Park, M. Kim, D. Kum and J. Choi: 3D Dual-Fusion: Dual-Domain Dual-Query Camera-LiDAR Fusion for 3D Object Detection. arXiv preprint arXiv:2211.13529 2022.
31 NIV-SSD 95.82 % 98.68 % 90.80 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
32 URFormer 95.81 % 98.52 % 93.03 % 0.1 s 1 core @ 2.5 Ghz (Python)
33 GLENet-VR code 95.81 % 96.85 % 90.91 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Zhang, Z. Zhu, J. Hou and Y. Yuan: GLENet: Boosting 3D object detectors with generative label uncertainty estimation. International Journal of Computer Vision 2023.
34 TSSTDet 95.81 % 96.65 % 93.05 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
35 test 95.80 % 98.39 % 92.86 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
36 DVF-V 95.77 % 96.60 % 90.89 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
A. Mahmoud, J. Hu and S. Waslander: Dense Voxel Fusion for 3D Object Detection. WACV 2023.
37 HAF-PVP_test 95.76 % 98.86 % 92.95 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
38 Fast-CLOCs 95.75 % 96.69 % 90.95 % 0.1 s GPU @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: Fast-CLOCs: Fast Camera-LiDAR Object Candidates Fusion for 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022.
39 SoRL 95.74 % 96.79 % 90.83 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
40 3D HANet code 95.73 % 98.61 % 92.96 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Q. Xia, Y. Chen, G. Cai, G. Chen, D. Xie, J. Su and Z. Wang: 3D HANet: A Flexible 3D Heatmap Auxiliary Network for Object Detection. IEEE Transactions on Geoscience and Remote Sensing 2023.
41 MAK code 95.71 % 96.68 % 90.86 % 0.03 s GPU @ 2.5 Ghz (Python)
42 DSGN++
This method uses stereo information.
code 95.70 % 98.08 % 88.27 % 0.2 s GeForce RTX 2080Ti
Y. Chen, S. Huang, S. Liu, B. Yu and J. Jia: DSGN++: Exploiting Visual-Spatial Relation for Stereo-Based 3D Detectors. IEEE Transactions on Pattern Analysis and Machine Intelligence 2022.
43 MAK_VOXEL_RCNN 95.67 % 98.63 % 92.97 % 0.03 s 1 core @ 2.5 Ghz (Python)
44 LVP 95.67 % 98.48 % 92.88 % 0.04 s 1 core @ 2.5 Ghz (Python)
45 GENet 95.63 % 96.53 % 90.82 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
46 CasA code 95.62 % 96.52 % 92.86 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.
47 BADet code 95.61 % 98.75 % 90.64 % 0.14 s 1 core @ 2.5 Ghz (C/C++)
R. Qian, X. Lai and X. Li: BADet: Boundary-Aware 3D Object Detection from Point Clouds. Pattern Recognition 2022.
48 SE-SSD
This method makes use of Velodyne laser scans.
code 95.60 % 96.69 % 90.53 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, L. Jiang and C. Fu: SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud. CVPR 2021.
49 SGFNet 95.60 % 98.46 % 92.74 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
50 FEIF3D
This method makes use of Velodyne laser scans.
95.59 % 96.44 % 92.95 % 0.1 s GPU @ 2.5 Ghz (Python)
51 PA-Det3D 95.59 % 96.37 % 90.97 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
52 PSMS-Net
This method makes use of Velodyne laser scans.
95.58 % 96.70 % 90.76 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
53 VDF 95.58 % 98.58 % 92.72 % 0.03 s GPU @ 2.5 Ghz (Python)
54 spark2 95.58 % 96.41 % 92.87 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
55 FARP-Net code 95.57 % 96.11 % 93.07 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
T. Xie, L. Wang, K. Wang, R. Li, X. Zhang, H. Zhang, L. Yang, H. Liu and J. Li: FARP-Net: Local-Global Feature Aggregation and Relation-Aware Proposals for 3D Object Detection. IEEE Transactions on Multimedia 2023.
56 FEMV-RCNN code 95.56 % 96.27 % 90.87 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
57 CAFI-Pillars 95.56 % 96.47 % 90.75 % 30ms NVIDIA Tesla P40 GPU
58 voxel_spark code 95.55 % 96.38 % 92.86 % 0.04 s GPU @ 2.5 Ghz (C/C++)
59 LoGoNet code 95.55 % 96.60 % 93.07 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
X. Li, T. Ma, Y. Hou, B. Shi, Y. Yang, Y. Liu, X. Wu, Q. Chen, Y. Li, Y. Qiao and others: LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross-Modal Fusion. CVPR 2023.
60 spark_voxel_rcnn code 95.55 % 96.41 % 92.84 % 0.04 s 1 core @ 2.5 Ghz (Python)
61 GD-MAE 95.54 % 98.38 % 90.42 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Yang, T. He, J. Liu, H. Chen, B. Wu, B. Lin, X. He and W. Ouyang: GD-MAE: Generative Decoder for MAE Pre- training on LiDAR Point Clouds. CVPR 2023.
62 spark 95.53 % 96.34 % 92.84 % 0.1 s 1 core @ 2.5 Ghz (Python)
63 DVF-PV 95.49 % 96.42 % 92.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
A. Mahmoud, J. Hu and S. Waslander: Dense Voxel Fusion for 3D Object Detection. WACV 2023.
64 SFD++ 95.48 % 98.40 % 92.61 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
65 SS-3DSSD code 95.47 % 96.31 % 90.55 % 0.014s 1 core @ 2.5 Ghz (C/C++)
66 3D-BCM 95.47 % 98.50 % 92.70 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
67 SPANet 95.46 % 96.54 % 90.47 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
Y. Ye: SPANet: Spatial and Part-Aware Aggregation Network for 3D Object Detection. Pacific Rim International Conference on Artificial Intelligence 2021.
68 Voxel_Spark_focal_we code 95.45 % 96.37 % 92.77 % 0.08 s 1 core @ 2.5 Ghz (Python)
69 Anonymous 95.44 % 96.41 % 92.85 % 0.1 s 1 core @ 2.5 Ghz (Python)
70 LGNet-Car code 95.43 % 96.52 % 92.73 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
71 DSASA code 95.40 % 96.14 % 92.46 % 0.05s GPU @ >3.5 Ghz (Python)
72 PG-RCNN code 95.40 % 96.66 % 90.55 % 0.06 s GPU @ 1.5 Ghz (Python)
I. Koo, I. Lee, S. Kim, H. Kim, W. Jeon and C. Kim: PG-RCNN: Semantic Surface Point Generation for 3D Object Detection. 2023.
73 Multi-Weights 95.39 % 96.38 % 90.60 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
74 Simi-fusion 95.38 % 98.35 % 92.87 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
75 SASA
This method makes use of Velodyne laser scans.
code 95.35 % 96.01 % 92.53 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
C. Chen, Z. Chen, J. Zhang and D. Tao: SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object Detection. arXiv preprint arXiv:2201.01976 2022.
76 TED-S Reproduced 95.33 % 98.45 % 92.75 % 0.1 s 1 core @ 2.5 Ghz (Python)
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77 SPG_mini
This method makes use of Velodyne laser scans.
code 95.32 % 96.23 % 92.68 % 0.09 s GPU @ 2.5 Ghz (Python)
Q. Xu, Y. Zhou, W. Wang, C. Qi and D. Anguelov: SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation. Proceedings of the IEEE conference on computer vision and pattern recognition (ICCV) 2021.
78 EQ-PVRCNN code 95.32 % 98.23 % 92.65 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, L. Jiang, Y. Sun, B. Schiele and J. Jia: A Unified Query-based Paradigm for Point Cloud Understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.
79 Focals Conv code 95.28 % 96.30 % 92.69 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Y. Chen, Y. Li, X. Zhang, J. Sun and J. Jia: Focal Sparse Convolutional Networks for 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.
80 CasA++ code 95.28 % 95.83 % 94.28 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.
81 RPF3D 95.25 % 96.31 % 90.51 % 0.1 s 1 core @ 2.5 Ghz (Python)
82 VoxSeT code 95.23 % 96.16 % 90.49 % 33 ms 1 core @ 2.5 Ghz (C/C++)
C. He, R. Li, S. Li and L. Zhang: Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds. CVPR 2022.
83 LGSLNet 95.22 % 98.00 % 92.72 % 0.1 s GPU @ 2.5 Ghz (Python)
84 PC-CNN-V2
This method makes use of Velodyne laser scans.
95.20 % 96.06 % 89.37 % 0.5 s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Du, M. Ang, S. Karaman and D. Rus: A General Pipeline for 3D Detection of Vehicles. 2018 IEEE International Conference on Robotics and Automation (ICRA) 2018.
85 R^2 R-CNN 95.19 % 96.38 % 92.57 % 0.1 s 1 core @ 2.5 Ghz (Python)
86 PR-SSD 95.18 % 97.64 % 92.48 % 0.02 s GPU @ 2.5 Ghz (Python)
87 VPFNet code 95.17 % 96.06 % 92.66 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
C. Wang, H. Chen and L. Fu: VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection. 2021.
88 F-PointNet
This method makes use of Velodyne laser scans.
code 95.17 % 95.85 % 85.42 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
89 EPNet++ 95.17 % 96.73 % 92.10 % 0.1 s GPU @ 2.5 Ghz (Python)
Z. Liu, T. Huang, B. Li, X. Chen, X. Wang and X. Bai: EPNet++: Cascade Bi-Directional Fusion for Multi-Modal 3D Object Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 2022.
90 SA-SSD code 95.16 % 97.92 % 90.15 % 0.04 s 1 core @ 2.5 Ghz (Python)
C. He, H. Zeng, J. Huang, X. Hua and L. Zhang: Structure Aware Single-stage 3D Object Detection from Point Cloud. CVPR 2020.
91 HMFI code 95.16 % 96.29 % 92.45 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
X. Li, B. Shi, Y. Hou, X. Wu, T. Ma, Y. Li and L. He: Homogeneous Multi-modal Feature Fusion and Interaction for 3D Object Detection. ECCV 2022.
92 GS-FPS code 95.15 % 95.83 % 92.39 % TBD s 1 core @ 2.5 Ghz (C/C++)
93 RBEV-Voxel code 95.15 % 96.43 % 90.32 % 0.08 s GPU @ 2.5 Ghz (Python)
94 USVLab BSAODet code 95.15 % 96.26 % 92.62 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
W. Xiao, Y. Peng, C. Liu, J. Gao, Y. Wu and X. Li: Balanced Sample Assignment and Objective for Single-Model Multi-Class 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2023.
95 TEDx 95.14 % 96.15 % 92.45 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
96 Pyramid R-CNN 95.13 % 95.88 % 92.62 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
J. Mao, M. Niu, H. Bai, X. Liang, H. Xu and C. Xu: Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection. ICCV 2021.
97 Voxel R-CNN code 95.11 % 96.49 % 92.45 % 0.04 s GPU @ 3.0 Ghz (C/C++)
J. Deng, S. Shi, P. Li, W. Zhou, Y. Zhang and H. Li: Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection . AAAI 2021.
98 3DSSD code 95.10 % 97.69 % 92.18 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
99 DTSSD 95.08 % 95.96 % 90.50 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
100 GF-pointnet 95.08 % 95.93 % 92.36 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
101 Anonymous 95.07 % 96.37 % 92.44 % 0.1 s GPU @ 2.5 Ghz (Python)
102 RAFDet 95.04 % 95.96 % 92.41 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
103 MonoSample (DID-M3D) 95.02 % 96.45 % 85.58 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
104 BSH-Det3D 95.02 % 96.34 % 92.36 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
105 HybridPillars 95.01 % 96.15 % 92.35 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
106 PDV code 95.00 % 96.07 % 92.44 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Hu, T. Kuai and S. Waslander: Point Density-Aware Voxels for LiDAR 3D Object Detection. CVPR 2022.
107 MVRA + I-FRCNN+ 94.98 % 95.87 % 82.52 % 0.18 s GPU @ 2.5 Ghz (Python)
H. Choi, H. Kang and Y. Hyun: Multi-View Reprojection Architecture for Orientation Estimation. The IEEE International Conference on Computer Vision (ICCV) Workshops 2019.
108 SIENet code 94.97 % 96.02 % 92.40 % 0.08 s 1 core @ 2.5 Ghz (Python)
Z. Li, Y. Yao, Z. Quan, W. Yang and J. Xie: SIENet: Spatial Information Enhancement Network for 3D Object Detection from Point Cloud. 2021.
109 VoTr-TSD code 94.94 % 95.97 % 92.44 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
J. Mao, Y. Xue, M. Niu, H. Bai, J. Feng, X. Liang, H. Xu and C. Xu: Voxel Transformer for 3D Object Detection. ICCV 2021.
110 L-AUG 94.92 % 95.84 % 92.22 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
T. Cortinhal, I. Gouigah and E. Aksoy: Semantics-aware LiDAR-Only Pseudo Point Cloud Generation for 3D Object Detection. 2023.
111 AMVFNet code 94.87 % 96.12 % 92.33 % 0.04 s GPU @ 2.5 Ghz (Python)
112 GraphAlign 94.87 % 98.06 % 92.47 % 0.03 s GPU @ 2.0 Ghz (Python)
113 CZY_PPF_Net 94.86 % 98.07 % 92.22 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
114 M3DeTR code 94.83 % 97.39 % 92.10 % n/a s GPU @ 1.0 Ghz (Python)
T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis and D. Manocha: M3DeTR: Multi-representation, Multi- scale, Mutual-relation 3D Object Detection with Transformers. 2021.
115 StructuralIF 94.81 % 96.14 % 92.12 % 0.02 s 8 cores @ 2.5 Ghz (Python)
J. Pei An: Deep structural information fusion for 3D object detection on LiDAR-camera system. Accepted in CVIU 2021.
116 spark_second_focal_w 94.80 % 95.45 % 92.02 % 0.1 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
117 Rnet 94.80 % 95.98 % 92.22 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
118 Under Blind Review#1 94.79 % 95.63 % 92.35 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
119 U_PV_V2_ep100_80 94.78 % 95.82 % 92.27 % 0... s 1 core @ 2.5 Ghz (Python)
120 DCAF code 94.78 % 96.31 % 90.20 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
121 focalnet 94.78 % 98.07 % 92.32 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
122 XView 94.77 % 95.89 % 92.23 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
123 U_PV_V2_ep_100_100 94.76 % 95.76 % 92.18 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
124 Spark_partA22 94.76 % 96.00 % 92.09 % 10 s 1 core @ 2.5 Ghz (Python)
125 LGNet-3classes code 94.76 % 98.13 % 92.15 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
126 focalnet 94.75 % 98.09 % 92.31 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
127 F3D 94.75 % 95.99 % 92.28 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
128 HA-PillarNet 94.75 % 95.91 % 92.16 % 0.05 s 1 core @ 2.5 Ghz (Python)
129 Spark_PartA2_Soft_fo code 94.74 % 95.80 % 92.13 % 0.1 s 1 core @ 2.5 Ghz (Python)
130 PV-PMRTNet 94.73 % 96.03 % 92.19 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
131 P2V-RCNN 94.73 % 96.03 % 92.34 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.
132 spark_second code 94.72 % 95.40 % 91.93 % . s 1 core @ 2.5 Ghz (Python)
133 sec_spark code 94.71 % 95.37 % 91.93 % 0.03 s GPU @ 2.5 Ghz (Python)
134 spark_second2 94.71 % 95.33 % 91.97 % 10 s 1 core @ 2.5 Ghz (Python)
135 SPG
This method makes use of Velodyne laser scans.
code 94.71 % 97.80 % 92.19 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Xu, Y. Zhou, W. Wang, C. Qi and D. Anguelov: SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation. Proceedings of the IEEE conference on computer vision and pattern recognition (ICCV) 2021.
136 CAT-Det 94.71 % 95.97 % 92.07 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Zhang, J. Chen and D. Huang: CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection. CVPR 2022.
137 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 94.70 % 98.17 % 92.04 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
138 spark-part2 94.69 % 95.71 % 92.09 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
139 OA-TSSD 94.68 % 95.43 % 91.91 % 20 s 8 cores @ 2.5 Ghz (C/C++)
140 OFFNet 94.68 % 96.18 % 92.07 % 0.1 s GPU @ 2.5 Ghz (Python)
141 SVGA-Net 94.67 % 96.05 % 91.86 % 0.03s 1 core @ 2.5 Ghz (Python + C/C++)
Q. He, Z. Wang, H. Zeng, Y. Zeng and Y. Liu: SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds. AAAI 2022.
142 RangeDet (Official) code 94.64 % 95.50 % 91.77 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
L. Fan, X. Xiong, F. Wang, N. Wang and Z. Zhang: RangeDet: In Defense of Range View for LiDAR-Based 3D Object Detection. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
143 U_VOXELRCNN_CAR_V3 94.64 % 96.02 % 91.94 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
144 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
code 94.64 % 95.86 % 92.10 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
145 PV-RCNN-Plus 94.63 % 95.76 % 92.17 % 1 s 1 core @ 2.5 Ghz (C/C++)
146 RangeIoUDet
This method makes use of Velodyne laser scans.
94.61 % 95.74 % 91.98 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, Z. Zhang, M. Zhang, X. Zhao and S. Pu: RangeIoUDet: Range Image Based Real-Time 3D Object Detector Optimized by Intersection Over Union. CVPR 2021.
147 PASS-PV-RCNN-Plus 94.59 % 95.79 % 92.10 % 1 s 1 core @ 2.5 Ghz (Python)
Anonymous: Leveraging Anchor-based LiDAR 3D Object Detection via Point Assisted Sample Selection. will submit to computer vision conference/journal 2024.
148 af 94.59 % 95.79 % 92.17 % 1 s GPU @ 2.5 Ghz (Python)
149 DVFENet 94.57 % 95.35 % 91.77 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection. Neurocomputing 2021.
150 GeVo 94.55 % 95.89 % 92.03 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
151 AAMVFNet code 94.53 % 95.89 % 91.98 % 0.04 s GPU @ 2.5 Ghz (Python)
152 TuSimple code 94.47 % 95.12 % 86.45 % 1.6 s GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016.
153 EPNet code 94.44 % 96.15 % 89.99 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
T. Huang, Z. Liu, X. Chen and X. Bai: EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection. ECCV 2020.
154 SERCNN
This method makes use of Velodyne laser scans.
94.42 % 96.33 % 89.96 % 0.1 s 1 core @ 2.5 Ghz (Python)
D. Zhou, J. Fang, X. Song, L. Liu, J. Yin, Y. Dai, H. Li and R. Yang: Joint 3D Instance Segmentation and Object Detection for Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020.
155 focal 94.41 % 98.28 % 92.09 % 100 s 1 core @ 2.5 Ghz (Python)
156 MSIT-Det 94.39 % 97.21 % 86.85 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
157 RealSynthesis-SECOND 94.34 % 95.14 % 91.34 % 0.05 s 1 core @ 2.5 Ghz (Python)
158 UberATG-MMF
This method makes use of Velodyne laser scans.
94.25 % 97.41 % 89.87 % 0.08 s GPU @ 2.5 Ghz (Python)
M. Liang*, B. Yang*, Y. Chen, R. Hu and R. Urtasun: Multi-Task Multi-Sensor Fusion for 3D Object Detection. CVPR 2019.
159 pointpillar_spark_fo 94.24 % 96.44 % 91.33 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
160 SRDL 94.24 % 95.86 % 91.80 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
161 SC-SSD 94.19 % 95.06 % 91.17 % 1 s 1 core @ 2.5 Ghz (C/C++)
162 u_second_v4_epoch_10 94.13 % 95.33 % 91.36 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
163 STNet 94.10 % 95.75 % 91.51 % 0.60 s 1 core @ 2.5 Ghz (Python)
164 pointpillars_spark code 94.04 % 96.88 % 91.17 % 0.02 s GPU @ 2.5 Ghz (C/C++)
165 RangeRCNN
This method makes use of Velodyne laser scans.
94.03 % 95.48 % 91.74 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, M. Zhang, Z. Zhang, X. Zhao and S. Pu: RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representation. arXiv preprint arXiv:2009.00206 2020.
166 Faraway-Frustum
This method makes use of Velodyne laser scans.
code 93.99 % 95.81 % 91.72 % 0.1 s GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-frustum: Dealing with lidar sparsity for 3D object detection using fusion. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) 2021.
167 DD3D code 93.99 % 94.69 % 89.37 % n/a s 1 core @ 2.5 Ghz (C/C++)
D. Park, R. Ambrus, V. Guizilini, J. Li and A. Gaidon: Is Pseudo-Lidar needed for Monocular 3D Object detection?. IEEE/CVF International Conference on Computer Vision (ICCV) .
168 spark_pointpillar code 93.98 % 96.88 % 91.11 % 0.02 s GPU @ 2.5 Ghz (Python)
169 spark_pointpillar2 93.97 % 96.66 % 91.03 % 10 s 1 core @ 2.5 Ghz (Python)
170 U_second_v4_ep_100_8 93.96 % 94.85 % 91.14 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
171 SIF 93.95 % 95.51 % 91.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
172 DDF 93.95 % 96.90 % 88.91 % 0.1 s 1 core @ 2.5 Ghz (Python)
173 Anonymous code 93.90 % 96.83 % 88.84 % 0.04 s 1 core @ 2.5 Ghz (Python)
174 MGAF-3DSSD code 93.87 % 94.45 % 86.37 % 0.1 s 1 core @ 2.5 Ghz (Python)
J. Li, H. Dai, L. Shao and Y. Ding: Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
175 3ONet 93.87 % 96.97 % 88.84 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
176 LPCG-Monoflex code 93.86 % 96.90 % 83.94 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
L. Peng, F. Liu, Z. Yu, S. Yan, D. Deng, Z. Yang, H. Liu and D. Cai: Lidar Point Cloud Guided Monocular 3D Object Detection. ECCV 2022.
177 MMLAB LIGA-Stereo
This method uses stereo information.
code 93.82 % 96.43 % 86.19 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
178 Sem-Aug
This method makes use of Velodyne laser scans.
93.77 % 96.79 % 88.78 % 0.1 s GPU @ 2.5 Ghz (Python)
L. Zhao, M. Wang and Y. Yue: Sem-Aug: Improving Camera-LiDAR Feature Fusion With Semantic Augmentation for 3D Vehicle Detection. IEEE Robotics and Automation Letters 2022.
179 DGEnhCL code 93.76 % 96.77 % 90.84 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
180 IMLIDAR(base) 93.75 % 96.76 % 88.78 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
181 Patches - EMP
This method makes use of Velodyne laser scans.
93.75 % 97.91 % 90.56 % 0.5 s GPU @ 2.5 Ghz (Python)
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
182 SoRL-V 93.73 % 96.87 % 88.73 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
183 CIA-SSD
This method makes use of Velodyne laser scans.
code 93.72 % 96.87 % 86.20 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, S. Chen, L. Jiang and C. Fu: CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud. AAAI 2021.
184 IIOU code 93.72 % 96.45 % 90.91 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
185 QD-3DT
This is an online method (no batch processing).
code 93.66 % 94.26 % 83.63 % 0.03 s GPU @ 2.5 Ghz (Python)
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.
186 MVAF-Net code 93.66 % 95.37 % 90.90 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
G. Wang, B. Tian, Y. Zhang, L. Chen, D. Cao and J. Wu: Multi-View Adaptive Fusion Network for 3D Object Detection. arXiv preprint arXiv:2011.00652 2020.
187 SSL-PointGNN code 93.65 % 96.61 % 88.53 % 0.56 s GPU @ 1.5 Ghz (Python)
E. Erçelik, E. Yurtsever, M. Liu, Z. Yang, H. Zhang, P. Topçam, M. Listl, Y. Çaylı and A. Knoll: 3D Object Detection with a Self-supervised Lidar Scene Flow Backbone. arXiv preprint arXiv:2205.00705 2022.
188 PA3DNet 93.62 % 96.57 % 88.65 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
M. Wang, L. Zhao and Y. Yue: PA3DNet: 3-D Vehicle Detection with Pseudo Shape Segmentation and Adaptive Camera- LiDAR Fusion. IEEE Transactions on Industrial Informatics 2023.
189 Anonymous 93.57 % 96.59 % 88.64 % 0.1 s GPU @ 2.5 Ghz (Python)
190 Anonymous 93.57 % 96.66 % 90.87 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
191 IA-SSD (multi) code 93.56 % 96.10 % 90.68 % 0.014 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
192 MonoLiG code 93.56 % 96.70 % 83.74 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
A. Hekimoglu, M. Schmidt and A. Ramiro: Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learning. 2023.
193 MonoPair 93.55 % 96.61 % 83.55 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
194 IA-SSDx 93.55 % 96.67 % 90.88 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
195 casx 93.55 % 96.67 % 90.88 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
196 MMAE
This method makes use of Velodyne laser scans.
93.55 % 96.52 % 90.53 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
197 IA-SSD (single) code 93.54 % 96.26 % 88.49 % 0.013 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
198 EBM3DOD code 93.54 % 96.81 % 88.33 % 0.12 s 1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.
199 fuf 93.53 % 96.74 % 88.43 % 10 s 1 core @ 2.5 Ghz (C/C++)
200 casxv1 93.52 % 96.67 % 90.85 % 0.01 s 1 core @ 2.5 Ghz (Python)
201 TADP 93.51 % 96.83 % 88.28 % 0.04 s GPU @ 2.5 Ghz (Python)
202 SeSame-voxel code 93.50 % 95.22 % 90.44 % N/A s TITAN RTX @ 1.35 Ghz (Python)
203 Deep MANTA 93.50 % 98.89 % 83.21 % 0.7 s GPU @ 2.5 Ghz (Python + C/C++)
F. Chabot, M. Chaouch, J. Rabarisoa, C. Teulière and T. Chateau: Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image. CVPR 2017.
204 Point-GNN
This method makes use of Velodyne laser scans.
code 93.50 % 96.58 % 88.35 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
205 BtcDet
This method makes use of Velodyne laser scans.
code 93.47 % 96.23 % 88.55 % 0.09 s GPU @ 2.5 Ghz (Python + C/C++)
Q. Xu, Y. Zhong and U. Neumann: Behind the Curtain: Learning Occluded Shapes for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2022.
206 MOPNet code 93.47 % 96.57 % 83.62 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
207 LVFSD 93.45 % 95.28 % 90.73 % 0.06 s
ERROR: Wrong syntax in BIBTEX file.
208 Struc info fusion II 93.45 % 96.72 % 88.31 % 0.05 s GPU @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.
209 EBM3DOD baseline code 93.45 % 96.72 % 88.25 % 0.05 s 1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.
210 IOUFusion 93.43 % 96.38 % 90.63 % 0.1 s GPU @ 2.5 Ghz (Python)
211 StereoDistill 93.43 % 97.61 % 87.71 % 0.4 s 1 core @ 2.5 Ghz (Python)
Z. Liu, X. Ye, X. Tan, D. Errui, Y. Zhou and X. Bai: StereoDistill: Pick the Cream from LiDAR for Distilling Stereo-based 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2023.
212 MonoLSS 93.42 % 96.19 % 83.62 % 0.04 s 1 core @ 2.5 Ghz (Python)
Z. Li, J. Jia and Y. Shi: MonoLSS: Learnable Sample Selection For Monocular 3D Detection. International Conference on 3D Vision 2024.
213 RRC code 93.40 % 95.68 % 87.37 % 3.6 s GPU @ 2.5 Ghz (C/C++)
J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y. Tai and L. Xu: Accurate Single Stage Detector Using Recurrent Rolling Convolution. CVPR 2017.
214 3D-CVF at SPA
This method makes use of Velodyne laser scans.
code 93.36 % 96.78 % 86.11 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
J. Yoo, Y. Kim, J. Kim and J. Choi: 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection. ECCV 2020.
215 IIOU_LDR code 93.33 % 96.33 % 88.22 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
216 RAFDet code 93.33 % 95.89 % 90.51 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
217 SNVC
This method uses stereo information.
code 93.32 % 96.33 % 85.81 % 1 s GPU @ 1.0 Ghz (Python)
S. Li, Z. Liu, Z. Shen and K. Cheng: Stereo Neural Vernier Caliper. Proceedings of the AAAI Conference on Artificial Intelligence 2022.
218 DFAF3D 93.32 % 96.58 % 90.24 % 0.05 s 1 core @ 2.5 Ghz (Python)
Q. Tang, X. Bai, J. Guo, B. Pan and W. Jiang: DFAF3D: A dual-feature-aware anchor-free single-stage 3D detector for point clouds. Image and Vision Computing 2023.
219 PVTr 93.32 % 94.71 % 90.82 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
220 Struc info fusion I 93.31 % 96.59 % 88.23 % 0.05 s 1 core @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.
221 ROT_S3D 93.30 % 96.28 % 88.20 % 0.1 s GPU @ 2.5 Ghz (Python)
222 CityBrainLab-CT3D code 93.30 % 96.28 % 90.58 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Sheng, S. Cai, Y. Liu, B. Deng, J. Huang, X. Hua and M. Zhao: Improving 3D Object Detection with Channel- wise Transformer. ICCV 2021.
223 MonoInsight 93.29 % 96.21 % 83.59 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
224 MonoInsight 93.29 % 96.21 % 83.59 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
225 DTSSD 93.25 % 95.85 % 90.42 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
226 HybridPillars (SSD) 93.23 % 95.84 % 88.07 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
227 STD code 93.22 % 96.14 % 90.53 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
228 DA-Net 93.22 % 96.59 % 90.71 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
229 SARPNET 93.21 % 96.07 % 88.09 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. Ye, H. Chen, C. Zhang, X. Hao and Z. Zhang: SARPNET: Shape Attention Regional Proposal Network for LiDAR-based 3D Object Detection. Neurocomputing 2019.
230 H^23D R-CNN code 93.20 % 96.20 % 90.55 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
J. Deng, W. Zhou, Y. Zhang and H. Li: From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2021.
231 Fast Point R-CNN
This method makes use of Velodyne laser scans.
93.18 % 96.13 % 87.68 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, S. Liu, X. Shen and J. Jia: Fast Point R-CNN. Proceedings of the IEEE international conference on computer vision (ICCV) 2019.
232 RAFDet 93.18 % 95.70 % 90.40 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
233 sensekitti code 93.17 % 94.79 % 84.38 % 4.5 s GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.
234 P2P code 93.14 % 96.67 % 88.01 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
235 SJTU-HW 93.11 % 96.30 % 82.21 % 0.85s GPU @ 1.5 Ghz (Python + C/C++)
S. Zhang, X. Zhao, L. Fang, F. Haiping and S. Haitao: LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR. IEEE International Conference on Image Processing 2018.
L. Fang, X. Zhao and S. Zhang: Small-objectness sensitive detection based on shifted single shot detector. Multimedia Tools and Applications 2018.
236 FromVoxelToPoint code 93.06 % 96.08 % 90.53 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to- Point Decoder. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
237 GA-RCNN 93.03 % 96.10 % 90.41 % 47ms 1 core @ 2.5 Ghz (C/C++)
238 MG 93.01 % 96.27 % 90.15 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
239 CLOCs_SecCas 92.95 % 95.43 % 89.21 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
240 WA 92.93 % 96.02 % 87.41 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
241 MonoCD 92.91 % 96.43 % 85.55 % n/a s 1 core @ 2.5 Ghz (Python)
242 FastDet 92.84 % 97.88 % 89.66 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
243 ACDet code 92.84 % 96.18 % 89.83 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
J. Xu, G. Wang, X. Zhang and G. Wan: ACDet: Attentive Cross-view Fusion for LiDAR-based 3D Object Detection. 3DV 2022.
244 HotSpotNet 92.81 % 96.21 % 89.80 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
245 PEF code 92.81 % 96.09 % 87.99 % N/A s 1 core @ 2.5 Ghz (C/C++)
246 MSAW 92.77 % 95.69 % 88.01 % 0.42 s 2 cores @ 2.5 Ghz (Python)
247 SegVoxelNet 92.73 % 96.00 % 87.60 % 0.04 s 1 core @ 2.5 Ghz (Python)
H. Yi, S. Shi, M. Ding, J. Sun, K. Xu, H. Zhou, Z. Wang, S. Li and G. Wang: SegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloud. ICRA 2020.
248 Patches
This method makes use of Velodyne laser scans.
92.72 % 96.34 % 87.63 % 0.15 s GPU @ 2.0 Ghz
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
249 Cube R-CNN code 92.72 % 95.78 % 84.81 % 0.05 s GPU @ 2.5 Ghz (Python)
G. Brazil, A. Kumar, J. Straub, N. Ravi, J. Johnson and G. Gkioxari: Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild. CVPR 2023.
250 CenterNet3D 92.69 % 95.76 % 89.81 % 0.04 s GPU @ 1.5 Ghz (Python)
G. Wang, B. Tian, Y. Ai, T. Xu, L. Chen and D. Cao: CenterNet3D:An Anchor free Object Detector for Autonomous Driving. 2020.
251 R-GCN 92.67 % 96.19 % 87.66 % 0.16 s GPU @ 2.5 Ghz (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
252 PI-RCNN 92.66 % 96.17 % 87.68 % 0.1 s 1 core @ 2.5 Ghz (Python)
L. Xie, C. Xiang, Z. Yu, G. Xu, Z. Yang, D. Cai and X. He: PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module. AAAI 2020 : The Thirty-Fourth AAAI Conference on Artificial Intelligence 2020.
253 PointPainting
This method makes use of Velodyne laser scans.
92.58 % 98.39 % 89.71 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
254 MMpointpillars 92.57 % 95.47 % 87.45 % 0.05 s 1 core @ 2.5 Ghz (Python)
255 VSAC 92.55 % 96.00 % 87.81 % 0.07 s 1 core @ 1.0 Ghz (Python)
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256 MLAFF 92.54 % 95.39 % 87.77 % 0.39 s 2 cores @ 2.5 Ghz (Python)
257 DASS 92.53 % 96.23 % 87.75 % 0.09 s 1 core @ 2.0 Ghz (Python)
O. Unal, L. Van Gool and D. Dai: Improving Point Cloud Semantic Segmentation by Learning 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2021.
258 3D IoU-Net 92.47 % 96.31 % 87.67 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, S. Krylov, Y. Ding and L. Shao: 3D IoU-Net: IoU Guided 3D Object Detector for Point Clouds. arXiv preprint arXiv:2004.04962 2020.
259 Associate-3Ddet code 92.45 % 95.61 % 87.32 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
L. Du, X. Ye, X. Tan, J. Feng, Z. Xu, E. Ding and S. Wen: Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection. The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
260 S-AT GCN 92.44 % 95.06 % 90.78 % 0.02 s GPU @ 2.0 Ghz (Python)
L. Wang, C. Wang, X. Zhang, T. Lan and J. Li: S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection. CoRR 2021.
261 CAT2 92.37 % 95.77 % 87.27 % 1 s 1 core @ 2.5 Ghz (C/C++)
262 HA PillarNet 92.37 % 95.38 % 87.40 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
263 PointRGCN 92.33 % 97.51 % 87.07 % 0.26 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
264 Sem-Aug-PointRCNN++ 92.32 % 95.65 % 87.62 % 0.1 s 8 cores @ 3.0 Ghz (Python)
L. Zhao, M. Wang and Y. Yue: Sem-Aug: Improving Camera-LiDAR Feature Fusion With Semantic Augmentation for 3D Vehicle Detection. IEEE Robotics and Automation Letters 2022.
265 OccupancyM3D 92.29 % 95.78 % 84.96 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
266 Harmonic PointPillar code 92.25 % 95.16 % 89.11 % 0.01 s 1 core @ 2.5 Ghz (Python)
H. Zhang, J. Mekala, Z. Nain, J. Park and H. Jung: 3D Harmonic Loss: Towards Task-consistent and Time-friendly 3D Object Detection for V2X Orchestration. will submit to IEEE Transactions on Vehicular Technology 2022.
267 F-ConvNet
This method makes use of Velodyne laser scans.
code 92.19 % 95.85 % 80.09 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
268 PFF3D
This method makes use of Velodyne laser scans.
code 92.15 % 95.37 % 87.54 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
269 PASS-PointPillar 92.09 % 95.20 % 88.73 % 1 s 1 core @ 2.5 Ghz (C/C++)
Anonymous: Leveraging Anchor-based LiDAR 3D Object Detection via Point Assisted Sample Selection. will submit to computer vision conference/journal 2024.
270 SDP+RPN 92.03 % 95.16 % 79.16 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 2015.
271 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 92.00 % 95.88 % 86.98 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
272 MMpp 91.97 % 95.21 % 87.04 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
273 XT-PartA2 91.92 % 95.38 % 89.23 % 0.1 s GPU @ >3.5 Ghz (Python)
274 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 91.90 % 95.92 % 87.11 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
275 mm3d_PartA2 91.88 % 95.27 % 89.21 % 0.1 s GPU @ >3.5 Ghz (Python)
276 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 91.86 % 95.03 % 89.06 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
277 mmFUSION code 91.84 % 95.69 % 87.05 % 1s 1 core @ 2.5 Ghz (Python)
J. Ahmad and A. Del Bue: mmFUSION: Multimodal Fusion for 3D Objects Detection. arXiv preprint arXiv:2311.04058 2023.
278 WeakM3D code 91.81 % 94.51 % 85.35 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
L. Peng, S. Yan, B. Wu, Z. Yang, X. He and D. Cai: WeakM3D: Towards Weakly Supervised Monocular 3D Object Detection. ICLR 2022.
279 Voxel-MAE+SECOND code 91.78 % 93.11 % 89.53 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
280 epBRM
This method makes use of Velodyne laser scans.
code 91.77 % 94.59 % 88.45 % 0.1 s GPU @ >3.5 Ghz (Python + C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
281 C-GCN 91.73 % 95.64 % 86.37 % 0.147 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
282 ITVD code 91.73 % 95.85 % 79.31 % 0.3 s GPU @ 2.5 Ghz (C/C++)
Y. Wei Liu: Improving Tiny Vehicle Detection in Complex Scenes. IEEE International Conference on Multimedia and Expo (ICME) 2018.
283 MM_SECOND code 91.71 % 95.14 % 86.75 % 0.05 s GPU @ >3.5 Ghz (Python)
284 SINet+ code 91.67 % 94.17 % 78.60 % 0.3 s TITAN X GPU
X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
285 Cascade MS-CNN code 91.60 % 94.26 % 78.84 % 0.25 s GPU @ 2.5 Ghz (C/C++)
Z. Cai and N. Vasconcelos: Cascade R-CNN: High Quality Object Detection and Instance Segmentation. arXiv preprint arXiv:1906.09756 2019.
Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A unified multi-scale deep convolutional neural network for fast object detection. European conference on computer vision 2016.
286 SeSame-pillar code 91.57 % 95.13 % 88.41 % N/A s TITAN RTX @ 1.35 Ghz (Python)
287 PointRGBNet 91.48 % 95.40 % 86.50 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
P. Xie Desheng: Real-time Detection of 3D Objects Based on Multi-Sensor Information Fusion. Automotive Engineering 2022.
288 MAFF-Net(DAF-Pillar) 91.46 % 94.38 % 83.89 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Z. Zhang, Z. Liang, M. Zhang, X. Zhao, Y. Ming, T. Wenming and S. Pu: MAFF-Net: Filter False Positive for 3D Vehicle Detection with Multi-modal Adaptive Feature Fusion. arXiv preprint arXiv:2009.10945 2020.
289 HRI-VoxelFPN 91.44 % 96.65 % 86.18 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
H. Kuang, B. Wang, J. An, M. Zhang and Z. Zhang: Voxel-FPN:multi-scale voxel feature aggregation in 3D object detection from point clouds. sensors 2020.
290 MonoAux-v2 code 91.43 % 94.42 % 79.20 % 0.04 s GPU @ 2.5 Ghz (Python)
291 TBD 91.39 % 96.76 % 81.51 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
292 EgoNet code 91.39 % 96.18 % 81.33 % 0.1 s GPU @ 1.5 Ghz (Python)
S. Li, Z. Yan, H. Li and K. Cheng: Exploring intermediate representation for monocular vehicle pose estimation. The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
293 MonoSKD code 91.34 % 96.68 % 83.69 % 0.04 s 1 core @ 2.5 Ghz (Python)
294 FDGNet code 91.31 % 96.44 % 83.51 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
295 Stereo CenterNet
This method uses stereo information.
91.27 % 96.61 % 83.50 % 0.04 s GPU @ 2.5 Ghz (Python)
Y. Shi, Y. Guo, Z. Mi and X. Li: Stereo CenterNet-based 3D object detection for autonomous driving. Neurocomputing 2022.
296 SHUD 91.21 % 94.30 % 81.31 % 0.04 s 1 core @ 2.5 Ghz (Python)
297 PointPillars
This method makes use of Velodyne laser scans.
code 91.19 % 94.00 % 88.17 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
298 MEDL-U 91.19 % 96.70 % 86.06 % 1 s GPU @ >3.5 Ghz (Python)
299 LTN 91.18 % 94.68 % 81.51 % 0.4 s GPU @ >3.5 Ghz (Python)
T. Wang, X. He, Y. Cai and G. Xiao: Learning a Layout Transfer Network for Context Aware Object Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
300 EOTL code 91.17 % 96.31 % 81.20 % TBD s 1 core @ 2.5 Ghz (Python + C/C++)
R. Yang, Z. Yan, T. Yang, Y. Wang and Y. Ruichek: Efficient Online Transfer Learning for Road Participants Detection in Autonomous Driving. IEEE Sensors Journal 2023.
301 MonoAux 91.17 % 94.14 % 81.35 % 0.04 s GPU @ 2.5 Ghz (Python)
302 WS3D
This method makes use of Velodyne laser scans.
91.15 % 95.13 % 86.52 % 0.1 s GPU @ 2.5 Ghz (Python)
Q. Meng, W. Wang, T. Zhou, J. Shen, L. Van Gool and D. Dai: Weakly Supervised 3D Object Detection from Lidar Point Cloud. 2020.
303 BA2-Det+MonoFlex 91.12 % 96.45 % 81.30 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
304 LWLANet code 91.12 % 94.22 % 81.22 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
305 MonoSGC 91.10 % 94.21 % 83.45 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
306 NeurOCS 91.08 % 96.39 % 81.20 % 0.1 s GPU @ 2.5 Ghz (Python)
Z. Min, B. Zhuang, S. Schulter, B. Liu, E. Dunn and M. Chandraker: NeurOCS: Neural NOCS Supervision for Monocular 3D Object Localization. CVPR 2023.
307 APDM code 91.07 % 92.91 % 87.90 % 0.7 s 1 core @ 2.5 Ghz (Python)
308 KM3D code 91.07 % 96.44 % 81.19 % 0.03 s 1 core @ 2.5 Ghz (Python)
P. Li: Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training. 2020.
309 DID-M3D code 91.04 % 94.29 % 81.31 % 0.04 s 1 core @ 2.5 Ghz (Python)
L. Peng, X. Wu, Z. Yang, H. Liu and D. Cai: DID-M3D: Decoupling Instance Depth for Monocular 3D Object Detection. ECCV 2022.
310 FII-CenterNet code 91.03 % 94.48 % 83.00 % 0.09 s GPU @ 2.5 Ghz (Python)
S. Fan, F. Zhu, S. Chen, H. Zhang, B. Tian, Y. Lv and F. Wang: FII-CenterNet: An Anchor-Free Detector With Foreground Attention for Traffic Object Detection. IEEE Transactions on Vehicular Technology 2021.
311 Aston-EAS 91.02 % 93.91 % 77.93 % 0.24 s GPU @ 2.5 Ghz (Python + C/C++)
J. Wei, J. He, Y. Zhou, K. Chen, Z. Tang and Z. Xiong: Enhanced Object Detection With Deep Convolutional Neural Networks for Advanced Driving Assistance. IEEE Transactions on Intelligent Transportation Systems 2019.
312 MonoFlex 91.02 % 96.01 % 83.38 % 0.03 s GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.
313 Mix-Teaching code 91.02 % 96.35 % 83.41 % 30 s 1 core @ 2.5 Ghz (C/C++)
L. Yang, X. Zhang, L. Wang, M. Zhu, C. Zhang and J. Li: Mix-Teaching: A Simple, Unified and Effective Semi-Supervised Learning Framework for Monocular 3D Object Detection. ArXiv 2022.
314 ARPNET 90.99 % 94.00 % 83.49 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
315 CIE 90.98 % 96.31 % 83.43 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Anonymities: Consistency of Implicit and Explicit Features Matters for Monocular 3D Object Detection. arXiv preprint arXiv:2207.07933 2022.
316 HINTED 90.97 % 95.16 % 85.55 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
317 PS++ 90.95 % 96.24 % 83.23 % 0.4 s 1 core @ 2.5 Ghz (C/C++)
318 DCD code 90.93 % 96.44 % 83.36 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
Y. Li, Y. Chen, J. He and Z. Zhang: Densely Constrained Depth Estimator for Monocular 3D Object Detection. European Conference on Computer Vision 2022.
319 prcnn_v18_80_100 90.88 % 96.21 % 85.85 % 0.1 s 1 core @ 2.5 Ghz (Python)
320 MonoEF 90.88 % 96.32 % 83.27 % 0.03 s 1 core @ 2.5 Ghz (Python)
Y. Zhou, Y. He, H. Zhu, C. Wang, H. Li and Q. Jiang: Monocular 3D Object Detection: An Extrinsic Parameter Free Approach. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
321 PatchNet code 90.87 % 93.82 % 79.62 % 0.4 s 1 core @ 2.5 Ghz (C/C++)
X. Ma, S. Liu, Z. Xia, H. Zhang, X. Zeng and W. Ouyang: Rethinking Pseudo-LiDAR Representation. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
322 MV3D
This method makes use of Velodyne laser scans.
90.83 % 96.47 % 78.63 % 0.36 s GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
323 monodle code 90.81 % 93.83 % 80.93 % 0.04 s GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for Monocular 3D Object Detection. CVPR 2021 .
324 MonoATT_V2 code 90.80 % 94.09 % 83.58 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
325 3D IoU Loss
This method makes use of Velodyne laser scans.
90.79 % 95.92 % 85.65 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
D. Zhou, J. Fang, X. Song, C. Guan, J. Yin, Y. Dai and R. Yang: IoU Loss for 2D/3D Object Detection. International Conference on 3D Vision (3DV) 2019.
326 SINet_VGG code 90.79 % 93.59 % 77.53 % 0.2 s TITAN X GPU
X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
327 HomoLoss(monoflex) code 90.69 % 95.92 % 80.91 % 0.04 s 1 core @ 2.5 Ghz (Python)
J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homography Loss for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
328 TANet code 90.67 % 93.67 % 85.31 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
329 MonoCInIS 90.60 % 96.05 % 82.43 % 0,13 s GPU @ 2.5 Ghz (C/C++)
J. Heylen, M. De Wolf, B. Dawagne, M. Proesmans, L. Van Gool, W. Abbeloos, H. Abdelkawy and D. Reino: MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.
330 SeSame-point code 90.55 % 95.78 % 87.62 % N/A s TITAN RTX @ 1.35 Ghz (Python)
331 MonoCDiT 90.53 % 96.01 % 80.69 % 0.05 s GPU @ >3.5 Ghz (Python)
332 DFSemONet(Baseline) 90.51 % 95.58 % 87.58 % 0.04 s GPU @ 2.5 Ghz (Python)
333 CG-Stereo
This method uses stereo information.
90.38 % 96.31 % 82.80 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
334 SCNet
This method makes use of Velodyne laser scans.
90.30 % 95.59 % 85.09 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
335 CMKD code 90.28 % 95.14 % 83.91 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Y. Hong, H. Dai and Y. Ding: Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection. ECCV 2022.
336 PS-fld code 90.27 % 95.75 % 82.32 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
Y. Chen, H. Dai and Y. Ding: Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
337 ODGS 90.20 % 95.79 % 85.23 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
338 MonoSTL 90.19 % 95.32 % 80.53 % na s 1 core @ 2.5 Ghz (Python)
339 Deep3DBox 90.19 % 94.71 % 76.82 % 1.5 s GPU @ 2.5 Ghz (C/C++)
A. Mousavian, D. Anguelov, J. Flynn and J. Kosecka: 3D Bounding Box Estimation Using Deep Learning and Geometry. CVPR 2017.
340 FQNet 90.17 % 94.72 % 76.78 % 0.5 s 1 core @ 2.5 Ghz (Python)
L. Liu, J. Lu, C. Xu, Q. Tian and J. Zhou: Deep Fitting Degree Scoring Network for Monocular 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
341 MonoSIM_v2 90.12 % 95.91 % 80.67 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
342 DeepStereoOP 90.06 % 95.15 % 79.91 % 3.4 s GPU @ 3.5 Ghz (Matlab + C/C++)
C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.
343 PI-SECOND code 89.99 % 95.31 % 86.86 % 0.05 s GPU @ >3.5 Ghz (Python + C/C++)
344 SubCNN 89.98 % 94.26 % 79.78 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.
345 MLOD
This method makes use of Velodyne laser scans.
code 89.97 % 94.88 % 84.98 % 0.12 s GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
346 GPP code 89.96 % 94.02 % 81.13 % 0.23 s GPU @ 1.5 Ghz (Python + C/C++)
A. Rangesh and M. Trivedi: Ground plane polling for 6dof pose estimation of objects on the road. IEEE Transactions on Intelligent Vehicles 2020.
347 AVOD
This method makes use of Velodyne laser scans.
code 89.88 % 95.17 % 82.83 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
348 SINet_PVA code 89.86 % 92.72 % 76.47 % 0.11 s TITAN X GPU
X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
349 MVAF-Net(3-classes) 89.67 % 95.69 % 86.79 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
350 MVAF-Net(3-classes) 89.62 % 95.62 % 86.76 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
351 3DOP
This method uses stereo information.
code 89.55 % 92.96 % 79.38 % 3s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
352 ADD code 89.53 % 94.82 % 81.60 % 0.1 s 1 core @ 2.5 Ghz (Python)
Z. Wu, Y. Wu, J. Pu, X. Li and X. Wang: Attention-based Depth Distillation with 3D-Aware Positional Encoding for Monocular 3D Object Detection. AAAI2023 .
353 IAFA 89.46 % 93.08 % 79.83 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
D. Zhou, X. Song, Y. Dai, J. Yin, F. Lu, M. Liao, J. Fang and L. Zhang: IAFA: Instance-Aware Feature Aggregation for 3D Object Detection from a Single Image. Proceedings of the Asian Conference on Computer Vision 2020.
354 Mono3D code 89.37 % 94.52 % 79.15 % 4.2 s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.
355 4d-MSCNN
This method uses stereo information.
code 89.37 % 92.40 % 77.00 % 0.3 min GPU @ 3.0 Ghz (Matlab + C/C++)
P. Ferraz, B. Oliveira, F. Ferreira, C. Silva Martins and others: Three-stage RGBD architecture for vehicle and pedestrian detection using convolutional neural networks and stereo vision. IET Intelligent Transport Systems 2020.
356 MonoDDE 89.19 % 96.76 % 81.60 % 0.04 s 1 core @ 2.5 Ghz (Python)
Z. Li, Z. Qu, Y. Zhou, J. Liu, H. Wang and L. Jiang: Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection. CVPR 2022.
357 MonoUNI code 88.96 % 94.30 % 78.95 % 0.04 s 1 core @ 2.5 Ghz (Python)
J. Jia, Z. Li and Y. Shi: MonoUNI: A Unified Vehicle and Infrastructure-side Monocular 3D Object Detection Network with Sufficient Depth Clues. Thirty-seventh Conference on Neural Information Processing Systems 2023.
358 AVOD-FPN
This method makes use of Velodyne laser scans.
code 88.92 % 94.70 % 84.13 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
359 PCT code 88.78 % 96.45 % 78.85 % 0.045 s 1 core @ 2.5 Ghz (C/C++)
L. Wang, L. Zhang, Y. Zhu, Z. Zhang, T. He, M. Li and X. Xue: Progressive Coordinate Transforms for Monocular 3D Object Detection. NeurIPS 2021.
360 OPA-3D code 88.77 % 96.50 % 76.55 % 0.04 s 1 core @ 3.5 Ghz (Python)
Y. Su, Y. Di, G. Zhai, F. Manhardt, J. Rambach, B. Busam, D. Stricker and F. Tombari: OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object Detection. IEEE Robotics and Automation Letters 2023.
361 DSC3D
This method uses stereo information.
88.74 % 96.56 % 76.41 % 0.31 s GPU @ 2.5 Ghz (Python)
362 AM3D 88.71 % 92.55 % 77.78 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
X. Ma, Z. Wang, H. Li, P. Zhang, W. Ouyang and X. Fan: Accurate Monocular Object Detection via Color- Embedded 3D Reconstruction for Autonomous Driving. Proceedings of the IEEE international Conference on Computer Vision (ICCV) 2019.
363 MS-CNN code 88.68 % 93.87 % 76.11 % 0.4 s GPU @ 2.5 Ghz (C/C++)
Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection. ECCV 2016.
364 MonoPSR code 88.50 % 93.63 % 73.36 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
365 Shift R-CNN (mono) code 88.48 % 94.07 % 78.34 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
366 RCD 88.46 % 92.52 % 83.73 % 0.1 s GPU @ 2.5 Ghz (Python)
A. Bewley, P. Sun, T. Mensink, D. Anguelov and C. Sminchisescu: Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection. Conference on Robot Learning (CoRL) 2020.
367 MM-MRFC
This method uses optical flow information.
This method makes use of Velodyne laser scans.
88.46 % 95.54 % 78.14 % 0.05 s GPU @ 2.5 Ghz (C/C++)
A. Costea, R. Varga and S. Nedevschi: Fast Boosting based Detection using Scale Invariant Multimodal Multiresolution Filtered Features. CVPR 2017.
368 MonoRoIDepth 88.45 % 93.99 % 78.50 % 1 s 1 core @ 2.5 Ghz (C/C++)
369 MonoDTR 88.41 % 93.90 % 76.20 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
K. Huang, T. Wu, H. Su and W. Hsu: MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer. CVPR 2022.
370 3DBN
This method makes use of Velodyne laser scans.
88.29 % 93.74 % 80.74 % 0.13s 1080Ti (Python+C/C++)
X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.
371 MonoCInIS 88.16 % 96.22 % 75.72 % 0,14 s GPU @ 2.5 Ghz (Python)
J. Heylen, M. De Wolf, B. Dawagne, M. Proesmans, L. Van Gool, W. Abbeloos, H. Abdelkawy and D. Reino: MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.
372 SVDM-VIEW 88.15 % 94.60 % 79.97 % 1 s 1 core @ 2.5 Ghz (Python)
373 MonoRUn code 87.91 % 95.48 % 78.10 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
374 SMOKE code 87.51 % 93.21 % 77.66 % 0.03 s GPU @ 2.5 Ghz (Python)
Z. Liu, Z. Wu and R. Tóth: SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation. 2020.
375 SH3D 87.33 % 95.79 % 77.76 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
376 CDN
This method uses stereo information.
code 87.19 % 95.85 % 79.43 % 0.6 s GPU @ 2.5 Ghz (Python)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems (NeurIPS) 2020.
377 RTM3D code 86.93 % 91.82 % 77.41 % 0.05 s GPU @ 1.0 Ghz (Python)
P. Li, H. Zhao, P. Liu and F. Cao: RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving. 2020.
378 MonoNeRD code 86.89 % 94.60 % 77.23 % na s 1 core @ 2.5 Ghz (Python)
J. Xu, L. Peng, H. Cheng, H. Li, W. Qian, K. Li, W. Wang and D. Cai: MonoNeRD: NeRF-like Representations for Monocular 3D Object Detection. ICCV 2023.
379 MonoRCNN code 86.78 % 91.98 % 66.97 % 0.07 s GPU @ 2.5 Ghz (Python)
X. Shi, Q. Ye, X. Chen, C. Chen, Z. Chen and T. Kim: Geometry-based Distance Decomposition for Monocular 3D Object Detection. ICCV 2021.
380 BirdNet+
This method makes use of Velodyne laser scans.
code 86.73 % 92.61 % 81.80 % 0.11 s Titan Xp (PyTorch)
A. Barrera, J. Beltrán, C. Guindel, J. Iglesias and F. García: BirdNet+: Two-Stage 3D Object Detection in LiDAR through a Sparsity-Invariant Bird’s Eye View. IEEE Access 2021.
381 MonoRCNN++ code 86.69 % 94.31 % 71.87 % 0.07 s GPU @ 2.5 Ghz (Python)
X. Shi, Z. Chen and T. Kim: Multivariate Probabilistic Monocular 3D Object Detection. WACV 2023.
382 DEVIANT code 86.64 % 94.42 % 76.69 % 0.04 s 1 GPU (Python)
A. Kumar, G. Brazil, E. Corona, A. Parchami and X. Liu: DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection. European Conference on Computer Vision (ECCV) 2022.
383 MonoAuxNorm 86.62 % 92.56 % 78.73 % 0.02 s GPU @ 2.5 Ghz (Python)
384 GUPNet code 86.45 % 94.15 % 74.18 % NA s 1 core @ 2.5 Ghz (Python + C/C++)
Y. Lu, X. Ma, L. Yang, T. Zhang, Y. Liu, Q. Chu, J. Yan and W. Ouyang: Geometry Uncertainty Projection Network for Monocular 3D Object Detection. arXiv preprint arXiv:2107.13774 2021.
385 DSGN
This method uses stereo information.
code 86.43 % 95.53 % 78.75 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
386 MonoAIU 86.30 % 94.06 % 71.53 % 0.03 s GPU @ 2.5 Ghz (Python)
387 MonoDETR code 86.17 % 93.99 % 76.19 % 0.04 s 1 core @ 2.5 Ghz (Python)
R. Zhang, H. Qiu, T. Wang, X. Xu, Z. Guo, Y. Qiao, P. Gao and H. Li: MonoDETR: Depth-aware Transformer for Monocular 3D Object Detection. arXiv preprint arXiv:2203.13310 2022.
388 Stereo R-CNN
This method uses stereo information.
code 85.98 % 93.98 % 71.25 % 0.3 s GPU @ 2.5 Ghz (Python)
P. Li, X. Chen and S. Shen: Stereo R-CNN based 3D Object Detection for Autonomous Driving. CVPR 2019.
389 Anonymous 85.82 % 94.15 % 71.22 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
390 MonoTRKD 85.79 % 94.44 % 79.87 % 40 s 1 core @ 2.5 Ghz (Python)
391 StereoFENet
This method uses stereo information.
85.70 % 91.48 % 77.62 % 0.15 s 1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
392 DE_Fusion 85.69 % 93.85 % 75.81 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
393 MonoSIM 85.65 % 93.99 % 78.58 % 0.16 s 1 core @ 2.5 Ghz (Python)
394 DMF
This method uses stereo information.
85.49 % 89.50 % 82.52 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
X. J. Chen and W. Xu: Disparity-Based Multiscale Fusion Network for Transportation Detection. IEEE Transactions on Intelligent Transportation Systems 2022.
395 ResNet-RRC_Car 85.33 % 91.45 % 74.27 % 0.06 s GPU @ 1.5 Ghz (Python + C/C++)
H. Jeon and others: High-Speed Car Detection Using ResNet- Based Recurrent Rolling Convolution. Proceedings of the IEEE conference on systems, man, and cybernetics 2018.
396 PL++ (SDN+GDC)
This method uses stereo information.
This method makes use of Velodyne laser scans.
code 85.15 % 94.95 % 77.78 % 0.6 s GPU @ 2.5 Ghz (C/C++)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.
397 M3D-RPN code 85.08 % 89.04 % 69.26 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
398 CDN-PL++
This method uses stereo information.
85.01 % 94.66 % 77.60 % 0.4 s GPU @ 2.5 Ghz (C/C++)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems 2020.
399 SDP+CRC (ft) 85.00 % 92.06 % 71.71 % 0.6 s GPU @ 2.5 Ghz (C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
400 Anonymous 84.99 % 93.63 % 77.44 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
401 SS3D 84.92 % 92.72 % 70.35 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
402 MonoFENet 84.63 % 91.68 % 76.71 % 0.15 s 1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
403 DLE code 84.45 % 94.66 % 62.10 % 0.06 s NVIDIA Tesla V100
C. Liu, S. Gu, L. Gool and R. Timofte: Deep Line Encoding for Monocular 3D Object Detection and Depth Prediction. Proceedings of the British Machine Vision Conference (BMVC) 2021.
404 MV3D (LIDAR)
This method makes use of Velodyne laser scans.
84.39 % 93.08 % 79.27 % 0.24 s GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
405 Complexer-YOLO
This method makes use of Velodyne laser scans.
84.16 % 91.92 % 79.62 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
406 MonOAPC 84.13 % 92.39 % 74.62 % 0035 s 1 core @ 2.5 Ghz (Python)
407 BKDStereo3D code 84.10 % 94.61 % 61.85 % 0.1 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
408 ZoomNet
This method uses stereo information.
code 83.92 % 94.22 % 69.00 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
L. Z. Xu: ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2020.
409 CMAN 83.74 % 89.74 % 65.35 % 0.15 s 1 core @ 2.5 Ghz (Python)
C. Yuanzhouhan Cao: CMAN: Leaning Global Structure Correlation for Monocular 3D Object Detection. IEEE Trans. Intell. Transport. Syst. 2022.
410 D4LCN code 83.67 % 90.34 % 65.33 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
411 MonoGhost 83.33 % 90.79 % 71.13 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
412 MonoLTKD 83.31 % 93.84 % 77.95 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
413 MonoLTKD_V3 83.31 % 93.84 % 77.95 % 0.04 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
414 Faster R-CNN code 83.16 % 88.97 % 72.62 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards Real- Time Object Detection with Region Proposal Networks. NIPS 2015.
415 SGM3D code 83.05 % 93.66 % 73.35 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
Z. Zhou, L. Du, X. Ye, Z. Zou, X. Tan, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection. RA-L 2022.
416 Pseudo-LiDAR++
This method uses stereo information.
code 82.90 % 94.46 % 75.45 % 0.4 s GPU @ 2.5 Ghz (Python)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.
417 Disp R-CNN
This method uses stereo information.
code 82.86 % 93.64 % 68.33 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
418 BS3D 82.72 % 95.35 % 70.01 % 22 ms Titan Xp
N. Gählert, J. Wan, M. Weber, J. Zöllner, U. Franke and J. Denzler: Beyond Bounding Boxes: Using Bounding Shapes for Real-Time 3D Vehicle Detection from Monocular RGB Images. 2019 IEEE Intelligent Vehicles Symposium (IV) 2019.
419 Disp R-CNN (velo)
This method uses stereo information.
code 82.64 % 93.45 % 70.45 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
420 HomoLoss(imvoxelnet) code 82.54 % 92.81 % 72.80 % 0.20 s 1 core @ 2.5 Ghz (Python)
J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homogrpahy Loss for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
421 YOLOStereo3D
This method uses stereo information.
code 82.15 % 94.81 % 62.17 % 0.1 s GPU 1080Ti
Y. Liu, L. Wang and M. Liu: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
422 Ground-Aware code 82.05 % 92.33 % 62.08 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
Y. Liu, Y. Yuan and M. Liu: Ground-aware Monocular 3D Object Detection for Autonomous Driving. IEEE Robotics and Automation Letters 2021.
423 FRCNN+Or code 82.00 % 92.91 % 68.79 % 0.09 s Titan Xp GPU
C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine 2018.
C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features. IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.
424 DDMP-3D 81.70 % 91.15 % 63.12 % 0.18 s 1 core @ 2.5 Ghz (Python)
L. Wang, L. Du, X. Ye, Y. Fu, G. Guo, X. Xue, J. Feng and L. Zhang: Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection. CVPR 2020.
425 BKDStereo3D w/o KD code 81.50 % 94.56 % 61.64 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
426 A3DODWTDA (image) code 81.25 % 78.96 % 70.56 % 0.8 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
427 RefineNet 81.01 % 91.91 % 65.67 % 0.20 s GPU @ 2.5 Ghz (Matlab + C++)
R. Rajaram, E. Bar and M. Trivedi: RefineNet: Refining Object Detectors for Autonomous Driving. IEEE Transactions on Intelligent Vehicles 2016.
R. Rajaram, E. Bar and M. Trivedi: RefineNet: Iterative Refinement for Accurate Object Localization. Intelligent Transportation Systems Conference 2016.
428 MonoTRKDv2 80.76 % 93.78 % 75.36 % 40 s 1 core @ 2.5 Ghz (Python)
429 CaDDN code 80.73 % 93.61 % 71.09 % 0.63 s GPU @ 2.5 Ghz (Python)
C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.
430 ESGN
This method uses stereo information.
80.58 % 93.07 % 70.68 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
A. Gao, Y. Pang, J. Nie, Z. Shao, J. Cao, Y. Guo and X. Li: ESGN: Efficient Stereo Geometry Network for Fast 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2022.
431 PGD-FCOS3D code 80.58 % 92.04 % 69.67 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
T. Wang, X. Zhu, J. Pang and D. Lin: Probabilistic and Geometric Depth: Detecting Objects in Perspective. Conference on Robot Learning (CoRL) 2021.
432 GrooMeD-NMS code 80.28 % 90.14 % 63.78 % 0.12 s 1 core @ 2.5 Ghz (Python)
A. Kumar, G. Brazil and X. Liu: GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection. CVPR 2021.
433 3D-GCK 80.19 % 89.55 % 68.08 % 24 ms Tesla V100
N. Gählert, J. Wan, N. Jourdan, J. Finkbeiner, U. Franke and J. Denzler: Single-Shot 3D Detection of Vehicles from Monocular RGB Images via Geometrically Constrained Keypoints in Real-Time. 2020 IEEE Intelligent Vehicles Symposium (IV) 2020.
434 YoloMono3D code 79.63 % 92.37 % 59.69 % 0.05 s GPU @ 2.5 Ghz (Python)
Y. Liu, L. Wang and L. Ming: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
435 A3DODWTDA
This method makes use of Velodyne laser scans.
code 79.15 % 82.98 % 68.30 % 0.08 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
436 ImVoxelNet code 79.09 % 89.80 % 69.45 % 0.2 s GPU @ 2.5 Ghz (Python)
D. Rukhovich, A. Vorontsova and A. Konushin: ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection. arXiv preprint arXiv:2106.01178 2021.
437 DFR-Net 78.81 % 90.13 % 60.40 % 0.18 s 1080 Ti (Pytorch)
Z. Zou, X. Ye, L. Du, X. Cheng, X. Tan, L. Zhang, J. Feng, X. Xue and E. Ding: The devil is in the task: Exploiting reciprocal appearance-localization features for monocular 3d object detection . ICCV 2021.
438 spLBP 78.66 % 81.66 % 61.69 % 1.5 s 8 cores @ 2.5 Ghz (Matlab + C/C++)
Q. Hu, S. Paisitkriangkrai, C. Shen, A. Hengel and F. Porikli: Fast Detection of Multiple Objects in Traffic Scenes With a Common Detection Framework. IEEE Trans. Intelligent Transportation Systems 2016.
439 FMF-occlusion-net 78.21 % 92.33 % 61.58 % 0.16 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Liu, H. Liu, Y. Wang, F. Sun and W. Huang: Fine-grained Multi-level Fusion for Anti- occlusion Monocular 3D Object Detection. IEEE Transactions on Image Processing 2022.
440 3D-SSMFCNN code 78.19 % 77.92 % 69.19 % 0.1 s GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.
441 SST [st]
This method uses stereo information.
78.01 % 90.78 % 70.97 % 1 s 1 core @ 2.5 Ghz (Python)
442 MonoGRNet code 77.94 % 88.65 % 63.31 % 0.04s NVIDIA P40
Z. Qin, J. Wang and Y. Lu: MonoGRNet: A Geometric Reasoning Network for 3D Object Localization. The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) 2019.
443 Aug3D-RPN 77.88 % 85.57 % 61.16 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth. 2021.
444 AutoShape code 77.66 % 86.51 % 64.40 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
Z. Liu, D. Zhou, F. Lu, J. Fang and L. Zhang: AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.
445 Reinspect code 77.48 % 90.27 % 66.73 % 2s 1 core @ 2.5 Ghz (C/C++)
R. Stewart, M. Andriluka and A. Ng: End-to-End People Detection in Crowded Scenes. CVPR 2016.
446 TS3D
This method uses stereo information.
77.23 % 92.39 % 57.28 % 0.09 s GPU @ 1.5 Ghz (Python + C/C++)
447 multi-task CNN 77.18 % 86.12 % 68.09 % 25.1 ms GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
448 Regionlets 76.99 % 88.75 % 60.49 % 1 s >8 cores @ 2.5 Ghz (C/C++)
X. Wang, M. Yang, S. Zhu and Y. Lin: Regionlets for Generic Object Detection. T-PAMI 2015.
W. Zou, X. Wang, M. Sun and Y. Lin: Generic Object Detection with Dense Neural Patterns and Regionlets. British Machine Vision Conference 2014.
C. Long, X. Wang, G. Hua, M. Yang and Y. Lin: Accurate Object Detection with Location Relaxation and Regionlets Relocalization. Asian Conference on Computer Vision 2014.
449 3DVP code 76.98 % 84.95 % 65.78 % 40 s 8 cores @ 3.5 Ghz (Matlab + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Data-Driven 3D Voxel Patterns for Object Category Recognition. IEEE Conference on Computer Vision and Pattern Recognition 2015.
450 Mobile Stereo R-CNN
This method uses stereo information.
76.73 % 90.08 % 62.23 % 1.8 s NVIDIA Jetson TX2
M. Hussein, M. Khalil and B. Abdullah: 3D Object Detection using Mobile Stereo R- CNN on Nvidia Jetson TX2. International Conference on Advanced Engineering, Technology and Applications (ICAETA) 2021.
451 SubCat code 76.36 % 84.10 % 60.56 % 0.7 s 6 cores @ 3.5 Ghz (Matlab + C/C++)
E. Ohn-Bar and M. Trivedi: Learning to Detect Vehicles by Clustering Appearance Patterns. T-ITS 2015.
452 GS3D 76.35 % 86.23 % 62.67 % 2 s 1 core @ 2.5 Ghz (C/C++)
B. Li, W. Ouyang, L. Sheng, X. Zeng and X. Wang: GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
453 AOG code 76.24 % 86.08 % 61.51 % 3 s 4 cores @ 2.5 Ghz (Matlab)
T. Wu, B. Li and S. Zhu: Learning And-Or Models to Represent Context and Occlusion for Car Detection and Viewpoint Estimation. TPAMI 2016.
B. Li, T. Wu and S. Zhu: Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model. ECCV 2014.
454 Pose-RCNN 75.83 % 89.59 % 64.06 % 2 s >8 cores @ 2.5 Ghz (Python)
M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on 2016.
455 Plane-Constraints 75.43 % 82.54 % 66.82 % 0.05 s 4 cores @ 3.0 Ghz (Python)
H. Yao, J. Chen, Z. Wang, X. Wang, X. Chai, Y. Qiu and P. Han: Vertex points are not enough: Monocular 3D object detection via intra-and inter-plane constraints. Neural Networks 2023.
456 3D FCN
This method makes use of Velodyne laser scans.
74.65 % 86.74 % 67.85 % >5 s 1 core @ 2.5 Ghz (C/C++)
B. Li: 3D Fully Convolutional Network for Vehicle Detection in Point Cloud. IROS 2017.
457 OC Stereo
This method uses stereo information.
code 74.60 % 87.39 % 62.56 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
458 Kinematic3D code 71.73 % 89.67 % 54.97 % 0.12 s 1 core @ 1.5 Ghz (C/C++)
G. Brazil, G. Pons-Moll, X. Liu and B. Schiele: Kinematic 3D Object Detection in Monocular Video. ECCV 2020 .
459 AOG-View 71.26 % 85.01 % 55.73 % 3 s 1 core @ 2.5 Ghz (Matlab, C/C++)
B. Li, T. Wu and S. Zhu: Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model. ECCV 2014.
460 GAC3D 70.73 % 83.30 % 52.23 % 0.25 s 1 core @ 2.5 Ghz (Python)
M. Bui, D. Ngo, H. Pham and D. Nguyen: GAC3D: improving monocular 3D object detection with ground-guide model and adaptive convolution. 2021.
461 MV-RGBD-RF
This method makes use of Velodyne laser scans.
70.70 % 77.89 % 57.41 % 4 s 4 cores @ 2.5 Ghz (C/C++)
A. Gonzalez, D. Vazquez, A. Lopez and J. Amores: On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts.. IEEE Trans. on Cybernetics 2016.
A. Gonzalez, G. Villalonga, J. Xu, D. Vazquez, J. Amores and A. Lopez: Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection. IEEE Intelligent Vehicles Symposium (IV) 2015.
462 Vote3Deep
This method makes use of Velodyne laser scans.
70.30 % 78.95 % 63.12 % 1.5 s 4 cores @ 2.5 Ghz (C/C++)
M. Engelcke, D. Rao, D. Zeng Wang, C. Hay Tong and I. Posner: Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks. ArXiv e-prints 2016.
463 ROI-10D 70.16 % 76.56 % 61.15 % 0.2 s GPU @ 3.5 Ghz (Python)
F. Manhardt, W. Kehl and A. Gaidon: ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape. Computer Vision and Pattern Recognition (CVPR) 2019.
464 BirdNet+ (legacy)
This method makes use of Velodyne laser scans.
code 68.05 % 92.10 % 65.61 % 0.1 s Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird’s Eye View. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
465 Decoupled-3D 67.92 % 87.78 % 54.53 % 0.08 s GPU @ 2.5 Ghz (C/C++)
Y. Cai, B. Li, Z. Jiao, H. Li, X. Zeng and X. Wang: Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation. AAAI 2020.
466 SparVox3D 67.88 % 83.76 % 52.56 % 0.05 s GPU @ 2.0 Ghz (Python)
E. Balatkan and F. Kıraç: Improving Regression Performance on Monocular 3D Object Detection Using Bin-Mixing and Sparse Voxel Data. 2021 6th International Conference on Computer Science and Engineering (UBMK) 2021.
467 Pseudo-Lidar
This method uses stereo information.
code 67.79 % 85.40 % 58.50 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Wang, W. Chao, D. Garg, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
468 OC-DPM 67.06 % 79.07 % 52.61 % 10 s 8 cores @ 2.5 Ghz (Matlab)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Occlusion Patterns for Object Class Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2013.
469 DPM-VOC+VP 66.72 % 82.15 % 49.01 % 8 s 1 core @ 2.5 Ghz (C/C++)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015.
470 BdCost48LDCF code 66.63 % 81.38 % 52.20 % 0.5 s 8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
471 RefinedMPL 65.24 % 88.29 % 53.20 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
472 MDPM-un-BB 64.06 % 79.74 % 49.07 % 60 s 4 core @ 2.5 Ghz (MATLAB)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
473 TLNet (Stereo)
This method uses stereo information.
code 63.53 % 76.92 % 54.58 % 0.1 s 1 core @ 2.5 Ghz (Python)
Z. Qin, J. Wang and Y. Lu: Triangulation Learning Network: from Monocular to Stereo 3D Object Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
474 PDV-Subcat 63.24 % 78.27 % 47.67 % 7 s 1 core @ 2.5 Ghz (C/C++)
J. Shen, X. Zuo, J. Li, W. Yang and H. Ling: A novel pixel neighborhood differential statistic feature for pedestrian and face detection . Pattern Recognition 2017.
475 MDSNet 62.74 % 85.94 % 50.27 % 0.05 s 1 core @ 2.5 Ghz (Python)
Z. Xie, Y. Song, J. Wu, Z. Li, C. Song and Z. Xu: MDS-Net: Multi-Scale Depth Stratification 3D Object Detection from Monocular Images. Sensors 2022.
476 MODet
This method makes use of Velodyne laser scans.
62.54 % 66.06 % 60.04 % 0.05 s GTX1080Ti
Y. Zhang, Z. Xiang, C. Qiao and S. Chen: Accurate and Real-Time Object Detection Based on Bird's Eye View on 3D Point Clouds. 2019 International Conference on 3D Vision (3DV) 2019.
477 CIE + DM3D 61.54 % 79.36 % 53.56 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Ananimities: Consistency of Implicit and Explicit Features Matters for Monocular 3D Object Detection. arXiv preprint arXiv:2207.07933 2022.
478 SubCat48LDCF code 61.16 % 78.86 % 44.69 % 0.5 s 8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
479 DPM-C8B1
This method uses stereo information.
60.21 % 75.24 % 44.73 % 15 s 4 cores @ 2.5 Ghz (Matlab + C/C++)
J. Yebes, L. Bergasa and M. García-Garrido: Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes. Sensors 2015.
J. Yebes, L. Bergasa, R. Arroyo and A. Lázaro: Supervised learning and evaluation of KITTI's cars detector with DPM. IV 2014.
480 SAMME48LDCF code 58.38 % 77.47 % 44.43 % 0.5 s 8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
481 LSVM-MDPM-sv 58.36 % 71.11 % 43.22 % 10 s 4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout. NIPS 2011.
482 BirdNet
This method makes use of Velodyne laser scans.
57.12 % 79.30 % 55.16 % 0.11 s Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
483 ACF-SC 56.60 % 69.90 % 43.61 % <0.3 s 1 core @ >3.5 Ghz (Matlab + C/C++)
C. Cadena, A. Dick and I. Reid: A Fast, Modular Scene Understanding System using Context-Aware Object Detection. Robotics and Automation (ICRA), 2015 IEEE International Conference on 2015.
484 LSVM-MDPM-us code 55.95 % 68.94 % 41.45 % 10 s 4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
485 ACF 54.09 % 63.05 % 41.81 % 0.2 s 1 core @ >3.5 Ghz (Matlab + C/C++)
P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014.
P. Doll\'ar: Piotr's Image and Video Matlab Toolbox (PMT). .
486 Mono3D_PLiDAR code 53.36 % 80.85 % 44.80 % 0.1 s NVIDIA GeForce 1080 (pytorch)
X. Weng and K. Kitani: Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud. arXiv:1903.09847 2019.
487 RT3D-GMP
This method uses stereo information.
51.95 % 62.41 % 39.14 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
488 VeloFCN
This method makes use of Velodyne laser scans.
51.82 % 70.53 % 45.70 % 1 s GPU @ 2.5 Ghz (Python + C/C++)
B. Li, T. Zhang and T. Xia: Vehicle Detection from 3D Lidar Using Fully Convolutional Network. RSS 2016 .
489 Init_Submit_240Orien 46.57 % 63.20 % 42.58 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
490 Orient240_Loc441 46.53 % 60.68 % 42.48 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
491 MonoGhost 46.53 % 60.68 % 42.48 % 0.03 s GPU @ 2.5 Ghz (Python)
492 Vote3D
This method makes use of Velodyne laser scans.
45.94 % 54.38 % 40.48 % 0.5 s 4 cores @ 2.8 Ghz (C/C++)
D. Wang and I. Posner: Voting for Voting in Online Point Cloud Object Detection. Proceedings of Robotics: Science and Systems 2015.
493 TopNet-HighRes
This method makes use of Velodyne laser scans.
45.85 % 58.04 % 41.11 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
494 RT3DStereo
This method uses stereo information.
45.81 % 56.53 % 37.63 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
495 Multimodal Detection
This method makes use of Velodyne laser scans.
code 45.46 % 63.91 % 37.25 % 0.06 s GPU @ 3.5 Ghz (Matlab + C/C++)
A. Asvadi, L. Garrote, C. Premebida, P. Peixoto and U. Nunes: Multimodal vehicle detection: fusing 3D- LIDAR and color camera data. Pattern Recognition Letters 2017.
496 RT3D
This method makes use of Velodyne laser scans.
39.69 % 50.33 % 40.04 % 0.09 s GPU @ 1.8Ghz
Y. Zeng, Y. Hu, S. Liu, J. Ye, Y. Han, X. Li and N. Sun: RT3D: Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving. IEEE Robotics and Automation Letters 2018.
497 tbd code 39.51 % 53.97 % 37.78 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
498 VoxelJones code 36.31 % 43.89 % 34.16 % .18 s 1 core @ 2.5 Ghz (Python + C/C++)
M. Motro and J. Ghosh: Vehicular Multi-object Tracking with Persistent Detector Failures. arXiv preprint arXiv:1907.11306 2019.
499 CSoR
This method makes use of Velodyne laser scans.
code 21.66 % 31.52 % 17.99 % 3.5 s 4 cores @ >3.5 Ghz (Python + C/C++)
L. Plotkin: PyDriver: Entwicklung eines Frameworks für räumliche Detektion und Klassifikation von Objekten in Fahrzeugumgebung. 2015.
500 mBoW
This method makes use of Velodyne laser scans.
21.59 % 35.22 % 16.89 % 10 s 1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
501 DepthCN
This method makes use of Velodyne laser scans.
code 21.18 % 37.45 % 16.08 % 2.3 s GPU @ 3.5 Ghz (Matlab)
A. Asvadi, L. Garrote, C. Premebida, P. Peixoto and U. Nunes: DepthCN: vehicle detection using 3D- LIDAR and convnet. IEEE ITSC 2017.
502 YOLOv2 code 14.31 % 26.74 % 10.94 % 0.02 s GPU @ 3.5 Ghz (C/C++)
J. Redmon, S. Divvala, R. Girshick and A. Farhadi: You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
503 TopNet-UncEst
This method makes use of Velodyne laser scans.
6.24 % 7.24 % 5.42 % 0.09 s NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
504 TopNet-Retina
This method makes use of Velodyne laser scans.
5.00 % 6.82 % 4.52 % 52ms GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
505 init 0.01 % 0.01 % 0.01 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
506 TopNet-DecayRate
This method makes use of Velodyne laser scans.
0.01 % 0.00 % 0.01 % 92 ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
507 LaserNet 0.00 % 0.00 % 0.00 % 12 ms GPU @ 2.5 Ghz (C/C++)
G. Meyer, A. Laddha, E. Kee, C. Vallespi-Gonzalez and C. Wellington: LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
508 DA3D+KM3D+v2-99 0.00 % 0.00 % 0.00 % 1 s GPU @ 2.5 Ghz (Python)
509 Neighbor-Vote 0.00 % 0.00 % 0.00 % 0.1 s GPU @ 2.5 Ghz (Python)
X. Chu, J. Deng, Y. Li, Z. Yuan, Y. Zhang, J. Ji and Y. Zhang: Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance Voting. ACM MM 2021.
510 DA3D+KM3D 0.00 % 0.00 % 0.00 % 0.02 s GPU @ 2.5 Ghz (Python)
511 DA3D 0.00 % 0.00 % 0.00 % 0.03 s 1 core @ 2.5 Ghz (Python)
Table as LaTeX | Only published Methods

Pedestrian


Method Setting Code Moderate Easy Hard Runtime Environment
1 LSFM 81.26 % 86.81 % 77.64 % 0.05 s 4 cores @ 2.5 Ghz (Python)
2 F-PointNet
This method makes use of Velodyne laser scans.
code 80.13 % 89.83 % 75.05 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
3 HHA-TFFEM
This method makes use of Velodyne laser scans.
78.53 % 87.01 % 74.70 % 0.14 s GPU @ 2.5 Ghz (Python + C/C++)
F. Tan, Z. Xia, Y. Ma and X. Feng: 3D Sensor Based Pedestrian Detection by Integrating Improved HHA Encoding and Two-Branch Feature Fusion. Remote Sensing 2022.
4 TuSimple code 78.40 % 88.87 % 73.66 % 1.6 s GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016.
5 RRC code 76.61 % 85.98 % 71.47 % 3.6 s GPU @ 2.5 Ghz (C/C++)
J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y. Tai and L. Xu: Accurate Single Stage Detector Using Recurrent Rolling Convolution. CVPR 2017.
6 WSSN
This method makes use of Velodyne laser scans.
76.42 % 84.91 % 71.86 % 0.37 s GPU @ >3.5 Ghz (Python + C/C++)
Z. Guo, W. Liao, Y. Xiao, P. Veelaert and W. Philips: Weak Segmentation Supervised Deep Neural Networks for Pedestrian Detection. Pattern Recognition 2021.
7 ECP Faster R-CNN 76.25 % 85.96 % 70.55 % 0.25 s GPU @ 2.5 Ghz (Python)
M. Braun, S. Krebs, F. Flohr and D. Gavrila: The EuroCity Persons Dataset: A Novel Benchmark for Object Detection. CoRR 2018.
8 Aston-EAS 76.07 % 86.71 % 70.02 % 0.24 s GPU @ 2.5 Ghz (Python + C/C++)
J. Wei, J. He, Y. Zhou, K. Chen, Z. Tang and Z. Xiong: Enhanced Object Detection With Deep Convolutional Neural Networks for Advanced Driving Assistance. IEEE Transactions on Intelligent Transportation Systems 2019.
9 MHN 75.99 % 87.21 % 69.50 % 0.39 s GPU @ 2.5 Ghz (Python)
J. Cao, Y. Pang, S. Zhao and X. Li: High-Level Semantic Networks for Multi- Scale Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2019.
10 FFNet code 75.81 % 87.17 % 69.86 % 1.07 s GPU @ 1.5 Ghz (Python)
C. Zhao, Y. Qian and M. Yang: Monocular Pedestrian Orientation Estimation Based on Deep 2D-3D Feedforward. Pattern Recognition 2019.
11 SJTU-HW 75.81 % 87.17 % 69.86 % 0.85s GPU @ 1.5 Ghz (Python + C/C++)
S. Zhang, X. Zhao, L. Fang, F. Haiping and S. Haitao: LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR. IEEE International Conference on Image Processing 2018.
L. Fang, X. Zhao and S. Zhang: Small-objectness sensitive detection based on shifted single shot detector. Multimedia Tools and Applications 2018.
12 MS-CNN code 74.89 % 85.71 % 68.99 % 0.4 s GPU @ 2.5 Ghz (C/C++)
Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection. ECCV 2016.
13 DD3D code 73.09 % 85.71 % 68.54 % n/a s 1 core @ 2.5 Ghz (C/C++)
D. Park, R. Ambrus, V. Guizilini, J. Li and A. Gaidon: Is Pseudo-Lidar needed for Monocular 3D Object detection?. IEEE/CVF International Conference on Computer Vision (ICCV) .
14 F-ConvNet
This method makes use of Velodyne laser scans.
code 72.91 % 83.63 % 67.18 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
15 GN 72.29 % 82.93 % 65.56 % 1 s GPU @ 2.5 Ghz (Matlab + C/C++)
S. Jung and K. Hong: Deep network aided by guiding network for pedestrian detection. Pattern Recognition Letters 2017.
16 SubCNN 72.27 % 84.88 % 66.82 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.
17 VMVS
This method makes use of Velodyne laser scans.
71.82 % 82.80 % 66.85 % 0.25 s GPU @ 2.5 Ghz (Python)
J. Ku, A. Pon, S. Walsh and S. Waslander: Improving 3D object detection for pedestrians with virtual multi-view synthesis orientation estimation. IROS 2019.
18 EOTL code 71.45 % 84.74 % 64.58 % TBD s 1 core @ 2.5 Ghz (Python + C/C++)
R. Yang, Z. Yan, T. Yang, Y. Wang and Y. Ruichek: Efficient Online Transfer Learning for Road Participants Detection in Autonomous Driving. IEEE Sensors Journal 2023.
19 IVA code 71.37 % 84.61 % 64.90 % 0.4 s GPU @ 2.5 Ghz (C/C++)
Y. Zhu, J. Wang, C. Zhao, H. Guo and H. Lu: Scale-adaptive Deconvolutional Regression Network for Pedestrian Detection. ACCV 2016.
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 2015.
20 MM-MRFC
This method uses optical flow information.
This method makes use of Velodyne laser scans.
70.76 % 83.79 % 64.81 % 0.05 s GPU @ 2.5 Ghz (C/C++)
A. Costea, R. Varga and S. Nedevschi: Fast Boosting based Detection using Scale Invariant Multimodal Multiresolution Filtered Features. CVPR 2017.
21 SDP+RPN 70.42 % 82.07 % 65.09 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 2015.
22 3DOP
This method uses stereo information.
code 69.57 % 83.17 % 63.48 % 3s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
23 MonoPSR code 68.56 % 85.60 % 63.34 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
24 DeepStereoOP 68.46 % 83.00 % 63.35 % 3.4 s GPU @ 3.5 Ghz (Matlab + C/C++)
C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.
25 sensekitti code 68.41 % 82.72 % 62.72 % 4.5 s GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.
26 MonoLSS 67.78 % 82.88 % 60.87 % 0.04 s 1 core @ 2.5 Ghz (Python)
Z. Li, J. Jia and Y. Shi: MonoLSS: Learnable Sample Selection For Monocular 3D Detection. International Conference on 3D Vision 2024.
27 Frustum-PointPillars code 67.51 % 76.80 % 63.81 % 0.06 s 4 cores @ 3.0 Ghz (Python)
A. Paigwar, D. Sierra-Gonzalez, \. Erkent and C. Laugier: Frustum-PointPillars: A Multi-Stage Approach for 3D Object Detection using RGB Camera and LiDAR. International Conference on Computer Vision, ICCV, Workshop on Autonomous Vehicle Vision 2021.
28 FII-CenterNet code 67.31 % 81.32 % 61.29 % 0.09 s GPU @ 2.5 Ghz (Python)
S. Fan, F. Zhu, S. Chen, H. Zhang, B. Tian, Y. Lv and F. Wang: FII-CenterNet: An Anchor-Free Detector With Foreground Attention for Traffic Object Detection. IEEE Transactions on Vehicular Technology 2021.
29 Mono3D code 67.29 % 80.30 % 62.23 % 4.2 s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.
30 Faster R-CNN code 66.24 % 79.97 % 61.09 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards Real- Time Object Detection with Region Proposal Networks. NIPS 2015.
31 VPFNet code 65.68 % 75.03 % 61.95 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
C. Wang, H. Chen and L. Fu: VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection. 2021.
32 EQ-PVRCNN code 65.01 % 77.19 % 61.95 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, L. Jiang, Y. Sun, B. Schiele and J. Jia: A Unified Query-based Paradigm for Point Cloud Understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.
33 CasA++ code 64.94 % 74.41 % 62.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.
34 TED code 64.74 % 74.26 % 62.08 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, C. Wen, W. Li, R. Yang and C. Wang: Transformation-Equivariant 3D Object Detection for Autonomous Driving. AAAI 2023.
35 LoGoNet code 64.55 % 72.47 % 62.24 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
X. Li, T. Ma, Y. Hou, B. Shi, Y. Yang, Y. Liu, X. Wu, Q. Chen, Y. Li, Y. Qiao and others: LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross-Modal Fusion. CVPR 2023.
36 SDP+CRC (ft) 64.36 % 79.22 % 59.16 % 0.6 s GPU @ 2.5 Ghz (C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
37 Pose-RCNN 63.54 % 80.07 % 57.02 % 2 s >8 cores @ 2.5 Ghz (Python)
M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on 2016.
38 USVLab BSAODet code 63.21 % 72.86 % 59.48 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
W. Xiao, Y. Peng, C. Liu, J. Gao, Y. Wu and X. Li: Balanced Sample Assignment and Objective for Single-Model Multi-Class 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2023.
39 MLF-DET 63.09 % 70.25 % 59.23 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
Z. Lin, Y. Shen, S. Zhou, S. Chen and N. Zheng: MLF-DET: Multi-Level Fusion for Cross- Modal 3D Object Detection. International Conference on Artificial Neural Networks 2023.
40 R^2 R-CNN 63.07 % 72.34 % 59.49 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
41 af 62.87 % 71.50 % 59.22 % 1 s GPU @ 2.5 Ghz (Python)
42 CFM 62.84 % 74.76 % 56.06 % <2 s GPU @ 2.5 Ghz (Matlab + C/C++)
Q. Hu, P. Wang, C. Shen, A. Hengel and F. Porikli: Pushing the Limits of Deep CNNs for Pedestrian Detection. IEEE Transactions on Circuits and Systems for Video Technology 2017.
43 CasA code 62.73 % 72.65 % 60.12 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.
44 Fast-CLOCs 62.57 % 76.22 % 60.13 % 0.1 s GPU @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: Fast-CLOCs: Fast Camera-LiDAR Object Candidates Fusion for 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022.
45 PiFeNet code 62.35 % 72.74 % 59.29 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
D. Le, H. Shi, H. Rezatofighi and J. Cai: Accurate and Real-time 3D Pedestrian Detection Using an Efficient Attentive Pillar Network. IEEE Robotics and Automation Letters 2022.
46 RPF3D 62.35 % 72.52 % 59.68 % 0.1 s 1 core @ 2.5 Ghz (Python)
47 HotSpotNet 62.31 % 71.43 % 59.24 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
48 IMLIDAR(base) 61.97 % 72.42 % 58.90 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
49 P2V-RCNN 61.83 % 71.76 % 59.29 % 0.1 s 2 cores @ 2.5 Ghz (Python)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.
50 MonoPair 61.57 % 78.81 % 56.51 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
51 monodle code 61.29 % 78.66 % 56.18 % 0.04 s GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for Monocular 3D Object Detection. CVPR 2021 .
52 RPN+BF code 61.22 % 77.06 % 55.22 % 0.6 s GPU @ 2.5 Ghz (Matlab + C/C++)
L. Zhang, L. Lin, X. Liang and K. He: Is Faster R-CNN Doing Well for Pedestrian Detection?. ECCV 2016.
53 PIPC-3Ddet code 61.15 % 68.23 % 57.53 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
54 focalnet 61.03 % 69.13 % 58.92 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
55 focalnet 60.99 % 69.06 % 58.89 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
56 Regionlets 60.83 % 73.79 % 54.72 % 1 s >8 cores @ 2.5 Ghz (C/C++)
X. Wang, M. Yang, S. Zhu and Y. Lin: Regionlets for Generic Object Detection. T-PAMI 2015.
W. Zou, X. Wang, M. Sun and Y. Lin: Generic Object Detection with Dense Neural Patterns and Regionlets. British Machine Vision Conference 2014.
C. Long, X. Wang, G. Hua, M. Yang and Y. Lin: Accurate Object Detection with Location Relaxation and Regionlets Relocalization. Asian Conference on Computer Vision 2014.
57 3DSSD code 60.51 % 72.33 % 56.28 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
58 3ONet 60.39 % 71.60 % 56.18 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
59 ACFNet 60.12 % 71.42 % 55.96 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
Y. Tian, X. Zhang, X. Wang, J. Xu, J. Wang, R. Ai, W. Gu and W. Ding: ACF-Net: Asymmetric Cascade Fusion for 3D Detection With LiDAR Point Clouds and Images. IEEE Transactions on Intelligent Vehicles 2023.
60 OFFNet 60.03 % 67.58 % 57.71 % 0.1 s GPU @ 2.5 Ghz (Python)
61 PV-PMRTNet 59.73 % 68.33 % 57.40 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
62 LGSLNet 59.58 % 68.54 % 57.34 % 0.1 s GPU @ 2.5 Ghz (Python)
63 IOUFusion 59.52 % 69.10 % 55.41 % 0.1 s GPU @ 2.5 Ghz (Python)
64 DPPFA-Net 59.52 % 67.68 % 56.87 % 0.1 s 1 core @ 2.5 Ghz (Python)
J. Wang, X. Kong, H. Nishikawa, Q. Lian and H. Tomiyama: Dynamic Point-Pixel Feature Alignment for Multi-modal 3D Object Detection. IEEE Internet of Things Journal 2023.
65 ACDet code 59.51 % 71.27 % 57.03 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
J. Xu, G. Wang, X. Zhang and G. Wan: ACDet: Attentive Cross-view Fusion for LiDAR-based 3D Object Detection. 3DV 2022.
66 PV-RCNN-Plus 59.26 % 67.99 % 56.41 % 1 s 1 core @ 2.5 Ghz (C/C++)
67 CZY_PPF_Net 59.26 % 67.81 % 57.04 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
68 QD-3DT
This is an online method (no batch processing).
code 59.26 % 78.41 % 54.37 % 0.03 s GPU @ 2.5 Ghz (Python)
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.
69 LGNet-3classes code 59.19 % 68.92 % 56.75 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
70 TANet code 59.07 % 69.90 % 56.44 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
71 MonoUNI code 58.97 % 76.17 % 53.99 % 0.04 s 1 core @ 2.5 Ghz (Python)
J. Jia, Z. Li and Y. Shi: MonoUNI: A Unified Vehicle and Infrastructure-side Monocular 3D Object Detection Network with Sufficient Depth Clues. Thirty-seventh Conference on Neural Information Processing Systems 2023.
72 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
code 58.81 % 66.93 % 56.57 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
73 PSMS-Net
This method makes use of Velodyne laser scans.
58.81 % 70.59 % 56.27 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
74 FEMV-RCNN code 58.78 % 68.31 % 56.39 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
75 SRDL 58.70 % 68.45 % 56.23 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
76 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 58.37 % 68.88 % 55.38 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
77 casxv1 58.34 % 71.44 % 55.96 % 0.01 s 1 core @ 2.5 Ghz (Python)
78 PASS-PV-RCNN-Plus 58.31 % 67.45 % 55.92 % 1 s 1 core @ 2.5 Ghz (Python)
Anonymous: Leveraging Anchor-based LiDAR 3D Object Detection via Point Assisted Sample Selection. will submit to computer vision conference/journal 2024.
79 F3D 58.25 % 67.94 % 55.96 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
80 focal 58.23 % 66.27 % 56.06 % 100 s 1 core @ 2.5 Ghz (Python)
81 Point-GNN
This method makes use of Velodyne laser scans.
code 58.20 % 71.59 % 54.06 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
82 DeepParts 58.15 % 71.47 % 51.92 % ~1 s GPU @ 2.5 Ghz (Matlab)
Y. Tian, P. Luo, X. Wang and X. Tang: Deep Learning Strong Parts for Pedestrian Detection. ICCV 2015.
83 CompACT-Deep 58.14 % 70.93 % 52.29 % 1 s 1 core @ 2.5 Ghz (Matlab + C/C++)
Z. Cai, M. Saberian and N. Vasconcelos: Learning Complexity-Aware Cascades for Deep Pedestrian Detection. ICCV 2015.
84 EPNet++ 58.10 % 68.58 % 55.58 % 0.1 s GPU @ 2.5 Ghz (Python)
Z. Liu, T. Huang, B. Li, X. Chen, X. Wang and X. Bai: EPNet++: Cascade Bi-Directional Fusion for Multi-Modal 3D Object Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 2022.
85 DSGN++
This method uses stereo information.
code 58.09 % 69.70 % 54.45 % 0.2 s GeForce RTX 2080Ti
Y. Chen, S. Huang, S. Liu, B. Yu and J. Jia: DSGN++: Exploiting Visual-Spatial Relation for Stereo-Based 3D Detectors. IEEE Transactions on Pattern Analysis and Machine Intelligence 2022.
86 IA-SSDx 58.01 % 71.00 % 53.95 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
87 casx 58.01 % 71.00 % 53.95 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
88 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 57.96 % 68.78 % 54.01 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
89 SVGA-Net 57.92 % 67.81 % 55.25 % 0.03s 1 core @ 2.5 Ghz (Python + C/C++)
Q. He, Z. Wang, H. Zeng, Y. Zeng and Y. Liu: SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds. AAAI 2022.
90 RAFDet 57.89 % 67.85 % 55.67 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
91 Rnet 57.87 % 66.14 % 54.87 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
92 AVOD-FPN
This method makes use of Velodyne laser scans.
code 57.87 % 67.95 % 55.23 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
93 DFAF3D 57.65 % 67.45 % 53.89 % 0.05 s 1 core @ 2.5 Ghz (Python)
Q. Tang, X. Bai, J. Guo, B. Pan and W. Jiang: DFAF3D: A dual-feature-aware anchor-free single-stage 3D detector for point clouds. Image and Vision Computing 2023.
94 HA-PillarNet 57.59 % 66.29 % 55.14 % 0.05 s 1 core @ 2.5 Ghz (Python)
95 PA-Det3D 57.55 % 66.80 % 55.18 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
96 U_PV_V2_ep100_80 57.50 % 66.11 % 55.21 % 0... s 1 core @ 2.5 Ghz (Python)
97 Faraway-Frustum
This method makes use of Velodyne laser scans.
code 57.35 % 67.88 % 54.42 % 0.1 s GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-frustum: Dealing with lidar sparsity for 3D object detection using fusion. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) 2021.
98 PDV code 57.34 % 65.94 % 54.21 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Hu, T. Kuai and S. Waslander: Point Density-Aware Voxels for LiDAR 3D Object Detection. CVPR 2022.
99 SIF 57.32 % 67.78 % 54.86 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
100 PG-RCNN code 57.31 % 67.77 % 54.83 % 0.06 s GPU @ 1.5 Ghz (Python)
I. Koo, I. Lee, S. Kim, H. Kim, W. Jeon and C. Kim: PG-RCNN: Semantic Surface Point Generation for 3D Object Detection. 2023.
101 VPA 57.27 % 70.06 % 54.83 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
102 FromVoxelToPoint code 57.26 % 68.26 % 54.74 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to- Point Decoder. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
103 u_second_v4_epoch_10 57.25 % 67.10 % 55.01 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
104 SemanticVoxels 57.22 % 67.62 % 54.90 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
J. Fei, W. Chen, P. Heidenreich, S. Wirges and C. Stiller: SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentation. MFI 2020.
105 LVFSD 57.20 % 67.44 % 54.67 % 0.06 s
ERROR: Wrong syntax in BIBTEX file.
106 Anonymous 57.12 % 68.51 % 53.06 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
107 IIOU code 57.05 % 66.36 % 53.06 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
108 Anonymous code 56.90 % 70.04 % 52.69 % 0.04 s 1 core @ 2.5 Ghz (Python)
109 DiffCandiDet 56.89 % 68.21 % 54.49 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
110 IA-SSD (single) code 56.87 % 66.69 % 54.68 % 0.013 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
111 CAT-Det 56.75 % 67.15 % 53.44 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Zhang, J. Chen and D. Huang: CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection. CVPR 2022.
112 FRCNN+Or code 56.68 % 71.64 % 51.53 % 0.09 s Titan Xp GPU
C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine 2018.
C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features. IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.
113 FilteredICF 56.53 % 69.79 % 50.32 % ~ 2 s >8 cores @ 2.5 Ghz (Matlab + C/C++)
S. Zhang, R. Benenson and B. Schiele: Filtered Channel Features for Pedestrian Detection. CVPR 2015.
114 ARPNET 56.42 % 69.08 % 52.69 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
115 MonoRUn code 56.40 % 73.05 % 51.40 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
116 RAFDet code 56.25 % 66.03 % 52.79 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
117 MV-RGBD-RF
This method makes use of Velodyne laser scans.
56.18 % 72.99 % 49.72 % 4 s 4 cores @ 2.5 Ghz (C/C++)
A. Gonzalez, D. Vazquez, A. Lopez and J. Amores: On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts.. IEEE Trans. on Cybernetics 2016.
A. Gonzalez, G. Villalonga, J. Xu, D. Vazquez, J. Amores and A. Lopez: Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection. IEEE Intelligent Vehicles Symposium (IV) 2015.
118 U_PV_V2_ep_100_100 56.18 % 64.52 % 54.12 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
119 U_second_v4_ep_100_8 56.03 % 65.94 % 53.94 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
120 HAF-PVP_test 55.96 % 65.29 % 53.25 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
121 HMFI code 55.96 % 66.20 % 53.24 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
X. Li, B. Shi, Y. Hou, X. Wu, T. Ma, Y. Li and L. He: Homogeneous Multi-modal Feature Fusion and Interaction for 3D Object Detection. ECCV 2022.
122 HybridPillars 55.90 % 64.07 % 53.46 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
123 STNet 55.90 % 65.63 % 53.70 % 0.60 s 1 core @ 2.5 Ghz (Python)
124 MGAF-3DSSD code 55.80 % 66.31 % 52.02 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
125 MG 55.70 % 64.24 % 52.17 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
126 GUPNet code 55.65 % 74.95 % 48.44 % NA s 1 core @ 2.5 Ghz (Python + C/C++)
Y. Lu, X. Ma, L. Yang, T. Zhang, Y. Liu, Q. Chu, J. Yan and W. Ouyang: Geometry Uncertainty Projection Network for Monocular 3D Object Detection. arXiv preprint arXiv:2107.13774 2021.
127 MLOD
This method makes use of Velodyne laser scans.
code 55.62 % 68.42 % 51.45 % 0.12 s GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
128 HybridPillars (SSD) 55.54 % 65.55 % 52.96 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
129 RAFDet 55.41 % 64.91 % 53.19 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
130 MLAFF 55.22 % 67.96 % 53.15 % 0.39 s 2 cores @ 2.5 Ghz (Python)
131 DEVIANT code 55.16 % 74.27 % 50.21 % 0.04 s 1 GPU (Python)
A. Kumar, G. Brazil, E. Corona, A. Parchami and X. Liu: DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection. European Conference on Computer Vision (ECCV) 2022.
132 PointPillars
This method makes use of Velodyne laser scans.
code 55.10 % 65.29 % 52.39 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
133 StereoDistill 55.09 % 69.00 % 50.95 % 0.4 s 1 core @ 2.5 Ghz (Python)
Z. Liu, X. Ye, X. Tan, D. Errui, Y. Zhou and X. Bai: StereoDistill: Pick the Cream from LiDAR for Distilling Stereo-based 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2023.
134 STD code 55.04 % 68.33 % 50.85 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
135 GeVo 55.04 % 63.95 % 52.93 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
136 DGEnhCL code 55.01 % 67.98 % 52.59 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
137 OPA-3D code 54.92 % 73.93 % 47.87 % 0.04 s 1 core @ 3.5 Ghz (Python)
Y. Su, Y. Di, G. Zhai, F. Manhardt, J. Rambach, B. Busam, D. Stricker and F. Tombari: OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object Detection. IEEE Robotics and Automation Letters 2023.
138 MSAW 54.91 % 68.16 % 52.64 % 0.42 s 2 cores @ 2.5 Ghz (Python)
139 SC-SSD 54.83 % 64.15 % 52.65 % 1 s 1 core @ 2.5 Ghz (C/C++)
140 Vote3Deep
This method makes use of Velodyne laser scans.
54.80 % 67.99 % 51.17 % 1.5 s 4 cores @ 2.5 Ghz (C/C++)
M. Engelcke, D. Rao, D. Zeng Wang, C. Hay Tong and I. Posner: Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks. ArXiv e-prints 2016.
141 M3DeTR code 54.78 % 63.15 % 52.49 % n/a s GPU @ 1.0 Ghz (Python)
T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis and D. Manocha: M3DeTR: Multi-representation, Multi- scale, Mutual-relation 3D Object Detection with Transformers. 2021.
142 DDF 54.64 % 67.48 % 50.41 % 0.1 s 1 core @ 2.5 Ghz (Python)
143 L-AUG 54.61 % 65.71 % 51.67 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
T. Cortinhal, I. Gouigah and E. Aksoy: Semantics-aware LiDAR-Only Pseudo Point Cloud Generation for 3D Object Detection. 2023.
144 RealSynthesis-SECOND 54.18 % 63.77 % 50.93 % 0.05 s 1 core @ 2.5 Ghz (Python)
145 epBRM
This method makes use of Velodyne laser scans.
code 54.13 % 62.90 % 51.95 % 0.10 s 1 core @ 2.5 Ghz (C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
146 DFSemONet(Baseline) 54.13 % 65.42 % 52.05 % 0.04 s GPU @ 2.5 Ghz (Python)
147 DVFENet 54.13 % 63.54 % 51.79 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection. Neurocomputing 2021.
148 DTSSD 53.84 % 62.75 % 51.66 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
149 XView 53.83 % 62.27 % 51.61 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
150 PointPainting
This method makes use of Velodyne laser scans.
53.76 % 61.86 % 50.61 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
151 MonoInsight 53.72 % 67.31 % 47.54 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
152 MonoInsight 53.72 % 67.31 % 47.54 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
153 PDV2 53.54 % 65.59 % 47.65 % 3.7 s 1 core @ 3.0 Ghz Matlab (C/C++)
J. Shen, X. Zuo, J. Li, W. Yang and H. Ling: A novel pixel neighborhood differential statistic feature for pedestrian and face detection . Pattern Recognition 2017.
154 PR-SSD 53.52 % 62.55 % 50.04 % 0.02 s GPU @ 2.5 Ghz (Python)
155 Mix-Teaching code 53.52 % 67.34 % 47.45 % 30 s 1 core @ 2.5 Ghz (C/C++)
L. Yang, X. Zhang, L. Wang, M. Zhu, C. Zhang and J. Li: Mix-Teaching: A Simple, Unified and Effective Semi-Supervised Learning Framework for Monocular 3D Object Detection. ArXiv 2022.
156 DA-Net 53.36 % 68.50 % 48.89 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
157 AMVFNet code 53.28 % 62.79 % 49.69 % 0.04 s GPU @ 2.5 Ghz (Python)
158 Cube R-CNN code 53.27 % 64.96 % 47.84 % 0.05 s GPU @ 2.5 Ghz (Python)
G. Brazil, A. Kumar, J. Straub, N. Ravi, J. Johnson and G. Gkioxari: Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild. CVPR 2023.
159 GF-pointnet 53.26 % 62.91 % 50.71 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
160 PVTr 53.23 % 62.15 % 51.04 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
161 TAFT 53.15 % 67.62 % 47.08 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
J. Shen, X. Zuo, W. Yang, D. Prokhorov, X. Mei and H. Ling: Differential Features for Pedestrian Detection: A Taylor Series Perspective. IEEE Transactions on Intelligent Transportation Systems 2018.
162 Disp R-CNN
This method uses stereo information.
code 52.98 % 71.79 % 48.20 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
163 PI-SECOND code 52.97 % 63.28 % 49.07 % 0.05 s GPU @ >3.5 Ghz (Python + C/C++)
164 Disp R-CNN (velo)
This method uses stereo information.
code 52.90 % 71.63 % 48.15 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
165 pAUCEnsT 52.88 % 65.84 % 46.97 % 60 s 1 core @ 2.5 Ghz (Matlab + C/C++)
S. Paisitkriangkrai, C. Shen and A. Hengel: Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning. arXiv 2014.
166 SparVox3D 52.84 % 69.33 % 48.49 % 0.05 s GPU @ 2.0 Ghz (Python)
E. Balatkan and F. Kıraç: Improving Regression Performance on Monocular 3D Object Detection Using Bin-Mixing and Sparse Voxel Data. 2021 6th International Conference on Computer Science and Engineering (UBMK) 2021.
167 AAMVFNet code 52.73 % 63.13 % 50.44 % 0.04 s GPU @ 2.5 Ghz (Python)
168 MonoAIU 52.68 % 71.73 % 45.61 % 0.03 s GPU @ 2.5 Ghz (Python)
169 PFF3D
This method makes use of Velodyne laser scans.
code 52.53 % 62.12 % 50.27 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
170 IA-SSD (multi) code 52.45 % 65.07 % 50.20 % 0.014 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
171 MVAF-Net(3-classes) 52.37 % 64.19 % 49.14 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
172 S-AT GCN 52.30 % 62.01 % 50.10 % 0.02 s GPU @ 2.0 Ghz (Python)
L. Wang, C. Wang, X. Zhang, T. Lan and J. Li: S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection. CoRR 2021.
173 MMLAB LIGA-Stereo
This method uses stereo information.
code 52.18 % 65.59 % 49.29 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
174 GS-FPS code 51.98 % 62.86 % 49.70 % TBD s 1 core @ 2.5 Ghz (C/C++)
175 HINTED 51.95 % 66.52 % 47.83 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
176 XT-PartA2 51.93 % 61.04 % 49.32 % 0.1 s GPU @ >3.5 Ghz (Python)
177 Plane-Constraints 51.57 % 64.64 % 46.98 % 0.05 s 4 cores @ 3.0 Ghz (Python)
H. Yao, J. Chen, Z. Wang, X. Wang, X. Chai, Y. Qiu and P. Han: Vertex points are not enough: Monocular 3D object detection via intra-and inter-plane constraints. Neural Networks 2023.
178 mm3d_PartA2 51.40 % 60.60 % 48.39 % 0.1 s GPU @ >3.5 Ghz (Python)
179 Shift R-CNN (mono) code 51.30 % 70.86 % 46.37 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
180 SeSame-voxel code 51.27 % 60.29 % 49.06 % N/A s TITAN RTX @ 1.35 Ghz (Python)
181 OA-TSSD 51.19 % 60.26 % 49.28 % 20 s 8 cores @ 2.5 Ghz (C/C++)
182 APDM code 51.08 % 61.99 % 48.24 % 0.7 s 1 core @ 2.5 Ghz (Python)
183 DTSSD 50.09 % 59.08 % 48.08 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
184 SeSame-point code 49.74 % 60.69 % 45.64 % N/A s TITAN RTX @ 1.35 Ghz (Python)
185 SCNet
This method makes use of Velodyne laser scans.
49.61 % 60.95 % 46.91 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
186 MM_SECOND code 49.60 % 60.59 % 46.70 % 0.05 s GPU @ >3.5 Ghz (Python)
187 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 49.41 % 58.93 % 46.44 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
188 MonoSIM_v2 49.01 % 63.65 % 42.86 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
189 HomoLoss(monoflex) code 48.97 % 63.77 % 44.60 % 0.04 s 1 core @ 2.5 Ghz (Python)
J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homography Loss for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
190 MMpointpillars 48.95 % 60.01 % 46.12 % 0.05 s 1 core @ 2.5 Ghz (Python)
191 PEF code 48.93 % 59.21 % 46.66 % N/A s 1 core @ 2.5 Ghz (C/C++)
192 Voxel-MAE+SECOND code 48.80 % 57.27 % 46.87 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
193 HA PillarNet 48.78 % 59.59 % 46.03 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
194 PS++ 48.73 % 61.33 % 44.66 % 0.4 s 1 core @ 2.5 Ghz (C/C++)
195 ACFD
This method makes use of Velodyne laser scans.
code 48.63 % 61.62 % 44.15 % 0.2 s 4 cores @ >3.5 Ghz (C/C++)
M. Dimitrievski, P. Veelaert and W. Philips: Semantically aware multilateral filter for depth upsampling in automotive LiDAR point clouds. IEEE Intelligent Vehicles Symposium, IV 2017, Los Angeles, CA, USA, June 11-14, 2017 2017.
196 R-CNN 48.57 % 62.88 % 43.05 % 4 s GPU @ 3.3 Ghz (C/C++)
J. Hosang, M. Omran, R. Benenson and B. Schiele: Taking a Deeper Look at Pedestrians. arXiv 2015.
197 prcnn_v18_80_100 48.53 % 60.07 % 45.72 % 0.1 s 1 core @ 2.5 Ghz (Python)
198 Anonymous 48.51 % 65.02 % 41.77 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
199 GraphAlign 48.47 % 55.17 % 46.68 % 0.03 s GPU @ 2.0 Ghz (Python)
200 IIOU_LDR code 48.40 % 60.07 % 46.41 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
201 ROT_S3D 48.11 % 59.38 % 46.18 % 0.1 s GPU @ 2.5 Ghz (Python)
202 MonoATT_V2 code 48.07 % 64.25 % 43.73 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
203 MonoTRKD 47.70 % 63.28 % 43.16 % 40 s 1 core @ 2.5 Ghz (Python)
204 MonoLiG code 47.69 % 62.87 % 43.27 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
A. Hekimoglu, M. Schmidt and A. Ramiro: Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learning. 2023.
205 MonoFlex 47.58 % 62.64 % 43.15 % 0.03 s GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.
206 BirdNet+
This method makes use of Velodyne laser scans.
code 47.50 % 54.78 % 45.53 % 0.11 s Titan Xp (PyTorch)
A. Barrera, J. Beltrán, C. Guindel, J. Iglesias and F. García: BirdNet+: Two-Stage 3D Object Detection in LiDAR through a Sparsity-Invariant Bird’s Eye View. IEEE Access 2021.
207 MVAF-Net(3-classes) 46.87 % 57.07 % 44.08 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
208 CMKD code 46.84 % 61.04 % 42.92 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Y. Hong, H. Dai and Y. Ding: Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection. ECCV 2022.
209 VSAC 46.33 % 59.10 % 44.07 % 0.07 s 1 core @ 1.0 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
210 MonOAPC 46.31 % 60.93 % 42.05 % 0035 s 1 core @ 2.5 Ghz (Python)
211 MMpp 46.09 % 56.10 % 43.62 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
212 SS3D 45.79 % 61.58 % 41.14 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
213 MonoRCNN++ code 45.76 % 60.29 % 39.39 % 0.07 s GPU @ 2.5 Ghz (Python)
X. Shi, Z. Chen and T. Kim: Multivariate Probabilistic Monocular 3D Object Detection. WACV 2023.
214 ACF 45.67 % 59.81 % 40.88 % 1 s 1 core @ 3.5 Ghz (Matlab + C/C++)
P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014.
215 SH3D 45.64 % 59.74 % 41.29 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
216 Fusion-DPM
This method makes use of Velodyne laser scans.
code 44.99 % 58.93 % 40.19 % ~ 30 s 1 core @ 3.5 Ghz (Matlab + C/C++)
C. Premebida, J. Carreira, J. Batista and U. Nunes: Pedestrian Detection Combining RGB and Dense LIDAR Data. IROS 2014.
217 ACF-MR 44.79 % 58.29 % 39.94 % 0.6 s 1 core @ 3.5 Ghz (C/C++)
R. Rajaram, E. Ohn-Bar and M. Trivedi: Looking at Pedestrians at Different Scales: A Multi-resolution Approach and Evaluations. T-ITS 2016.
218 MonoTRKDv2 44.54 % 59.66 % 40.12 % 40 s 1 core @ 2.5 Ghz (Python)
219 P2P code 44.30 % 55.25 % 42.40 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
220 SeSame-pillar code 44.21 % 52.67 % 41.95 % N/A s TITAN RTX @ 1.35 Ghz (Python)
221 LPCG-Monoflex code 44.13 % 62.44 % 39.46 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
L. Peng, F. Liu, Z. Yu, S. Yan, D. Deng, Z. Yang, H. Liu and D. Cai: Lidar Point Cloud Guided Monocular 3D Object Detection. ECCV 2022.
222 HA-SSVM 43.87 % 58.76 % 38.81 % 21 s 1 core @ >3.5 Ghz (Matlab + C/C++)
J. Xu, S. Ramos, D. Vázquez and A. López: Hierarchical Adaptive Structural SVM for Domain Adaptation. IJCV 2016.
223 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 43.86 % 54.55 % 40.99 % 0.0047s 1 core @ 2.5 Ghz (python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
224 MonoEF 43.73 % 58.79 % 39.45 % 0.03 s 1 core @ 2.5 Ghz (Python)
Y. Zhou, Y. He, H. Zhu, C. Wang, H. Li and Q. Jiang: Monocular 3D Object Detection: An Extrinsic Parameter Free Approach. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
225 Anonymous 43.65 % 55.61 % 39.54 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
226 DSC3D
This method uses stereo information.
43.64 % 58.46 % 39.31 % 0.31 s GPU @ 2.5 Ghz (Python)
227 D4LCN code 43.50 % 59.55 % 37.12 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
228 DMF
This method uses stereo information.
43.43 % 52.99 % 41.29 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
X. J. Chen and W. Xu: Disparity-Based Multiscale Fusion Network for Transportation Detection. IEEE Transactions on Intelligent Transportation Systems 2022.
229 MonoDDE 43.36 % 57.80 % 39.00 % 0.04 s 1 core @ 2.5 Ghz (Python)
Z. Li, Z. Qu, Y. Zhou, J. Liu, H. Wang and L. Jiang: Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection. CVPR 2022.
230 DPM-VOC+VP 43.26 % 59.21 % 38.12 % 8 s 1 core @ 2.5 Ghz (C/C++)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015.
231 ACF-SC 42.97 % 53.30 % 38.12 % <0.3 s 1 core @ >3.5 Ghz (Matlab + C/C++)
C. Cadena, A. Dick and I. Reid: A Fast, Modular Scene Understanding System using Context-Aware Object Detection. Robotics and Automation (ICRA), 2015 IEEE International Conference on 2015.
232 OccupancyM3D 42.95 % 57.08 % 38.28 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
233 MonoDTR 42.86 % 59.44 % 38.57 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
K. Huang, T. Wu, H. Su and W. Hsu: MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer. CVPR 2022.
234 SquaresICF code 42.61 % 57.08 % 37.85 % 1 s GPU @ >3.5 Ghz (C/C++)
R. Benenson, M. Mathias, T. Tuytelaars and L. Gool: Seeking the strongest rigid detector. CVPR 2013.
235 CG-Stereo
This method uses stereo information.
42.54 % 54.64 % 38.45 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
236 MonoAuxNorm 42.32 % 56.80 % 37.86 % 0.02 s GPU @ 2.5 Ghz (Python)
237 BirdNet+ (legacy)
This method makes use of Velodyne laser scans.
code 41.97 % 51.38 % 40.15 % 0.1 s Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird’s Eye View. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
238 DDMP-3D 41.54 % 56.73 % 35.52 % 0.18 s 1 core @ 2.5 Ghz (Python)
L. Wang, L. Du, X. Ye, Y. Fu, G. Guo, X. Xue, J. Feng and L. Zhang: Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection. CVPR 2020.
239 CSW3D
This method makes use of Velodyne laser scans.
41.50 % 53.76 % 37.25 % 0.03 s 4 cores @ 2.5 Ghz (C/C++)
J. Hu, T. Wu, H. Fu, Z. Wang and K. Ding: Cascaded Sliding Window Based Real-Time 3D Region Proposal for Pedestrian Detection. ROBIO 2019.
240 M3D-RPN code 41.46 % 56.64 % 37.31 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
241 YOLOStereo3D
This method uses stereo information.
code 41.46 % 56.20 % 37.07 % 0.1 s GPU 1080Ti
Y. Liu, L. Wang and M. Liu: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
242 fuf 41.42 % 53.95 % 37.48 % 10 s 1 core @ 2.5 Ghz (C/C++)
243 BKDStereo3D code 41.17 % 55.94 % 34.99 % 0.1 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
244 CIE 41.04 % 53.27 % 37.73 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Anonymities: Consistency of Implicit and Explicit Features Matters for Monocular 3D Object Detection. arXiv preprint arXiv:2207.07933 2022.
245 SubCat 40.50 % 53.75 % 35.66 % 1.2 s 6 cores @ 2.5 Ghz (Matlab + C/C++)
E. Ohn-Bar and M. Trivedi: Fast and Robust Object Detection Using Visual Subcategories. Computer Vision and Pattern Recognition Workshops Mobile Vision 2014.
246 PS-fld code 40.47 % 55.47 % 36.65 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
Y. Chen, H. Dai and Y. Ding: Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
247 DSGN
This method uses stereo information.
code 39.93 % 49.28 % 38.13 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
248 RT3D-GMP
This method uses stereo information.
39.83 % 55.56 % 35.18 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
249 SparsePool code 39.59 % 50.81 % 35.91 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
250 SparsePool code 39.43 % 50.94 % 35.78 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
251 AVOD
This method makes use of Velodyne laser scans.
code 39.43 % 50.90 % 35.75 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
252 ACF 39.12 % 48.42 % 35.03 % 0.2 s 1 core @ >3.5 Ghz (Matlab + C/C++)
P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014.
P. Doll\'ar: Piotr's Image and Video Matlab Toolbox (PMT). .
253 MonoLTKD 38.60 % 54.33 % 34.12 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
254 MonoLTKD_V3 38.60 % 54.33 % 34.12 % 0.04 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
255 LSVM-MDPM-sv 37.26 % 50.74 % 33.13 % 10 s 4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout. NIPS 2011.
256 ODGS 37.12 % 46.01 % 34.56 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
257 BKDStereo3D w/o KD code 37.02 % 50.58 % 32.92 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
258 multi-task CNN 37.00 % 49.38 % 33.46 % 25.1 ms GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
259 MonoSIM 36.71 % 49.49 % 33.24 % 0.16 s 1 core @ 2.5 Ghz (Python)
260 FastDet 36.60 % 45.47 % 34.70 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
261 Complexer-YOLO
This method makes use of Velodyne laser scans.
36.45 % 42.16 % 32.91 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
262 LSVM-MDPM-us code 35.92 % 48.73 % 31.70 % 10 s 4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
263 SVDM-VIEW 35.90 % 48.27 % 32.44 % 1 s 1 core @ 2.5 Ghz (Python)
264 CMAN 34.96 % 49.73 % 30.92 % 0.15 s 1 core @ 2.5 Ghz (Python)
C. Yuanzhouhan Cao: CMAN: Leaning Global Structure Correlation for Monocular 3D Object Detection. IEEE Trans. Intell. Transport. Syst. 2022.
265 Aug3D-RPN 34.95 % 47.22 % 30.64 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth. 2021.
266 FMF-occlusion-net 34.74 % 49.26 % 30.37 % 0.16 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Liu, H. Liu, Y. Wang, F. Sun and W. Huang: Fine-grained Multi-level Fusion for Anti- occlusion Monocular 3D Object Detection. IEEE Transactions on Image Processing 2022.
267 TS3D
This method uses stereo information.
34.44 % 48.70 % 30.26 % 0.09 s GPU @ 1.5 Ghz (Python + C/C++)
268 MonoNeRD code 34.43 % 46.50 % 31.06 % na s 1 core @ 2.5 Ghz (Python)
J. Xu, L. Peng, H. Cheng, H. Li, W. Qian, K. Li, W. Wang and D. Cai: MonoNeRD: NeRF-like Representations for Monocular 3D Object Detection. ICCV 2023.
269 PointRGBNet 33.92 % 44.35 % 30.43 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
P. Xie Desheng: Real-time Detection of 3D Objects Based on Multi-Sensor Information Fusion. Automotive Engineering 2022.
270 PGD-FCOS3D code 33.67 % 48.30 % 29.76 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
T. Wang, X. Zhu, J. Pang and D. Lin: Probabilistic and Geometric Depth: Detecting Objects in Perspective. Conference on Robot Learning (CoRL) 2021.
271 Vote3D
This method makes use of Velodyne laser scans.
33.04 % 42.66 % 30.59 % 0.5 s 4 cores @ 2.8 Ghz (C/C++)
D. Wang and I. Posner: Voting for Voting in Online Point Cloud Object Detection. Proceedings of Robotics: Science and Systems 2015.
272 ESGN
This method uses stereo information.
32.60 % 44.09 % 29.10 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
A. Gao, Y. Pang, J. Nie, Z. Shao, J. Cao, Y. Guo and X. Li: ESGN: Efficient Stereo Geometry Network for Fast 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2022.
273 SGM3D code 32.48 % 45.03 % 28.58 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
Z. Zhou, L. Du, X. Ye, Z. Zou, X. Tan, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection. RA-L 2022.
274 CaDDN code 32.42 % 46.35 % 29.98 % 0.63 s GPU @ 2.5 Ghz (Python)
C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.
275 DFR-Net 31.84 % 45.20 % 27.94 % 0.18 s 1080 Ti (Pytorch)
Z. Zou, X. Ye, L. Du, X. Cheng, X. Tan, L. Zhang, J. Feng, X. Xue and E. Ding: The devil is in the task: Exploiting reciprocal appearance-localization features for monocular 3d object detection . ICCV 2021.
276 OC Stereo
This method uses stereo information.
code 30.79 % 43.50 % 28.40 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
277 mBoW
This method makes use of Velodyne laser scans.
30.26 % 41.52 % 26.34 % 10 s 1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
278 BirdNet
This method makes use of Velodyne laser scans.
30.07 % 36.82 % 28.40 % 0.11 s Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
279 RT3DStereo
This method uses stereo information.
29.30 % 41.12 % 25.25 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
280 MDSNet 29.25 % 41.64 % 26.01 % 0.05 s 1 core @ 2.5 Ghz (Python)
Z. Xie, Y. Song, J. Wu, Z. Li, C. Song and Z. Xu: MDS-Net: Multi-Scale Depth Stratification 3D Object Detection from Monocular Images. Sensors 2022.
281 SST [st]
This method uses stereo information.
26.78 % 38.41 % 24.58 % 1 s 1 core @ 2.5 Ghz (Python)
282 DPM-C8B1
This method uses stereo information.
25.34 % 36.40 % 22.00 % 15 s 4 cores @ 2.5 Ghz (Matlab + C/C++)
J. Yebes, L. Bergasa and M. García-Garrido: Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes. Sensors 2015.
J. Yebes, L. Bergasa, R. Arroyo and A. Lázaro: Supervised learning and evaluation of KITTI's cars detector with DPM. IV 2014.
283 RefinedMPL 20.81 % 30.41 % 18.72 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
284 TopNet-Retina
This method makes use of Velodyne laser scans.
16.45 % 22.37 % 15.43 % 52ms GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
285 TopNet-HighRes
This method makes use of Velodyne laser scans.
15.28 % 21.22 % 13.89 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
286 YOLOv2 code 11.46 % 15.37 % 9.67 % 0.02 s GPU @ 3.5 Ghz (C/C++)
J. Redmon, S. Divvala, R. Girshick and A. Farhadi: You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
287 MonoGhost_Ped_Cycl 9.80 % 13.31 % 9.91 % 0.03 s 1 core @ 2.5 Ghz (Python)
288 TopNet-UncEst
This method makes use of Velodyne laser scans.
8.58 % 13.00 % 7.38 % 0.09 s NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
289 BIP-HETERO 7.05 % 8.51 % 6.30 % ~2 s 1 core @ 2.5 Ghz (C/C++)
A. Mekonnen, F. Lerasle, A. Herbulot and C. Briand: People Detection with Heterogeneous Features and Explicit Optimization on Computation Time. Pattern Recognition (ICPR), 2014 22nd International Conference on 2014.
290 init 0.03 % 0.03 % 0.03 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
291 TopNet-DecayRate
This method makes use of Velodyne laser scans.
0.01 % 0.01 % 0.01 % 92 ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
292 DA3D+KM3D+v2-99 0.00 % 0.00 % 0.00 % 1 s GPU @ 2.5 Ghz (Python)
293 DA3D+KM3D 0.00 % 0.00 % 0.00 % 0.02 s GPU @ 2.5 Ghz (Python)
294 DA3D 0.00 % 0.00 % 0.00 % 0.03 s 1 core @ 2.5 Ghz (Python)
Table as LaTeX | Only published Methods

Cyclist


Method Setting Code Moderate Easy Hard Runtime Environment
1 OFFNet 86.24 % 89.05 % 81.37 % 0.1 s GPU @ 2.5 Ghz (Python)
2 TED code 84.36 % 92.60 % 78.43 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, C. Wen, W. Li, R. Yang and C. Wang: Transformation-Equivariant 3D Object Detection for Autonomous Driving. AAAI 2023.
3 CasA++ code 84.26 % 92.38 % 78.42 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.
4 LoGoNet code 84.00 % 90.14 % 77.97 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
X. Li, T. Ma, Y. Hou, B. Shi, Y. Yang, Y. Liu, X. Wu, Q. Chen, Y. Li, Y. Qiao and others: LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross-Modal Fusion. CVPR 2023.
5 CasA code 83.21 % 92.86 % 77.12 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.
6 MLF-DET 81.95 % 87.34 % 74.79 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
Z. Lin, Y. Shen, S. Zhou, S. Chen and N. Zheng: MLF-DET: Multi-Level Fusion for Cross- Modal 3D Object Detection. International Conference on Artificial Neural Networks 2023.
7 VPA 81.85 % 89.87 % 75.16 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
8 HMFI code 81.76 % 89.35 % 74.93 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
X. Li, B. Shi, Y. Hou, X. Wu, T. Ma, Y. Li and L. He: Homogeneous Multi-modal Feature Fusion and Interaction for 3D Object Detection. ECCV 2022.
9 IMLIDAR(base) 81.72 % 90.91 % 75.19 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
10 RangeIoUDet
This method makes use of Velodyne laser scans.
81.67 % 90.43 % 74.90 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, Z. Zhang, M. Zhang, X. Zhao and S. Pu: RangeIoUDet: Range Image Based Real-Time 3D Object Detector Optimized by Intersection Over Union. CVPR 2021.
11 USVLab BSAODet code 81.36 % 86.82 % 74.40 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
W. Xiao, Y. Peng, C. Liu, J. Gao, Y. Wu and X. Li: Balanced Sample Assignment and Objective for Single-Model Multi-Class 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2023.
12 PV-PMRTNet 81.12 % 89.07 % 74.38 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
13 HA-PillarNet 81.03 % 89.88 % 75.05 % 0.05 s 1 core @ 2.5 Ghz (Python)
14 U_PV_V2_ep100_80 80.85 % 89.46 % 74.01 % 0... s 1 core @ 2.5 Ghz (Python)
15 CAT-Det 80.70 % 87.94 % 73.86 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Zhang, J. Chen and D. Huang: CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection. CVPR 2022.
16 SPG_mini
This method makes use of Velodyne laser scans.
code 80.58 % 87.77 % 74.86 % 0.09 s GPU @ 2.5 Ghz (Python)
Q. Xu, Y. Zhou, W. Wang, C. Qi and D. Anguelov: SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation. Proceedings of the IEEE conference on computer vision and pattern recognition (ICCV) 2021.
17 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
code 80.57 % 88.65 % 74.81 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
18 PIPC-3Ddet code 80.49 % 89.69 % 73.73 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
19 BtcDet
This method makes use of Velodyne laser scans.
code 80.46 % 88.41 % 74.59 % 0.09 s GPU @ 2.5 Ghz (Python + C/C++)
Q. Xu, Y. Zhong and U. Neumann: Behind the Curtain: Learning Occluded Shapes for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2022.
20 Rnet 80.44 % 87.56 % 73.55 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
21 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 80.42 % 86.62 % 73.64 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
22 EQ-PVRCNN code 80.37 % 89.07 % 74.20 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, L. Jiang, Y. Sun, B. Schiele and J. Jia: A Unified Query-based Paradigm for Point Cloud Understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.
23 Anonymous code 80.21 % 89.61 % 73.14 % 0.04 s 1 core @ 2.5 Ghz (Python)
24 PSMS-Net
This method makes use of Velodyne laser scans.
80.10 % 88.76 % 73.27 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
25 HINTED 80.04 % 86.76 % 73.45 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
26 PDV code 79.84 % 88.76 % 73.04 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Hu, T. Kuai and S. Waslander: Point Density-Aware Voxels for LiDAR 3D Object Detection. CVPR 2022.
27 CZY_PPF_Net 79.75 % 87.34 % 73.05 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
28 PEF code 79.70 % 91.33 % 72.64 % N/A s 1 core @ 2.5 Ghz (C/C++)
29 U_PV_V2_ep_100_100 79.60 % 86.87 % 73.32 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
30 GeVo 79.54 % 89.03 % 73.17 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
31 M3DeTR code 79.29 % 87.38 % 72.46 % n/a s GPU @ 1.0 Ghz (Python)
T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis and D. Manocha: M3DeTR: Multi-representation, Multi- scale, Mutual-relation 3D Object Detection with Transformers. 2021.
32 DiffCandiDet 79.27 % 88.76 % 72.87 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
33 PASS-PV-RCNN-Plus 79.22 % 86.26 % 72.68 % 1 s 1 core @ 2.5 Ghz (Python)
Anonymous: Leveraging Anchor-based LiDAR 3D Object Detection via Point Assisted Sample Selection. will submit to computer vision conference/journal 2024.
34 HAF-PVP_test 79.19 % 89.84 % 71.93 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
35 PV-RCNN-Plus 79.01 % 86.50 % 72.88 % 1 s 1 core @ 2.5 Ghz (C/C++)
36 HotSpotNet 78.81 % 86.06 % 71.74 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
37 HybridPillars 78.81 % 87.30 % 72.25 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
38 IA-SSD (single) code 78.71 % 88.99 % 72.03 % 0.013 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
39 PG-RCNN code 78.69 % 88.17 % 72.16 % 0.06 s GPU @ 1.5 Ghz (Python)
I. Koo, I. Lee, S. Kim, H. Kim, W. Jeon and C. Kim: PG-RCNN: Semantic Surface Point Generation for 3D Object Detection. 2023.
40 3ONet 78.51 % 90.18 % 71.38 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
41 DDF 78.41 % 89.27 % 71.46 % 0.1 s 1 core @ 2.5 Ghz (Python)
42 PA-Det3D 78.31 % 87.56 % 71.82 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
43 focalnet 78.30 % 85.05 % 73.30 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
44 focalnet 78.29 % 85.19 % 73.15 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
45 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 78.29 % 88.90 % 71.19 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
46 LGNet-3classes code 78.21 % 84.45 % 71.71 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
47 F-ConvNet
This method makes use of Velodyne laser scans.
code 78.05 % 86.75 % 68.12 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
48 PointPainting
This method makes use of Velodyne laser scans.
78.04 % 87.70 % 69.27 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
49 mm3d_PartA2 78.02 % 89.05 % 71.63 % 0.1 s GPU @ >3.5 Ghz (Python)
50 FEMV-RCNN code 77.83 % 87.12 % 70.66 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
51 DFAF3D 77.74 % 87.20 % 70.77 % 0.05 s 1 core @ 2.5 Ghz (Python)
Q. Tang, X. Bai, J. Guo, B. Pan and W. Jiang: DFAF3D: A dual-feature-aware anchor-free single-stage 3D detector for point clouds. Image and Vision Computing 2023.
52 IA-SSDx 77.58 % 88.94 % 70.80 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
53 casx 77.58 % 88.94 % 70.80 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
54 PVTr 77.49 % 89.71 % 70.58 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
55 RPF3D 77.20 % 89.45 % 70.36 % 0.1 s 1 core @ 2.5 Ghz (Python)
56 GraphAlign 77.15 % 84.72 % 72.34 % 0.03 s GPU @ 2.0 Ghz (Python)
57 RAFDet 77.00 % 87.23 % 70.90 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
58 Anonymous 76.98 % 88.37 % 70.15 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
59 P2V-RCNN 76.93 % 88.40 % 70.35 % 0.1 s 2 cores @ 2.5 Ghz (Python)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.
60 EOTL code 76.88 % 85.62 % 66.04 % TBD s 1 core @ 2.5 Ghz (Python + C/C++)
R. Yang, Z. Yan, T. Yang, Y. Wang and Y. Ruichek: Efficient Online Transfer Learning for Road Participants Detection in Autonomous Driving. IEEE Sensors Journal 2023.
61 RRC code 76.81 % 86.81 % 66.59 % 3.6 s GPU @ 2.5 Ghz (C/C++)
J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y. Tai and L. Xu: Accurate Single Stage Detector Using Recurrent Rolling Convolution. CVPR 2017.
62 XT-PartA2 76.70 % 87.40 % 71.40 % 0.1 s GPU @ >3.5 Ghz (Python)
63 af 76.63 % 85.66 % 71.49 % 1 s GPU @ 2.5 Ghz (Python)
64 RAFDet 76.40 % 88.57 % 69.83 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
65 GF-pointnet 76.31 % 85.23 % 69.74 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
66 AAMVFNet code 76.28 % 85.63 % 69.91 % 0.04 s GPU @ 2.5 Ghz (Python)
67 ACFNet 76.15 % 86.92 % 71.33 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
Y. Tian, X. Zhang, X. Wang, J. Xu, J. Wang, R. Ai, W. Gu and W. Ding: ACF-Net: Asymmetric Cascade Fusion for 3D Detection With LiDAR Point Clouds and Images. IEEE Transactions on Intelligent Vehicles 2023.
68 AMVFNet code 75.81 % 84.77 % 69.81 % 0.04 s GPU @ 2.5 Ghz (Python)
69 F3D 75.75 % 88.38 % 69.02 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
70 DA-Net 75.64 % 86.64 % 71.06 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
71 PR-SSD 75.64 % 85.78 % 70.35 % 0.02 s GPU @ 2.5 Ghz (Python)
72 DTSSD 75.55 % 87.11 % 69.43 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
73 casxv1 75.52 % 88.33 % 68.68 % 0.01 s 1 core @ 2.5 Ghz (Python)
74 ACDet code 75.41 % 88.54 % 69.45 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
J. Xu, G. Wang, X. Zhang and G. Wan: ACDet: Attentive Cross-view Fusion for LiDAR-based 3D Object Detection. 3DV 2022.
75 u_second_v4_epoch_10 75.38 % 87.96 % 69.54 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
76 MS-CNN code 75.30 % 84.88 % 65.27 % 0.4 s GPU @ 2.5 Ghz (C/C++)
Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection. ECCV 2016.
77 LGSLNet 75.29 % 84.01 % 70.60 % 0.1 s GPU @ 2.5 Ghz (Python)
78 STNet 75.22 % 87.47 % 68.28 % 0.60 s 1 core @ 2.5 Ghz (Python)
79 TuSimple code 75.22 % 83.68 % 65.22 % 1.6 s GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016.
80 SVGA-Net 75.14 % 85.13 % 68.14 % 0.03s 1 core @ 2.5 Ghz (Python + C/C++)
Q. He, Z. Wang, H. Zeng, Y. Zeng and Y. Liu: SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds. AAAI 2022.
81 Point-GNN
This method makes use of Velodyne laser scans.
code 75.08 % 85.75 % 68.69 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
82 Fast-CLOCs 75.07 % 89.73 % 67.93 % 0.1 s GPU @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: Fast-CLOCs: Fast Camera-LiDAR Object Candidates Fusion for 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022.
83 DTSSD 74.81 % 86.18 % 68.26 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
84 OA-TSSD 74.79 % 87.90 % 68.52 % 20 s 8 cores @ 2.5 Ghz (C/C++)
85 Deep3DBox 74.78 % 84.36 % 64.05 % 1.5 s GPU @ 2.5 Ghz (C/C++)
A. Mousavian, D. Anguelov, J. Flynn and J. Kosecka: 3D Bounding Box Estimation Using Deep Learning and Geometry. CVPR 2017.
86 U_second_v4_ep_100_8 74.75 % 84.43 % 67.71 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
87 prcnn_v18_80_100 74.74 % 86.53 % 67.00 % 0.1 s 1 core @ 2.5 Ghz (Python)
88 VPFNet code 74.52 % 82.60 % 66.04 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
C. Wang, H. Chen and L. Fu: VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection. 2021.
89 SC-SSD 74.45 % 84.37 % 67.85 % 1 s 1 core @ 2.5 Ghz (C/C++)
90 RealSynthesis-SECOND 74.37 % 87.41 % 66.91 % 0.05 s 1 core @ 2.5 Ghz (Python)
91 3DSSD code 74.12 % 87.09 % 67.67 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
92 focal 74.03 % 84.16 % 69.35 % 100 s 1 core @ 2.5 Ghz (Python)
93 SDP+RPN 73.85 % 82.59 % 64.87 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 2015.
94 SRDL 73.68 % 85.44 % 66.94 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
95 DVFENet 73.66 % 85.45 % 67.10 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection. Neurocomputing 2021.
96 Faraway-Frustum
This method makes use of Velodyne laser scans.
code 73.63 % 85.43 % 66.64 % 0.1 s GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-frustum: Dealing with lidar sparsity for 3D object detection using fusion. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) 2021.
97 MM_SECOND code 73.63 % 85.26 % 67.06 % 0.05 s GPU @ >3.5 Ghz (Python)
98 sensekitti code 73.48 % 82.90 % 64.03 % 4.5 s GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.
99 PI-SECOND code 73.44 % 87.02 % 67.23 % 0.05 s GPU @ >3.5 Ghz (Python + C/C++)
100 L-AUG 73.43 % 83.88 % 68.12 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
T. Cortinhal, I. Gouigah and E. Aksoy: Semantics-aware LiDAR-Only Pseudo Point Cloud Generation for 3D Object Detection. 2023.
101 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 73.42 % 86.21 % 66.45 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
102 RAFDet code 73.40 % 84.98 % 67.27 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
103 HybridPillars (SSD) 73.24 % 82.36 % 67.38 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
104 IIOU code 73.20 % 86.92 % 66.11 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
105 SIF 73.19 % 85.18 % 65.41 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
106 F-PointNet
This method makes use of Velodyne laser scans.
code 73.16 % 86.86 % 65.21 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
107 FromVoxelToPoint code 73.16 % 87.07 % 65.98 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to- Point Decoder. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
108 XView 73.16 % 88.02 % 65.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
109 S-AT GCN 72.81 % 82.79 % 66.72 % 0.02 s GPU @ 2.0 Ghz (Python)
L. Wang, C. Wang, X. Zhang, T. Lan and J. Li: S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection. CoRR 2021.
110 H^23D R-CNN code 72.73 % 85.50 % 65.81 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
J. Deng, W. Zhou, Y. Zhang and H. Li: From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2021.
111 MG 72.54 % 84.19 % 65.93 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
112 IOUFusion 72.49 % 86.41 % 65.60 % 0.1 s GPU @ 2.5 Ghz (Python)
113 SeSame-voxel code 72.25 % 84.16 % 65.39 % N/A s TITAN RTX @ 1.35 Ghz (Python)
114 GS-FPS code 72.17 % 84.60 % 66.52 % TBD s 1 core @ 2.5 Ghz (C/C++)
115 P2P code 72.17 % 82.32 % 65.65 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
116 Voxel-MAE+SECOND code 72.09 % 82.55 % 65.45 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
117 MonoPSR code 72.08 % 82.06 % 62.43 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
118 ARPNET 71.95 % 84.96 % 65.21 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
119 SubCNN 71.72 % 79.36 % 62.74 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.
120 STD code 71.63 % 83.99 % 64.92 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
121 DGEnhCL code 71.39 % 85.86 % 62.42 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
122 LVFSD 71.09 % 85.81 % 64.75 % 0.06 s
ERROR: Wrong syntax in BIBTEX file.
123 IA-SSD (multi) code 70.46 % 84.98 % 65.55 % 0.014 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
124 MGAF-3DSSD code 70.41 % 86.42 % 63.26 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
125 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 70.18 % 82.86 % 63.55 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
126 fuf 69.67 % 84.99 % 62.91 % 10 s 1 core @ 2.5 Ghz (C/C++)
127 SeSame-point code 69.59 % 87.27 % 62.84 % N/A s TITAN RTX @ 1.35 Ghz (Python)
128 IIOU_LDR code 69.49 % 83.10 % 64.59 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
129 PointPillars
This method makes use of Velodyne laser scans.
code 68.98 % 83.97 % 62.17 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
130 Vote3Deep
This method makes use of Velodyne laser scans.
68.82 % 78.41 % 62.50 % 1.5 s 4 cores @ 2.5 Ghz (C/C++)
M. Engelcke, D. Rao, D. Zeng Wang, C. Hay Tong and I. Posner: Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks. ArXiv e-prints 2016.
131 3DOP
This method uses stereo information.
code 68.71 % 80.52 % 61.07 % 3s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
132 ROT_S3D 68.53 % 84.35 % 63.76 % 0.1 s GPU @ 2.5 Ghz (Python)
133 Pose-RCNN 68.40 % 81.53 % 59.43 % 2 s >8 cores @ 2.5 Ghz (Python)
M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on 2016.
134 EPNet++ 68.30 % 80.27 % 63.00 % 0.1 s GPU @ 2.5 Ghz (Python)
Z. Liu, T. Huang, B. Li, X. Chen, X. Wang and X. Bai: EPNet++: Cascade Bi-Directional Fusion for Multi-Modal 3D Object Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 2022.
135 TANet code 68.20 % 82.24 % 62.13 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
136 IVA code 67.57 % 78.48 % 58.83 % 0.4 s GPU @ 2.5 Ghz (C/C++)
Y. Zhu, J. Wang, C. Zhao, H. Guo and H. Lu: Scale-adaptive Deconvolutional Regression Network for Pedestrian Detection. ACCV 2016.
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 2015.
137 MSAW 67.22 % 82.14 % 62.13 % 0.42 s 2 cores @ 2.5 Ghz (Python)
138 DeepStereoOP 67.22 % 79.35 % 58.60 % 3.4 s GPU @ 3.5 Ghz (Matlab + C/C++)
C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.
139 Cube R-CNN code 66.98 % 81.99 % 58.56 % 0.05 s GPU @ 2.5 Ghz (Python)
G. Brazil, A. Kumar, J. Straub, N. Ravi, J. Johnson and G. Gkioxari: Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild. CVPR 2023.
140 MMpp 66.87 % 80.54 % 60.69 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
141 SeSame-pillar code 66.76 % 77.99 % 60.45 % N/A s TITAN RTX @ 1.35 Ghz (Python)
142 MMpointpillars 66.66 % 77.03 % 61.25 % 0.05 s 1 core @ 2.5 Ghz (Python)
143 APDM code 66.65 % 80.16 % 60.51 % 0.7 s 1 core @ 2.5 Ghz (Python)
144 HA PillarNet 66.63 % 78.41 % 60.09 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
145 FII-CenterNet code 66.54 % 79.04 % 57.76 % 0.09 s GPU @ 2.5 Ghz (Python)
S. Fan, F. Zhu, S. Chen, H. Zhang, B. Tian, Y. Lv and F. Wang: FII-CenterNet: An Anchor-Free Detector With Foreground Attention for Traffic Object Detection. IEEE Transactions on Vehicular Technology 2021.
146 epBRM
This method makes use of Velodyne laser scans.
code 66.51 % 79.65 % 60.31 % 0.10 s 1 core @ 2.5 Ghz (C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
147 PFF3D
This method makes use of Velodyne laser scans.
code 66.25 % 79.44 % 60.11 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
148 DD3D code 65.98 % 81.13 % 58.86 % n/a s 1 core @ 2.5 Ghz (C/C++)
D. Park, R. Ambrus, V. Guizilini, J. Li and A. Gaidon: Is Pseudo-Lidar needed for Monocular 3D Object detection?. IEEE/CVF International Conference on Computer Vision (ICCV) .
149 PointRGBNet 65.98 % 79.87 % 59.75 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
P. Xie Desheng: Real-time Detection of 3D Objects Based on Multi-Sensor Information Fusion. Automotive Engineering 2022.
150 VSAC 65.90 % 82.03 % 59.18 % 0.07 s 1 core @ 1.0 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
151 BirdNet+
This method makes use of Velodyne laser scans.
code 65.40 % 72.96 % 60.23 % 0.11 s Titan Xp (PyTorch)
A. Barrera, J. Beltrán, C. Guindel, J. Iglesias and F. García: BirdNet+: Two-Stage 3D Object Detection in LiDAR through a Sparsity-Invariant Bird’s Eye View. IEEE Access 2021.
152 Mono3D code 65.15 % 77.19 % 57.88 % 4.2 s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.
153 MLAFF 64.41 % 80.28 % 58.52 % 0.39 s 2 cores @ 2.5 Ghz (Python)
154 DMF
This method uses stereo information.
63.39 % 74.69 % 56.96 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
X. J. Chen and W. Xu: Disparity-Based Multiscale Fusion Network for Transportation Detection. IEEE Transactions on Intelligent Transportation Systems 2022.
155 PiFeNet code 63.34 % 78.05 % 56.46 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
D. Le, H. Shi, H. Rezatofighi and J. Cai: Accurate and Real-time 3D Pedestrian Detection Using an Efficient Attentive Pillar Network. IEEE Robotics and Automation Letters 2022.
156 FastDet 62.93 % 75.79 % 57.31 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
157 Faster R-CNN code 62.86 % 72.40 % 54.97 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards Real- Time Object Detection with Region Proposal Networks. NIPS 2015.
158 SCNet
This method makes use of Velodyne laser scans.
62.50 % 78.48 % 56.34 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
159 DSGN++
This method uses stereo information.
code 62.10 % 77.71 % 55.78 % 0.2 s GeForce RTX 2080Ti
Y. Chen, S. Huang, S. Liu, B. Yu and J. Jia: DSGN++: Exploiting Visual-Spatial Relation for Stereo-Based 3D Detectors. IEEE Transactions on Pattern Analysis and Machine Intelligence 2022.
160 StereoDistill 61.46 % 80.92 % 54.64 % 0.4 s 1 core @ 2.5 Ghz (Python)
Z. Liu, X. Ye, X. Tan, D. Errui, Y. Zhou and X. Bai: StereoDistill: Pick the Cream from LiDAR for Distilling Stereo-based 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2023.
161 DFSemONet(Baseline) 61.46 % 77.65 % 55.87 % 0.04 s GPU @ 2.5 Ghz (Python)
162 AVOD-FPN
This method makes use of Velodyne laser scans.
code 60.79 % 70.38 % 55.37 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
163 SDP+CRC (ft) 60.72 % 75.63 % 53.00 % 0.6 s GPU @ 2.5 Ghz (C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
164 MVAF-Net(3-classes) 60.04 % 74.01 % 55.71 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
165 MonoInsight 59.86 % 76.21 % 51.17 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
166 MonoInsight 59.86 % 76.21 % 51.17 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
167 Complexer-YOLO
This method makes use of Velodyne laser scans.
59.78 % 66.94 % 55.63 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
168 MVAF-Net(3-classes) 59.54 % 72.32 % 55.44 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
169 Mix-Teaching code 58.65 % 75.15 % 50.54 % 30 s 1 core @ 2.5 Ghz (C/C++)
L. Yang, X. Zhang, L. Wang, M. Zhu, C. Zhang and J. Li: Mix-Teaching: A Simple, Unified and Effective Semi-Supervised Learning Framework for Monocular 3D Object Detection. ArXiv 2022.
170 Regionlets 58.52 % 71.12 % 50.83 % 1 s >8 cores @ 2.5 Ghz (C/C++)
X. Wang, M. Yang, S. Zhu and Y. Lin: Regionlets for Generic Object Detection. T-PAMI 2015.
W. Zou, X. Wang, M. Sun and Y. Lin: Generic Object Detection with Dense Neural Patterns and Regionlets. British Machine Vision Conference 2014.
C. Long, X. Wang, G. Hua, M. Yang and Y. Lin: Accurate Object Detection with Location Relaxation and Regionlets Relocalization. Asian Conference on Computer Vision 2014.
171 MonoLiG code 58.35 % 80.41 % 51.21 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
A. Hekimoglu, M. Schmidt and A. Ramiro: Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learning. 2023.
172 FRCNN+Or code 57.01 % 70.99 % 50.14 % 0.09 s Titan Xp GPU
C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine 2018.
C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features. IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.
173 QD-3DT
This is an online method (no batch processing).
code 56.51 % 75.55 % 49.70 % 0.03 s GPU @ 2.5 Ghz (Python)
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.
174 MonoPair 56.37 % 74.77 % 48.37 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
175 MLOD
This method makes use of Velodyne laser scans.
code 56.04 % 75.35 % 49.11 % 0.12 s GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
176 Anonymous 55.47 % 74.27 % 47.25 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
177 MonoATT_V2 code 54.84 % 78.21 % 47.98 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
178 MonoFlex 54.76 % 72.41 % 46.21 % 0.03 s GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.
179 MonoLSS 54.63 % 74.54 % 47.98 % 0.04 s 1 core @ 2.5 Ghz (Python)
Z. Li, J. Jia and Y. Shi: MonoLSS: Learnable Sample Selection For Monocular 3D Detection. International Conference on 3D Vision 2024.
180 BirdNet+ (legacy)
This method makes use of Velodyne laser scans.
code 54.61 % 74.97 % 50.29 % 0.1 s Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird’s Eye View. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
181 MMLAB LIGA-Stereo
This method uses stereo information.
code 54.57 % 74.40 % 48.11 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
182 HomoLoss(monoflex) code 54.12 % 70.14 % 46.16 % 0.04 s 1 core @ 2.5 Ghz (Python)
J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homography Loss for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
183 MonoUNI code 53.71 % 71.68 % 45.26 % 0.04 s 1 core @ 2.5 Ghz (Python)
J. Jia, Z. Li and Y. Shi: MonoUNI: A Unified Vehicle and Infrastructure-side Monocular 3D Object Detection Network with Sufficient Depth Clues. Thirty-seventh Conference on Neural Information Processing Systems 2023.
184 monodle code 53.29 % 70.78 % 45.01 % 0.04 s GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for Monocular 3D Object Detection. CVPR 2021 .
185 LPCG-Monoflex code 53.04 % 72.36 % 46.11 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
L. Peng, F. Liu, Z. Yu, S. Yan, D. Deng, Z. Yang, H. Liu and D. Cai: Lidar Point Cloud Guided Monocular 3D Object Detection. ECCV 2022.
186 AVOD
This method makes use of Velodyne laser scans.
code 52.60 % 66.45 % 46.39 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
187 PS++ 52.29 % 72.60 % 46.82 % 0.4 s 1 core @ 2.5 Ghz (C/C++)
188 CMKD code 51.76 % 73.18 % 45.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Y. Hong, H. Dai and Y. Ding: Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection. ECCV 2022.
189 MonOAPC 51.68 % 68.18 % 44.08 % 0035 s 1 core @ 2.5 Ghz (Python)
190 SH3D 51.59 % 73.71 % 44.86 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
191 MonoDDE 51.10 % 70.85 % 44.02 % 0.04 s 1 core @ 2.5 Ghz (Python)
Z. Li, Z. Qu, Y. Zhou, J. Liu, H. Wang and L. Jiang: Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection. CVPR 2022.
192 MonoDTR 49.48 % 64.93 % 42.76 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
K. Huang, T. Wu, H. Su and W. Hsu: MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer. CVPR 2022.
193 MonoRUn code 49.13 % 67.47 % 43.41 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
194 MonoRCNN++ code 48.84 % 67.78 % 42.44 % 0.07 s GPU @ 2.5 Ghz (Python)
X. Shi, Z. Chen and T. Kim: Multivariate Probabilistic Monocular 3D Object Detection. WACV 2023.
195 CG-Stereo
This method uses stereo information.
48.46 % 69.98 % 42.41 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
196 MonoSIM_v2 48.20 % 68.66 % 41.69 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
197 BirdNet
This method makes use of Velodyne laser scans.
47.64 % 64.91 % 44.59 % 0.11 s Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
198 MonoAIU 46.87 % 69.33 % 40.57 % 0.03 s GPU @ 2.5 Ghz (Python)
199 DEVIANT code 46.42 % 67.71 % 39.44 % 0.04 s 1 GPU (Python)
A. Kumar, G. Brazil, E. Corona, A. Parchami and X. Liu: DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection. European Conference on Computer Vision (ECCV) 2022.
200 Disp R-CNN (velo)
This method uses stereo information.
code 46.37 % 63.22 % 40.15 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
201 Disp R-CNN
This method uses stereo information.
code 46.37 % 63.24 % 40.15 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
202 SparsePool code 44.57 % 60.53 % 40.37 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
203 Anonymous 43.40 % 67.49 % 38.66 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
204 Shift R-CNN (mono) code 42.96 % 63.24 % 38.22 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
205 D4LCN code 42.86 % 65.29 % 36.29 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
206 GUPNet code 42.78 % 67.11 % 37.94 % NA s 1 core @ 2.5 Ghz (Python + C/C++)
Y. Lu, X. Ma, L. Yang, T. Zhang, Y. Liu, Q. Chu, J. Yan and W. Ouyang: Geometry Uncertainty Projection Network for Monocular 3D Object Detection. arXiv preprint arXiv:2107.13774 2021.
207 MonoAuxNorm 42.37 % 62.78 % 36.88 % 0.02 s GPU @ 2.5 Ghz (Python)
208 M3D-RPN code 41.54 % 61.54 % 35.23 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
209 MonoEF 41.19 % 51.06 % 35.70 % 0.03 s 1 core @ 2.5 Ghz (Python)
Y. Zhou, Y. He, H. Zhu, C. Wang, H. Li and Q. Jiang: Monocular 3D Object Detection: An Extrinsic Parameter Free Approach. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
210 PS-fld code 41.13 % 58.13 % 35.90 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
Y. Chen, H. Dai and Y. Ding: Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
211 Plane-Constraints 41.01 % 58.71 % 35.35 % 0.05 s 4 cores @ 3.0 Ghz (Python)
H. Yao, J. Chen, Z. Wang, X. Wang, X. Chai, Y. Qiu and P. Han: Vertex points are not enough: Monocular 3D object detection via intra-and inter-plane constraints. Neural Networks 2023.
212 MV-RGBD-RF
This method makes use of Velodyne laser scans.
40.94 % 51.10 % 34.83 % 4 s 4 cores @ 2.5 Ghz (C/C++)
A. Gonzalez, D. Vazquez, A. Lopez and J. Amores: On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts.. IEEE Trans. on Cybernetics 2016.
A. Gonzalez, G. Villalonga, J. Xu, D. Vazquez, J. Amores and A. Lopez: Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection. IEEE Intelligent Vehicles Symposium (IV) 2015.
213 MonoTRKDv2 39.61 % 56.76 % 34.50 % 40 s 1 core @ 2.5 Ghz (Python)
214 MonoTRKD 39.12 % 58.34 % 34.59 % 40 s 1 core @ 2.5 Ghz (Python)
215 MonoLTKD 38.84 % 58.63 % 33.99 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
216 MonoLTKD_V3 38.84 % 58.63 % 33.99 % 0.04 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
217 DDMP-3D 38.62 % 58.70 % 34.10 % 0.18 s 1 core @ 2.5 Ghz (Python)
L. Wang, L. Du, X. Ye, Y. Fu, G. Guo, X. Xue, J. Feng and L. Zhang: Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection. CVPR 2020.
218 CMAN 38.36 % 58.12 % 31.79 % 0.15 s 1 core @ 2.5 Ghz (Python)
C. Yuanzhouhan Cao: CMAN: Leaning Global Structure Correlation for Monocular 3D Object Detection. IEEE Trans. Intell. Transport. Syst. 2022.
219 OPA-3D code 38.35 % 55.98 % 33.83 % 0.04 s 1 core @ 3.5 Ghz (Python)
Y. Su, Y. Di, G. Zhai, F. Manhardt, J. Rambach, B. Busam, D. Stricker and F. Tombari: OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object Detection. IEEE Robotics and Automation Letters 2023.
220 Aug3D-RPN 36.69 % 51.49 % 30.04 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth. 2021.
221 SparsePool code 36.26 % 44.21 % 32.57 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
222 SS3D 35.48 % 52.97 % 31.07 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
223 DSGN
This method uses stereo information.
code 35.15 % 49.10 % 31.41 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
224 pAUCEnsT 34.90 % 50.51 % 30.35 % 60 s 1 core @ 2.5 Ghz (Matlab + C/C++)
S. Paisitkriangkrai, C. Shen and A. Hengel: Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning. arXiv 2014.
225 TopNet-Retina
This method makes use of Velodyne laser scans.
31.98 % 47.51 % 29.84 % 52ms GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
226 DFR-Net 31.93 % 48.34 % 27.95 % 0.18 s 1080 Ti (Pytorch)
Z. Zou, X. Ye, L. Du, X. Cheng, X. Tan, L. Zhang, J. Feng, X. Xue and E. Ding: The devil is in the task: Exploiting reciprocal appearance-localization features for monocular 3d object detection . ICCV 2021.
227 SVDM-VIEW 31.75 % 47.79 % 27.83 % 1 s 1 core @ 2.5 Ghz (Python)
228 OccupancyM3D 30.22 % 41.76 % 26.32 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
229 CIE 30.10 % 38.03 % 26.99 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Anonymities: Consistency of Implicit and Explicit Features Matters for Monocular 3D Object Detection. arXiv preprint arXiv:2207.07933 2022.
230 MonoNeRD code 29.89 % 45.35 % 26.49 % na s 1 core @ 2.5 Ghz (Python)
J. Xu, L. Peng, H. Cheng, H. Li, W. Qian, K. Li, W. Wang and D. Cai: MonoNeRD: NeRF-like Representations for Monocular 3D Object Detection. ICCV 2023.
231 OC Stereo
This method uses stereo information.
code 28.76 % 43.18 % 24.80 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
232 Vote3D
This method makes use of Velodyne laser scans.
27.99 % 39.81 % 25.19 % 0.5 s 4 cores @ 2.8 Ghz (C/C++)
D. Wang and I. Posner: Voting for Voting in Online Point Cloud Object Detection. Proceedings of Robotics: Science and Systems 2015.
233 SGM3D code 27.89 % 42.21 % 24.73 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
Z. Zhou, L. Du, X. Ye, Z. Zou, X. Tan, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection. RA-L 2022.
234 LSVM-MDPM-us code 27.81 % 37.66 % 24.83 % 10 s 4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
235 DPM-VOC+VP 27.73 % 41.58 % 24.61 % 8 s 1 core @ 2.5 Ghz (C/C++)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015.
236 RefinedMPL 27.17 % 44.47 % 22.84 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
237 CaDDN code 27.13 % 40.03 % 23.23 % 0.63 s GPU @ 2.5 Ghz (Python)
C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.
238 PGD-FCOS3D code 26.48 % 44.28 % 23.03 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
T. Wang, X. Zhu, J. Pang and D. Lin: Probabilistic and Geometric Depth: Detecting Objects in Perspective. Conference on Robot Learning (CoRL) 2021.
239 LSVM-MDPM-sv 26.05 % 35.70 % 23.56 % 10 s 4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout. NIPS 2011.
240 SST [st]
This method uses stereo information.
25.96 % 41.39 % 22.78 % 1 s 1 core @ 2.5 Ghz (Python)
241 DPM-C8B1
This method uses stereo information.
25.57 % 41.47 % 21.93 % 15 s 4 cores @ 2.5 Ghz (Matlab + C/C++)
J. Yebes, L. Bergasa and M. García-Garrido: Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes. Sensors 2015.
J. Yebes, L. Bergasa, R. Arroyo and A. Lázaro: Supervised learning and evaluation of KITTI's cars detector with DPM. IV 2014.
242 FMF-occlusion-net 23.59 % 37.41 % 21.20 % 0.16 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Liu, H. Liu, Y. Wang, F. Sun and W. Huang: Fine-grained Multi-level Fusion for Anti- occlusion Monocular 3D Object Detection. IEEE Transactions on Image Processing 2022.
243 RT3D-GMP
This method uses stereo information.
22.90 % 33.64 % 19.87 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
244 MonoSIM 20.28 % 27.90 % 17.23 % 0.16 s 1 core @ 2.5 Ghz (Python)
245 mBoW
This method makes use of Velodyne laser scans.
17.63 % 26.66 % 16.02 % 10 s 1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
246 MonoGhost_Ped_Cycl 17.42 % 24.56 % 15.40 % 0.03 s 1 core @ 2.5 Ghz (Python)
247 MDSNet 16.64 % 28.23 % 14.14 % 0.05 s 1 core @ 2.5 Ghz (Python)
Z. Xie, Y. Song, J. Wu, Z. Li, C. Song and Z. Xu: MDS-Net: Multi-Scale Depth Stratification 3D Object Detection from Monocular Images. Sensors 2022.
248 TopNet-HighRes
This method makes use of Velodyne laser scans.
13.98 % 22.86 % 14.52 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
249 ESGN
This method uses stereo information.
13.45 % 21.13 % 11.72 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
A. Gao, Y. Pang, J. Nie, Z. Shao, J. Cao, Y. Guo and X. Li: ESGN: Efficient Stereo Geometry Network for Fast 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2022.
250 RT3DStereo
This method uses stereo information.
12.96 % 19.58 % 11.47 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
251 TopNet-UncEst
This method makes use of Velodyne laser scans.
12.00 % 18.14 % 11.85 % 0.09 s NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
252 init 0.13 % 0.08 % 0.07 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
253 YOLOv2 code 0.06 % 0.15 % 0.07 % 0.02 s GPU @ 3.5 Ghz (C/C++)
J. Redmon, S. Divvala, R. Girshick and A. Farhadi: You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
254 TopNet-DecayRate
This method makes use of Velodyne laser scans.
0.04 % 0.00 % 0.04 % 92 ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
255 DA3D+KM3D+v2-99 0.00 % 0.00 % 0.00 % 1 s GPU @ 2.5 Ghz (Python)
256 DA3D+KM3D 0.00 % 0.00 % 0.00 % 0.02 s GPU @ 2.5 Ghz (Python)
257 DA3D 0.00 % 0.00 % 0.00 % 0.03 s 1 core @ 2.5 Ghz (Python)
Table as LaTeX | Only published Methods

Object Detection and Orientation Estimation Evaluation

Cars


Method Setting Code Moderate Easy Hard Runtime Environment
1 MB3D 97.72 % 98.75 % 92.81 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
2 LVP(84.92) 97.66 % 98.68 % 92.81 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
3 MM-UniTOD 97.44 % 98.63 % 94.30 % 0.04 s 1 core @ 2.5 Ghz (Python)
4 HPC-Net 97.40 % 98.56 % 92.76 % 0.18 s 1 core @ 2.5 Ghz (C/C++)
5 UDeerPEP code 97.39 % 98.40 % 94.80 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Z. Dong, H. Ji, X. Huang, W. Zhang, X. Zhan and J. Chen: PeP: a Point enhanced Painting method for unified point cloud tasks. 2023.
6 VirConv-S code 96.46 % 96.99 % 93.74 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, C. Wen, S. Shi and C. Wang: Virtual Sparse Convolution for Multimodal 3D Object Detection. CVPR 2023.
7 GraR-VoI code 96.29 % 96.81 % 91.06 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph. ECCV 2022.
8 DAF-SSD 96.19 % 96.93 % 88.64 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
9 MLF-DET 96.09 % 96.87 % 88.78 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
Z. Lin, Y. Shen, S. Zhou, S. Chen and N. Zheng: MLF-DET: Multi-Level Fusion for Cross- Modal 3D Object Detection. International Conference on Artificial Neural Networks 2023.
10 GraR-Po code 96.09 % 96.83 % 90.99 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph. ECCV 2022.
11 SFD code 96.05 % 98.95 % 90.96 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
X. Wu, L. Peng, H. Yang, L. Xie, C. Huang, C. Deng, H. Liu and D. Cai: Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completion. CVPR 2022.
12 VPFNet code 96.04 % 96.63 % 90.99 % 0.06 s 2 cores @ 2.5 Ghz (Python)
H. Zhu, J. Deng, Y. Zhang, J. Ji, Q. Mao, H. Li and Y. Zhang: VPFNet: Improving 3D Object Detection with Virtual Point based LiDAR and Stereo Data Fusion. IEEE Transactions on Multimedia 2022.
13 VirConv-T code 96.01 % 98.64 % 93.12 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, C. Wen, S. Shi and C. Wang: Virtual Sparse Convolution for Multimodal 3D Object Detection. CVPR 2023.
14 HDet3D 96.00 % 96.69 % 90.84 % 0.07 s >8 cores @ 2.5 Ghz (Python)
15 TED code 95.96 % 96.63 % 93.24 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, C. Wen, W. Li, R. Yang and C. Wang: Transformation-Equivariant 3D Object Detection for Autonomous Driving. AAAI 2023.
16 RDIoU code 95.95 % 98.77 % 90.90 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Sheng, S. Cai, N. Zhao, B. Deng, J. Huang, X. Hua, M. Zhao and G. Lee: Rethinking IoU-based Optimization for Single- stage 3D Object Detection. ECCV 2022.
17 ACFNet 95.95 % 96.64 % 93.17 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
Y. Tian, X. Zhang, X. Wang, J. Xu, J. Wang, R. Ai, W. Gu and W. Ding: ACF-Net: Asymmetric Cascade Fusion for 3D Detection With LiDAR Point Clouds and Images. IEEE Transactions on Intelligent Vehicles 2023.
18 CLOCs code 95.93 % 96.77 % 90.93 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection . 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
19 PVFusion code 95.93 % 98.67 % 90.91 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
20 GraR-Vo code 95.92 % 96.66 % 92.78 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph. ECCV 2022.
21 LFT 95.87 % 99.15 % 88.47 % 0.1s 1 core @ 2.5 Ghz (C/C++)
22 PVT-SSD 95.83 % 96.74 % 90.58 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Yang, W. Wang, M. Chen, B. Lin, T. He, H. Chen, X. He and W. Ouyang: PVT-SSD: Single-Stage 3D Object Detector with Point-Voxel Transformer. CVPR 2023.
23 CLOCs_PVCas code 95.79 % 96.74 % 90.81 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection . 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
24 3D Dual-Fusion code 95.76 % 96.53 % 93.01 % 0.1 s 1 core @ 2.5 Ghz (Python)
Y. Kim, K. Park, M. Kim, D. Kum and J. Choi: 3D Dual-Fusion: Dual-Domain Dual-Query Camera-LiDAR Fusion for 3D Object Detection. arXiv preprint arXiv:2211.13529 2022.
25 PIPC-3Ddet code 95.75 % 96.79 % 90.79 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
26 GLENet-VR code 95.73 % 96.84 % 90.80 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Zhang, Z. Zhu, J. Hou and Y. Yuan: GLENet: Boosting 3D object detectors with generative label uncertainty estimation. International Journal of Computer Vision 2023.
27 GraR-Pi code 95.72 % 98.57 % 92.55 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph. ECCV 2022.
28 VPA 95.71 % 96.70 % 90.81 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
29 NIV-SSD 95.71 % 98.66 % 90.64 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
30 DiffCandiDet 95.70 % 96.57 % 92.75 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
31 OcTr 95.69 % 96.44 % 90.78 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
C. Zhou, Y. Zhang, J. Chen and D. Huang: OcTr: Octree-based Transformer for 3D Object Detection. CVPR 2023.
32 CDF 95.66 % 96.21 % 90.48 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
33 SoRL 95.65 % 96.78 % 90.72 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
34 DVF-V 95.63 % 96.59 % 90.71 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
A. Mahmoud, J. Hu and S. Waslander: Dense Voxel Fusion for 3D Object Detection. WACV 2023.
35 test 95.61 % 98.37 % 92.55 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
36 MAK code 95.60 % 96.67 % 90.72 % 0.03 s GPU @ 2.5 Ghz (Python)
37 URFormer 95.59 % 98.45 % 92.69 % 0.1 s 1 core @ 2.5 Ghz (Python)
38 DSGN++
This method uses stereo information.
code 95.58 % 98.04 % 88.09 % 0.2 s GeForce RTX 2080Ti
Y. Chen, S. Huang, S. Liu, B. Yu and J. Jia: DSGN++: Exploiting Visual-Spatial Relation for Stereo-Based 3D Detectors. IEEE Transactions on Pattern Analysis and Machine Intelligence 2022.
39 HAF-PVP_test 95.57 % 98.85 % 92.64 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
40 Fast-CLOCs 95.57 % 96.66 % 90.70 % 0.1 s GPU @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: Fast-CLOCs: Fast Camera-LiDAR Object Candidates Fusion for 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022.
41 MAK_VOXEL_RCNN 95.56 % 98.62 % 92.82 % 0.03 s 1 core @ 2.5 Ghz (Python)
42 TSSTDet 95.56 % 96.54 % 92.71 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
43 3D HANet code 95.54 % 98.59 % 92.66 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Q. Xia, Y. Chen, G. Cai, G. Chen, D. Xie, J. Su and Z. Wang: 3D HANet: A Flexible 3D Heatmap Auxiliary Network for Object Detection. IEEE Transactions on Geoscience and Remote Sensing 2023.
44 PA-Det3D 95.53 % 96.36 % 90.87 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
45 FARP-Net code 95.53 % 96.10 % 92.98 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
T. Xie, L. Wang, K. Wang, R. Li, X. Zhang, H. Zhang, L. Yang, H. Liu and J. Li: FARP-Net: Local-Global Feature Aggregation and Relation-Aware Proposals for 3D Object Detection. IEEE Transactions on Multimedia 2023.
46 CasA code 95.53 % 96.51 % 92.71 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.
47 GENet 95.50 % 96.51 % 90.66 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
48 FEIF3D
This method makes use of Velodyne laser scans.
95.49 % 96.42 % 92.78 % 0.1 s GPU @ 2.5 Ghz (Python)
49 FEMV-RCNN code 95.49 % 96.27 % 90.76 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
50 LVP 95.47 % 98.45 % 92.56 % 0.04 s 1 core @ 2.5 Ghz (Python)
51 spark2 95.45 % 96.38 % 92.70 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
52 LoGoNet code 95.44 % 96.59 % 92.89 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
X. Li, T. Ma, Y. Hou, B. Shi, Y. Yang, Y. Liu, X. Wu, Q. Chen, Y. Li, Y. Qiao and others: LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross-Modal Fusion. CVPR 2023.
53 spark_voxel_rcnn code 95.44 % 96.40 % 92.68 % 0.04 s 1 core @ 2.5 Ghz (Python)
54 SGFNet 95.43 % 98.42 % 92.46 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
55 CAFI-Pillars 95.43 % 96.46 % 90.56 % 30ms NVIDIA Tesla P40 GPU
56 spark 95.42 % 96.33 % 92.68 % 0.1 s 1 core @ 2.5 Ghz (Python)
57 voxel_spark code 95.42 % 96.36 % 92.68 % 0.04 s GPU @ 2.5 Ghz (C/C++)
58 PSMS-Net
This method makes use of Velodyne laser scans.
95.42 % 96.67 % 90.54 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
59 SS-3DSSD code 95.39 % 96.30 % 90.43 % 0.014s 1 core @ 2.5 Ghz (C/C++)
60 VDF 95.37 % 98.55 % 92.41 % 0.03 s GPU @ 2.5 Ghz (Python)
61 GD-MAE 95.36 % 98.31 % 90.19 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Yang, T. He, J. Liu, H. Chen, B. Wu, B. Lin, X. He and W. Ouyang: GD-MAE: Generative Decoder for MAE Pre- training on LiDAR Point Clouds. CVPR 2023.
62 Voxel_Spark_focal_we code 95.35 % 96.37 % 92.63 % 0.08 s 1 core @ 2.5 Ghz (Python)
63 DVF-PV 95.35 % 96.40 % 92.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
A. Mahmoud, J. Hu and S. Waslander: Dense Voxel Fusion for 3D Object Detection. WACV 2023.
64 SFD++ 95.35 % 98.38 % 92.39 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
65 BADet code 95.34 % 98.65 % 90.28 % 0.14 s 1 core @ 2.5 Ghz (C/C++)
R. Qian, X. Lai and X. Li: BADet: Boundary-Aware 3D Object Detection from Point Clouds. Pattern Recognition 2022.
66 Anonymous 95.33 % 96.39 % 92.65 % 0.1 s 1 core @ 2.5 Ghz (Python)
67 DSASA code 95.32 % 96.13 % 92.29 % 0.05s GPU @ >3.5 Ghz (Python)
68 LGNet-Car code 95.31 % 96.51 % 92.55 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
69 Multi-Weights 95.30 % 96.37 % 90.50 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
70 SASA
This method makes use of Velodyne laser scans.
code 95.29 % 96.00 % 92.42 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
C. Chen, Z. Chen, J. Zhang and D. Tao: SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object Detection. arXiv preprint arXiv:2201.01976 2022.
71 3D-BCM 95.27 % 98.47 % 92.39 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
72 PG-RCNN code 95.27 % 96.64 % 90.37 % 0.06 s GPU @ 1.5 Ghz (Python)
I. Koo, I. Lee, S. Kim, H. Kim, W. Jeon and C. Kim: PG-RCNN: Semantic Surface Point Generation for 3D Object Detection. 2023.
73 Focals Conv code 95.23 % 96.29 % 92.60 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Y. Chen, Y. Li, X. Zhang, J. Sun and J. Jia: Focal Sparse Convolutional Networks for 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.
74 EQ-PVRCNN code 95.20 % 98.22 % 92.47 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, L. Jiang, Y. Sun, B. Schiele and J. Jia: A Unified Query-based Paradigm for Point Cloud Understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.
75 TED-S Reproduced 95.19 % 98.43 % 92.55 % 0.1 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
76 CasA++ code 95.17 % 95.81 % 94.10 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.
77 Simi-fusion 95.17 % 98.27 % 92.58 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
78 SE-SSD
This method makes use of Velodyne laser scans.
code 95.17 % 96.55 % 90.00 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, L. Jiang and C. Fu: SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud. CVPR 2021.
79 LGSLNet 95.14 % 97.98 % 92.61 % 0.1 s GPU @ 2.5 Ghz (Python)
80 VoxSeT code 95.13 % 96.15 % 90.38 % 33 ms 1 core @ 2.5 Ghz (C/C++)
C. He, R. Li, S. Li and L. Zhang: Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds. CVPR 2022.
81 PR-SSD 95.10 % 97.62 % 92.33 % 0.02 s GPU @ 2.5 Ghz (Python)
82 RPF3D 95.08 % 96.26 % 90.28 % 0.1 s 1 core @ 2.5 Ghz (Python)
83 GS-FPS code 95.06 % 95.82 % 92.24 % TBD s 1 core @ 2.5 Ghz (C/C++)
84 R^2 R-CNN 95.06 % 96.35 % 92.36 % 0.1 s 1 core @ 2.5 Ghz (Python)
85 HMFI code 95.05 % 96.28 % 92.28 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
X. Li, B. Shi, Y. Hou, X. Wu, T. Ma, Y. Li and L. He: Homogeneous Multi-modal Feature Fusion and Interaction for 3D Object Detection. ECCV 2022.
86 SPANet 95.03 % 96.31 % 89.99 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
Y. Ye: SPANet: Spatial and Part-Aware Aggregation Network for 3D Object Detection. Pacific Rim International Conference on Artificial Intelligence 2021.
87 Pyramid R-CNN 95.03 % 95.87 % 92.46 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
J. Mao, M. Niu, H. Bai, X. Liang, H. Xu and C. Xu: Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection. ICCV 2021.
88 VPFNet code 95.01 % 96.03 % 92.41 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
C. Wang, H. Chen and L. Fu: VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection. 2021.
89 EPNet++ 95.00 % 96.70 % 91.82 % 0.1 s GPU @ 2.5 Ghz (Python)
Z. Liu, T. Huang, B. Li, X. Chen, X. Wang and X. Bai: EPNet++: Cascade Bi-Directional Fusion for Multi-Modal 3D Object Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 2022.
90 GF-pointnet 94.99 % 95.92 % 92.22 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
91 USVLab BSAODet code 94.99 % 96.23 % 92.36 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
W. Xiao, Y. Peng, C. Liu, J. Gao, Y. Wu and X. Li: Balanced Sample Assignment and Objective for Single-Model Multi-Class 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2023.
92 RBEV-Voxel code 94.99 % 96.41 % 90.10 % 0.08 s GPU @ 2.5 Ghz (Python)
93 DTSSD 94.98 % 95.95 % 90.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
94 Voxel R-CNN code 94.96 % 96.47 % 92.24 % 0.04 s GPU @ 3.0 Ghz (C/C++)
J. Deng, S. Shi, P. Li, W. Zhou, Y. Zhang and H. Li: Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection . AAAI 2021.
95 TEDx 94.94 % 96.11 % 92.15 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
96 Anonymous 94.91 % 96.32 % 92.22 % 0.1 s GPU @ 2.5 Ghz (Python)
97 PDV code 94.91 % 96.06 % 92.30 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Hu, T. Kuai and S. Waslander: Point Density-Aware Voxels for LiDAR 3D Object Detection. CVPR 2022.
98 RAFDet 94.91 % 95.93 % 92.22 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
99 HybridPillars 94.89 % 96.13 % 92.16 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
100 SIENet code 94.85 % 96.01 % 92.23 % 0.08 s 1 core @ 2.5 Ghz (Python)
Z. Li, Y. Yao, Z. Quan, W. Yang and J. Xie: SIENet: Spatial Information Enhancement Network for 3D Object Detection from Point Cloud. 2021.
101 BSH-Det3D 94.85 % 96.31 % 92.13 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
102 VoTr-TSD code 94.81 % 95.95 % 92.24 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
J. Mao, Y. Xue, M. Niu, H. Bai, J. Feng, X. Liang, H. Xu and C. Xu: Voxel Transformer for 3D Object Detection. ICCV 2021.
103 GraphAlign 94.79 % 98.04 % 92.35 % 0.03 s GPU @ 2.0 Ghz (Python)
104 L-AUG 94.76 % 95.80 % 91.94 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
T. Cortinhal, I. Gouigah and E. Aksoy: Semantics-aware LiDAR-Only Pseudo Point Cloud Generation for 3D Object Detection. 2023.
105 CZY_PPF_Net 94.74 % 98.03 % 92.03 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
106 AMVFNet code 94.71 % 96.09 % 92.10 % 0.04 s GPU @ 2.5 Ghz (Python)
107 Under Blind Review#1 94.70 % 95.62 % 92.21 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
108 M3DeTR code 94.70 % 97.37 % 91.89 % n/a s GPU @ 1.0 Ghz (Python)
T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis and D. Manocha: M3DeTR: Multi-representation, Multi- scale, Mutual-relation 3D Object Detection with Transformers. 2021.
109 MonoSample (DID-M3D) 94.69 % 96.30 % 85.10 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
110 Spark_partA22 94.67 % 95.99 % 91.95 % 10 s 1 core @ 2.5 Ghz (Python)
111 spark_second_focal_w 94.67 % 95.43 % 91.82 % 0.1 s 1 core @ 2.5 Ghz (Python)
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112 U_PV_V2_ep100_80 94.67 % 95.80 % 92.09 % 0... s 1 core @ 2.5 Ghz (Python)
113 Rnet 94.66 % 95.95 % 91.99 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
114 XView 94.66 % 95.88 % 92.07 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
115 Spark_PartA2_Soft_fo code 94.65 % 95.79 % 91.99 % 0.1 s 1 core @ 2.5 Ghz (Python)
116 LGNet-3classes code 94.65 % 98.12 % 91.97 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
117 StructuralIF 94.64 % 96.12 % 91.85 % 0.02 s 8 cores @ 2.5 Ghz (Python)
J. Pei An: Deep structural information fusion for 3D object detection on LiDAR-camera system. Accepted in CVIU 2021.
118 F3D 94.64 % 95.98 % 92.09 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
119 HA-PillarNet 94.63 % 95.89 % 92.00 % 0.05 s 1 core @ 2.5 Ghz (Python)
120 focalnet 94.62 % 98.05 % 92.13 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
121 U_PV_V2_ep_100_100 94.62 % 95.73 % 91.99 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
122 DCAF code 94.62 % 96.29 % 89.97 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
123 spark-part2 94.61 % 95.70 % 91.96 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
124 PV-PMRTNet 94.60 % 96.02 % 92.00 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
125 focalnet 94.60 % 98.08 % 92.12 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
126 P2V-RCNN 94.59 % 96.01 % 92.13 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.
127 spark_second code 94.59 % 95.39 % 91.73 % . s 1 core @ 2.5 Ghz (Python)
128 OFFNet 94.58 % 96.17 % 91.91 % 0.1 s GPU @ 2.5 Ghz (Python)
129 CAT-Det 94.57 % 95.95 % 91.88 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Zhang, J. Chen and D. Huang: CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection. CVPR 2022.
130 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 94.57 % 98.15 % 91.85 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
131 spark_second2 94.57 % 95.32 % 91.77 % 10 s 1 core @ 2.5 Ghz (Python)
132 sec_spark code 94.57 % 95.36 % 91.73 % 0.03 s GPU @ 2.5 Ghz (Python)
133 OA-TSSD 94.52 % 95.38 % 91.67 % 20 s 8 cores @ 2.5 Ghz (C/C++)
134 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
code 94.52 % 95.84 % 91.93 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
135 RangeDet (Official) code 94.51 % 95.48 % 91.57 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
L. Fan, X. Xiong, F. Wang, N. Wang and Z. Zhang: RangeDet: In Defense of Range View for LiDAR-Based 3D Object Detection. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
136 PV-RCNN-Plus 94.49 % 95.71 % 91.98 % 1 s 1 core @ 2.5 Ghz (C/C++)
137 U_VOXELRCNN_CAR_V3 94.49 % 96.00 % 91.71 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
138 MVRA + I-FRCNN+ 94.46 % 95.66 % 81.74 % 0.18 s GPU @ 2.5 Ghz (Python)
H. Choi, H. Kang and Y. Hyun: Multi-View Reprojection Architecture for Orientation Estimation. The IEEE International Conference on Computer Vision (ICCV) Workshops 2019.
139 SVGA-Net 94.45 % 96.02 % 91.54 % 0.03s 1 core @ 2.5 Ghz (Python + C/C++)
Q. He, Z. Wang, H. Zeng, Y. Zeng and Y. Liu: SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds. AAAI 2022.
140 PASS-PV-RCNN-Plus 94.45 % 95.77 % 91.89 % 1 s 1 core @ 2.5 Ghz (Python)
Anonymous: Leveraging Anchor-based LiDAR 3D Object Detection via Point Assisted Sample Selection. will submit to computer vision conference/journal 2024.
141 GeVo 94.44 % 95.87 % 91.86 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
142 DVFENet 94.44 % 95.33 % 91.55 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection. Neurocomputing 2021.
143 af 94.43 % 95.77 % 91.93 % 1 s GPU @ 2.5 Ghz (Python)
144 RangeIoUDet
This method makes use of Velodyne laser scans.
94.42 % 95.69 % 91.70 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, Z. Zhang, M. Zhang, X. Zhao and S. Pu: RangeIoUDet: Range Image Based Real-Time 3D Object Detector Optimized by Intersection Over Union. CVPR 2021.
145 AAMVFNet code 94.34 % 95.85 % 91.73 % 0.04 s GPU @ 2.5 Ghz (Python)
146 focal 94.31 % 98.27 % 91.91 % 100 s 1 core @ 2.5 Ghz (Python)
147 MSIT-Det 94.27 % 97.20 % 86.71 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
148 SERCNN
This method makes use of Velodyne laser scans.
94.24 % 96.31 % 89.71 % 0.1 s 1 core @ 2.5 Ghz (Python)
D. Zhou, J. Fang, X. Song, L. Liu, J. Yin, Y. Dai, H. Li and R. Yang: Joint 3D Instance Segmentation and Object Detection for Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020.
149 EPNet code 94.22 % 96.13 % 89.68 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
T. Huang, Z. Liu, X. Chen and X. Bai: EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection. ECCV 2020.
150 RealSynthesis-SECOND 94.17 % 95.10 % 91.07 % 0.05 s 1 core @ 2.5 Ghz (Python)
151 SRDL 94.08 % 95.83 % 91.55 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
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