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 VirConv-S 97.27 % 98.00 % 94.53 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
2 VoCo 97.19 % 98.27 % 94.59 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
3 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.
4 VirConv-T 96.38 % 98.93 % 93.56 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
5 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.
6 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.
7 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.
8 NIV-SSD 96.14 % 96.90 % 88.73 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
9 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.
10 ACF-Net 96.06 % 96.68 % 93.36 % n/a s 1 core @ 2.5 Ghz (C/C++)
11 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.
12 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.
13 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.
14 ImpDet 96.00 % 96.73 % 90.96 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
15 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.
16 CityBrainLab 95.96 % 96.59 % 90.94 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
17 VirConv-L 95.96 % 98.74 % 93.19 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
18 SPT 95.92 % 96.57 % 91.04 % 0.1 s GPU @ 2.5 Ghz (Python)
19 PVT-SSD 95.90 % 96.75 % 90.69 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
20 BiProDet 95.89 % 96.25 % 93.25 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
21 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.
22 OcTr 95.84 % 96.48 % 90.99 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
23 3D Dual-Fusion 95.82 % 96.54 % 93.11 % 0.1 s 1 core @ 2.5 Ghz (Python)
24 GLENet-VR 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. arXiv preprint arXiv:2207.02466 2022.
25 NSAW code 95.80 % 98.59 % 92.91 % 0.1 s 1 core @ 2.5 Ghz (Python)
26 HCPVF 95.80 % 96.62 % 93.03 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
27 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.
28 LightCPC code 95.76 % 96.56 % 90.79 % 0.02 s 1 core @ 2.5 Ghz (Python + C/C++)
29 LIVOX_Det
This method makes use of Velodyne laser scans.
95.75 % 98.62 % 93.05 % n/a s 1 core @ 2.5 Ghz (Python + C/C++)
30 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.
31 HRNet++ 95.72 % 98.82 % 90.81 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
32 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. arXiv preprint arXiv:2204.03039 2022.
33 SGFusion 95.67 % 96.57 % 92.69 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
34 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.
35 VoxelGraphRCNN 95.62 % 96.66 % 90.76 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
36 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.
37 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.
38 HRNet 95.58 % 96.63 % 92.86 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
39 FARP-Net code 95.57 % 96.11 % 93.07 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
40 LoGoNet 95.55 % 96.60 % 93.07 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
41 GT3D 95.51 % 96.58 % 92.80 % 0.1 s 1 core @ 2.5 Ghz (Python)
42 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.
43 Anonymous 95.47 % 98.35 % 90.55 % n/a s 1 core @ 2.5 Ghz (C/C++)
44 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.
45 LGNet 95.43 % 96.52 % 92.73 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
46 TBD 95.43 % 96.06 % 90.38 % 0.1 s 1 core @ 2.5 Ghz (Python)
47 Anonymous 95.41 % 96.49 % 90.68 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
48 GLENet 95.40 % 96.61 % 90.57 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
49 PTA-RCNN 95.39 % 96.40 % 92.40 % 0.08 s 1 core @ 2.5 Ghz (Python)
50 PV-DT3D 95.36 % 96.34 % 92.70 % 1.4 s 1 core @ 2.5 Ghz (C/C++)
51 DCGNN 95.36 % 96.39 % 90.37 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
52 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.
53 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.
54 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.
55 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.
56 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.
57 ATT_SSD 95.27 % 95.87 % 90.56 % 0.01 s 1 core @ 2.5 Ghz (Python)
58 ChTR3D 95.26 % 96.22 % 90.51 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
59 ChTR3D 95.23 % 96.13 % 90.45 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
60 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.
61 TTT_SSD 95.21 % 95.73 % 92.45 % TBD s 1 core @ 2.5 Ghz (C/C++)
62 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.
63 VGT-RCNN 95.19 % 96.38 % 92.55 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
64 BSConv 95.18 % 97.90 % 92.28 % 0.1 s 1 core @ 2.5 Ghz (Java)
65 4cls-center-hirige 95.17 % 98.39 % 85.83 % 0.01 s 1 core @ 2.5 Ghz (Python)
66 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.
67 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.
68 EPNet++ 95.17 % 96.73 % 92.10 % 0.1 s GPU @ 2.5 Ghz (Python)
Z. Liu, H. tengteng, B. Li, X. Chen, X. Wang and X. Bai: EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object Detection. arXiv preprint arXiv:2112.11088 2021.
69 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.
70 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.
71 IPS 95.16 % 95.86 % 90.57 % TBD s 1 core @ 2.5 Ghz (C/C++)
72 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.
73 GS-FPS 95.13 % 95.73 % 90.55 % TBD s 1 core @ 2.5 Ghz (C/C++)
74 VGRCNN++ 95.12 % 96.48 % 92.50 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
75 SWA code 95.11 % 95.91 % 92.43 % 0.18 s 1 core @ 2.5 Ghz (C/C++)
76 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.
77 AGS-SSD[la] 95.10 % 97.83 % 92.40 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
78 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.
79 VGRCNN 95.07 % 96.24 % 92.38 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
80 VG-RCNN 95.06 % 96.41 % 92.45 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
81 GV-RCNN code 95.06 % 96.29 % 92.43 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
82 GEO_LOC 95.05 % 95.93 % 90.43 % TBD s 1 core @ 2.5 Ghz (C/C++)
83 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.
84 Anonymous
This method makes use of Velodyne laser scans.
95.00 % 96.30 % 92.35 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
85 USVLab BSAODet 94.99 % 96.22 % 92.30 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
86 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.
87 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.
88 SGDA3D 94.96 % 96.02 % 92.36 % 0.07 s 1 core @ 2.5 Ghz (Python)
89 ChTR3D 94.95 % 96.32 % 90.25 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
90 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.
91 NV-RCNN 94.92 % 95.86 % 92.30 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
92 Semantical PVRCNN 94.92 % 95.97 % 92.36 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
93 GVNet-V2 94.92 % 96.29 % 92.21 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
94 KPSCC code 94.90 % 95.98 % 92.11 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
95 CZY_PPF_Net2 94.86 % 98.07 % 92.22 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
96 GVNet code 94.86 % 96.30 % 92.22 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
97 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.
98 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.
99 CSVoxel-RCNN 94.81 % 96.23 % 92.09 % 0.03 s GPU @ 1.0 Ghz (Python)
100 CZY_3917 94.80 % 98.00 % 92.21 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
101 Under Blind Review#2 94.80 % 95.95 % 92.26 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
102 DCCA 94.77 % 95.75 % 92.27 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
103 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.
104 PVE 94.75 % 96.01 % 92.13 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
105 VGA-RCNN 94.74 % 95.98 % 92.06 % 0.07 s 1 core @ 2.5 Ghz (Python)
106 PSA-SSD 94.74 % 95.80 % 90.21 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
107 TBD 94.74 % 95.88 % 91.96 % 0.06 s GPU @ 2.5 Ghz (Python)
108 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.
109 DTE3D 94.73 % 96.06 % 91.84 % 0.15s 1 core @ 2.5 Ghz (C/C++)
110 SC-Voxel-RCNN 94.71 % 96.13 % 91.94 % 0.12 s GPU @ 1.0 Ghz (Python)
111 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.
112 IKT3D
This method makes use of Velodyne laser scans.
94.71 % 95.92 % 92.15 % 0.05 s 1 core @ 2.5 Ghz (Python)
113 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.
114 Anonymous 94.70 % 96.12 % 92.05 % 0.03s
115 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.
116 DKDet 94.68 % 96.03 % 91.92 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
117 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.
118 BASA 94.65 % 96.07 % 90.02 % 1s 1 core @ 2.5 Ghz (python)
119 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.
120 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.
121 CZY_PPF_Net 94.63 % 96.19 % 90.14 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
122 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.
123 USVLab BSAODet (S) 94.60 % 96.10 % 90.03 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
124 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.
125 DGT-Det3D code 94.55 % 96.20 % 91.74 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
126 VPNet 94.51 % 96.07 % 92.04 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
127 WGVRF 94.50 % 95.97 % 90.06 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
128 TVTr 94.50 % 97.66 % 91.94 % 0.08 s 1 core @ 2.5 Ghz (Python)
129 SPVB-SSD 94.49 % 95.80 % 91.90 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
130 CZY 94.47 % 96.00 % 90.08 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
131 MVMM code 94.47 % 95.76 % 89.98 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
132 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.
133 src-hirige 94.46 % 95.92 % 88.91 % 0.01 s 1 core @ 2.5 Ghz (Python)
134 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.
135 U_RVRCNN_V2_1 94.42 % 95.76 % 91.75 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
136 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.
137 DCAN-Second code 94.41 % 97.08 % 91.37 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
138 TCDVF 94.31 % 95.02 % 91.33 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
139 multi-center-hirige 94.27 % 96.87 % 88.51 % 0.01 s 1 core @ 2.5 Ghz (Python)
140 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.
141 singal-hirige 94.24 % 95.38 % 87.76 % 0.01 s 1 core @ 2.5 Ghz (Python)
142 SRDL 94.24 % 95.86 % 91.80 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
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143 PVRCNN_8369 94.24 % 95.84 % 91.80 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
144 TBD 94.22 % 95.00 % 91.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
145 U_PVRCNN_V2 94.18 % 95.83 % 91.58 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
146 DGT-Det3D 94.03 % 95.55 % 91.58 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
147 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.
148 VueronNet 94.03 % 96.70 % 87.58 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
149 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.
150 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) .
151 SIF 93.95 % 95.51 % 91.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
152 U_SECOND_V4 93.94 % 95.76 % 89.68 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
153 TBD 93.89 % 94.52 % 86.44 % 0.1 s 1 core @ 2.5 Ghz (Python)
154 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.
155 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.
156 YOLOv5x6_1920 93.82 % 96.64 % 81.54 % 0.05 s GPU @ 3.5 Ghz (Python)
157 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.
158 DKAnet 93.79 % 95.16 % 89.27 % 0.05 s 1 core @ 2.0 Ghz (Python)
159 PA-RCNN code 93.79 % 96.58 % 86.43 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
160 Sem-Aug 93.77 % 96.79 % 88.78 % 0.08 s GPU @ 2.5 Ghz (Python)
161 CF-ctdep-tv-ta 93.75 % 95.08 % 91.08 % 1 s 1 core @ 2.5 Ghz (C/C++)
162 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.
163 CAD 93.73 % 96.84 % 88.74 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
164 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.
165 Anonymous 93.68 % 95.19 % 91.12 % 1 1 core @ 2.5 Ghz (Python)
166 SECOND_7862 93.68 % 95.19 % 91.12 % 1 s 1 core @ 2.5 Ghz (Python)
167 CF-base-tv 93.68 % 94.86 % 90.87 % 1 s 1 core @ 2.5 Ghz (C/C++)
168 DTFI 93.67 % 95.17 % 91.10 % 0.03 s 1 core @ 2.5 Ghz (Python)
169 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.
170 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.
171 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.
172 PA3DNet 93.62 % 96.57 % 88.65 % 0.05 s GPU @ 2.5 Ghz (Python)
173 multi-ssd-hirige 93.57 % 95.05 % 83.79 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
174 SSAL-Mono 93.57 % 96.63 % 83.74 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
175 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.
176 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.
177 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.
178 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.
179 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.
180 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.
181 FV2P v2 93.49 % 94.33 % 90.85 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
182 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.
183 HPV-RCNN 93.45 % 96.10 % 88.29 % 0.15 s 1 core @ 2.5 Ghz (Python)
184 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.
185 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.
186 TBD code 93.44 % 96.38 % 90.36 % 0.1 s GPU @ 2.5 Ghz (Python)
187 StereoDistill 93.43 % 97.61 % 87.71 % 0.4 s 1 core @ 2.5 Ghz (Python)
188 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.
189 3D-CVF at SPA
This method makes use of Velodyne laser scans.
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.
190 PVTr 93.36 % 94.54 % 90.82 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
191 Anonymous 93.34 % 96.44 % 83.76 % 40 s 1 core @ 2.5 Ghz (C/C++)
192 3SNet 93.33 % 96.40 % 90.65 % 0.07 s GPU @ 2.5 Ghz (Python)
193 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.
194 CF-ctdep-tv 93.31 % 94.89 % 90.87 % 1 s 1 core @ 2.5 Ghz (C/C++)
195 Anomynous 93.31 % 96.45 % 90.59 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
196 YOLOv5x6_1280 93.31 % 95.92 % 85.44 % 0.019 s GPU @ >3.5 Ghz (Python)
197 YOLOv5x6_1280_Q 93.31 % 95.88 % 85.45 % 0.016 s GPU @ >3.5 Ghz (Python)
198 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.
199 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.
200 ITCA-SSD code 93.27 % 96.65 % 88.14 % 0.05 s 1 core @ 2.5 Ghz (Python)
201 Reprod-Two-Branch 93.26 % 94.83 % 90.61 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
202 BAIR 93.26 % 96.33 % 85.49 % 0.04 s 1 core @ 2.5 Ghz (Python)
203 SPNet code 93.23 % 95.99 % 92.60 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
204 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.
205 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.
206 CFF-ep25 93.20 % 94.81 % 90.76 % 1 s 1 core @ 2.5 Ghz (C/C++)
207 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.
208 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.
209 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.
210 MonoASS 93.15 % 96.09 % 85.34 % 0.04 s 1 core @ 2.5 Ghz (Python)
211 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.
212 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.
213 CFF-tv 93.06 % 94.68 % 90.61 % 1 s 1 core @ 2.5 Ghz (C/C++)
214 3DSeMoDLE code 93.04 % 96.21 % 81.01 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
215 GS-FPS-LT 93.04 % 95.77 % 90.21 % TBD s 1 core @ 2.5 Ghz (C/C++)
216 Mono3DMethod 93.00 % 96.25 % 83.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
217 cp-tv 92.99 % 94.52 % 90.36 % 1 s 1 core @ 2.5 Ghz (C/C++)
218 FSFNet 92.99 % 96.36 % 89.99 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
219 Anonymous 92.99 % 96.18 % 87.80 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
220 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.
221 cff-tv-v2-ep25 92.94 % 94.65 % 90.62 % 1 s 1 core @ 2.5 Ghz (C/C++)
222 KPP3D code 92.93 % 98.38 % 89.93 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
223 Anonymous 92.87 % 95.57 % 89.93 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
224 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.
225 mbdf-netv1 code 92.83 % 96.00 % 89.90 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
226 CF-base-train 92.82 % 94.98 % 90.19 % 1 s 1 core @ 2.5 Ghz (C/C++)
227 MDNet 92.82 % 96.14 % 85.37 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
228 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.
229 Anonymous 92.74 % 95.56 % 89.82 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
230 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.
231 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.
232 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.
233 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.
234 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.
235 Anonymous 92.62 % 95.58 % 89.74 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
236 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.
237 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.
238 Anonymous 92.51 % 95.88 % 85.35 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
239 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.
240 CSNet8306 code 92.47 % 96.05 % 87.61 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
241 CSNet 92.46 % 95.99 % 89.24 % 0.1 s 1 core @ 2.5 Ghz (Python)
242 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.
243 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.
244 CAT 92.42 % 95.94 % 87.36 % 1 s 1 core @ 2.5 Ghz (Python)
245 DD3Dv2 code 92.41 % 95.41 % 87.27 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
246 LazyTorch-CP-Small-P 92.38 % 94.71 % 90.04 % 1 s 1 core @ 2.5 Ghz (C/C++)
247 LazyTorch-CP-Infer-O 92.35 % 94.76 % 90.09 % 1 s 1 core @ 2.5 Ghz (C/C++)
248 cp-tv-kp 92.34 % 94.32 % 90.25 % 1 s 1 core @ 2.5 Ghz (C/C++)
249 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.
250 Sem-Aug-PointRCNN++ 92.32 % 95.65 % 87.62 % 0.1 s 8 cores @ 3.0 Ghz (Python)
251 CSNet8299 code 92.31 % 96.14 % 87.54 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
252 PSM_stereo 92.27 % 95.63 % 87.24 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
253 CenterFuse 92.26 % 95.04 % 89.24 % 0.059 sec/frame 2 x V100
254 Harmonic PointPillar code 92.25 % 95.16 % 89.11 % 0.01 s 1 core @ 2.5 Ghz (Python)
H. Zhang, M. Mekala, Z. Nain, D. Yang, J. Park and H. Jung: Harmonic 3D: Time-friendly and Task- consistent LiDAR-based 3D Object Detection on Edge. will submit to IEEE Transactions on Vehicle Technology 2022.
255 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.
256 Self-Calib Conv 92.17 % 94.45 % 90.19 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
257 PSA-Det3D 92.17 % 95.53 % 89.67 % 0.1 s GPU @ 2.5 Ghz (Python)
258 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.
259 AFTD 92.14 % 95.56 % 87.45 % 1 s 1 core @ 2.5 Ghz (Python + C/C++)
260 cp-tv-kp-io-sc 92.11 % 95.02 % 88.97 % 1 s 1 core @ 2.5 Ghz (C/C++)
261 CF-cd-io-tv 92.06 % 94.80 % 88.80 % 1 s 1 core @ 2.5 Ghz (C/C++)
262 SSL_PP code 92.04 % 95.97 % 84.91 % 16ms GPU @ 1.5 Ghz (Python)
263 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.
264 KeyFuse2B 92.01 % 94.57 % 90.56 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
265 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.
266 CrazyTensor-CP 91.95 % 93.64 % 89.23 % 1 s 1 core @ 2.5 Ghz (Python)
267 MonoATT code 91.92 % 95.57 % 86.24 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
268 ZMMPP 91.92 % 94.93 % 88.86 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
269 CFF-tv-v2 91.91 % 94.72 % 90.57 % 1 s 1 core @ 2.5 Ghz (C/C++)
270 Dune-DCF-e09 91.90 % 94.97 % 88.74 % 1 s 1 core @ 2.5 Ghz (C/C++)
271 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.
272 CrazyTensor-CF 91.89 % 94.98 % 88.70 % 1 s 1 core @ 2.5 Ghz (C/C++)
273 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.
274 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.
275 KeyPoint-IoUHead 91.79 % 94.65 % 88.61 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
276 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.
277 CAD
This method uses stereo information.
This method makes use of Velodyne laser scans.
91.76 % 94.97 % 88.46 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
278 TBD 91.75 % 94.53 % 86.54 % 0.1 s 1 core @ 2.5 Ghz (Python)
279 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.
280 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.
281 Dune-DCF-e11 91.68 % 94.69 % 88.57 % 1 s 1 core @ 2.5 Ghz (C/C++)
282 City-CF-fixed 91.67 % 94.87 % 88.80 % 1 s 1 core @ 2.5 Ghz (C/C++)
283 Dune-DCF-e15 91.67 % 94.72 % 88.53 % 1 s 1 core @ 2.5 Ghz (C/C++)
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 CF-ctdep-train 91.64 % 94.81 % 90.16 % 1 s 1 core @ 2.5 Ghz (C/C++)
286 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.
287 CenterPoint (pcdet) 91.58 % 93.99 % 89.15 % 0.051 sec/frame 2 x V100
288 TBD 91.56 % 94.52 % 86.29 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
289 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.
290 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.
291 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.
292 new_stereo 91.39 % 95.01 % 86.77 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
293 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.
294 IoU-2B 91.38 % 94.81 % 85.63 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
295 T_PVRCNN 91.36 % 95.02 % 88.45 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
296 GPENet code 91.36 % 94.11 % 83.40 % 0.02 s GPU @ 2.5 Ghz (Python)
297 City-CF 91.35 % 94.54 % 88.38 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
298 T_PVRCNN_V2 91.31 % 95.59 % 88.22 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
299 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.
300 SAD 91.21 % 96.58 % 81.29 % 0.05 s 1 core @ 2.5 Ghz (python)
301 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.
302 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.
303 SAD 91.16 % 96.44 % 83.49 % 0.05 s 1 core @ 2.5 Ghz (python)
304 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.
305 NeurOCS 91.13 % 96.46 % 81.28 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
306 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.
307 Pseudo-Stereo++ 91.06 % 96.23 % 83.39 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
308 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.
309 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.
310 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.
311 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.
312 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.
313 TempM3D 90.99 % 96.48 % 81.21 % 0.07 s 1 core @ 2.5 Ghz (Python)
314 CIE 90.98 % 96.31 % 83.43 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
315 cff-tv-t 90.98 % 94.68 % 84.39 % 1 s 1 core @ 2.5 Ghz (C/C++)
316 monopd code 90.93 % 96.44 % 83.36 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
317 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.
318 MoGDE 90.91 % 96.47 % 83.66 % 0.03 s GPU @ 2.5 Ghz (Python)
319 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.
320 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.
321 PS 90.85 % 96.20 % 83.08 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
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 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.
325 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.
326 Anonymous 90.78 % 96.42 % 83.51 % 40 s 1 core @ 2.5 Ghz (C/C++)
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 MonoPPM code 90.66 % 93.88 % 80.99 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
330 MF 90.66 % 93.57 % 86.15 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
331 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.
332 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.
333 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.
334 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.
335 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.
336 MonoAug 90.24 % 95.59 % 80.47 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
337 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.
338 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.
339 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.
340 variance_point 90.01 % 95.79 % 87.50 % 0.05 s 1 core @ 2.5 Ghz (Python)
341 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.
342 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.
343 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.
344 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.
345 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.
346 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.
347 ADD code 89.53 % 94.82 % 81.60 % 0.1 s 1 core @ 2.5 Ghz (Python)
348 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.
349 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.
350 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.
351 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.
352 MonoAD 88.99 % 94.43 % 76.76 % 0.03 s GPU @ 2.5 Ghz (Python)
353 MM3D 88.98 % 95.12 % 81.35 % NA s 1 core @ 2.5 Ghz (C/C++)
354 MonoEdge-RCNN 88.97 % 94.19 % 74.23 % 0.05 s 1 core @ 2.5 Ghz (Python)
355 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.
356 Shape-Aware 88.85 % 94.26 % 81.33 % 0.05 s 1 core @ 2.5 Ghz (Python)
357 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.
358 OPA-3D code 88.77 % 96.50 % 76.55 % 0.04 s 1 core @ 3.5 Ghz (Python)
359 Lite-FPN-GUPNet 88.76 % 96.45 % 76.54 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
360 MonoInsight 88.72 % 94.23 % 76.57 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
361 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.
362 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.
363 MonoA^2 88.68 % 94.06 % 78.95 % na s 1 core @ 2.5 Ghz (C/C++)
364 Anonymous 88.62 % 92.96 % 79.44 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
365 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.
366 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.
367 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.
368 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.
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 DEPT 88.00 % 96.45 % 78.40 % 0.03 s 1 core @ 2.5 Ghz (Python)
373 EW code 87.94 % 92.22 % 78.32 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
374 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.
375 MonoAug 87.76 % 93.65 % 77.91 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
376 CZY 87.55 % 94.63 % 82.97 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
377 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.
378 zongmuDistill 87.47 % 95.44 % 79.97 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
379 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.
380 BiResFPN 87.06 % 86.03 % 75.85 % 0.071s 1 core @ 2.5 Ghz (C/C++)
381 MonoA^2(new) 87.06 % 94.17 % 78.21 % na s 1 core @ 2.5 Ghz (C/C++)
382 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.
383 MonoNeRD 86.89 % 94.60 % 77.23 % na s 1 core @ 2.5 Ghz (C/C++)
384 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.
385 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.
386 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.
387 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.
388 BCA 86.62 % 94.38 % 76.67 % 0.17 s GPU @ 2.5 Ghz (Python)
389 UPF_3D
This method uses stereo information.
86.61 % 96.12 % 79.53 % 0.29 s 1 core @ 2.5 Ghz (Python)
390 MK3D 86.48 % 94.40 % 74.21 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
391 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.
392 GCDR 86.45 % 94.15 % 74.18 % 0.28 s 1 core @ 2.5 Ghz (Python)
393 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.
394 OBMO_GUPNet 86.27 % 96.49 % 76.47 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
395 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.
396 TBD 86.03 % 91.50 % 78.52 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
397 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.
398 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.
399 City-ICN 85.70 % 93.91 % 73.41 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
400 Anonymous 85.65 % 92.64 % 78.83 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
401 MonoPCNS 85.56 % 92.57 % 78.41 % 0.14 s GPU @ 2.5 Ghz (Python)
402 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.
403 LT-M3OD 85.42 % 93.99 % 75.77 % 0.03 s 1 core @ 2.5 Ghz (Python)
404 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.
405 MDT code 85.32 % 93.80 % 75.35 % 0.01 s 1 core @ 2.5 Ghz (Python)
406 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.
407 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 .
408 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.
409 M3DGAF 85.01 % 93.33 % 77.55 % 0.07 s 1 core @ 2.5 Ghz (Python)
410 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.
411 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.
412 MDS-Mono3D 84.85 % 95.06 % 74.84 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
413 2D_Detector 84.64 % 95.19 % 74.80 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
414 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.
415 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.
416 DSC3D
This method uses stereo information.
84.42 % 94.78 % 62.07 % 0.06 s GPU @ 2.5 Ghz (Python)
417 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.
418 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.
419 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.
420 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.
421 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.
422 Anonymous 83.24 % 93.53 % 73.60 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
423 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.
424 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.
425 SparseLiDAR_fusion 82.91 % 93.77 % 70.83 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
426 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.
427 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.
428 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.
429 CrazyTensor-ICN 82.70 % 90.79 % 70.61 % 1 s 1 core @ 2.5 Ghz (C/C++)
430 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.
431 HBD 82.64 % 93.13 % 75.14 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
432 Yolo5x6_Ghost 82.54 % 91.04 % 72.74 % 0.03 s GPU @ 2.5 Ghz (Python)
433 Yolo5x6_Ghost 82.54 % 91.04 % 72.74 % 0.03 s GPU @ 2.5 Ghz (Python)
434 Yolov5ObjectDetector 82.54 % 93.39 % 72.65 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
435 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.
436 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.
437 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.
438 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.
439 Res 81.89 % 92.60 % 72.18 % 0.03 s 1 core @ 2.5 Ghz (Python)
440 GHos_3d 81.89 % 92.60 % 72.18 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
441 ALI_TRY1 81.89 % 92.60 % 72.18 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
442 Anonymous 81.71 % 96.77 % 69.38 % 40 s 1 core @ 2.5 Ghz (C/C++)
443 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.
444 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.
445 ART 81.03 % 90.97 % 74.44 % 20ms s 1 core @ 2.5 Ghz (C/C++)
446 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.
447 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.
448 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.
449 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.
450 FD 80.38 % 92.08 % 75.65 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
451 AMNet 80.30 % 88.43 % 74.19 % 0.03 s GPU @ 1.0 Ghz (Python)
452 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.
453 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.
454 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.
455 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.
456 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.
457 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.
458 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.
459 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.
460 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.
461 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.
462 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.
463 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.
464 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.
465 SARM3D 77.41 % 90.20 % 68.24 % 0.03 s GPU @ 2.5 Ghz (Python)
466 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.
467 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.
468 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.
469 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.
470 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.
471 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.
472 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.
473 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.
474 Ghost3D object detec 75.21 % 85.65 % 67.60 % 0.03 s 1 core @ 2.5 Ghz (Python)
475 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.
476 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.
477 MonoXiver 72.63 % 87.68 % 64.07 % 0.03s GPU @ 2.5 Ghz (Python)
478 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 .
479 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.
480 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.
481 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.
482 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.
483 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.
484 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.
485 BEV_GHOST 67.98 % 72.02 % 62.55 % 0.1 s 1 core @ 2.5 Ghz (Python)
486 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.
487 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.
488 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.
489 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.
490 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.
491 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.
492 aliii0 66.55 % 81.40 % 51.53 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
493 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.
494 BSM3D 64.16 % 87.11 % 56.08 % 0.03 s 1 core @ 2.5 Ghz (Python)
495 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.
496 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.
497 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.
498 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.
499 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.
500 CIE + DM3D 61.54 % 79.36 % 53.56 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
501 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.
502 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.
503 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.
504 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.
505 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.
506 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.
507 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.
508 Anonymous 54.16 % 71.33 % 47.50 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
509 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). .
510 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.
511 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.
512 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 .
513 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.
514 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.
515 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.
516 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.
517 CDTrack3D code 44.58 % 53.11 % 37.41 % 0.0106 s NVIDIA RTX 3090 GPU, i9 10850k CPU
518 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.
519 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.
520 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.
521 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.
522 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.
523 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.
524 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.
525 MonoDET code 5.80 % 6.41 % 5.13 % 0.04 s 1 core @ 2.5 Ghz (Python)
526 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.
527 test code 4.63 % 1.85 % 4.96 % 50 s 1 core @ 2.5 Ghz (Python)
528 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.
529 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.
530 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.
Table as LaTeX | Only published Methods

Pedestrian


Method Setting Code Moderate Easy Hard Runtime Environment
1 VueronNet 80.75 % 89.91 % 76.56 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
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 4cls-center-hirige 79.49 % 88.78 % 73.86 % 0.01 s 1 core @ 2.5 Ghz (Python)
4 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.
5 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.
6 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.
7 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.
8 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.
9 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.
10 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.
11 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.
12 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.
13 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.
14 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) .
15 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.
16 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.
17 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.
18 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.
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 BAIR 67.95 % 83.26 % 62.81 % 0.04 s 1 core @ 2.5 Ghz (Python)
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 MonoASS 67.39 % 82.58 % 62.26 % 0.04 s 1 core @ 2.5 Ghz (Python)
29 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.
30 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.
31 YOLOv5x6_1280_Q 66.41 % 81.39 % 61.35 % 0.016 s GPU @ >3.5 Ghz (Python)
32 CAD 66.40 % 76.10 % 63.58 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
33 GPENet code 66.37 % 80.47 % 60.72 % 0.02 s GPU @ 2.5 Ghz (Python)
34 YOLOv5x6_1280 66.33 % 81.29 % 61.23 % 0.019 s GPU @ >3.5 Ghz (Python)
35 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.
36 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.
37 BiProDet 65.50 % 75.07 % 63.09 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
38 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.
39 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.
40 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.
41 LoGoNet 64.55 % 72.47 % 62.24 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
42 YOLOv5x6_1920 64.53 % 81.88 % 57.44 % 0.05 s GPU @ 3.5 Ghz (Python)
43 yolov5m-cityperson 64.47 % 74.73 % 59.92 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
44 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.
45 DCAN-Second code 64.09 % 74.11 % 61.44 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
46 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.
47 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.
48 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.
49 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.
50 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. arXiv preprint arXiv:2112.15458 2022.
51 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.
52 variance_point 62.19 % 72.24 % 58.54 % 0.05 s 1 core @ 2.5 Ghz (Python)
53 VoCo 62.11 % 69.00 % 59.15 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
54 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.
55 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.
56 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 .
57 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.
58 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.
59 CFF-tv 60.73 % 71.73 % 58.02 % 1 s 1 core @ 2.5 Ghz (C/C++)
60 CFF-ep25 60.60 % 71.09 % 58.00 % 1 s 1 core @ 2.5 Ghz (C/C++)
61 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.
62 cff-tv-v2-ep25 60.27 % 71.00 % 57.68 % 1 s 1 core @ 2.5 Ghz (C/C++)
63 Reprod-Two-Branch 60.24 % 71.39 % 57.66 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
64 SGDA3D 60.17 % 68.03 % 58.06 % 0.07 s 1 core @ 2.5 Ghz (Python)
65 KeyFuse2B 60.16 % 70.32 % 57.49 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
66 ACF-Net 60.12 % 71.42 % 55.96 % n/a s 1 core @ 2.5 Ghz (C/C++)
67 CFF-tv-v2 60.10 % 70.11 % 57.57 % 1 s 1 core @ 2.5 Ghz (C/C++)
68 USVLab BSAODet (S) 60.08 % 70.93 % 57.40 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
69 USVLab BSAODet 59.83 % 69.64 % 57.31 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
70 PA-RCNN code 59.81 % 69.94 % 57.40 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
71 3DSeMoDLE code 59.65 % 78.87 % 54.61 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
72 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.
73 CZY_PPF_Net2 59.26 % 67.81 % 57.04 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
74 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.
75 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.
76 U_RVRCNN_V2_1 58.97 % 68.45 % 56.64 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
77 Anonymous
This method makes use of Velodyne laser scans.
58.96 % 68.15 % 56.46 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
78 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.
79 TBD 58.77 % 67.92 % 55.33 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
80 SRDL 58.70 % 68.45 % 56.23 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
81 PVRCNN_8369 58.70 % 68.43 % 56.24 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
82 CF-ctdep-tv-ta 58.54 % 68.36 % 56.11 % 1 s 1 core @ 2.5 Ghz (C/C++)
83 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.
84 Semantical PVRCNN 58.30 % 65.54 % 56.36 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
85 VPNet 58.28 % 68.78 % 55.91 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
86 CZY_3917 58.26 % 66.64 % 56.01 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
87 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.
88 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.
89 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.
90 EPNet++ 58.10 % 68.58 % 55.58 % 0.1 s GPU @ 2.5 Ghz (Python)
Z. Liu, H. tengteng, B. Li, X. Chen, X. Wang and X. Bai: EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object Detection. arXiv preprint arXiv:2112.11088 2021.
91 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. arXiv preprint arXiv:2204.03039 2022.
92 Under Blind Review#2 58.01 % 67.39 % 55.77 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
93 CF-base-tv 57.99 % 68.06 % 55.47 % 1 s 1 core @ 2.5 Ghz (C/C++)
94 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.
95 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.
96 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.
97 Lite-FPN-GUPNet 57.56 % 76.82 % 50.42 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
98 LazyTorch-CP-Infer-O 57.47 % 67.75 % 55.08 % 1 s 1 core @ 2.5 Ghz (C/C++)
99 CenterPoint (pcdet) 57.38 % 67.60 % 54.98 % 0.051 sec/frame 2 x V100
100 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.
101 LazyTorch-CP-Small-P 57.35 % 67.67 % 55.04 % 1 s 1 core @ 2.5 Ghz (C/C++)
102 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.
103 SIF 57.32 % 67.78 % 54.86 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
104 CF-ctdep-tv 57.27 % 67.17 % 54.80 % 1 s 1 core @ 2.5 Ghz (C/C++)
105 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.
106 Self-Calib Conv 57.25 % 66.38 % 55.44 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
107 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.
108 DGT-Det3D 57.04 % 66.94 % 54.73 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
109 WGVRF 56.97 % 64.88 % 53.74 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
110 DGT-Det3D code 56.89 % 66.53 % 54.39 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
111 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.
112 Anonymous 56.84 % 67.23 % 54.40 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
113 CZY_PPF_Net 56.79 % 66.00 % 54.44 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
114 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.
115 FV2P v2 56.74 % 66.99 % 54.48 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
116 MVMM code 56.72 % 65.69 % 54.54 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
117 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.
118 City-CF-fixed 56.62 % 67.73 % 53.85 % 1 s 1 core @ 2.5 Ghz (C/C++)
119 U_PVRCNN_V2 56.58 % 65.92 % 54.26 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
120 AFTD 56.57 % 68.13 % 53.15 % 1 s 1 core @ 2.5 Ghz (Python + C/C++)
121 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.
122 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.
123 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.
124 cp-tv-kp 56.24 % 65.11 % 54.09 % 1 s 1 core @ 2.5 Ghz (C/C++)
125 3SNet 56.23 % 65.13 % 52.60 % 0.07 s GPU @ 2.5 Ghz (Python)
126 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.
127 U_SECOND_V4 56.09 % 66.55 % 53.56 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
128 TBD 55.96 % 66.20 % 53.25 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
129 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.
130 DTE3D 55.83 % 66.41 % 52.21 % 0.15s 1 core @ 2.5 Ghz (C/C++)
131 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.
132 cp-tv 55.74 % 64.83 % 53.76 % 1 s 1 core @ 2.5 Ghz (C/C++)
133 GCDR 55.65 % 74.95 % 48.44 % 0.28 s 1 core @ 2.5 Ghz (Python)
134 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.
135 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.
136 Dune-DCF-e11 55.62 % 66.39 % 53.00 % 1 s 1 core @ 2.5 Ghz (C/C++)
137 CZY 55.47 % 64.52 % 53.26 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
138 CF-ctdep-train 55.42 % 65.48 % 52.84 % 1 s 1 core @ 2.5 Ghz (C/C++)
139 PSA-Det3D 55.25 % 65.04 % 51.81 % 0.1 s GPU @ 2.5 Ghz (Python)
140 KeyPoint-IoUHead 55.20 % 66.99 % 51.68 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
141 BCA 55.20 % 74.33 % 50.26 % 0.17 s GPU @ 2.5 Ghz (Python)
142 CF-base-train 55.19 % 65.78 % 52.47 % 1 s 1 core @ 2.5 Ghz (C/C++)
143 TCDVF 55.18 % 65.12 % 52.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
144 CenterFuse 55.17 % 67.88 % 52.55 % 0.059 sec/frame 2 x V100
145 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.
146 Anonymous 55.15 % 66.68 % 52.57 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
147 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.
148 Dune-DCF-e15 55.09 % 65.54 % 52.36 % 1 s 1 core @ 2.5 Ghz (C/C++)
149 StereoDistill 55.09 % 69.00 % 50.95 % 0.4 s 1 core @ 2.5 Ghz (Python)
150 CrazyTensor-CP 55.05 % 64.92 % 52.88 % 1 s 1 core @ 2.5 Ghz (Python)
151 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.
152 VGA-RCNN 55.03 % 65.16 % 52.42 % 0.07 s 1 core @ 2.5 Ghz (Python)
153 Dune-DCF-e09 55.01 % 65.56 % 52.43 % 1 s 1 core @ 2.5 Ghz (C/C++)
154 OPA-3D code 54.92 % 73.93 % 47.87 % 0.04 s 1 core @ 3.5 Ghz (Python)
155 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.
156 City-CF 54.79 % 65.01 % 52.24 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
157 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.
158 IoU-2B 54.61 % 69.65 % 51.43 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
159 T_PVRCNN_V2 54.51 % 64.23 % 52.26 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
160 GS-FPS-LT 54.14 % 63.45 % 51.67 % TBD s 1 core @ 2.5 Ghz (C/C++)
161 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.
162 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.
163 aliii0 54.03 % 68.48 % 48.04 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
164 KPSCC code 54.03 % 63.53 % 51.89 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
165 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.
166 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.
167 cff-tv-t 53.69 % 68.26 % 49.75 % 1 s 1 core @ 2.5 Ghz (C/C++)
168 T_PVRCNN 53.69 % 63.29 % 51.32 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
169 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.
170 GS-FPS 53.46 % 62.71 % 50.25 % TBD s 1 core @ 2.5 Ghz (C/C++)
171 BASA 53.43 % 62.16 % 49.86 % 1s 1 core @ 2.5 Ghz (python)
172 PSA-SSD 53.43 % 63.07 % 51.34 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
173 IKT3D
This method makes use of Velodyne laser scans.
53.39 % 61.55 % 51.28 % 0.05 s 1 core @ 2.5 Ghz (Python)
174 IPS 53.38 % 62.38 % 51.15 % TBD s 1 core @ 2.5 Ghz (C/C++)
175 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.
176 MK3D 53.13 % 74.57 % 48.20 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
177 Mono3DMethod 53.12 % 69.34 % 46.69 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
178 PVTr 53.11 % 61.97 % 51.00 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
179 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.
180 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.
181 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.
182 NV-RCNN 52.86 % 62.28 % 50.72 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
183 CF-cd-io-tv 52.86 % 65.30 % 50.17 % 1 s 1 core @ 2.5 Ghz (C/C++)
184 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.
185 cp-tv-kp-io-sc 52.77 % 64.59 % 50.47 % 1 s 1 core @ 2.5 Ghz (C/C++)
186 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.
187 Anonymous 52.52 % 63.78 % 49.89 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
188 MDNet 52.46 % 68.56 % 47.69 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
189 SECOND_7862 52.46 % 60.95 % 49.88 % 1 s 1 core @ 2.5 Ghz (Python)
190 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.
191 CAD
This method uses stereo information.
This method makes use of Velodyne laser scans.
52.40 % 61.87 % 49.99 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
192 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.
193 HPV-RCNN 52.25 % 62.30 % 49.68 % 0.15 s 1 core @ 2.5 Ghz (Python)
194 SWA code 52.20 % 60.60 % 49.08 % 0.18 s 1 core @ 2.5 Ghz (C/C++)
195 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.
196 DD3Dv2 code 52.08 % 65.08 % 48.52 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
197 CrazyTensor-CF 52.00 % 62.19 % 49.49 % 1 s 1 core @ 2.5 Ghz (C/C++)
198 ATT_SSD 51.76 % 61.25 % 49.79 % 0.01 s 1 core @ 2.5 Ghz (Python)
199 GEO_LOC 51.57 % 61.00 % 49.42 % TBD s 1 core @ 2.5 Ghz (C/C++)
200 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.
201 TTT_SSD 51.09 % 60.19 % 48.95 % TBD s 1 core @ 2.5 Ghz (C/C++)
202 AGS-SSD[la] 50.90 % 60.46 % 48.60 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
203 TBD 50.45 % 60.47 % 47.02 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
204 Shape-Aware 49.91 % 66.87 % 45.08 % 0.05 s 1 core @ 2.5 Ghz (Python)
205 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.
206 LightCPC code 49.41 % 58.24 % 46.99 % 0.02 s 1 core @ 2.5 Ghz (Python + C/C++)
207 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.
208 MonoAug 49.10 % 65.03 % 44.42 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
209 Pseudo-Stereo++ 49.02 % 61.65 % 44.96 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
210 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.
211 MonoAug 48.93 % 64.70 % 44.37 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
212 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.
213 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.
214 ZMMPP 48.39 % 57.33 % 46.15 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
215 PS 48.39 % 60.99 % 44.42 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
216 KPP3D code 47.76 % 57.06 % 45.35 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
217 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.
218 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.
219 SSAL-Mono 47.36 % 62.31 % 43.03 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
220 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.
221 M3DGAF 46.70 % 61.58 % 42.25 % 0.07 s 1 core @ 2.5 Ghz (Python)
222 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.
223 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.
224 BiResFPN 45.74 % 62.59 % 40.97 % 0.071s 1 core @ 2.5 Ghz (C/C++)
225 DEPT 45.69 % 60.30 % 41.26 % 0.03 s 1 core @ 2.5 Ghz (Python)
226 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.
227 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.
228 MonoA^2 44.89 % 59.60 % 40.56 % na s 1 core @ 2.5 Ghz (C/C++)
229 MonoInsight 44.81 % 58.85 % 40.44 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
230 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.
231 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.
232 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.
233 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.
234 DSC3D
This method uses stereo information.
43.85 % 60.56 % 39.43 % 0.06 s GPU @ 2.5 Ghz (Python)
235 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.
236 Yolo5x6_Ghost 43.70 % 59.74 % 39.06 % 0.03 s GPU @ 2.5 Ghz (Python)
237 Yolo5x6_Ghost 43.70 % 59.74 % 39.06 % 0.03 s GPU @ 2.5 Ghz (Python)
238 Anonymous 43.68 % 58.24 % 39.56 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
239 City-ICN 43.56 % 60.56 % 38.71 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
240 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.
241 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.
242 CrazyTensor-ICN 43.43 % 60.32 % 38.58 % 1 s 1 core @ 2.5 Ghz (C/C++)
243 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.
244 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.
245 MoGDE 43.17 % 59.10 % 39.06 % 0.03 s GPU @ 2.5 Ghz (Python)
246 Res 43.10 % 58.82 % 38.56 % 0.03 s 1 core @ 2.5 Ghz (Python)
247 GHos_3d 43.10 % 58.82 % 38.56 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
248 ALI_TRY1 43.10 % 58.82 % 38.56 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
249 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.
250 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.
251 Yolov5ObjectDetector 42.74 % 58.46 % 38.32 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
252 LT-M3OD 42.72 % 57.14 % 38.38 % 0.03 s 1 core @ 2.5 Ghz (Python)
253 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.
254 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.
255 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.
256 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.
257 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.
258 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 .
259 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.
260 CIE 41.04 % 53.27 % 37.73 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
261 CZY 41.01 % 50.27 % 38.93 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
262 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.
263 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.
264 MonoAD 40.39 % 56.38 % 36.17 % 0.03 s GPU @ 2.5 Ghz (Python)
265 Anonymous 39.94 % 55.67 % 35.69 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
266 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.
267 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.
268 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.
269 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.
270 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.
271 SPT 39.41 % 46.02 % 37.86 % 0.1 s GPU @ 2.5 Ghz (Python)
272 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). .
273 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.
274 BSM3D 37.12 % 50.45 % 33.02 % 0.03 s 1 core @ 2.5 Ghz (Python)
275 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.
276 MonoPCNS 36.61 % 49.77 % 33.01 % 0.14 s GPU @ 2.5 Ghz (Python)
277 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.
278 SparseLiDAR_fusion 35.95 % 50.48 % 31.71 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
279 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.
280 MM3D 35.63 % 47.80 % 32.72 % NA s 1 core @ 2.5 Ghz (C/C++)
281 BEV_GHOST 35.35 % 46.76 % 31.94 % 0.1 s 1 core @ 2.5 Ghz (Python)
282 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.
283 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.
284 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.
285 MonoNeRD 34.43 % 46.50 % 31.06 % na s 1 core @ 2.5 Ghz (C/C++)
286 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.
287 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.
288 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.
289 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.
290 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.
291 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.
292 SARM3D 32.33 % 42.60 % 29.23 % 0.03 s GPU @ 2.5 Ghz (Python)
293 HBD 31.99 % 44.66 % 28.21 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
294 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.
295 MonoXiver 31.80 % 42.82 % 28.71 % 0.03s GPU @ 2.5 Ghz (Python)
296 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.
297 CDTrack3D code 30.62 % 41.66 % 26.69 % 0.0106 s NVIDIA RTX 3090 GPU, i9 10850k CPU
298 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.
299 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.
300 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.
301 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.
302 AMNet 28.50 % 37.11 % 25.83 % 0.03 s GPU @ 1.0 Ghz (Python)
303 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.
304 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.
305 DCD code 17.08 % 23.35 % 15.03 % 1 s 1 core @ 2.5 Ghz (C/C++)
306 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.
307 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.
308 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.
309 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.
310 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.
311 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.
Table as LaTeX | Only published Methods

Cyclist


Method Setting Code Moderate Easy Hard Runtime Environment
1 BiProDet 84.44 % 90.16 % 77.71 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
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 84.00 % 90.14 % 77.97 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
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 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.
7 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.
8 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.
9 Semantical PVRCNN 80.60 % 88.70 % 73.21 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
10 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.
11 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.
12 CAD 80.53 % 91.65 % 73.56 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
13 SGDA3D 80.49 % 87.77 % 72.51 % 0.07 s 1 core @ 2.5 Ghz (Python)
14 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.
15 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.
16 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.
17 SPT 80.21 % 88.89 % 73.79 % 0.1 s GPU @ 2.5 Ghz (Python)
18 USVLab BSAODet 80.04 % 88.51 % 73.38 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
19 VoCo 80.00 % 87.66 % 74.48 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
20 Under Blind Review#2 79.94 % 87.22 % 73.58 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
21 Anonymous
This method makes use of Velodyne laser scans.
79.92 % 87.70 % 73.16 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
22 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.
23 CF-ctdep-tv-ta 79.76 % 90.48 % 73.04 % 1 s 1 core @ 2.5 Ghz (C/C++)
24 CZY_PPF_Net2 79.75 % 87.34 % 73.05 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
25 PA-RCNN code 79.42 % 89.01 % 72.72 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
26 IKT3D
This method makes use of Velodyne laser scans.
79.38 % 87.43 % 72.87 % 0.05 s 1 core @ 2.5 Ghz (Python)
27 USVLab BSAODet (S) 79.34 % 89.04 % 72.50 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
28 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.
29 CFF-ep25 79.20 % 88.66 % 72.11 % 1 s 1 core @ 2.5 Ghz (C/C++)
30 CZY_PPF_Net 79.03 % 88.09 % 73.83 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
31 CFF-tv 78.94 % 88.99 % 71.88 % 1 s 1 core @ 2.5 Ghz (C/C++)
32 Reprod-Two-Branch 78.92 % 88.74 % 71.93 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
33 3SNet 78.88 % 86.83 % 73.73 % 0.07 s GPU @ 2.5 Ghz (Python)
34 cff-tv-v2-ep25 78.86 % 88.39 % 71.68 % 1 s 1 core @ 2.5 Ghz (C/C++)
35 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.
36 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.
37 DGT-Det3D code 78.69 % 87.54 % 71.86 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
38 CFF-tv-v2 78.59 % 87.90 % 71.64 % 1 s 1 core @ 2.5 Ghz (C/C++)
39 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.
40 CF-ctdep-tv 78.16 % 87.85 % 71.62 % 1 s 1 core @ 2.5 Ghz (C/C++)
41 CZY_3917 78.11 % 86.51 % 71.65 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
42 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.
43 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.
44 U_RVRCNN_V2_1 77.90 % 86.33 % 71.12 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
45 CF-base-tv 77.87 % 86.17 % 71.35 % 1 s 1 core @ 2.5 Ghz (C/C++)
46 TBD 77.82 % 86.21 % 71.45 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
47 TCDVF 77.66 % 86.38 % 71.38 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
48 DCAN-Second code 77.63 % 90.34 % 71.98 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
49 KeyFuse2B 77.58 % 89.00 % 70.66 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
50 VGA-RCNN 77.50 % 85.80 % 70.89 % 0.07 s 1 core @ 2.5 Ghz (Python)
51 PVTr 77.39 % 89.56 % 70.23 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
52 KPSCC code 77.26 % 87.25 % 71.05 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
53 IPS 77.19 % 89.29 % 70.72 % TBD s 1 core @ 2.5 Ghz (C/C++)
54 CZY 77.13 % 88.80 % 70.33 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
55 WGVRF 77.01 % 85.88 % 70.70 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
56 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.
57 LightCPC code 76.92 % 87.79 % 70.75 % 0.02 s 1 core @ 2.5 Ghz (Python + C/C++)
58 PSA-Det3D 76.87 % 86.83 % 70.34 % 0.1 s GPU @ 2.5 Ghz (Python)
59 MVMM code 76.85 % 84.87 % 72.10 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
60 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.
61 U_PVRCNN_V2 76.72 % 86.37 % 70.09 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
62 DGT-Det3D 76.71 % 86.58 % 70.08 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
63 GS-FPS-LT 76.50 % 86.71 % 70.53 % TBD s 1 core @ 2.5 Ghz (C/C++)
64 NV-RCNN 76.31 % 88.34 % 69.70 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
65 CenterFuse 76.29 % 89.88 % 67.96 % 0.059 sec/frame 2 x V100
66 ACF-Net 76.15 % 86.92 % 71.33 % n/a s 1 core @ 2.5 Ghz (C/C++)
67 FV2P v2 75.83 % 88.58 % 69.05 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
68 GS-FPS 75.78 % 86.48 % 70.99 % TBD s 1 core @ 2.5 Ghz (C/C++)
69 BASA 75.75 % 86.41 % 70.26 % 1s 1 core @ 2.5 Ghz (python)
70 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.
71 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.
72 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.
73 PSA-SSD 75.15 % 86.11 % 70.01 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
74 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.
75 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.
76 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.
77 TBD 75.02 % 88.92 % 68.08 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
78 cp-tv 74.89 % 84.13 % 68.41 % 1 s 1 core @ 2.5 Ghz (C/C++)
79 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.
80 ATT_SSD 74.75 % 87.02 % 69.63 % 0.01 s 1 core @ 2.5 Ghz (Python)
81 SWA code 74.63 % 86.25 % 69.93 % 0.18 s 1 core @ 2.5 Ghz (C/C++)
82 GEO_LOC 74.60 % 86.95 % 69.67 % TBD s 1 core @ 2.5 Ghz (C/C++)
83 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.
84 Self-Calib Conv 74.32 % 84.08 % 68.02 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
85 TTT_SSD 74.32 % 86.17 % 68.32 % TBD s 1 core @ 2.5 Ghz (C/C++)
86 4cls-center-hirige 74.15 % 83.63 % 64.69 % 0.01 s 1 core @ 2.5 Ghz (Python)
87 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.
88 HPV-RCNN 74.12 % 85.69 % 67.04 % 0.15 s 1 core @ 2.5 Ghz (Python)
89 TBD 74.06 % 84.24 % 68.91 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
90 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.
91 U_SECOND_V4 73.81 % 86.35 % 67.26 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
92 AGS-SSD[la] 73.72 % 85.27 % 67.95 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
93 cp-tv-kp 73.68 % 84.33 % 67.48 % 1 s 1 core @ 2.5 Ghz (C/C++)
94 PVRCNN_8369 73.68 % 85.41 % 66.98 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
95 SRDL 73.68 % 85.44 % 66.94 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
96 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.
97 cp-tv-kp-io-sc 73.66 % 87.24 % 66.98 % 1 s 1 core @ 2.5 Ghz (C/C++)
98 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.
99 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.
100 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.
101 SIF 73.19 % 85.18 % 65.41 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
102 CF-cd-io-tv 73.17 % 87.63 % 65.99 % 1 s 1 core @ 2.5 Ghz (C/C++)
103 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.
104 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.
105 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.
106 VPNet 72.88 % 85.66 % 66.16 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
107 City-CF-fixed 72.86 % 86.69 % 66.70 % 1 s 1 core @ 2.5 Ghz (C/C++)
108 T_PVRCNN_V2 72.81 % 86.48 % 65.32 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
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 T_PVRCNN 72.79 % 85.61 % 66.40 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
111 KPP3D code 72.73 % 82.91 % 66.30 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
112 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.
113 IoU-2B 72.71 % 89.27 % 63.76 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
114 KeyPoint-IoUHead 72.69 % 87.00 % 65.91 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
115 City-CF 72.68 % 86.44 % 66.22 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
116 ZMMPP 72.65 % 82.01 % 66.11 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
117 SECOND_7862 72.51 % 84.05 % 66.14 % 1 s 1 core @ 2.5 Ghz (Python)
118 Dune-DCF-e15 72.38 % 86.51 % 66.02 % 1 s 1 core @ 2.5 Ghz (C/C++)
119 CF-base-train 72.24 % 86.19 % 65.04 % 1 s 1 core @ 2.5 Ghz (C/C++)
120 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.
121 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.
122 variance_point 71.87 % 83.16 % 63.53 % 0.05 s 1 core @ 2.5 Ghz (Python)
123 Dune-DCF-e11 71.82 % 86.21 % 65.26 % 1 s 1 core @ 2.5 Ghz (C/C++)
124 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.
125 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.
126 CF-ctdep-train 71.61 % 85.47 % 64.23 % 1 s 1 core @ 2.5 Ghz (C/C++)
127 cff-tv-t 71.22 % 88.33 % 63.92 % 1 s 1 core @ 2.5 Ghz (C/C++)
128 Anonymous 71.18 % 82.55 % 64.62 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
129 Anonymous 70.87 % 82.26 % 64.77 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
130 LazyTorch-CP-Infer-O 70.65 % 83.62 % 64.20 % 1 s 1 core @ 2.5 Ghz (C/C++)
131 CenterPoint (pcdet) 70.54 % 83.36 % 64.16 % 0.051 sec/frame 2 x V100
132 LazyTorch-CP-Small-P 70.48 % 83.46 % 64.07 % 1 s 1 core @ 2.5 Ghz (C/C++)
133 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.
134 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.
135 Anonymous 70.37 % 82.59 % 64.89 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
136 Dune-DCF-e09 70.28 % 83.14 % 64.12 % 1 s 1 core @ 2.5 Ghz (C/C++)
137 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.
138 CrazyTensor-CP 69.31 % 83.67 % 62.97 % 1 s 1 core @ 2.5 Ghz (Python)
139 CrazyTensor-CF 69.23 % 85.28 % 62.37 % 1 s 1 core @ 2.5 Ghz (C/C++)
140 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.
141 AFTD 68.83 % 88.94 % 62.04 % 1 s 1 core @ 2.5 Ghz (Python + C/C++)
142 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.
143 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.
144 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.
145 EPNet++ 68.30 % 80.27 % 63.00 % 0.1 s GPU @ 2.5 Ghz (Python)
Z. Liu, H. tengteng, B. Li, X. Chen, X. Wang and X. Bai: EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object Detection. arXiv preprint arXiv:2112.11088 2021.
146 DTE3D 68.23 % 82.63 % 61.99 % 0.15s 1 core @ 2.5 Ghz (C/C++)
147 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.
148 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.
149 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.
150 VueronNet 66.92 % 82.71 % 59.01 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
151 CAD
This method uses stereo information.
This method makes use of Velodyne laser scans.
66.75 % 80.00 % 59.59 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
152 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.
153 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.
154 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.
155 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) .
156 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.
157 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.
158 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.
159 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.
160 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. arXiv preprint arXiv:2112.15458 2022.
161 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.
162 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.
163 CZY 62.20 % 73.99 % 56.07 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
164 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. arXiv preprint arXiv:2204.03039 2022.
165 StereoDistill 61.46 % 80.92 % 54.64 % 0.4 s 1 core @ 2.5 Ghz (Python)
166 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.
167 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.
168 BAIR 59.97 % 76.29 % 51.80 % 0.04 s 1 core @ 2.5 Ghz (Python)
169 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.
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 SSAL-Mono 58.45 % 80.54 % 49.15 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
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 GPENet code 55.96 % 71.89 % 48.36 % 0.02 s GPU @ 2.5 Ghz (Python)
177 MonoASS 55.92 % 75.72 % 49.08 % 0.04 s 1 core @ 2.5 Ghz (Python)
178 MDNet 55.58 % 69.87 % 46.54 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
179 MoGDE 55.22 % 79.09 % 46.54 % 0.03 s GPU @ 2.5 Ghz (Python)
180 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.
181 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.
182 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.
183 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.
184 YOLOv5x6_1280 54.07 % 71.49 % 47.82 % 0.019 s GPU @ >3.5 Ghz (Python)
185 YOLOv5x6_1280_Q 53.78 % 71.22 % 47.60 % 0.016 s GPU @ >3.5 Ghz (Python)
186 Shape-Aware 53.48 % 70.94 % 46.95 % 0.05 s 1 core @ 2.5 Ghz (Python)
187 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 .
188 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.
189 Pseudo-Stereo++ 52.87 % 72.93 % 46.56 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
190 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.
191 YOLOv5x6_1920 52.29 % 75.21 % 45.67 % 0.05 s GPU @ 3.5 Ghz (Python)
192 PS 51.87 % 71.65 % 46.48 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
193 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.
194 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.
195 Mono3DMethod 50.24 % 67.46 % 43.86 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
196 3DSeMoDLE code 49.71 % 69.51 % 43.11 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
197 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.
198 M3DGAF 49.25 % 64.62 % 42.82 % 0.07 s 1 core @ 2.5 Ghz (Python)
199 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.
200 DEPT 49.04 % 69.01 % 42.71 % 0.03 s 1 core @ 2.5 Ghz (Python)
201 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.
202 MonoAug 48.61 % 69.31 % 42.24 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
203 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.
204 Anonymous 48.01 % 66.15 % 41.50 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
205 MonoInsight 47.72 % 67.52 % 41.00 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
206 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.
207 BCA 46.50 % 67.68 % 39.47 % 0.17 s GPU @ 2.5 Ghz (Python)
208 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.
209 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.
210 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.
211 LT-M3OD 45.87 % 63.54 % 39.19 % 0.03 s 1 core @ 2.5 Ghz (Python)
212 MonoAug 45.48 % 65.80 % 39.05 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
213 DD3Dv2 code 45.35 % 63.42 % 39.61 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
214 Lite-FPN-GUPNet 45.16 % 64.84 % 38.38 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
215 MonoAD 44.87 % 66.02 % 39.94 % 0.03 s GPU @ 2.5 Ghz (Python)
216 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.
217 MonoA^2 44.12 % 59.98 % 37.93 % na s 1 core @ 2.5 Ghz (C/C++)
218 MK3D 43.41 % 64.38 % 36.70 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
219 Anonymous 43.31 % 64.66 % 36.52 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
220 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.
221 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.
222 GCDR 42.78 % 67.11 % 37.94 % 0.28 s 1 core @ 2.5 Ghz (Python)
223 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.
224 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 .
225 BiResFPN 41.47 % 61.79 % 35.99 % 0.071s 1 core @ 2.5 Ghz (C/C++)
226 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.
227 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.
228 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.
229 CrazyTensor-ICN 38.69 % 59.42 % 33.90 % 1 s 1 core @ 2.5 Ghz (C/C++)
230 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.
231 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.
232 OPA-3D code 38.35 % 55.98 % 33.83 % 0.04 s 1 core @ 3.5 Ghz (Python)
233 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.
234 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.
235 BSM3D 35.89 % 54.56 % 30.71 % 0.03 s 1 core @ 2.5 Ghz (Python)
236 City-ICN 35.87 % 53.37 % 29.37 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
237 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.
238 Yolo5x6_Ghost 35.20 % 50.96 % 31.53 % 0.03 s GPU @ 2.5 Ghz (Python)
239 Yolo5x6_Ghost 35.20 % 50.96 % 31.53 % 0.03 s GPU @ 2.5 Ghz (Python)
240 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.
241 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.
242 Res 34.56 % 49.83 % 30.90 % 0.03 s 1 core @ 2.5 Ghz (Python)
243 GHos_3d 34.56 % 49.83 % 30.90 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
244 ALI_TRY1 34.56 % 49.83 % 30.90 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
245 SparseLiDAR_fusion 33.00 % 48.74 % 28.68 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
246 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.
247 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.
248 Yolov5ObjectDetector 31.69 % 45.65 % 29.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
249 AMNet 31.01 % 45.93 % 27.06 % 0.03 s GPU @ 1.0 Ghz (Python)
250 CIE 30.10 % 38.03 % 26.99 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
251 MonoNeRD 29.89 % 45.35 % 26.49 % na s 1 core @ 2.5 Ghz (C/C++)
252 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.
253 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.
254 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.
255 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.
256 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.
257 SARM3D 27.67 % 42.08 % 23.40 % 0.03 s GPU @ 2.5 Ghz (Python)
258 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.
259 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.
260 MonoXiver 26.95 % 39.81 % 23.32 % 0.03s GPU @ 2.5 Ghz (Python)
261 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.
262 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.
263 MM3D 25.98 % 35.92 % 22.18 % NA s 1 core @ 2.5 Ghz (C/C++)
264 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.
265 HBD 24.75 % 35.65 % 22.00 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
266 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.
267 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.
268 MonoPCNS 22.75 % 31.31 % 20.12 % 0.14 s GPU @ 2.5 Ghz (Python)
269 BEV_GHOST 20.80 % 29.52 % 19.38 % 0.1 s 1 core @ 2.5 Ghz (Python)
270 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.
271 CDTrack3D code 17.28 % 27.04 % 13.92 % 0.0106 s NVIDIA RTX 3090 GPU, i9 10850k CPU
272 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.
273 DCD code 14.71 % 18.66 % 13.83 % 1 s 1 core @ 2.5 Ghz (C/C++)
274 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.
275 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.
276 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.
277 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.
278 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.
279 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.
Table as LaTeX | Only published Methods

Object Detection and Orientation Estimation Evaluation

Cars


Method Setting Code Moderate Easy Hard Runtime Environment
1 VoCo 97.11 % 98.25 % 94.46 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
2 VirConv-S 96.46 % 96.99 % 93.74 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
3 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.
4 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.
5 NIV-SSD 96.06 % 96.89 % 88.63 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
6 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.
7 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.
8 VirConv-T 96.01 % 98.64 % 93.12 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
9 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.
10 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.
11 ACF-Net 95.95 % 96.64 % 93.17 % n/a s 1 core @ 2.5 Ghz (C/C++)
12 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.
13 ImpDet 95.92 % 96.72 % 90.85 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
14 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.
15 SPT 95.88 % 96.56 % 90.97 % 0.1 s GPU @ 2.5 Ghz (Python)
16 CityBrainLab 95.86 % 96.58 % 90.81 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
17 BiProDet 95.85 % 96.25 % 93.17 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
18 PVT-SSD 95.83 % 96.74 % 90.58 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
19 VirConv-L 95.80 % 98.73 % 92.92 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
20 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.
21 3D Dual-Fusion 95.76 % 96.53 % 93.01 % 0.1 s 1 core @ 2.5 Ghz (Python)
22 GLENet-VR 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. arXiv preprint arXiv:2207.02466 2022.
23 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.
24 HCPVF 95.70 % 96.61 % 92.87 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
25 OcTr 95.69 % 96.44 % 90.78 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
26 LightCPC code 95.66 % 96.55 % 90.65 % 0.02 s 1 core @ 2.5 Ghz (Python + C/C++)
27 NSAW code 95.65 % 98.57 % 92.63 % 0.1 s 1 core @ 2.5 Ghz (Python)
28 LIVOX_Det
This method makes use of Velodyne laser scans.
95.64 % 98.60 % 92.90 % n/a s 1 core @ 2.5 Ghz (Python + C/C++)
29 HRNet++ 95.63 % 98.81 % 90.70 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
30 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.
31 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. arXiv preprint arXiv:2204.03039 2022.
32 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.
33 VoxelGraphRCNN 95.53 % 96.65 % 90.65 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
34 FARP-Net code 95.53 % 96.10 % 92.98 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
35 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.
36 HRNet 95.50 % 96.62 % 92.73 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
37 LoGoNet 95.44 % 96.59 % 92.89 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
38 SGFusion 95.44 % 96.54 % 92.29 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
39 GT3D 95.43 % 96.57 % 92.67 % 0.1 s 1 core @ 2.5 Ghz (Python)
40 Anonymous 95.37 % 98.34 % 90.41 % n/a s 1 core @ 2.5 Ghz (C/C++)
41 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.
42 TBD 95.34 % 96.05 % 90.25 % 0.1 s 1 core @ 2.5 Ghz (Python)
43 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.
44 LGNet 95.31 % 96.51 % 92.55 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
45 GLENet 95.30 % 96.60 % 90.44 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
46 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.
47 PTA-RCNN 95.28 % 96.39 % 92.21 % 0.08 s 1 core @ 2.5 Ghz (Python)
48 Anonymous 95.23 % 96.47 % 90.45 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
49 PV-DT3D 95.23 % 96.33 % 92.53 % 1.4 s 1 core @ 2.5 Ghz (C/C++)
50 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.
51 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.
52 ATT_SSD 95.18 % 95.86 % 90.42 % 0.01 s 1 core @ 2.5 Ghz (Python)
53 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.
54 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.
55 ChTR3D 95.16 % 96.21 % 90.37 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
56 ChTR3D 95.13 % 96.12 % 90.31 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
57 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.
58 TTT_SSD 95.12 % 95.72 % 92.29 % TBD s 1 core @ 2.5 Ghz (C/C++)
59 VGT-RCNN 95.11 % 96.37 % 92.42 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
60 BSConv 95.08 % 97.89 % 92.09 % 0.1 s 1 core @ 2.5 Ghz (Java)
61 IPS 95.08 % 95.85 % 90.44 % TBD s 1 core @ 2.5 Ghz (C/C++)
62 GS-FPS 95.06 % 95.72 % 90.44 % TBD s 1 core @ 2.5 Ghz (C/C++)
63 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.
64 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.
65 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.
66 SWA code 95.01 % 95.90 % 92.27 % 0.18 s 1 core @ 2.5 Ghz (C/C++)
67 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.
68 EPNet++ 95.00 % 96.70 % 91.82 % 0.1 s GPU @ 2.5 Ghz (Python)
Z. Liu, H. tengteng, B. Li, X. Chen, X. Wang and X. Bai: EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object Detection. arXiv preprint arXiv:2112.11088 2021.
69 AGS-SSD[la] 95.00 % 97.82 % 92.23 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
70 VGRCNN++ 94.98 % 96.46 % 92.29 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
71 GEO_LOC 94.96 % 95.92 % 90.29 % TBD s 1 core @ 2.5 Ghz (C/C++)
72 GV-RCNN code 94.96 % 96.28 % 92.28 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
73 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.
74 VGRCNN 94.95 % 96.23 % 92.22 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
75 VG-RCNN 94.92 % 96.40 % 92.20 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
76 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.
77 Anonymous
This method makes use of Velodyne laser scans.
94.87 % 96.29 % 92.16 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
78 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.
79 ChTR3D 94.84 % 96.31 % 90.12 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
80 SGDA3D 94.82 % 96.00 % 92.14 % 0.07 s 1 core @ 2.5 Ghz (Python)
81 KPSCC code 94.81 % 95.97 % 91.96 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
82 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.
83 Semantical PVRCNN 94.81 % 95.96 % 92.19 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
84 NV-RCNN 94.79 % 95.84 % 92.09 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
85 USVLab BSAODet 94.79 % 96.18 % 92.05 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
86 GVNet-V2 94.77 % 96.28 % 91.98 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
87 CZY_PPF_Net2 94.74 % 98.03 % 92.03 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
88 GVNet code 94.72 % 96.29 % 92.00 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
89 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.
90 CZY_3917 94.70 % 97.98 % 92.03 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
91 Under Blind Review#2 94.69 % 95.94 % 92.09 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
92 DCCA 94.67 % 95.74 % 92.10 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
93 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.
94 CSVoxel-RCNN 94.66 % 96.21 % 91.84 % 0.03 s GPU @ 1.0 Ghz (Python)
95 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.
96 PSA-SSD 94.63 % 95.78 % 90.06 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
97 PVE 94.63 % 96.00 % 91.95 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
98 TBD 94.60 % 95.87 % 91.71 % 0.06 s GPU @ 2.5 Ghz (Python)
99 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.
100 IKT3D
This method makes use of Velodyne laser scans.
94.59 % 95.91 % 91.95 % 0.05 s 1 core @ 2.5 Ghz (Python)
101 VGA-RCNN 94.57 % 95.94 % 91.80 % 0.07 s 1 core @ 2.5 Ghz (Python)
102 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.
103 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.
104 BASA 94.55 % 96.06 % 89.84 % 1s 1 core @ 2.5 Ghz (python)
105 Anonymous 94.54 % 96.10 % 91.79 % 0.03s
106 SC-Voxel-RCNN 94.54 % 96.10 % 91.67 % 0.12 s GPU @ 1.0 Ghz (Python)
107 DTE3D 94.54 % 96.04 % 91.53 % 0.15s 1 core @ 2.5 Ghz (C/C++)
108 DKDet 94.53 % 96.00 % 91.66 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
109 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.
110 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.
111 CZY_PPF_Net 94.50 % 96.16 % 89.97 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
112 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.
113 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.
114 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.
115 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.
116 DGT-Det3D code 94.41 % 96.17 % 91.49 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
117 USVLab BSAODet (S) 94.36 % 96.06 % 89.71 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
118 VPNet 94.35 % 96.04 % 91.78 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
119 MVMM code 94.33 % 95.75 % 89.78 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
120 SPVB-SSD 94.31 % 95.78 % 91.61 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
121 WGVRF 94.28 % 95.92 % 89.78 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
122 U_RVRCNN_V2_1 94.26 % 95.74 % 91.50 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
123 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.
124 CZY 94.24 % 95.98 % 89.80 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
125 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.
126 TVTr 94.22 % 97.62 % 91.56 % 0.08 s 1 core @ 2.5 Ghz (Python)
127 TCDVF 94.18 % 95.00 % 91.12 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
128 TBD 94.09 % 94.97 % 91.14 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
129 SRDL 94.08 % 95.83 % 91.55 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
130 PVRCNN_8369 94.08 % 95.81 % 91.54 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
131 U_PVRCNN_V2 94.04 % 95.80 % 91.31 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
132 RangeRCNN
This method makes use of Velodyne laser scans.
93.90 % 95.47 % 91.53 % 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.
133 DGT-Det3D 93.89 % 95.53 % 91.34 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
134 TBD 93.80 % 94.52 % 86.34 % 0.1 s 1 core @ 2.5 Ghz (Python)
135 U_SECOND_V4 93.80 % 95.74 % 89.46 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
136 SIF 93.79 % 95.48 % 91.30 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
137 DD3D code 93.78 % 94.67 % 88.99 % 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) .
138 MGAF-3DSSD code 93.77 % 94.45 % 86.25 % 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.
139 MMLAB LIGA-Stereo
This method uses stereo information.
code 93.71 % 96.40 % 86.00 % 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.
140 Sem-Aug 93.69 % 96.78 % 88.69 % 0.08 s GPU @ 2.5 Ghz (Python)
141 PA-RCNN code 93.67 % 96.57 % 86.31 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
142 CAD 93.63 % 96.82 % 88.59 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
143 CF-ctdep-tv-ta 93.61 % 95.07 % 90.90 %