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 VoCo 97.19 % 98.27 % 94.59 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
2 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.
3 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.
4 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.
5 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.
6 NIV-SSD 96.14 % 96.90 % 88.73 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
7 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.
8 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.
9 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.
10 TED 96.03 % 96.64 % 93.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
11 ImpDet 96.00 % 96.73 % 90.96 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
12 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.
13 CityBrainLab 95.96 % 96.59 % 90.94 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
14 PE-RCVN 95.94 % 96.90 % 90.98 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
15 SPT 95.92 % 96.57 % 91.04 % 0.1 s GPU @ 2.5 Ghz (Python)
16 PVT-SSD 95.90 % 96.75 % 90.69 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
17 Anonymous 95.89 % 96.25 % 93.25 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
18 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.
19 3D Dual-Fusion 95.82 % 96.54 % 93.11 % 0.1 s 1 core @ 2.5 Ghz (Python)
20 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.
21 NSAW code 95.80 % 98.59 % 92.91 % 0.1 s 1 core @ 2.5 Ghz (Python)
22 HCPVF 95.80 % 96.62 % 93.03 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
23 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. 2022.
24 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++)
25 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.
26 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.
27 TBD 95.69 % 96.33 % 93.07 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
28 SGFusion 95.67 % 96.57 % 92.69 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
29 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.
30 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.
31 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.
32 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. 2022.
33 Anonymous 95.47 % 98.35 % 90.55 % n/a s 1 core @ 2.5 Ghz (C/C++)
34 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.
35 LGNet 95.43 % 96.52 % 92.73 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
36 TBD 95.43 % 96.06 % 90.38 % 0.1 s 1 core @ 2.5 Ghz (Python)
37 Anonymous 95.41 % 96.49 % 90.68 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
38 GLENet 95.40 % 96.61 % 90.57 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
39 PTA-RCNN 95.39 % 96.40 % 92.40 % 0.08 s 1 core @ 2.5 Ghz (Python)
40 PV-DT3D 95.36 % 96.34 % 92.70 % 1.4 s 1 core @ 2.5 Ghz (C/C++)
41 DCGNN 95.36 % 96.39 % 90.37 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
42 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.
43 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.
44 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.
45 anonymous 95.31 % 96.36 % 92.57 % 0.09 s GPU @ 2.5 Ghz (Python)
46 VoxelGraphRCNN 95.29 % 98.25 % 92.68 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
47 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.
48 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.
49 ChTR3D 95.26 % 96.22 % 90.51 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
50 DGDNH 95.24 % 98.36 % 92.69 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
51 ChTR3D 95.23 % 96.13 % 90.45 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
52 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.
53 TBD 95.21 % 95.73 % 92.45 % TBD s 1 core @ 2.5 Ghz (C/C++)
54 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.
55 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.
56 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.
57 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.
58 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.
59 HMFI code 95.16 % 96.29 % 92.45 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
60 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.
61 VGRCNN++ 95.12 % 96.48 % 92.50 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
62 SWA code 95.11 % 95.91 % 92.43 % 0.18 s 1 core @ 2.5 Ghz (C/C++)
63 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.
64 AGS-SSD[la] 95.10 % 97.83 % 92.40 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
65 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.
66 VGRCNN 95.07 % 96.24 % 92.38 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
67 VG-RCNN 95.06 % 96.41 % 92.45 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
68 GV-RCNN code 95.06 % 96.29 % 92.43 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
69 PV-RCNN++ code 95.05 % 96.08 % 92.42 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
70 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.
71 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++)
72 ATT_SSD 94.99 % 96.02 % 92.18 % 0.01 s 1 core @ 2.5 Ghz (Python)
73 USVLab BSAODet 94.99 % 96.22 % 92.30 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
74 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.
75 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.
76 SGDA3D 94.96 % 96.02 % 92.36 % 0.07 s 1 core @ 2.5 Ghz (Python)
77 ChTR3D 94.95 % 96.32 % 90.25 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
78 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.
79 NV-RCNN 94.92 % 95.86 % 92.30 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
80 Semantical PVRCNN 94.92 % 95.97 % 92.36 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
81 GVNet-V2 94.92 % 96.29 % 92.21 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
82 TBD code 94.90 % 95.98 % 92.11 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
83 CZY_PPF_Net2 94.86 % 98.07 % 92.22 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
84 GVNet code 94.86 % 96.30 % 92.22 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
85 TBD 94.85 % 97.04 % 92.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
86 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.
87 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.
88 CSVoxel-RCNN 94.81 % 96.23 % 92.09 % 0.03 s GPU @ 1.0 Ghz (Python)
89 CZY_3917 94.80 % 98.00 % 92.21 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
90 E^2-PV-RCNN 94.80 % 95.95 % 92.26 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
X. Li, Y. Zhang and D. Kong: E^2-PV-RCNN: improving 3D object detection via enhancing keypoint features. Multimedia Tools and Applications 2022.
91 SRIF-RCNN 94.79 % 95.63 % 92.35 % 0.0947 s 1 core @ 2.5 Ghz (C/C++)
X. Li and D. Kong: SRIF-RCNN: Sparsely Represented Inputs Fusion of Different Sensors for 3D Object Detection. Applied Intelligence 2022.
92 DCCA 94.77 % 95.75 % 92.27 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
93 VCRCNN 94.77 % 96.06 % 92.28 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
94 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.
95 PVE 94.75 % 96.01 % 92.13 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
96 VGA-RCNN 94.74 % 95.98 % 92.06 % 0.07 s 1 core @ 2.5 Ghz (Python)
97 PSA-SSD 94.74 % 95.80 % 90.21 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
98 TBD 94.74 % 95.88 % 91.96 % 0.06 s GPU @ 2.5 Ghz (Python)
99 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.
100 DTE3D 94.73 % 96.06 % 91.84 % 0.19 s 1 core @ 2.5 Ghz (C/C++)
101 SC-Voxel-RCNN 94.71 % 96.13 % 91.94 % 0.12 s GPU @ 1.0 Ghz (Python)
102 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.
103 IKT3D
This method makes use of Velodyne laser scans.
94.71 % 95.92 % 92.15 % 0.05 s 1 core @ 2.5 Ghz (Python)
104 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.
105 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.
106 DKDet 94.68 % 96.03 % 91.92 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
107 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.
108 DDet 94.66 % 95.82 % 92.17 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
109 FPV-SSD 94.66 % 96.92 % 91.78 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
110 BASA 94.65 % 96.07 % 90.02 % 1s 1 core @ 2.5 Ghz (python)
111 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.
112 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.
113 CZY_PPF_Net 94.63 % 96.19 % 90.14 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
114 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.
115 USVLab BSAODet (S) 94.60 % 96.10 % 90.03 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
116 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.
117 DGT-Det3D code 94.55 % 96.20 % 91.74 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
118 VPNet 94.51 % 96.07 % 92.04 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
119 WGVRF 94.50 % 95.97 % 90.06 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
120 MVTr 94.50 % 97.66 % 91.94 % 0.08 s 1 core @ 2.5 Ghz (Python)
121 SPVB-SSD 94.49 % 95.80 % 91.90 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
122 CZY 94.47 % 96.00 % 90.08 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
123 MVMM code 94.47 % 95.76 % 89.98 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
124 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.
125 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.
126 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.
127 DCAN-Second code 94.41 % 97.08 % 91.37 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
128 TCDVF 94.31 % 95.02 % 91.33 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
129 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.
130 SRDL 94.24 % 95.86 % 91.80 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
131 FusionDetv1 94.23 % 95.84 % 91.80 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
132 TBD 94.22 % 95.00 % 91.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
133 SARFE 94.18 % 95.74 % 91.57 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
134 DGT-Det3D 94.03 % 95.55 % 91.58 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
135 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.
136 VueronNet 94.03 % 96.70 % 87.58 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
137 DGIST MT-CNN 94.00 % 95.25 % 86.76 % 0.09 s GPU @ 1.0 Ghz (Python)
138 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.
139 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) .
140 SIF 93.95 % 95.51 % 91.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
141 U_SECOND_V4 93.94 % 95.76 % 89.68 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
142 B-FPS 93.92 % 95.57 % 89.61 % 0.1 s 1 core @ 2.5 Ghz (Java)
143 TBD 93.89 % 94.52 % 86.44 % 0.1 s 1 core @ 2.5 Ghz (Python)
144 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.
145 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.
146 YOLOv5x6_1920 93.82 % 96.64 % 81.54 % 0.05 s GPU @ 3.5 Ghz (Python)
147 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.
148 DKAnet 93.79 % 95.16 % 89.27 % 0.05 s 1 core @ 2.0 Ghz (Python)
149 PA-RCNN code 93.79 % 96.58 % 86.43 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
150 Sem-Aug 93.77 % 96.79 % 88.78 % 0.08 s GPU @ 2.5 Ghz (Python)
151 CF-ctdep-tv-ta 93.75 % 95.08 % 91.08 % 1 s 1 core @ 2.5 Ghz (C/C++)
152 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.
153 CAD 93.73 % 96.84 % 88.74 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
154 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.
155 Anonymous 93.68 % 95.19 % 91.12 % 1 1 core @ 2.5 Ghz (Python)
156 SECOND_7862 93.68 % 95.19 % 91.12 % 1 s 1 core @ 2.5 Ghz (Python)
157 CF-base-tv 93.68 % 94.86 % 90.87 % 1 s 1 core @ 2.5 Ghz (C/C++)
158 DTFI 93.67 % 95.17 % 91.10 % 0.03 s 1 core @ 2.5 Ghz (Python)
159 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.
160 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.
161 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.
162 PA3DNet 93.62 % 96.57 % 88.65 % 0.05 s GPU @ 2.5 Ghz (Python)
163 KpNet 93.60 % 96.76 % 85.98 % 0.42 s 1 core @ 2.5 Ghz (C/C++)
164 SPD-Net 93.57 % 96.59 % 88.64 % 0.1 s 2 cores @ 3.0 Ghz (Python)
165 DVF 93.57 % 96.46 % 88.58 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
166 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.
167 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.
168 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.
169 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.
170 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.
171 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.
172 FV2P v2 93.49 % 94.33 % 90.85 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
173 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.
174 HPV-RCNN 93.45 % 96.10 % 88.29 % 0.15 s 1 core @ 2.5 Ghz (Python)
175 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.
176 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.
177 TBD code 93.44 % 96.38 % 90.36 % 0.1 s GPU @ 2.5 Ghz (Python)
178 StereoDistill 93.43 % 97.61 % 87.71 % 0.4 s 1 core @ 2.5 Ghz (Python)
179 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.
180 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.
181 PVTr 93.36 % 94.54 % 90.82 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
182 Anonymous 93.34 % 96.44 % 83.76 % 40 s 1 core @ 2.5 Ghz (C/C++)
183 DSASNet 93.33 % 96.55 % 88.47 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
184 3SNet 93.33 % 96.40 % 90.65 % 0.07 s GPU @ 2.5 Ghz (Python)
185 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.
186 FS-Net
This method makes use of Velodyne laser scans.
93.32 % 96.39 % 90.64 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
187 CF-ctdep-tv 93.31 % 94.89 % 90.87 % 1 s 1 core @ 2.5 Ghz (C/C++)
188 YOLOv5x6_1280 93.31 % 95.92 % 85.44 % 0.019 s GPU @ >3.5 Ghz (Python)
189 YOLOv5x6_1280_Q 93.31 % 95.88 % 85.45 % 0.016 s GPU @ >3.5 Ghz (Python)
190 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.
191 VPN 93.30 % 96.19 % 88.30 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
192 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.
193 ITCA-SSD code 93.27 % 96.65 % 88.14 % 0.05 s 1 core @ 2.5 Ghz (Python)
194 Reprod-Two-Branch 93.26 % 94.83 % 90.61 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
195 SPNet code 93.23 % 95.99 % 92.60 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
196 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.
197 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.
198 CFF-ep25 93.20 % 94.81 % 90.76 % 1 s 1 core @ 2.5 Ghz (C/C++)
199 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.
200 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.
201 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.
202 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.
203 SGNet 93.08 % 96.43 % 90.53 % 0.09 s GPU @ 2.5 Ghz (Python)
204 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.
205 CFF-tv 93.06 % 94.68 % 90.61 % 1 s 1 core @ 2.5 Ghz (C/C++)
206 cp-tv 92.99 % 94.52 % 90.36 % 1 s 1 core @ 2.5 Ghz (C/C++)
207 FSFNet 92.99 % 96.36 % 89.99 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
208 Anonymous 92.99 % 96.18 % 87.80 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
209 MSADet 92.95 % 96.18 % 89.95 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
210 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.
211 cff-tv-v2-ep25 92.94 % 94.65 % 90.62 % 1 s 1 core @ 2.5 Ghz (C/C++)
212 GT3D 92.93 % 96.35 % 90.24 % 0.1 s 1 core @ 2.5 Ghz (Python)
213 KPP3D code 92.93 % 98.38 % 89.93 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
214 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.
215 mbdf-netv1 code 92.83 % 96.00 % 89.90 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
216 CF-base-train 92.82 % 94.98 % 90.19 % 1 s 1 core @ 2.5 Ghz (C/C++)
217 MDNet 92.82 % 96.14 % 85.37 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
218 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.
219 Anonymous 92.74 % 95.56 % 89.82 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
220 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.
221 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.
222 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.
223 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.
224 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.
225 MFANet 92.62 % 95.58 % 89.74 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
226 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.
227 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.
228 Anonymous 92.51 % 95.88 % 85.35 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
229 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.
230 CSNet8306 code 92.47 % 96.05 % 87.61 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
231 CSNet 92.46 % 95.99 % 89.24 % 0.1 s 1 core @ 2.5 Ghz (Python)
232 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.
233 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.
234 CAT 92.42 % 95.94 % 87.36 % 1 s 1 core @ 2.5 Ghz (Python)
235 DD3Dv2 code 92.41 % 95.41 % 87.27 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
236 LazyTorch-CP-Small-P 92.38 % 94.71 % 90.04 % 1 s 1 core @ 2.5 Ghz (C/C++)
237 LazyTorch-CP-Infer-O 92.35 % 94.76 % 90.09 % 1 s 1 core @ 2.5 Ghz (C/C++)
238 cp-tv-kp 92.34 % 94.32 % 90.25 % 1 s 1 core @ 2.5 Ghz (C/C++)
239 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.
240 Sem-Aug-PointRCNN++ 92.32 % 95.65 % 87.62 % 0.1 s 8 cores @ 3.0 Ghz (Python)
241 CSNet8299 code 92.31 % 96.14 % 87.54 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
242 PSM_stereo 92.27 % 95.63 % 87.24 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
243 CenterFuse 92.26 % 95.04 % 89.24 % 0.059 sec/frame 2 x V100
244 Harmonic PointPillar code 92.25 % 95.16 % 89.11 % 0.01 s 1 core @ 2.5 Ghz (Python)
H. Zhang, M. Mekala, Z. Nain, 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 Intelligent Transportation Systems 2022.
245 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.
246 Self-Calib Conv 92.17 % 94.45 % 90.19 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
247 PSA-Det3D 92.17 % 95.53 % 89.67 % 0.1 s GPU @ 2.5 Ghz (Python)
248 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.
249 AFTD 92.14 % 95.56 % 87.45 % 1 s 1 core @ 2.5 Ghz (Python + C/C++)
250 cp-tv-kp-io-sc 92.11 % 95.02 % 88.97 % 1 s 1 core @ 2.5 Ghz (C/C++)
251 CF-cd-io-tv 92.06 % 94.80 % 88.80 % 1 s 1 core @ 2.5 Ghz (C/C++)
252 SSL_PP code 92.04 % 95.97 % 84.91 % 16ms GPU @ 1.5 Ghz (Python)
253 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.
254 KeyFuse2B 92.01 % 94.57 % 90.56 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
255 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.
256 Anonymous 91.97 % 95.79 % 89.30 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
257 CrazyTensor-CP 91.95 % 93.64 % 89.23 % 1 s 1 core @ 2.5 Ghz (Python)
258 ZMMPP 91.92 % 94.93 % 88.86 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
259 CFF-tv-v2 91.91 % 94.72 % 90.57 % 1 s 1 core @ 2.5 Ghz (C/C++)
260 Dune-DCF-e09 91.90 % 94.97 % 88.74 % 1 s 1 core @ 2.5 Ghz (C/C++)
261 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.
262 CrazyTensor-CF 91.89 % 94.98 % 88.70 % 1 s 1 core @ 2.5 Ghz (C/C++)
263 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.
264 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.
265 KeyPoint-IoUHead 91.79 % 94.65 % 88.61 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
266 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.
267 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++)
268 TBD 91.75 % 94.53 % 86.54 % 0.1 s 1 core @ 2.5 Ghz (Python)
269 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.
270 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.
271 Dune-DCF-e11 91.68 % 94.69 % 88.57 % 1 s 1 core @ 2.5 Ghz (C/C++)
272 City-CF-fixed 91.67 % 94.87 % 88.80 % 1 s 1 core @ 2.5 Ghz (C/C++)
273 Dune-DCF-e15 91.67 % 94.72 % 88.53 % 1 s 1 core @ 2.5 Ghz (C/C++)
274 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.
275 SFD-Retina
This method uses stereo information.
This method uses optical flow information.
This method makes use of Velodyne laser scans.
91.64 % 94.85 % 82.13 % 0.04 s GPU @ 2.5 Ghz (Python)
276 CF-ctdep-train 91.64 % 94.81 % 90.16 % 1 s 1 core @ 2.5 Ghz (C/C++)
277 HS3D code 91.62 % 95.51 % 86.94 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
278 mono3d 91.60 % 94.60 % 84.86 % 0.03 s GPU @ 2.5 Ghz (Python)
279 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.
280 GFD-Retina
This method uses stereo information.
This method uses optical flow information.
This method makes use of Velodyne laser scans.
91.59 % 94.55 % 81.80 % 0.07 s GPU @ 2.5 Ghz (Python)
281 CenterPoint (pcdet) 91.58 % 93.99 % 89.15 % 0.051 sec/frame 2 x V100
282 TBD 91.56 % 94.52 % 86.29 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
283 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.
284 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.
285 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.
286 new_stereo 91.39 % 95.01 % 86.77 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
287 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.
288 IoU-2B 91.38 % 94.81 % 85.63 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
289 T_PVRCNN 91.36 % 95.02 % 88.45 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
290 GPENet code 91.36 % 94.11 % 83.40 % 0.02 s GPU @ 2.5 Ghz (Python)
291 City-CF 91.35 % 94.54 % 88.38 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
292 PP-PCdet code 91.32 % 94.82 % 88.18 % 0.01 s 1 core @ 2.5 Ghz (Python)
293 T_PVRCNN_V2 91.31 % 95.59 % 88.22 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
294 Contrastive PP code 91.29 % 96.93 % 88.11 % 0.01 s 1 core @ 2.5 Ghz (Python)
295 Slime 91.27 % 96.55 % 81.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
296 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.
297 SAD 91.21 % 96.58 % 81.29 % 0.05 s 1 core @ 2.5 Ghz (python)
298 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.
299 LTN 91.18 % 94.68 % 81.51 % 0.4 s GPU @ >3.5 Ghz (Python)
T. Wang, X. He, Y. Cai and G. Xiao: Learning a Layout Transfer Network for Context Aware Object Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
300 SAD 91.16 % 96.44 % 83.49 % 0.05 s 1 core @ 2.5 Ghz (python)
301 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.
302 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.
303 CGPS-3DV code 91.06 % 96.23 % 83.39 % CGPS-3DV 1 core @ 2.5 Ghz (C/C++)
304 3D_att
This method makes use of Velodyne laser scans.
91.05 % 96.70 % 85.88 % 0.17 s GPU @ 2.5 Ghz (Python)
305 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.
306 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.
307 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.
308 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.
309 Mix-Teaching 91.02 % 96.35 % 83.41 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
310 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.
311 TempM3D 90.99 % 96.48 % 81.21 % 0.05 s 1 core @ 2.5 Ghz (Python)
312 VMDet 90.98 % 96.31 % 83.43 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
313 cff-tv-t 90.98 % 94.68 % 84.39 % 1 s 1 core @ 2.5 Ghz (C/C++)
314 monopd code 90.93 % 96.44 % 83.36 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
315 DDS code 90.93 % 96.44 % 83.36 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
316 MoGDE 90.91 % 96.47 % 83.66 % 0.03 s GPU @ 2.5 Ghz (Python)
317 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.
318 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.
319 PS code 90.85 % 96.20 % 83.08 % PS s 1 core @ 2.5 Ghz (C/C++)
320 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.
321 MonoDistill 90.81 % 95.92 % 81.08 % 0.04 s 1 core @ 2.5 Ghz (Python)
322 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 .
323 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.
324 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.
325 Anonymous 90.78 % 96.42 % 83.51 % 40 s 1 core @ 2.5 Ghz (C/C++)
326 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.
327 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.
328 MonoPPM code 90.66 % 93.88 % 80.99 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
329 MF 90.66 % 93.57 % 86.15 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
330 MonoGround 90.63 % 93.94 % 80.80 % 0.03 s 1 core @ 2.5 Ghz (Python)
331 MonoEdge 90.62 % 93.52 % 80.91 % 0.05 s GPU @ 2.5 Ghz (Python)
332 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.
333 CG-Stereo
This method uses stereo information.
90.38 % 96.31 % 82.80 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
334 MonoEdge-Rotate 90.30 % 93.53 % 80.60 % 0.05 s GPU @ 2.5 Ghz (Python)
335 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.
336 CMKD* 90.28 % 95.14 % 83.91 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
337 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.
338 MonoAug 90.24 % 95.59 % 80.47 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
339 Deep3DBox 90.19 % 94.71 % 76.82 % 1.5 s GPU @ 2.5 Ghz (C/C++)
A. Mousavian, D. Anguelov, J. Flynn and J. Kosecka: 3D Bounding Box Estimation Using Deep Learning and Geometry. CVPR 2017.
340 FQNet 90.17 % 94.72 % 76.78 % 0.5 s 1 core @ 2.5 Ghz (Python)
L. Liu, J. Lu, C. Xu, Q. Tian and J. Zhou: Deep Fitting Degree Scoring Network for Monocular 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
341 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.
342 variance_point 90.01 % 95.79 % 87.50 % 0.05 s 1 core @ 2.5 Ghz (Python)
343 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.
344 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.
345 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.
346 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.
347 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.
348 Digging_M3D 89.77 % 93.73 % 79.68 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
349 TBD_BD code 89.73 % 94.18 % 86.84 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
350 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.
351 ADD code 89.53 % 94.82 % 81.60 % 0.1 s 1 core @ 2.5 Ghz (Python)
352 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.
353 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.
354 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.
355 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.
356 MonoAD 88.99 % 94.43 % 76.76 % 0.03 s GPU @ 2.5 Ghz (Python)
357 MonoEdge-RCNN 88.97 % 94.19 % 74.23 % 0.05 s 1 core @ 2.5 Ghz (Python)
358 AVOD-FPN
This method makes use of Velodyne laser scans.
code 88.92 % 94.70 % 84.13 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
359 Keypoint-3D 88.87 % 93.31 % 76.10 % 14 s 1 core @ 2.5 Ghz (C/C++)
360 Shape-Aware 88.85 % 94.26 % 81.33 % 0.05 s 1 core @ 2.5 Ghz (Python)
361 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.
362 OPA-3D code 88.77 % 96.50 % 76.55 % 0.04 s 1 core @ 3.5 Ghz (Python)
363 Lite-FPN-GUPNet 88.76 % 96.45 % 76.54 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
364 MonoInsight 88.72 % 94.23 % 76.57 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
365 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.
366 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.
367 MM-Retina
This method uses stereo information.
This method uses optical flow information.
This method makes use of Velodyne laser scans.
88.63 % 93.74 % 78.56 % 0.04 s GPU @ 2.5 Ghz (Python)
368 Anonymous 88.62 % 92.96 % 79.44 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
369 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.
370 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.
371 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.
372 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.
373 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.
374 anonymity 88.41 % 95.28 % 81.68 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
375 anonymity 88.40 % 95.03 % 81.67 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
376 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.
377 MonoCon code 88.22 % 93.59 % 76.18 % 0.02 s GPU @ 2.5 Ghz (Python)
T. Xianpeng Liu: Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection. AAAI 2022.
378 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.
379 DEPT 88.00 % 96.45 % 78.40 % 0.03 s 1 core @ 2.5 Ghz (Python)
380 EW code 87.94 % 92.22 % 78.32 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
381 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.
382 SAIC_ADC_Mono3D code 87.78 % 95.21 % 80.14 % 50 s GPU @ 2.5 Ghz (Python)
383 MonoAug 87.76 % 93.65 % 77.91 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
384 CZY 87.55 % 94.63 % 82.97 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
385 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.
386 zongmuDistill 87.47 % 95.44 % 79.97 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
387 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.
388 mono3d code 87.07 % 93.28 % 77.72 % TBD TBD
389 BiResFPN 87.06 % 86.03 % 75.85 % 0.071s 1 core @ 2.5 Ghz (C/C++)
390 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.
391 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.
392 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.
393 Anonymous code 86.69 % 94.31 % 71.87 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
394 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.
395 UPF_3D
This method uses stereo information.
86.61 % 96.12 % 79.53 % 0.29 s 1 core @ 2.5 Ghz (Python)
396 MK3D 86.48 % 94.40 % 74.21 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
397 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.
398 GCDR 86.45 % 94.15 % 74.18 % 0.28 s 1 core @ 2.5 Ghz (Python)
399 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.
400 gupnet_se 86.33 % 94.27 % 74.08 % 0.03s 1 core @ 2.5 Ghz (C/C++)
401 OBMO_GUPNet 86.27 % 96.49 % 76.47 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
402 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.
403 TBD 86.03 % 91.50 % 78.52 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
404 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.
405 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.
406 City-ICN 85.70 % 93.91 % 73.41 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
407 Anonymous 85.65 % 92.64 % 78.83 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
408 MonoPCNS 85.56 % 92.57 % 78.41 % 0.14 s GPU @ 2.5 Ghz (Python)
409 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.
410 LT-M3OD 85.42 % 93.99 % 75.77 % 0.03 s 1 core @ 2.5 Ghz (Python)
411 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.
412 MDT code 85.32 % 93.80 % 75.35 % 0.01 s 1 core @ 2.5 Ghz (Python)
413 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.
414 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 .
415 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.
416 M3DGAF 85.01 % 93.33 % 77.55 % 0.07 s 1 core @ 2.5 Ghz (Python)
417 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.
418 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.
419 MDS-Mono3D 84.85 % 95.06 % 74.84 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
420 ZongmuMono3d code 84.64 % 93.06 % 75.29 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
421 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.
422 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.
423 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.
424 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.
425 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.
426 CMAN 83.74 % 89.74 % 65.35 % 0.15 s 1 core @ 2.5 Ghz (Python)
427 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.
428 Anonymous 83.24 % 93.53 % 73.60 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
429 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.
430 ppt 83.13 % 85.27 % 77.48 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
431 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.
432 SparseLiDAR_fusion 82.91 % 93.77 % 70.83 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
433 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.
434 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.
435 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.
436 CrazyTensor-ICN 82.70 % 90.79 % 70.61 % 1 s 1 core @ 2.5 Ghz (C/C++)
437 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.
438 HBD 82.64 % 93.13 % 75.14 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
439 Yolo5x6_Ghost 82.54 % 91.04 % 72.74 % 0.03 s GPU @ 2.5 Ghz (Python)
440 Yolo5x6_Ghost 82.54 % 91.04 % 72.74 % 0.03 s GPU @ 2.5 Ghz (Python)
441 Yolov5ObjectDetector 82.54 % 93.39 % 72.65 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
442 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.
443 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.
444 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.
445 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.
446 Anonymous 81.71 % 96.77 % 69.38 % 40 s 1 core @ 2.5 Ghz (C/C++)
447 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.
448 SD3DOD 81.64 % 92.60 % 75.97 % 0.04 s GPU @ 2.5 Ghz (Python)
449 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.
450 ART 81.03 % 90.97 % 74.44 % 20ms s 1 core @ 2.5 Ghz (C/C++)
451 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.
452 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.
453 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.
454 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.
455 FD 80.38 % 92.08 % 75.65 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
456 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.
457 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.
458 EM code 80.05 % 87.00 % 66.77 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
459 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.
460 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.
461 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.
462 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.
463 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.
464 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.
465 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.
466 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.
467 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.
468 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.
469 SARM3D 77.41 % 90.20 % 68.24 % 0.03 s GPU @ 2.5 Ghz (Python)
470 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.
471 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.
472 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.
473 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.
474 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.
475 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.
476 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.
477 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.
478 Ghost3D object detec 75.21 % 85.65 % 67.60 % 0.03 s 1 core @ 2.5 Ghz (Python)
479 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.
480 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.
481 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 .
482 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.
483 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.
484 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.
485 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.
486 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.
487 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.
488 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.
489 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.
490 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.
491 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.
492 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.
493 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.
494 MM 65.65 % 88.80 % 56.53 % 1 s 1 core @ 2.5 Ghz (C/C++)
495 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.
496 BSM3D 64.16 % 87.11 % 56.08 % 0.03 s 1 core @ 2.5 Ghz (Python)
497 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.
498 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.
499 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.
500 MDSNet 62.74 % 85.94 % 50.27 % 0.07 s 1 core @ 2.5 Ghz (Python)
501 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.
502 VMDet_Boost 61.54 % 79.36 % 53.56 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
503 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.
504 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.
505 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.
506 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.
507 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.
508 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.
509 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.
510 Anonymous 54.16 % 71.33 % 47.50 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
511 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). .
512 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.
513 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.
514 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 .
515 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.
516 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.
517 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.
518 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.
519 CDTrack3D code 44.58 % 53.11 % 37.41 % 0.0106 s NVIDIA RTX 3090 GPU, i9 10850k CPU
520 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.
521 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.
522 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.
523 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.
524 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.
525 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.
526 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.
527 MonoDET code 5.80 % 6.41 % 5.13 % 0.04 s 1 core @ 2.5 Ghz (Python)
528 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.
529 test code 4.63 % 1.85 % 4.96 % 50 s 1 core @ 2.5 Ghz (Python)
530 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.
531 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.
532 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 DGIST MT-CNN 80.78 % 89.66 % 76.52 % 0.09 s GPU @ 1.0 Ghz (Python)
2 VueronNet 80.75 % 89.91 % 76.56 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
3 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.
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 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.
27 GFD-Retina
This method uses stereo information.
This method uses optical flow information.
This method makes use of Velodyne laser scans.
67.39 % 81.79 % 62.24 % 0.07 s GPU @ 2.5 Ghz (Python)
28 FII-CenterNet code 67.31 % 81.32 % 61.29 % 0.09 s GPU @ 2.5 Ghz (Python)
S. Fan, F. Zhu, S. Chen, H. Zhang, B. Tian, Y. Lv and F. Wang: FII-CenterNet: An Anchor-Free Detector With Foreground Attention for Traffic Object Detection. IEEE Transactions on Vehicular Technology 2021.
29 Mono3D code 67.29 % 80.30 % 62.23 % 4.2 s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.
30 SFD-Retina
This method uses stereo information.
This method uses optical flow information.
This method makes use of Velodyne laser scans.
67.17 % 82.48 % 62.02 % 0.04 s GPU @ 2.5 Ghz (Python)
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 Anonymous 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 MM-Retina
This method uses stereo information.
This method uses optical flow information.
This method makes use of Velodyne laser scans.
64.91 % 79.88 % 59.60 % 0.04 s GPU @ 2.5 Ghz (Python)
41 TED 64.74 % 74.26 % 62.08 % 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 PV-RCNN++ code 63.47 % 72.82 % 60.96 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
48 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.
49 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.
50 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.
51 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.
52 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.
53 variance_point 62.19 % 72.24 % 58.54 % 0.05 s 1 core @ 2.5 Ghz (Python)
54 VoCo 62.11 % 69.00 % 59.15 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
55 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.
56 PE-RCVN 61.64 % 69.49 % 59.55 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
57 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.
58 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 .
59 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.
60 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.
61 CFF-tv 60.73 % 71.73 % 58.02 % 1 s 1 core @ 2.5 Ghz (C/C++)
62 CFF-ep25 60.60 % 71.09 % 58.00 % 1 s 1 core @ 2.5 Ghz (C/C++)
63 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.
64 cff-tv-v2-ep25 60.27 % 71.00 % 57.68 % 1 s 1 core @ 2.5 Ghz (C/C++)
65 Reprod-Two-Branch 60.24 % 71.39 % 57.66 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
66 SGDA3D 60.17 % 68.03 % 58.06 % 0.07 s 1 core @ 2.5 Ghz (Python)
67 KeyFuse2B 60.16 % 70.32 % 57.49 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
68 FS-Net
This method makes use of Velodyne laser scans.
60.13 % 69.08 % 57.91 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
69 CFF-tv-v2 60.10 % 70.11 % 57.57 % 1 s 1 core @ 2.5 Ghz (C/C++)
70 USVLab BSAODet (S) 60.08 % 70.93 % 57.40 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
71 USVLab BSAODet 59.83 % 69.64 % 57.31 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
72 PA-RCNN code 59.81 % 69.94 % 57.40 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
73 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.
74 VPN 59.48 % 70.97 % 55.29 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
75 CZY_PPF_Net2 59.26 % 67.81 % 57.04 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
76 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.
77 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.
78 SARFE 59.07 % 68.25 % 56.74 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
79 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++)
80 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.
81 TBD 58.77 % 67.92 % 55.33 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
82 SRDL 58.70 % 68.45 % 56.23 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
83 FusionDetv1 58.68 % 68.44 % 56.22 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
84 CF-ctdep-tv-ta 58.54 % 68.36 % 56.11 % 1 s 1 core @ 2.5 Ghz (C/C++)
85 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.
86 Semantical PVRCNN 58.30 % 65.54 % 56.36 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
87 VPNet 58.28 % 68.78 % 55.91 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
88 CZY_3917 58.26 % 66.64 % 56.01 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
89 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.
90 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.
91 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.
92 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.
93 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.
94 E^2-PV-RCNN 58.01 % 67.39 % 55.77 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
X. Li, Y. Zhang and D. Kong: E^2-PV-RCNN: improving 3D object detection via enhancing keypoint features. Multimedia Tools and Applications 2022.
95 CF-base-tv 57.99 % 68.06 % 55.47 % 1 s 1 core @ 2.5 Ghz (C/C++)
96 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.
97 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.
98 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.
99 Lite-FPN-GUPNet 57.56 % 76.82 % 50.42 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
100 LazyTorch-CP-Infer-O 57.47 % 67.75 % 55.08 % 1 s 1 core @ 2.5 Ghz (C/C++)
101 CenterPoint (pcdet) 57.38 % 67.60 % 54.98 % 0.051 sec/frame 2 x V100
102 SGNet 57.36 % 65.93 % 53.82 % 0.09 s GPU @ 2.5 Ghz (Python)
103 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.
104 LazyTorch-CP-Small-P 57.35 % 67.67 % 55.04 % 1 s 1 core @ 2.5 Ghz (C/C++)
105 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.
106 SIF 57.32 % 67.78 % 54.86 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
107 CF-ctdep-tv 57.27 % 67.17 % 54.80 % 1 s 1 core @ 2.5 Ghz (C/C++)
108 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.
109 Self-Calib Conv 57.25 % 66.38 % 55.44 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
110 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.
111 DGT-Det3D 57.04 % 66.94 % 54.73 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
112 WGVRF 56.97 % 64.88 % 53.74 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
113 DGT-Det3D code 56.89 % 66.53 % 54.39 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
114 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.
115 DDet 56.81 % 65.02 % 54.65 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
116 CZY_PPF_Net 56.79 % 66.00 % 54.44 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
117 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.
118 FV2P v2 56.74 % 66.99 % 54.48 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
119 MVMM code 56.72 % 65.69 % 54.54 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
120 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.
121 City-CF-fixed 56.62 % 67.73 % 53.85 % 1 s 1 core @ 2.5 Ghz (C/C++)
122 AFTD 56.57 % 68.13 % 53.15 % 1 s 1 core @ 2.5 Ghz (Python + C/C++)
123 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.
124 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.
125 TBD 56.42 % 65.60 % 53.61 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
126 TBD 56.42 % 65.60 % 53.61 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
127 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.
128 cp-tv-kp 56.24 % 65.11 % 54.09 % 1 s 1 core @ 2.5 Ghz (C/C++)
129 3SNet 56.23 % 65.13 % 52.60 % 0.07 s GPU @ 2.5 Ghz (Python)
130 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.
131 U_SECOND_V4 56.09 % 66.55 % 53.56 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
132 TBD 55.96 % 66.20 % 53.25 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
133 HMFI code 55.96 % 66.20 % 53.24 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
134 DTE3D 55.83 % 66.41 % 52.21 % 0.19 s 1 core @ 2.5 Ghz (C/C++)
135 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.
136 cp-tv 55.74 % 64.83 % 53.76 % 1 s 1 core @ 2.5 Ghz (C/C++)
137 VCRCNN 55.66 % 64.59 % 53.53 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
138 GCDR 55.65 % 74.95 % 48.44 % 0.28 s 1 core @ 2.5 Ghz (Python)
139 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.
140 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.
141 Dune-DCF-e11 55.62 % 66.39 % 53.00 % 1 s 1 core @ 2.5 Ghz (C/C++)
142 gupnet_se 55.50 % 74.80 % 50.57 % 0.03s 1 core @ 2.5 Ghz (C/C++)
143 CZY 55.47 % 64.52 % 53.26 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
144 CF-ctdep-train 55.42 % 65.48 % 52.84 % 1 s 1 core @ 2.5 Ghz (C/C++)
145 MSADet 55.31 % 66.73 % 52.97 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
146 PSA-Det3D 55.25 % 65.04 % 51.81 % 0.1 s GPU @ 2.5 Ghz (Python)
147 KeyPoint-IoUHead 55.20 % 66.99 % 51.68 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
148 CF-base-train 55.19 % 65.78 % 52.47 % 1 s 1 core @ 2.5 Ghz (C/C++)
149 TCDVF 55.18 % 65.12 % 52.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
150 CenterFuse 55.17 % 67.88 % 52.55 % 0.059 sec/frame 2 x V100
151 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.
152 Anonymous 55.15 % 66.68 % 52.57 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
153 TBD 55.14 % 63.44 % 53.89 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
154 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.
155 Dune-DCF-e15 55.09 % 65.54 % 52.36 % 1 s 1 core @ 2.5 Ghz (C/C++)
156 StereoDistill 55.09 % 69.00 % 50.95 % 0.4 s 1 core @ 2.5 Ghz (Python)
157 CrazyTensor-CP 55.05 % 64.92 % 52.88 % 1 s 1 core @ 2.5 Ghz (Python)
158 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.
159 VGA-RCNN 55.03 % 65.16 % 52.42 % 0.07 s 1 core @ 2.5 Ghz (Python)
160 Dune-DCF-e09 55.01 % 65.56 % 52.43 % 1 s 1 core @ 2.5 Ghz (C/C++)
161 OPA-3D code 54.92 % 73.93 % 47.87 % 0.04 s 1 core @ 3.5 Ghz (Python)
162 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.
163 City-CF 54.79 % 65.01 % 52.24 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
164 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.
165 DSASNet 54.67 % 64.59 % 51.05 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
166 IoU-2B 54.61 % 69.65 % 51.43 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
167 T_PVRCNN_V2 54.51 % 64.23 % 52.26 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
168 tbd 54.44 % 64.96 % 50.57 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
169 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.
170 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.
171 TBD code 54.03 % 63.53 % 51.89 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
172 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.
173 ATT_SSD 53.76 % 63.62 % 51.64 % 0.01 s 1 core @ 2.5 Ghz (Python)
174 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.
175 cff-tv-t 53.69 % 68.26 % 49.75 % 1 s 1 core @ 2.5 Ghz (C/C++)
176 T_PVRCNN 53.69 % 63.29 % 51.32 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
177 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.
178 Mix-Teaching 53.52 % 67.34 % 47.45 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
179 BASA 53.43 % 62.16 % 49.86 % 1s 1 core @ 2.5 Ghz (python)
180 PSA-SSD 53.43 % 63.07 % 51.34 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
181 IKT3D
This method makes use of Velodyne laser scans.
53.39 % 61.55 % 51.28 % 0.05 s 1 core @ 2.5 Ghz (Python)
182 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.
183 MK3D 53.13 % 74.57 % 48.20 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
184 PVTr 53.11 % 61.97 % 51.00 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
185 FPV-SSD 53.10 % 62.00 % 50.67 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
186 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.
187 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.
188 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.
189 NV-RCNN 52.86 % 62.28 % 50.72 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
190 CF-cd-io-tv 52.86 % 65.30 % 50.17 % 1 s 1 core @ 2.5 Ghz (C/C++)
191 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.
192 cp-tv-kp-io-sc 52.77 % 64.59 % 50.47 % 1 s 1 core @ 2.5 Ghz (C/C++)
193 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.
194 MFANet 52.52 % 63.78 % 49.89 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
195 HS3D code 52.50 % 63.73 % 49.78 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
196 MDNet 52.46 % 68.56 % 47.69 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
197 SECOND_7862 52.46 % 60.95 % 49.88 % 1 s 1 core @ 2.5 Ghz (Python)
198 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.
199 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++)
200 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.
201 HPV-RCNN 52.25 % 62.30 % 49.68 % 0.15 s 1 core @ 2.5 Ghz (Python)
202 SWA code 52.20 % 60.60 % 49.08 % 0.18 s 1 core @ 2.5 Ghz (C/C++)
203 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.
204 DD3Dv2 code 52.08 % 65.08 % 48.52 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
205 CrazyTensor-CF 52.00 % 62.19 % 49.49 % 1 s 1 core @ 2.5 Ghz (C/C++)
206 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.
207 TBD 51.09 % 60.19 % 48.95 % TBD s 1 core @ 2.5 Ghz (C/C++)
208 AGS-SSD[la] 50.90 % 60.46 % 48.60 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
209 TBD_BD code 50.60 % 60.91 % 48.47 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
210 TBD 50.45 % 60.47 % 47.02 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
211 MonoGround 50.35 % 63.96 % 45.75 % 0.03 s 1 core @ 2.5 Ghz (Python)
212 Shape-Aware 49.91 % 66.87 % 45.08 % 0.05 s 1 core @ 2.5 Ghz (Python)
213 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.
214 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.
215 MonoAug 49.10 % 65.03 % 44.42 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
216 CGPS-3DV code 49.02 % 61.65 % 44.96 % CGPS-3DV 1 core @ 2.5 Ghz (C/C++)
217 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.
218 MonoAug 48.93 % 64.70 % 44.37 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
219 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.
220 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.
221 ZMMPP 48.39 % 57.33 % 46.15 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
222 PS code 48.39 % 60.99 % 44.42 % PS s 1 core @ 2.5 Ghz (C/C++)
223 MonoEdge 48.26 % 61.11 % 42.37 % 0.05 s GPU @ 2.5 Ghz (Python)
224 KPP3D code 47.76 % 57.06 % 45.35 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
225 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.
226 MonoEdge-Rotate 47.56 % 62.79 % 43.02 % 0.05 s GPU @ 2.5 Ghz (Python)
227 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.
228 Contrastive PP code 47.05 % 56.47 % 45.04 % 0.01 s 1 core @ 2.5 Ghz (Python)
229 CMKD* 46.84 % 61.04 % 42.92 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
230 mono3d 46.73 % 58.34 % 41.92 % 0.03 s GPU @ 2.5 Ghz (Python)
231 M3DGAF 46.70 % 61.58 % 42.25 % 0.07 s 1 core @ 2.5 Ghz (Python)
232 PP-PCdet code 45.82 % 54.17 % 43.68 % 0.01 s 1 core @ 2.5 Ghz (Python)
233 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.
234 Anonymous code 45.76 % 60.29 % 39.39 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
235 BiResFPN 45.74 % 62.59 % 40.97 % 0.071s 1 core @ 2.5 Ghz (C/C++)
236 DEPT 45.69 % 60.30 % 41.26 % 0.03 s 1 core @ 2.5 Ghz (Python)
237 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.
238 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.
239 MonoInsight 44.81 % 58.85 % 40.44 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
240 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.
241 GT3D 44.60 % 57.33 % 40.41 % 0.1 s 1 core @ 2.5 Ghz (Python)
242 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.
243 SAIC_ADC_Mono3D code 43.96 % 59.27 % 39.83 % 50 s GPU @ 2.5 Ghz (Python)
244 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.
245 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.
246 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.
247 Yolo5x6_Ghost 43.70 % 59.74 % 39.06 % 0.03 s GPU @ 2.5 Ghz (Python)
248 Yolo5x6_Ghost 43.70 % 59.74 % 39.06 % 0.03 s GPU @ 2.5 Ghz (Python)
249 Anonymous 43.68 % 58.24 % 39.56 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
250 City-ICN 43.56 % 60.56 % 38.71 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
251 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.
252 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.
253 CrazyTensor-ICN 43.43 % 60.32 % 38.58 % 1 s 1 core @ 2.5 Ghz (C/C++)
254 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.
255 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.
256 MoGDE 43.17 % 59.10 % 39.06 % 0.03 s GPU @ 2.5 Ghz (Python)
257 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.
258 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.
259 Yolov5ObjectDetector 42.74 % 58.46 % 38.32 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
260 LT-M3OD 42.72 % 57.14 % 38.38 % 0.03 s 1 core @ 2.5 Ghz (Python)
261 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.
262 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.
263 MonoCon code 42.44 % 56.57 % 36.34 % 0.02 s GPU @ 2.5 Ghz (Python)
T. Xianpeng Liu: Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection. AAAI 2022.
264 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.
265 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.
266 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.
267 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 .
268 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.
269 CZY 41.01 % 50.27 % 38.93 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
270 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.
271 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.
272 MonoAD 40.39 % 56.38 % 36.17 % 0.03 s GPU @ 2.5 Ghz (Python)
273 Anonymous 39.94 % 55.67 % 35.69 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
274 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.
275 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.
276 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.
277 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.
278 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.
279 SPT 39.41 % 46.02 % 37.86 % 0.1 s GPU @ 2.5 Ghz (Python)
280 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). .
281 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.
282 BSM3D 37.12 % 50.45 % 33.02 % 0.03 s 1 core @ 2.5 Ghz (Python)
283 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.
284 MonoPCNS 36.61 % 49.77 % 33.01 % 0.14 s GPU @ 2.5 Ghz (Python)
285 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.
286 SparseLiDAR_fusion 35.95 % 50.48 % 31.71 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
287 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.
288 mono3d code 35.86 % 48.61 % 32.10 % TBD TBD
289 CMAN 34.96 % 49.73 % 30.92 % 0.15 s 1 core @ 2.5 Ghz (Python)
290 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.
291 anonymity 34.41 % 47.09 % 30.59 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
292 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.
293 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.
294 ZongmuMono3d code 33.47 % 45.86 % 29.84 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
295 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.
296 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.
297 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.
298 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.
299 SARM3D 32.33 % 42.60 % 29.23 % 0.03 s GPU @ 2.5 Ghz (Python)
300 HBD 31.99 % 44.66 % 28.21 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
301 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.
302 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.
303 CDTrack3D code 30.62 % 41.66 % 26.69 % 0.0106 s NVIDIA RTX 3090 GPU, i9 10850k CPU
304 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.
305 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.
306 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.
307 MDSNet 29.25 % 41.64 % 26.01 % 0.07 s 1 core @ 2.5 Ghz (Python)
308 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.
309 MM 25.20 % 35.72 % 21.90 % 1 s 1 core @ 2.5 Ghz (C/C++)
310 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.
311 DCD code 17.08 % 23.35 % 15.03 % 1 s 1 core @ 2.5 Ghz (C/C++)
312 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.
313 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.
314 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.
315 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.
316 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.
317 EM code 1.70 % 1.67 % 1.61 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
318 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 Anonymous 84.44 % 90.16 % 77.71 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
2 TED 84.36 % 92.60 % 78.43 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
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 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.
5 SARFE 82.00 % 90.01 % 75.30 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
6 SGNet 82.00 % 91.55 % 75.30 % 0.09 s GPU @ 2.5 Ghz (Python)
7 HMFI code 81.76 % 89.35 % 74.93 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
8 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.
9 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.
10 Semantical PVRCNN 80.60 % 88.70 % 73.21 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
11 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.
12 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.
13 FS-Net
This method makes use of Velodyne laser scans.
80.54 % 87.05 % 73.81 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
14 CAD 80.53 % 91.65 % 73.56 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
15 SGDA3D 80.49 % 87.77 % 72.51 % 0.07 s 1 core @ 2.5 Ghz (Python)
16 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.
17 VCRCNN 80.46 % 87.34 % 73.68 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
18 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.
19 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.
20 SPT 80.21 % 88.89 % 73.79 % 0.1 s GPU @ 2.5 Ghz (Python)
21 USVLab BSAODet 80.04 % 88.51 % 73.38 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
22 VoCo 80.00 % 87.66 % 74.48 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
23 E^2-PV-RCNN 79.94 % 87.22 % 73.58 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
X. Li, Y. Zhang and D. Kong: E^2-PV-RCNN: improving 3D object detection via enhancing keypoint features. Multimedia Tools and Applications 2022.
24 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++)
25 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.
26 CF-ctdep-tv-ta 79.76 % 90.48 % 73.04 % 1 s 1 core @ 2.5 Ghz (C/C++)
27 CZY_PPF_Net2 79.75 % 87.34 % 73.05 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
28 DSASNet 79.56 % 87.41 % 73.08 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
29 DDet 79.47 % 88.65 % 72.64 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
30 PA-RCNN code 79.42 % 89.01 % 72.72 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
31 IKT3D
This method makes use of Velodyne laser scans.
79.38 % 87.43 % 72.87 % 0.05 s 1 core @ 2.5 Ghz (Python)
32 USVLab BSAODet (S) 79.34 % 89.04 % 72.50 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
33 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.
34 PV-RCNN++ code 79.22 % 85.76 % 72.35 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
35 CFF-ep25 79.20 % 88.66 % 72.11 % 1 s 1 core @ 2.5 Ghz (C/C++)
36 CZY_PPF_Net 79.03 % 88.09 % 73.83 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
37 CFF-tv 78.94 % 88.99 % 71.88 % 1 s 1 core @ 2.5 Ghz (C/C++)
38 Reprod-Two-Branch 78.92 % 88.74 % 71.93 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
39 3SNet 78.88 % 86.83 % 73.73 % 0.07 s GPU @ 2.5 Ghz (Python)
40 cff-tv-v2-ep25 78.86 % 88.39 % 71.68 % 1 s 1 core @ 2.5 Ghz (C/C++)
41 TBD 78.83 % 84.96 % 72.18 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
42 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.
43 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.
44 PE-RCVN 78.71 % 90.77 % 71.83 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
45 DGT-Det3D code 78.69 % 87.54 % 71.86 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
46 CFF-tv-v2 78.59 % 87.90 % 71.64 % 1 s 1 core @ 2.5 Ghz (C/C++)
47 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.
48 CF-ctdep-tv 78.16 % 87.85 % 71.62 % 1 s 1 core @ 2.5 Ghz (C/C++)
49 CZY_3917 78.11 % 86.51 % 71.65 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
50 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.
51 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.
52 CF-base-tv 77.87 % 86.17 % 71.35 % 1 s 1 core @ 2.5 Ghz (C/C++)
53 TBD 77.82 % 86.21 % 71.45 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
54 TCDVF 77.66 % 86.38 % 71.38 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
55 DCAN-Second code 77.63 % 90.34 % 71.98 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
56 KeyFuse2B 77.58 % 89.00 % 70.66 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
57 VGA-RCNN 77.50 % 85.80 % 70.89 % 0.07 s 1 core @ 2.5 Ghz (Python)
58 PVTr 77.39 % 89.56 % 70.23 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
59 TBD code 77.26 % 87.25 % 71.05 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
60 CZY 77.13 % 88.80 % 70.33 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
61 WGVRF 77.01 % 85.88 % 70.70 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
62 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.
63 PSA-Det3D 76.87 % 86.83 % 70.34 % 0.1 s GPU @ 2.5 Ghz (Python)
64 MVMM code 76.85 % 84.87 % 72.10 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
65 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.
66 DGT-Det3D 76.71 % 86.58 % 70.08 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
67 NV-RCNN 76.31 % 88.34 % 69.70 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
68 CenterFuse 76.29 % 89.88 % 67.96 % 0.059 sec/frame 2 x V100
69 ATT_SSD 75.90 % 88.76 % 70.39 % 0.01 s 1 core @ 2.5 Ghz (Python)
70 FV2P v2 75.83 % 88.58 % 69.05 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
71 BASA 75.75 % 86.41 % 70.26 % 1s 1 core @ 2.5 Ghz (python)
72 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.
73 FPV-SSD 75.33 % 83.30 % 68.10 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
74 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.
75 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.
76 PSA-SSD 75.15 % 86.11 % 70.01 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
77 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.
78 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.
79 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.
80 TBD 75.02 % 88.92 % 68.08 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
81 cp-tv 74.89 % 84.13 % 68.41 % 1 s 1 core @ 2.5 Ghz (C/C++)
82 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.
83 DGIST MT-CNN 74.77 % 86.95 % 66.05 % 0.09 s GPU @ 1.0 Ghz (Python)
84 SWA code 74.63 % 86.25 % 69.93 % 0.18 s 1 core @ 2.5 Ghz (C/C++)
85 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.
86 Self-Calib Conv 74.32 % 84.08 % 68.02 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
87 TBD 74.32 % 86.17 % 68.32 % TBD s 1 core @ 2.5 Ghz (C/C++)
88 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.
89 HPV-RCNN 74.12 % 85.69 % 67.04 % 0.15 s 1 core @ 2.5 Ghz (Python)
90 TBD 74.06 % 84.24 % 68.91 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
91 MSADet 74.06 % 87.54 % 68.55 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
92 VPN 73.99 % 89.56 % 66.86 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
93 SDP+RPN 73.85 % 82.59 % 64.87 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 2015.
94 U_SECOND_V4 73.81 % 86.35 % 67.26 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
95 AGS-SSD[la] 73.72 % 85.27 % 67.95 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
96 FusionDetv1 73.69 % 85.39 % 66.94 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
97 cp-tv-kp 73.68 % 84.33 % 67.48 % 1 s 1 core @ 2.5 Ghz (C/C++)
98 SRDL 73.68 % 85.44 % 66.94 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
99 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.
100 cp-tv-kp-io-sc 73.66 % 87.24 % 66.98 % 1 s 1 core @ 2.5 Ghz (C/C++)
101 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.
102 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.
103 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.
104 SIF 73.19 % 85.18 % 65.41 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
105 CF-cd-io-tv 73.17 % 87.63 % 65.99 % 1 s 1 core @ 2.5 Ghz (C/C++)
106 F-PointNet
This method makes use of Velodyne laser scans.
code 73.16 % 86.86 % 65.21 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
107 FromVoxelToPoint code 73.16 % 87.07 % 65.98 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to- Point Decoder. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
108 XView 73.16 % 88.02 % 65.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
109 HS3D code 73.02 % 84.59 % 67.13 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
110 VPNet 72.88 % 85.66 % 66.16 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
111 City-CF-fixed 72.86 % 86.69 % 66.70 % 1 s 1 core @ 2.5 Ghz (C/C++)
112 T_PVRCNN_V2 72.81 % 86.48 % 65.32 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
113 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.
114 T_PVRCNN 72.79 % 85.61 % 66.40 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
115 KPP3D code 72.73 % 82.91 % 66.30 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
116 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.
117 IoU-2B 72.71 % 89.27 % 63.76 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
118 KeyPoint-IoUHead 72.69 % 87.00 % 65.91 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
119 City-CF 72.68 % 86.44 % 66.22 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
120 ZMMPP 72.65 % 82.01 % 66.11 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
121 SECOND_7862 72.51 % 84.05 % 66.14 % 1 s 1 core @ 2.5 Ghz (Python)
122 Dune-DCF-e15 72.38 % 86.51 % 66.02 % 1 s 1 core @ 2.5 Ghz (C/C++)
123 CF-base-train 72.24 % 86.19 % 65.04 % 1 s 1 core @ 2.5 Ghz (C/C++)
124 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.
125 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.
126 variance_point 71.87 % 83.16 % 63.53 % 0.05 s 1 core @ 2.5 Ghz (Python)
127 Dune-DCF-e11 71.82 % 86.21 % 65.26 % 1 s 1 core @ 2.5 Ghz (C/C++)
128 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.
129 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.
130 CF-ctdep-train 71.61 % 85.47 % 64.23 % 1 s 1 core @ 2.5 Ghz (C/C++)
131 cff-tv-t 71.22 % 88.33 % 63.92 % 1 s 1 core @ 2.5 Ghz (C/C++)
132 Anonymous 70.87 % 82.26 % 64.77 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
133 LazyTorch-CP-Infer-O 70.65 % 83.62 % 64.20 % 1 s 1 core @ 2.5 Ghz (C/C++)
134 CenterPoint (pcdet) 70.54 % 83.36 % 64.16 % 0.051 sec/frame 2 x V100
135 LazyTorch-CP-Small-P 70.48 % 83.46 % 64.07 % 1 s 1 core @ 2.5 Ghz (C/C++)
136 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.
137 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.
138 MFANet 70.37 % 82.59 % 64.89 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
139 Dune-DCF-e09 70.28 % 83.14 % 64.12 % 1 s 1 core @ 2.5 Ghz (C/C++)
140 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.
141 TBD_BD code 69.56 % 83.36 % 63.39 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
142 CrazyTensor-CP 69.31 % 83.67 % 62.97 % 1 s 1 core @ 2.5 Ghz (Python)
143 CrazyTensor-CF 69.23 % 85.28 % 62.37 % 1 s 1 core @ 2.5 Ghz (C/C++)
144 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.
145 AFTD 68.83 % 88.94 % 62.04 % 1 s 1 core @ 2.5 Ghz (Python + C/C++)
146 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.
147 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.
148 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.
149 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.
150 DTE3D 68.23 % 82.63 % 61.99 % 0.19 s 1 core @ 2.5 Ghz (C/C++)
151 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.
152 Contrastive PP code 67.62 % 79.63 % 60.81 % 0.01 s 1 core @ 2.5 Ghz (Python)
153 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.
154 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.
155 TBD 67.15 % 82.44 % 60.87 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
156 TBD 67.15 % 82.44 % 60.87 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
157 VueronNet 66.92 % 82.71 % 59.01 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
158 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++)
159 PP-PCdet code 66.58 % 78.44 % 60.22 % 0.01 s 1 core @ 2.5 Ghz (Python)
160 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.
161 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.
162 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.
163 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) .
164 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.
165 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.
166 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.
167 tbd 64.31 % 79.83 % 58.14 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
168 GT3D 63.41 % 80.86 % 56.47 % 0.1 s 1 core @ 2.5 Ghz (Python)
169 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.
170 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.
171 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.
172 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.
173 CZY 62.20 % 73.99 % 56.07 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
174 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.
175 StereoDistill 61.46 % 80.92 % 54.64 % 0.4 s 1 core @ 2.5 Ghz (Python)
176 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.
177 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.
178 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.
179 Mix-Teaching 58.65 % 75.15 % 50.54 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
180 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.
181 SFD-Retina
This method uses stereo information.
This method uses optical flow information.
This method makes use of Velodyne laser scans.
57.71 % 76.35 % 50.56 % 0.04 s GPU @ 2.5 Ghz (Python)
182 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.
183 GFD-Retina
This method uses stereo information.
This method uses optical flow information.
This method makes use of Velodyne laser scans.
56.93 % 74.59 % 49.91 % 0.07 s GPU @ 2.5 Ghz (Python)
184 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.
185 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.
186 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.
187 GPENet code 55.96 % 71.89 % 48.36 % 0.02 s GPU @ 2.5 Ghz (Python)
188 MDNet 55.58 % 69.87 % 46.54 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
189 MoGDE 55.22 % 79.09 % 46.54 % 0.03 s GPU @ 2.5 Ghz (Python)
190 MM-Retina
This method uses stereo information.
This method uses optical flow information.
This method makes use of Velodyne laser scans.
54.90 % 69.95 % 48.49 % 0.04 s GPU @ 2.5 Ghz (Python)
191 mono3d 54.82 % 70.77 % 47.55 % 0.03 s GPU @ 2.5 Ghz (Python)
192 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.
193 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.
194 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.
195 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.
196 YOLOv5x6_1280 54.07 % 71.49 % 47.82 % 0.019 s GPU @ >3.5 Ghz (Python)
197 YOLOv5x6_1280_Q 53.78 % 71.22 % 47.60 % 0.016 s GPU @ >3.5 Ghz (Python)
198 Shape-Aware 53.48 % 70.94 % 46.95 % 0.05 s 1 core @ 2.5 Ghz (Python)
199 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 .
200 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.
201 CGPS-3DV code 52.87 % 72.93 % 46.56 % CGPS-3DV 1 core @ 2.5 Ghz (C/C++)
202 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.
203 YOLOv5x6_1920 52.29 % 75.21 % 45.67 % 0.05 s GPU @ 3.5 Ghz (Python)
204 MonoEdge-Rotate 51.89 % 68.91 % 45.33 % 0.05 s GPU @ 2.5 Ghz (Python)
205 PS code 51.87 % 71.65 % 46.48 % PS s 1 core @ 2.5 Ghz (C/C++)
206 CMKD* 51.76 % 73.18 % 45.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
207 MonoGround 51.67 % 67.78 % 45.11 % 0.03 s 1 core @ 2.5 Ghz (Python)
208 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.
209 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.
210 MonoEdge 49.33 % 65.93 % 42.44 % 0.05 s GPU @ 2.5 Ghz (Python)
211 M3DGAF 49.25 % 64.62 % 42.82 % 0.07 s 1 core @ 2.5 Ghz (Python)
212 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.
213 DEPT 49.04 % 69.01 % 42.71 % 0.03 s 1 core @ 2.5 Ghz (Python)
214 Anonymous code 48.84 % 67.78 % 42.44 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
215 MonoAug 48.61 % 69.31 % 42.24 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
216 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.
217 Anonymous 48.01 % 66.15 % 41.50 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
218 MonoInsight 47.72 % 67.52 % 41.00 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
219 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.
220 anonymity 47.21 % 66.52 % 41.56 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
221 gupnet_se 46.47 % 68.66 % 37.89 % 0.03s 1 core @ 2.5 Ghz (C/C++)
222 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.
223 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.
224 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.
225 LT-M3OD 45.87 % 63.54 % 39.19 % 0.03 s 1 core @ 2.5 Ghz (Python)
226 anonymity 45.69 % 65.71 % 40.11 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
227 MonoAug 45.48 % 65.80 % 39.05 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
228 DD3Dv2 code 45.35 % 63.42 % 39.61 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
229 Lite-FPN-GUPNet 45.16 % 64.84 % 38.38 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
230 MonoAD 44.87 % 66.02 % 39.94 % 0.03 s GPU @ 2.5 Ghz (Python)
231 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.
232 MK3D 43.41 % 64.38 % 36.70 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
233 Anonymous 43.31 % 64.66 % 36.52 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
234 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.
235 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.
236 GCDR 42.78 % 67.11 % 37.94 % 0.28 s 1 core @ 2.5 Ghz (Python)
237 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.
238 MonoCon code 42.49 % 59.39 % 35.94 % 0.02 s GPU @ 2.5 Ghz (Python)
T. Xianpeng Liu: Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection. AAAI 2022.
239 SAIC_ADC_Mono3D code 42.37 % 60.72 % 36.57 % 50 s GPU @ 2.5 Ghz (Python)
240 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 .
241 BiResFPN 41.47 % 61.79 % 35.99 % 0.071s 1 core @ 2.5 Ghz (C/C++)
242 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.
243 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.
244 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.
245 CrazyTensor-ICN 38.69 % 59.42 % 33.90 % 1 s 1 core @ 2.5 Ghz (C/C++)
246 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.
247 CMAN 38.36 % 58.12 % 31.79 % 0.15 s 1 core @ 2.5 Ghz (Python)
248 OPA-3D code 38.35 % 55.98 % 33.83 % 0.04 s 1 core @ 3.5 Ghz (Python)
249 mono3d code 37.18 % 49.10 % 32.04 % TBD TBD
250 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.
251 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.
252 BSM3D 35.89 % 54.56 % 30.71 % 0.03 s 1 core @ 2.5 Ghz (Python)
253 City-ICN 35.87 % 53.37 % 29.37 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
254 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.
255 Yolo5x6_Ghost 35.20 % 50.96 % 31.53 % 0.03 s GPU @ 2.5 Ghz (Python)
256 Yolo5x6_Ghost 35.20 % 50.96 % 31.53 % 0.03 s GPU @ 2.5 Ghz (Python)
257 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.
258 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.
259 SparseLiDAR_fusion 33.00 % 48.74 % 28.68 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
260 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.
261 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.
262 Yolov5ObjectDetector 31.69 % 45.65 % 29.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
263 ZongmuMono3d code 31.56 % 44.68 % 27.48 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
264 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.
265 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.
266 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.
267 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.
268 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.
269 SARM3D 27.67 % 42.08 % 23.40 % 0.03 s GPU @ 2.5 Ghz (Python)
270 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.
271 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.
272 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.
273 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.
274 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.
275 HBD 24.75 % 35.65 % 22.00 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
276 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.
277 MonoPCNS 22.75 % 31.31 % 20.12 % 0.14 s GPU @ 2.5 Ghz (Python)
278 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.
279 CDTrack3D code 17.28 % 27.04 % 13.92 % 0.0106 s NVIDIA RTX 3090 GPU, i9 10850k CPU
280 MDSNet 16.64 % 28.23 % 14.14 % 0.07 s 1 core @ 2.5 Ghz (Python)
281 DCD code 14.71 % 18.66 % 13.83 % 1 s 1 core @ 2.5 Ghz (C/C++)
282 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.
283 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.
284 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.
285 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.
286 MM 6.79 % 11.33 % 6.28 % 1 s 1 core @ 2.5 Ghz (C/C++)
287 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.
288 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 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.
3 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.
4 NIV-SSD 96.06 % 96.89 % 88.63 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
5 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.
6 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.
7 TED 95.96 % 96.63 % 93.24 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
8 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.
9 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.
10 ImpDet 95.92 % 96.72 % 90.85 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
11 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.
12 SPT 95.88 % 96.56 % 90.97 % 0.1 s GPU @ 2.5 Ghz (Python)
13 CityBrainLab 95.86 % 96.58 % 90.81 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
14 PE-RCVN 95.85 % 96.89 % 90.87 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
15 Anonymous 95.85 % 96.25 % 93.17 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
16 PVT-SSD 95.83 % 96.74 % 90.58 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
17 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.
18 3D Dual-Fusion 95.76 % 96.53 % 93.01 % 0.1 s 1 core @ 2.5 Ghz (Python)
19 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.
20 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.
21 HCPVF 95.70 % 96.61 % 92.87 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
22 NSAW code 95.65 % 98.57 % 92.63 % 0.1 s 1 core @ 2.5 Ghz (Python)
23 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++)
24 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. 2022.
25 TBD 95.62 % 96.32 % 92.95 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
26 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.
27 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.
28 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.
29 SGFusion 95.44 % 96.54 % 92.29 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
30 Anonymous 95.37 % 98.34 % 90.41 % n/a s 1 core @ 2.5 Ghz (C/C++)
31 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. 2022.
32 TBD 95.34 % 96.05 % 90.25 % 0.1 s 1 core @ 2.5 Ghz (Python)
33 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.
34 LGNet 95.31 % 96.51 % 92.55 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
35 GLENet 95.30 % 96.60 % 90.44 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
36 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.
37 PTA-RCNN 95.28 % 96.39 % 92.21 % 0.08 s 1 core @ 2.5 Ghz (Python)
38 Anonymous 95.23 % 96.47 % 90.45 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
39 PV-DT3D 95.23 % 96.33 % 92.53 % 1.4 s 1 core @ 2.5 Ghz (C/C++)
40 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.
41 VoxelGraphRCNN 95.21 % 98.24 % 92.54 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
42 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.
43 anonymous 95.19 % 96.35 % 92.38 % 0.09 s GPU @ 2.5 Ghz (Python)
44 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.
45 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.
46 ChTR3D 95.16 % 96.21 % 90.37 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
47 DGDNH 95.15 % 98.35 % 92.55 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
48 ChTR3D 95.13 % 96.12 % 90.31 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
49 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.
50 TBD 95.12 % 95.72 % 92.29 % TBD s 1 core @ 2.5 Ghz (C/C++)
51 HMFI code 95.05 % 96.28 % 92.28 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
52 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.
53 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.
54 SWA code 95.01 % 95.90 % 92.27 % 0.18 s 1 core @ 2.5 Ghz (C/C++)
55 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.
56 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.
57 AGS-SSD[la] 95.00 % 97.82 % 92.23 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
58 VGRCNN++ 94.98 % 96.46 % 92.29 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
59 GV-RCNN code 94.96 % 96.28 % 92.28 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
60 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.
61 VGRCNN 94.95 % 96.23 % 92.22 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
62 VG-RCNN 94.92 % 96.40 % 92.20 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
63 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.
64 PV-RCNN++ code 94.90 % 96.07 % 92.22 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
65 ATT_SSD 94.88 % 96.01 % 92.00 % 0.01 s 1 core @ 2.5 Ghz (Python)
66 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++)
67 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.
68 ChTR3D 94.84 % 96.31 % 90.12 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
69 SGDA3D 94.82 % 96.00 % 92.14 % 0.07 s 1 core @ 2.5 Ghz (Python)
70 TBD code 94.81 % 95.97 % 91.96 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
71 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.
72 Semantical PVRCNN 94.81 % 95.96 % 92.19 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
73 NV-RCNN 94.79 % 95.84 % 92.09 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
74 USVLab BSAODet 94.79 % 96.18 % 92.05 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
75 GVNet-V2 94.77 % 96.28 % 91.98 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
76 CZY_PPF_Net2 94.74 % 98.03 % 92.03 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
77 TBD 94.73 % 97.01 % 92.18 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
78 GVNet code 94.72 % 96.29 % 92.00 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
79 SRIF-RCNN 94.70 % 95.62 % 92.21 % 0.0947 s 1 core @ 2.5 Ghz (C/C++)
X. Li and D. Kong: SRIF-RCNN: Sparsely Represented Inputs Fusion of Different Sensors for 3D Object Detection. Applied Intelligence 2022.
80 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.
81 CZY_3917 94.70 % 97.98 % 92.03 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
82 E^2-PV-RCNN 94.69 % 95.94 % 92.09 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
X. Li, Y. Zhang and D. Kong: E^2-PV-RCNN: improving 3D object detection via enhancing keypoint features. Multimedia Tools and Applications 2022.
83 DCCA 94.67 % 95.74 % 92.10 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
84 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.
85 CSVoxel-RCNN 94.66 % 96.21 % 91.84 % 0.03 s GPU @ 1.0 Ghz (Python)
86 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.
87 VCRCNN 94.64 % 96.05 % 92.09 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
88 PSA-SSD 94.63 % 95.78 % 90.06 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
89 PVE 94.63 % 96.00 % 91.95 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
90 TBD 94.60 % 95.87 % 91.71 % 0.06 s GPU @ 2.5 Ghz (Python)
91 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.
92 IKT3D
This method makes use of Velodyne laser scans.
94.59 % 95.91 % 91.95 % 0.05 s 1 core @ 2.5 Ghz (Python)
93 VGA-RCNN 94.57 % 95.94 % 91.80 % 0.07 s 1 core @ 2.5 Ghz (Python)
94 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.
95 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.
96 BASA 94.55 % 96.06 % 89.84 % 1s 1 core @ 2.5 Ghz (python)
97 SC-Voxel-RCNN 94.54 % 96.10 % 91.67 % 0.12 s GPU @ 1.0 Ghz (Python)
98 DDet 94.54 % 95.80 % 91.99 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
99 DTE3D 94.54 % 96.04 % 91.53 % 0.19 s 1 core @ 2.5 Ghz (C/C++)
100 DKDet 94.53 % 96.00 % 91.66 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
101 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.
102 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.
103 CZY_PPF_Net 94.50 % 96.16 % 89.97 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
104 FPV-SSD 94.48 % 96.89 % 91.52 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
105 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.
106 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.
107 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.
108 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.
109 DGT-Det3D code 94.41 % 96.17 % 91.49 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
110 USVLab BSAODet (S) 94.36 % 96.06 % 89.71 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
111 VPNet 94.35 % 96.04 % 91.78 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
112 MVMM code 94.33 % 95.75 % 89.78 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
113 SPVB-SSD 94.31 % 95.78 % 91.61 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
114 WGVRF 94.28 % 95.92 % 89.78 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
115 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.
116 CZY 94.24 % 95.98 % 89.80 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
117 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.
118 MVTr 94.22 % 97.62 % 91.56 % 0.08 s 1 core @ 2.5 Ghz (Python)
119 TCDVF 94.18 % 95.00 % 91.12 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
120 TBD 94.09 % 94.97 % 91.14 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
121 SRDL 94.08 % 95.83 % 91.55 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
122 FusionDetv1 94.07 % 95.82 % 91.54 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
123 SARFE 94.07 % 95.73 % 91.40 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
124 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.
125 DGT-Det3D 93.89 % 95.53 % 91.34 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
126 TBD 93.80 % 94.52 % 86.34 % 0.1 s 1 core @ 2.5 Ghz (Python)
127 U_SECOND_V4 93.80 % 95.74 % 89.46 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
128 B-FPS 93.80 % 95.56 % 89.42 % 0.1 s 1 core @ 2.5 Ghz (Java)