Bird's Eye View Evaluation 2017


The bird's eye view benchmark consists of 7481 training images and 7518 test images as well as the corresponding point clouds, comprising a total of 80.256 labeled objects. For evaluation, we compute precision-recall curves. To rank the methods we compute average precision. 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 bird's eye view detection performance using the PASCAL criteria also used for 2D object detection. Far objects are thus filtered based on their bounding box height in the image plane. As only objects also appearing on the image plane are labeled, objects in don't car areas do not count as false positives. We note that the evaluation does not take care of ignoring detections that are not visible on the image plane — these detections might give rise to false positives. For cars we require a bounding box overlap of 70% in bird's eye view, while for pedestrians and cyclists we require an overlap of 50%. 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 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 GraR-Po 92.12 % 95.79 % 87.11 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
2 TED 92.05 % 95.44 % 87.30 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
3 LIVOX_Det
This method makes use of Velodyne laser scans.
92.05 % 95.60 % 89.22 % n/a s 1 core @ 2.5 Ghz (Python + C/C++)
4 VPFNet 91.86 % 93.02 % 86.94 % 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. 2021.
5 SFD code 91.85 % 95.64 % 86.83 % 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 SE-SSD
This method makes use of Velodyne laser scans.
code 91.84 % 95.68 % 86.72 % 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.
7 GraR-Vo 91.72 % 95.27 % 86.51 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
8 PVT-SSD 91.63 % 95.23 % 86.43 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
9 CityBrainLab 91.62 % 94.78 % 86.68 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
10 SPANet 91.59 % 95.59 % 86.53 % 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.
11 CasA 91.54 % 95.19 % 86.82 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
12 GraR-Pi 91.52 % 95.06 % 86.42 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
13 DGDNH 91.36 % 95.03 % 88.79 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
14 BADet code 91.32 % 95.23 % 86.48 % 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.
15 Anonymous 91.32 % 95.42 % 88.38 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
16 CasA++ 91.22 % 94.57 % 88.43 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
17 Anonymous 91.14 % 94.04 % 86.33 % n/a s 1 core @ 2.5 Ghz (C/C++)
18 SGFusion 91.11 % 94.76 % 86.27 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
19 Anonymous 91.04 % 94.76 % 86.31 % n/a s 1 core @ 2.5 Ghz (Python + C/C++)
20 SA-SSD code 91.03 % 95.03 % 85.96 % 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.
21 anonymous 90.90 % 92.96 % 86.34 % 0.09 s GPU @ 2.5 Ghz (Python)
22 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 90.65 % 94.98 % 86.14 % 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.
23 VueronNet code 90.56 % 94.67 % 85.31 % 0.06 s 1 core @ 2.0 Ghz (Python)
24 ST-RCNN
This method makes use of Velodyne laser scans.
90.53 % 94.58 % 86.08 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
25 VPFNet code 90.52 % 93.94 % 86.25 % 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.
26 PDV code 90.48 % 94.56 % 86.23 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Hu, T. Kuai and S. Waslander: Point Density-Aware Voxels for LiDAR 3D Object Detection. CVPR 2022.
27 VCRCNN 90.42 % 94.55 % 86.11 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
28 M3DeTR code 90.37 % 94.41 % 85.98 % n/a s GPU @ 1.0 Ghz (Python)
T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis and D. Manocha: M3DeTR: Multi-representation, Multi- scale, Mutual-relation 3D Object Detection with Transformers. 2021.
29 TBD 90.37 % 93.82 % 87.72 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
30 DDet 90.34 % 94.16 % 86.01 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
31 VoTr-TSD code 90.34 % 94.03 % 86.14 % 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.
32 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
code 90.13 % 92.42 % 85.93 % 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.
33 XView 90.12 % 92.27 % 85.94 % 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.
34 GraR-VoI 90.10 % 95.69 % 86.85 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
35 CAT-Det 90.07 % 92.59 % 85.82 % 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.
36 IKT3D
This method makes use of Velodyne laser scans.
90.06 % 92.14 % 85.74 % 0.05 s 1 core @ 2.5 Ghz (Python)
37 FPV-SSD 89.93 % 91.45 % 85.21 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
38 SVGA-Net 89.88 % 92.07 % 85.59 % 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.
39 EBM3DOD code 89.86 % 95.64 % 84.56 % 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.
40 CIA-SSD
This method makes use of Velodyne laser scans.
code 89.84 % 93.74 % 82.39 % 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.
41 CLOCs_PVCas code 89.80 % 93.05 % 86.57 % 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.
42 PE-RCVN 89.79 % 95.55 % 84.78 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
43 Anonymous 89.76 % 95.41 % 86.42 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
44 GLENet-VR 89.76 % 93.48 % 84.89 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
45 EBM3DOD baseline code 89.63 % 95.44 % 84.34 % 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.
46 HCPVF 89.62 % 93.20 % 86.72 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
47 DSASNet 89.59 % 93.41 % 84.81 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
48 3SNet 89.58 % 93.26 % 84.80 % 0.07 s GPU @ 2.5 Ghz (Python)
49 CAD 89.57 % 93.03 % 84.71 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
50 3D-CVF at SPA
This method makes use of Velodyne laser scans.
89.56 % 93.52 % 82.45 % 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.
51 ImpDet 89.55 % 92.74 % 84.41 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
52 Struc info fusion II 89.54 % 95.26 % 82.31 % 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.
53 KpNet 89.53 % 93.34 % 81.95 % 0.42 s 1 core @ 2.5 Ghz (C/C++)
54 SASA
This method makes use of Velodyne laser scans.
code 89.51 % 92.87 % 86.35 % 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.
55 Fast-CLOCs 89.49 % 93.03 % 86.40 % 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.
56 KpNet 89.49 % 93.29 % 81.92 % 42 s 1 core @ 2.5 Ghz (C/C++)
57 IA-SSD (single) code 89.48 % 93.14 % 84.42 % 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.
58 CLOCs code 89.48 % 92.91 % 86.42 % 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.
59 SA3DNet
This method uses stereo information.
This method makes use of Velodyne laser scans.
89.46 % 93.11 % 84.60 % 0.05 s GPU @ 2.5 Ghz (Python)
60 DVF-V 89.42 % 93.12 % 86.50 % 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.
61 Struc info fusion I 89.38 % 94.91 % 84.29 % 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.
62 JPVNet 89.36 % 92.78 % 84.37 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
63 BtcDet
This method makes use of Velodyne laser scans.
code 89.34 % 92.81 % 84.55 % 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.
64 IA-SSD (multi) code 89.33 % 92.79 % 84.35 % 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.
65 Anonymous 89.27 % 92.79 % 86.53 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
66 TBD 89.24 % 92.59 % 85.99 % 0.1 s 1 core @ 2.5 Ghz (Python)
67 ATT_SSD 89.22 % 92.82 % 85.90 % 0.01 s 1 core @ 2.5 Ghz (Python)
68 TBD code 89.21 % 92.88 % 85.87 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
69 DVF-PV 89.20 % 93.08 % 86.28 % 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.
70 TF3D
This method makes use of Velodyne laser scans.
89.19 % 93.10 % 84.41 % 0.1 s 2 cores @ 3.0 Ghz (Python)
71 STD code 89.19 % 94.74 % 86.42 % 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.
72 FS-Net
This method makes use of Velodyne laser scans.
89.18 % 92.88 % 84.39 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
73 Point-GNN
This method makes use of Velodyne laser scans.
code 89.17 % 93.11 % 83.90 % 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.
74 HMFI code 89.17 % 93.04 % 86.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
75 SSL-PointGNN code 89.16 % 92.92 % 83.99 % 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.
76 Anonymous 89.15 % 92.47 % 85.97 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
77 SGNet 89.14 % 93.04 % 86.54 % 0.09 s GPU @ 2.5 Ghz (Python)
78 DGCN 89.14 % 92.62 % 83.90 % 0.1 s GPU @ 2.5 Ghz (Python)
79 USVLab BSAODet 89.13 % 92.92 % 86.41 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
80 SPG_mini
This method makes use of Velodyne laser scans.
code 89.12 % 92.80 % 86.27 % 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.
81 ITCA-SSD code 89.12 % 93.19 % 83.99 % 0.05 s 1 core @ 2.5 Ghz (Python)
82 EQ-PVRCNN code 89.09 % 94.55 % 86.42 % 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.
83 SPT 89.09 % 94.87 % 84.38 % 0.1 s GPU @ 2.5 Ghz (Python)
84 MSADet 89.08 % 92.76 % 85.66 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
85 VoxSeT code 89.07 % 92.70 % 86.29 % 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.
86 3DSSD code 89.02 % 92.66 % 85.86 % 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.
87 EPNet++ 89.00 % 95.41 % 85.73 % 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.
88 Focals Conv code 89.00 % 92.67 % 86.33 % 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.
89 SECOND 88.98 % 92.01 % 83.67 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
90 LGNet 88.98 % 92.83 % 86.26 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
91 ISE-RCNN 88.97 % 92.86 % 86.28 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
92 PTA-RCNN 88.94 % 92.32 % 85.63 % 0.08 s 1 core @ 2.5 Ghz (Python)
93 GV-RCNN code 88.94 % 94.52 % 86.24 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
94 TBD 88.94 % 92.03 % 86.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
95 SPNet code 88.92 % 92.29 % 86.16 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
96 Sem-Aug v1 code 88.92 % 92.59 % 84.29 % 0.04 s GPU @ 3.5 Ghz (Python)
97 H^23D R-CNN code 88.87 % 92.85 % 86.07 % 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.
98 Pyramid R-CNN 88.84 % 92.19 % 86.21 % 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.
99 CityBrainLab-CT3D code 88.83 % 92.36 % 84.07 % 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.
100 Voxel R-CNN code 88.83 % 94.85 % 86.13 % 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.
101 HVNet 88.82 % 92.83 % 83.38 % 0.03 s GPU @ 2.0 Ghz (Python)
M. Ye, S. Xu and T. Cao: HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection. CVPR 2020.
102 GLENet 88.81 % 92.22 % 84.13 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
103 AGS-SSD[la] 88.80 % 92.61 % 85.44 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
104 FV2P v2 88.80 % 92.22 % 84.24 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
105 mbdf-netv1 code 88.77 % 94.45 % 83.90 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
106 SRIF-RCNN 88.77 % 92.10 % 86.06 % 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.
107 Anonymous 88.75 % 92.09 % 84.06 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
108 PV-RCNN++ code 88.74 % 92.66 % 85.97 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
109 SPG
This method makes use of Velodyne laser scans.
code 88.70 % 94.33 % 85.98 % 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.
110 MVMM code 88.70 % 92.17 % 85.47 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
111 ISE-RCNN-PV 88.69 % 92.31 % 86.10 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
112 DCAN-Second code 88.68 % 92.76 % 85.32 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
113 SIENet code 88.65 % 92.38 % 86.03 % 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.
114 P2V-RCNN 88.63 % 92.72 % 86.14 % 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.
115 FromVoxelToPoint code 88.61 % 92.23 % 86.11 % 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.
116 RangeIoUDet
This method makes use of Velodyne laser scans.
88.59 % 92.28 % 85.83 % 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.
117 WGVRF 88.56 % 92.45 % 85.69 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
118 DCCA 88.55 % 92.29 % 85.85 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
119 GVNet-V2 88.54 % 92.26 % 85.71 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
120 EPNet code 88.47 % 94.22 % 83.69 % 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.
121 GT3D 88.46 % 92.22 % 83.81 % 0.1 s 1 core @ 2.5 Ghz (Python)
122 CenterNet3D 88.46 % 91.80 % 83.62 % 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.
123 GVNet code 88.43 % 92.19 % 85.63 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
124 USVLab BSAODet (S) 88.42 % 92.19 % 85.55 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
125 RangeRCNN
This method makes use of Velodyne laser scans.
88.40 % 92.15 % 85.74 % 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.
126 Patches
This method makes use of Velodyne laser scans.
88.39 % 92.72 % 83.19 % 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.
127 3D IoU-Net 88.38 % 94.76 % 81.93 % 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.
128 StructuralIF 88.38 % 91.78 % 85.67 % 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.
129 CSVoxel-RCNN 88.37 % 92.07 % 85.51 % 0.03 s GPU @ 1.0 Ghz (Python)
130 NV-RCNN 88.36 % 91.41 % 85.72 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
131 DKDet 88.32 % 92.21 % 85.46 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
132 CenterFuse 88.31 % 91.54 % 83.39 % 0.059 sec/frame 2 x V100
133 SARFE 88.28 % 92.35 % 85.50 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
134 FusionDetv2-v4 88.27 % 92.05 % 85.38 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
135 TBD 88.26 % 91.44 % 85.44 % 0.06 s GPU @ 2.5 Ghz (Python)
136 KPP3D code 88.25 % 93.93 % 83.26 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
137 CLOCs_SecCas 88.23 % 91.16 % 82.63 % 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.
138 SPVB-SSD 88.23 % 91.82 % 85.46 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
139 UberATG-MMF
This method makes use of Velodyne laser scans.
88.21 % 93.67 % 81.99 % 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.
140 Patches - EMP
This method makes use of Velodyne laser scans.
88.17 % 94.49 % 84.75 % 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.
141 SRDL 88.17 % 92.01 % 85.43 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
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142 CF-cd-io-tv 88.16 % 91.32 % 83.26 % 1 s 1 core @ 2.5 Ghz (C/C++)
143 FusionDetv1 88.13 % 91.91 % 85.40 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
144 PSA-Det3D 88.13 % 92.08 % 85.35 % 0.1 s GPU @ 2.5 Ghz (Python)
145 PointPainting
This method makes use of Velodyne laser scans.
88.11 % 92.45 % 83.36 % 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.
146 SERCNN
This method makes use of Velodyne laser scans.
88.10 % 94.11 % 83.43 % 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.
147 Associate-3Ddet code 88.09 % 91.40 % 82.96 % 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.
148 HotSpotNet 88.09 % 94.06 % 83.24 % 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.
149 Faraway-Frustum
This method makes use of Velodyne laser scans.
code 88.08 % 91.90 % 85.35 % 0.1 s GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-frustum: Dealing with lidar sparsity for 3D object detection using fusion. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) 2021.
150 NV2P-RCNN 88.08 % 93.44 % 85.32 % 0.1 s GPU @ 2.5 Ghz (Python)
151 VPN 88.06 % 90.94 % 83.24 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
152 TBD 88.04 % 91.31 % 84.79 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
153 SC-Voxel-RCNN 88.02 % 91.45 % 85.22 % 0.12 s GPU @ 1.0 Ghz (Python)
154 CZY 88.00 % 91.85 % 85.22 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
155 UberATG-HDNET
This method makes use of Velodyne laser scans.
87.98 % 93.13 % 81.23 % 0.05 s GPU @ 2.5 Ghz (Python)
B. Yang, M. Liang and R. Urtasun: HDNET: Exploiting HD Maps for 3D Object Detection. 2nd Conference on Robot Learning (CoRL) 2018.
156 TCDVF 87.94 % 91.21 % 84.66 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
157 DGT-Det3D 87.88 % 91.70 % 85.14 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
158 FusionDetv2-v5 87.86 % 91.92 % 83.07 % 0.05 s 1 core @ 2.5 Ghz (Java + C/C++)
159 Fast Point R-CNN
This method makes use of Velodyne laser scans.
87.84 % 90.87 % 80.52 % 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.
160 CSNet 87.84 % 92.23 % 82.93 % 0.1 s 1 core @ 2.5 Ghz (Python)
161 CF-ctdep-tv-ta 87.81 % 90.73 % 84.97 % 1 s 1 core @ 2.5 Ghz (C/C++)
162 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 87.79 % 91.70 % 84.61 % 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.
163 cp-tv-kp-io-sc 87.78 % 90.98 % 84.04 % 1 s 1 core @ 2.5 Ghz (C/C++)
164 SIF 87.76 % 91.44 % 85.15 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
165 MVAF-Net code 87.73 % 91.95 % 85.00 % 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.
166 Reprod-Two-Branch 87.69 % 90.69 % 84.72 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
167 DKAnet 87.68 % 91.07 % 84.03 % 0.05 s 1 core @ 2.0 Ghz (Python)
168 DVFENet 87.68 % 90.93 % 84.60 % 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.
169 S-AT GCN 87.68 % 90.85 % 84.20 % 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.
170 TBD 87.67 % 91.02 % 82.42 % 0.1 s 1 core @ 2.5 Ghz (Python)
171 CFF-tv-v2 87.67 % 90.70 % 84.58 % 1 s 1 core @ 2.5 Ghz (C/C++)
172 CFF-ep25 87.66 % 90.60 % 84.71 % 1 s 1 core @ 2.5 Ghz (C/C++)
173 TBD 87.62 % 90.86 % 82.29 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
174 CF-base-tv 87.60 % 90.28 % 84.52 % 1 s 1 core @ 2.5 Ghz (C/C++)
175 AutoAlign 87.60 % 91.72 % 84.44 % 0.1 s 1 core @ 2.5 Ghz (Python)
176 KeyFuse2B 87.59 % 90.70 % 84.58 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
177 MODet
This method makes use of Velodyne laser scans.
87.56 % 90.80 % 82.69 % 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.
178 CFF-tv 87.55 % 90.56 % 84.59 % 1 s 1 core @ 2.5 Ghz (C/C++)
179 cff-tv-v2-ep25 87.55 % 90.26 % 84.53 % 1 s 1 core @ 2.5 Ghz (C/C++)
180 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 87.53 % 91.99 % 81.03 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
181 TBD 87.51 % 90.76 % 80.15 % 0.1 s 1 core @ 2.5 Ghz (Python)
182 DTFI 87.51 % 91.01 % 84.25 % 0.03 s 1 core @ 2.5 Ghz (Python)
183 CF-ctdep-tv 87.50 % 90.56 % 84.65 % 1 s 1 core @ 2.5 Ghz (C/C++)
184 PointRGCN 87.49 % 91.63 % 80.73 % 0.26 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
185 Anonymous 87.48 % 90.98 % 84.22 % 1 1 core @ 2.5 Ghz (Python)
186 MGAF-3DSSD code 87.47 % 92.70 % 82.19 % 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.
187 PC-CNN-V2
This method makes use of Velodyne laser scans.
87.40 % 91.19 % 79.35 % 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.
188 HS3D code 87.40 % 91.97 % 82.85 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
189 PVTr 87.39 % 91.21 % 84.77 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
190 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 87.39 % 92.13 % 82.72 % 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.
191 Sem-Aug 87.37 % 93.35 % 82.43 % 0.08 s GPU @ 2.5 Ghz (Python)
192 MAFF-Net(DAF-Pillar) 87.34 % 90.79 % 77.66 % 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.
193 KeyPoint-IoUHead 87.32 % 90.36 % 83.23 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
194 ZMMPP 87.25 % 90.47 % 82.42 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
195 HRI-VoxelFPN 87.21 % 92.75 % 79.82 % 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.
196 epBRM
This method makes use of Velodyne laser scans.
code 87.13 % 90.70 % 81.92 % 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.
197 3D_att
This method makes use of Velodyne laser scans.
87.09 % 93.14 % 81.92 % 0.17 s GPU @ 2.5 Ghz (Python)
198 Contrastive PP code 87.06 % 92.99 % 81.96 % 0.01 s 1 core @ 2.5 Ghz (Python)
199 DVF 87.05 % 92.76 % 84.13 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
200 T_PVRCNN 86.97 % 91.63 % 82.20 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
201 SARPNET 86.92 % 92.21 % 81.68 % 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.
202 cff-tv-t 86.92 % 91.04 % 80.46 % 1 s 1 core @ 2.5 Ghz (C/C++)
203 CZY_3917 86.90 % 90.69 % 82.22 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
204 CF-base-train 86.88 % 90.03 % 83.16 % 1 s 1 core @ 2.5 Ghz (C/C++)
205 Self-Calib Conv 86.86 % 90.00 % 83.88 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
206 T_PVRCNN_V2 86.85 % 91.54 % 81.82 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
207 ARPNET 86.81 % 90.06 % 79.41 % 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.
208 C-GCN 86.78 % 91.11 % 80.09 % 0.147 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
209 IoU-2B 86.74 % 90.92 % 80.40 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
210 cp-tv-kp 86.58 % 89.58 % 83.64 % 1 s 1 core @ 2.5 Ghz (C/C++)
211 PointPillars
This method makes use of Velodyne laser scans.
code 86.56 % 90.07 % 82.81 % 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.
212 TANet code 86.54 % 91.58 % 81.19 % 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.
213 cp-tv 86.52 % 89.55 % 83.45 % 1 s 1 core @ 2.5 Ghz (C/C++)
214 SCNet
This method makes use of Velodyne laser scans.
86.48 % 90.07 % 81.30 % 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.
215 CF-ctdep-train 86.46 % 89.57 % 82.03 % 1 s 1 core @ 2.5 Ghz (C/C++)
216 CSNet8306 code 86.44 % 92.57 % 81.36 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
217 SegVoxelNet 86.37 % 91.62 % 83.04 % 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.
218 Dune-DCF-e09 86.36 % 89.33 % 81.77 % 1 s 1 core @ 2.5 Ghz (C/C++)
219 Dune-DCF-e11 86.32 % 89.32 % 81.78 % 1 s 1 core @ 2.5 Ghz (C/C++)
220 PP-PCdet code 86.32 % 89.86 % 81.62 % 0.01 s 1 core @ 2.5 Ghz (Python)
221 3D IoU Loss
This method makes use of Velodyne laser scans.
86.22 % 91.36 % 81.20 % 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.
222 Dune-DCF-e15 86.21 % 88.99 % 81.62 % 1 s 1 core @ 2.5 Ghz (C/C++)
223 TBD_BD code 86.12 % 91.00 % 81.66 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
224 CrazyTensor-CF 86.10 % 89.13 % 81.61 % 1 s 1 core @ 2.5 Ghz (C/C++)
225 City-CF-fixed 86.09 % 89.94 % 81.73 % 1 s 1 core @ 2.5 Ghz (C/C++)
226 R-GCN 86.05 % 91.91 % 81.05 % 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.
227 UberATG-PIXOR++
This method makes use of Velodyne laser scans.
86.01 % 93.28 % 80.11 % 0.035 s GPU @ 2.5 Ghz (Python)
B. Yang, M. Liang and R. Urtasun: HDNET: Exploiting HD Maps for 3D Object Detection. 2nd Conference on Robot Learning (CoRL) 2018.
228 SSL_PP code 85.93 % 92.19 % 80.40 % 16ms GPU @ 1.5 Ghz (Python)
229 CSNet8299 code 85.91 % 91.64 % 80.95 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
230 Sem-Aug-PointRCNN++ 85.88 % 91.68 % 83.37 % 0.1 s 8 cores @ 3.0 Ghz (Python)
231 DASS 85.85 % 91.74 % 80.97 % 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.
232 F-ConvNet
This method makes use of Velodyne laser scans.
code 85.84 % 91.51 % 76.11 % 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.
233 City-CF 85.83 % 89.20 % 81.61 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
234 PI-RCNN 85.81 % 91.44 % 81.00 % 0.1 s 1 core @ 2.5 Ghz (Python)
L. Xie, C. Xiang, Z. Yu, G. Xu, Z. Yang, D. Cai and X. He: PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module. AAAI 2020 : The Thirty-Fourth AAAI Conference on Artificial Intelligence 2020.
235 LazyTorch-CP-Infer-O 85.74 % 89.19 % 81.35 % 1 s 1 core @ 2.5 Ghz (C/C++)
236 PointRGBNet 85.73 % 91.39 % 80.68 % 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.
237 AFTD 85.63 % 90.61 % 82.28 % 1 s 1 core @ 2.5 Ghz (Python + C/C++)
238 LazyTorch-CP-Small-P 85.63 % 89.10 % 81.27 % 1 s 1 core @ 2.5 Ghz (C/C++)
239 CrazyTensor-CP 85.55 % 87.94 % 82.63 % 1 s 1 core @ 2.5 Ghz (Python)
240 Sem-Aug-PointRCNN code 85.50 % 89.75 % 83.13 % 0.1 s GPU @ 3.5 Ghz (C/C++)
241 variance_point 85.39 % 91.90 % 81.13 % 0.05 s 1 core @ 2.5 Ghz (Python)
242 UberATG-ContFuse
This method makes use of Velodyne laser scans.
85.35 % 94.07 % 75.88 % 0.06 s GPU @ 2.5 Ghz (Python)
M. Liang, B. Yang, S. Wang and R. Urtasun: Deep Continuous Fusion for Multi-Sensor 3D Object Detection. ECCV 2018.
243 new_stereo 85.24 % 90.74 % 82.10 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
244 PSM_stereo 85.12 % 90.26 % 80.21 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
245 PFF3D
This method makes use of Velodyne laser scans.
code 85.08 % 89.61 % 80.42 % 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.
246 CenterPoint (pcdet) 85.05 % 88.47 % 81.19 % 0.051 sec/frame 2 x V100
247 AVOD
This method makes use of Velodyne laser scans.
code 84.95 % 89.75 % 78.32 % 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.
248 WS3D
This method makes use of Velodyne laser scans.
84.93 % 90.96 % 77.96 % 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.
249 AVOD-FPN
This method makes use of Velodyne laser scans.
code 84.82 % 90.99 % 79.62 % 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.
250 MF 84.72 % 88.58 % 78.17 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
251 F-PointNet
This method makes use of Velodyne laser scans.
code 84.67 % 91.17 % 74.77 % 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.
252 FusionDetv2-baseline 84.31 % 90.38 % 79.23 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
253 3DBN
This method makes use of Velodyne laser scans.
83.94 % 89.66 % 76.50 % 0.13s 1080Ti (Python+C/C++)
X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.
254 MLOD
This method makes use of Velodyne laser scans.
code 82.68 % 90.25 % 77.97 % 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.
255 BirdNet+
This method makes use of Velodyne laser scans.
code 81.85 % 87.43 % 75.36 % 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.
256 TBD 81.53 % 87.90 % 74.26 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
257 FD 81.47 % 88.34 % 75.07 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
258 CZY 81.21 % 89.10 % 76.13 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
259 AEC3D 80.37 % 86.81 % 74.26 % 18 ms GPU @ 2.5 Ghz (Python)
260 DMF
This method uses stereo information.
80.29 % 84.64 % 76.05 % 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.
261 UberATG-PIXOR
This method makes use of Velodyne laser scans.
80.01 % 83.97 % 74.31 % 0.035 s TITAN Xp (Python)
B. Yang, W. Luo and R. Urtasun: PIXOR: Real-time 3D Object Detection from Point Clouds. CVPR 2018.
262 MV3D (LIDAR)
This method makes use of Velodyne laser scans.
78.98 % 86.49 % 72.23 % 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.
263 DSGN++
This method uses stereo information.
code 78.94 % 88.55 % 69.74 % 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.
264 MV3D
This method makes use of Velodyne laser scans.
78.93 % 86.62 % 69.80 % 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.
265 StereoDistill 78.59 % 89.03 % 69.34 % 0.4 s 1 core @ 2.5 Ghz (Python)
266 Anonymous 77.40 % 90.76 % 70.00 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
267 SD3DOD 76.96 % 86.82 % 70.05 % 0.04 s GPU @ 2.5 Ghz (Python)
268 MMLAB LIGA-Stereo
This method uses stereo information.
code 76.78 % 88.15 % 67.40 % 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.
269 RCD 75.83 % 82.26 % 69.61 % 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.
270 LaserNet 74.52 % 79.19 % 68.45 % 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.
271 PL++ (SDN+GDC)
This method uses stereo information.
This method makes use of Velodyne laser scans.
code 73.80 % 84.61 % 65.59 % 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.
272 SNVC
This method uses stereo information.
code 73.61 % 86.88 % 64.49 % 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.
273 A3DODWTDA
This method makes use of Velodyne laser scans.
code 73.26 % 79.58 % 62.77 % 0.08 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
274 Anonymous 71.23 % 86.67 % 64.08 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
275 ppt 70.21 % 72.17 % 65.26 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
276 Complexer-YOLO
This method makes use of Velodyne laser scans.
68.96 % 77.24 % 64.95 % 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.
277 PS++ code 68.36 % 84.64 % 59.01 % PS++ s 1 core @ 2.5 Ghz (C/C++)
278 TopNet-Retina
This method makes use of Velodyne laser scans.
68.16 % 80.16 % 63.43 % 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.
279 CG-Stereo
This method uses stereo information.
66.44 % 85.29 % 58.95 % 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.
280 PLUME
This method uses stereo information.
66.27 % 82.97 % 56.70 % 0.15 s GPU @ 2.5 Ghz (Python)
Y. Wang, B. Yang, R. Hu, M. Liang and R. Urtasun: PLUME: Efficient 3D Object Detection from Stereo Images. IROS 2021.
281 CDN
This method uses stereo information.
code 66.24 % 83.32 % 57.65 % 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.
282 PS code 65.33 % 83.75 % 56.14 % PS s 1 core @ 2.5 Ghz (C/C++)
283 DSGN
This method uses stereo information.
code 65.05 % 82.90 % 56.60 % 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.
284 TopNet-DecayRate
This method makes use of Velodyne laser scans.
64.60 % 79.74 % 58.04 % 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.
285 UPF_3D
This method uses stereo information.
63.58 % 85.53 % 56.56 % 0.29 s 1 core @ 2.5 Ghz (Python)
286 BirdNet+ (legacy)
This method makes use of Velodyne laser scans.
code 63.33 % 84.80 % 61.23 % 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.
287 3D FCN
This method makes use of Velodyne laser scans.
61.67 % 70.62 % 55.61 % >5 s 1 core @ 2.5 Ghz (C/C++)
B. Li: 3D Fully Convolutional Network for Vehicle Detection in Point Cloud. IROS 2017.
288 CDN-PL++
This method uses stereo information.
61.04 % 81.27 % 52.84 % 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.
289 BirdNet
This method makes use of Velodyne laser scans.
59.83 % 84.17 % 57.35 % 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.
290 TopNet-UncEst
This method makes use of Velodyne laser scans.
59.67 % 72.05 % 51.67 % 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.
291 RT3D-GMP
This method uses stereo information.
59.00 % 69.14 % 45.49 % 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.
292 Disp R-CNN (velo)
This method uses stereo information.
code 58.62 % 79.76 % 47.73 % 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.
293 ESGN
This method uses stereo information.
58.12 % 78.10 % 49.28 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
294 Pseudo-LiDAR++
This method uses stereo information.
code 58.01 % 78.31 % 51.25 % 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.
295 Disp R-CNN
This method uses stereo information.
code 57.98 % 79.61 % 47.09 % 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.
296 ZoomNet
This method uses stereo information.
code 54.91 % 72.94 % 44.14 % 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.
297 ART 54.23 % 75.05 % 48.19 % 20ms s 1 core @ 2.5 Ghz (C/C++)
298 VoxelJones code 53.96 % 66.21 % 47.66 % .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.
299 TopNet-HighRes
This method makes use of Velodyne laser scans.
53.05 % 67.84 % 46.99 % 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.
300 OC Stereo
This method uses stereo information.
code 51.47 % 68.89 % 42.97 % 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.
301 YOLOStereo3D
This method uses stereo information.
code 50.28 % 76.10 % 36.86 % 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.
302 RT3DStereo
This method uses stereo information.
46.82 % 58.81 % 38.38 % 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.
303 Pseudo-Lidar
This method uses stereo information.
code 45.00 % 67.30 % 38.40 % 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.
304 RT3D
This method makes use of Velodyne laser scans.
44.00 % 56.44 % 42.34 % 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.
305 Stereo CenterNet
This method uses stereo information.
42.12 % 62.97 % 35.37 % 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.
306 Stereo R-CNN
This method uses stereo information.
code 41.31 % 61.92 % 33.42 % 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.
307 SparseLiDAR_fusion 38.99 % 52.57 % 32.86 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
308 GCDR 37.34 % 50.85 % 30.51 % 0.28 s 1 core @ 2.5 Ghz (Python)
309 VMDet_Boost 33.13 % 46.17 % 28.80 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
310 StereoFENet
This method uses stereo information.
32.96 % 49.29 % 25.90 % 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.
311 Anonymous 30.81 % 43.11 % 26.81 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
312 Digging_M3D 28.84 % 39.74 % 26.08 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
313 Mobile Stereo R-CNN
This method uses stereo information.
28.78 % 44.51 % 22.30 % 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.
314 VMDet 28.50 % 41.41 % 23.88 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
315 Anonymous 27.70 % 37.81 % 24.61 % 40 s 1 core @ 2.5 Ghz (C/C++)
316 SARM3D 26.81 % 34.17 % 23.68 % 0.03 s GPU @ 2.5 Ghz (Python)
317 CMKD* 25.82 % 38.98 % 22.80 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
318 MoGDE 25.60 % 38.38 % 22.91 % 0.03 s GPU @ 2.5 Ghz (Python)
319 LPCG-Monoflex 24.81 % 35.96 % 21.86 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
320 Anonymous 24.78 % 33.38 % 22.00 % 40 s 1 core @ 2.5 Ghz (C/C++)
321 DD3Dv2 code 24.67 % 35.70 % 21.73 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
322 Mix-Teaching 24.23 % 35.74 % 20.80 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
323 MonoInsight 24.23 % 34.85 % 20.87 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
324 anonymity 23.92 % 36.92 % 21.68 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
325 PS-fld code 23.76 % 32.64 % 20.64 % 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.
326 SCSTSV-MonoFlex 23.71 % 34.59 % 20.41 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
327 anonymity 23.61 % 36.80 % 21.31 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
328 MonoDDE 23.46 % 33.58 % 20.37 % 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.
329 DD3D code 23.41 % 32.35 % 20.42 % 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) .
330 DID-M3D 22.76 % 32.95 % 19.83 % 0.04 s 1 core @ 2.5 Ghz (Python)
331 MonoDistill 22.59 % 31.87 % 19.72 % 0.04 s 1 core @ 2.5 Ghz (Python)
332 zongmuDistill 22.56 % 33.48 % 19.88 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
333 OPA-3D code 22.53 % 33.54 % 19.22 % 0.04 s 1 core @ 3.5 Ghz (Python)
334 Shape-Aware 22.13 % 32.55 % 18.94 % 0.05 s 1 core @ 2.5 Ghz (Python)
335 MonoCon code 22.10 % 31.12 % 19.00 % 0.02 s GPU @ 2.5 Ghz (Python)
T. Xianpeng Liu: Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection. AAAI 2022.
336 Anonymous 22.05 % 31.75 % 19.44 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
337 gupnet_se 21.98 % 32.82 % 18.70 % 0.03s 1 core @ 2.5 Ghz (C/C++)
338 ZongmuMono3d code 21.78 % 33.18 % 18.71 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
339 MDNet 21.71 % 33.31 % 18.49 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
340 Lite-FPN-GUPNet 21.53 % 31.68 % 18.38 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
341 DDS code 21.50 % 32.55 % 18.25 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
342 MonoDETR code 21.45 % 32.20 % 18.68 % 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.
343 OBMO_GUPNet 21.41 % 30.81 % 18.37 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
344 M3DGAF 21.39 % 31.34 % 19.28 % 0.07 s 1 core @ 2.5 Ghz (Python)
345 mono3d code 21.39 % 32.17 % 18.47 % TBD TBD
346 SGM3D 21.37 % 31.49 % 18.43 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
Z. Zhou, L. Du, X. Ye, Z. Zou, X. Tan, E. Ding, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection. 2021.
347 monopd code 21.29 % 32.12 % 18.08 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
348 DEPT 21.22 % 30.85 % 18.47 % 0.03 s 1 core @ 2.5 Ghz (Python)
349 GUPNet code 21.19 % 30.29 % 18.20 % 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.
350 HBD 20.91 % 29.87 % 18.22 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
351 GPENet code 20.79 % 30.31 % 18.21 % 0.02 s GPU @ 2.5 Ghz (Python)
352 mono3d 20.75 % 31.58 % 17.66 % 0.03 s GPU @ 2.5 Ghz (Python)
353 LT-M3OD 20.74 % 29.40 % 17.83 % 0.03 s 1 core @ 2.5 Ghz (Python)
354 HomoLoss(monoflex) code 20.68 % 29.60 % 17.81 % 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.
355 MonoFlex 20.67 % 30.95 % 17.72 % 0.03 s 1 core @ 2.5 Ghz (Python)
356 Anonymous 20.47 % 33.17 % 17.31 % 40 s 1 core @ 2.5 Ghz (C/C++)
357 MonoGround 20.47 % 30.07 % 17.74 % 0.03 s 1 core @ 2.5 Ghz (Python)
358 EW code 20.38 % 28.88 % 17.59 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
359 MonoDTR 20.38 % 28.59 % 17.14 % 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.
360 MonoEdge 20.35 % 28.80 % 17.57 % 0.05 s GPU @ 2.5 Ghz (Python)
361 SAIC_ADC_Mono3D code 20.20 % 27.09 % 18.78 % 50 s GPU @ 2.5 Ghz (Python)
362 MonoEdge-Rotate 20.16 % 31.19 % 17.35 % 0.05 s GPU @ 2.5 Ghz (Python)
363 MDSNet 20.14 % 32.81 % 15.77 % 0.07 s 1 core @ 2.5 Ghz (Python)
364 AutoShape code 20.08 % 30.66 % 15.95 % 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.
365 MonoEdge-RCNN 20.07 % 27.62 % 16.34 % 0.05 s 1 core @ 2.5 Ghz (Python)
366 M3DSSD++ code 20.03 % 32.18 % 16.47 % 0.16s 1 core @ 2.5 Ghz (C/C++)
367 MAOLoss code 19.95 % 28.29 % 16.94 % 0.05 s 1 core @ 2.5 Ghz (Python)
368 ANM 19.82 % 29.89 % 16.77 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
369 EM code 19.80 % 30.61 % 16.55 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
370 MonoFlex 19.75 % 28.23 % 16.89 % 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.
371 MonoEF 19.70 % 29.03 % 17.26 % 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.
372 K3D 19.60 % 28.31 % 17.62 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
373 HomoLoss(imvoxelnet) code 19.25 % 29.18 % 16.21 % 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.
374 MonoAug 19.19 % 28.20 % 16.15 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
375 MK3D 19.18 % 29.11 % 15.78 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
376 DFR-Net 19.17 % 28.17 % 14.84 % 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.
377 SwinMono3D 19.15 % 29.65 % 14.09 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
378 DLE code 19.05 % 31.09 % 14.13 % 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.
379 PCT code 19.03 % 29.65 % 15.92 % 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.
380 Anonymous code 18.96 % 26.54 % 16.01 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
381 CaDDN code 18.91 % 27.94 % 17.19 % 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.
382 monodle code 18.89 % 24.79 % 16.00 % 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 .
383 MonoFar 18.68 % 25.89 % 16.30 % 0.04 s 1 core @ 2.5 Ghz (Python)
384 none 18.66 % 26.19 % 15.79 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
385 Neighbor-Vote 18.65 % 27.39 % 16.54 % 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.
386 GrooMeD-NMS code 18.27 % 26.19 % 14.05 % 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.
387 MonoRCNN code 18.11 % 25.48 % 14.10 % 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.
388 Ground-Aware code 17.98 % 29.81 % 13.08 % 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.
389 MP-Mono 17.96 % 25.36 % 13.84 % 0.16 s GPU @ 2.5 Ghz (Python)
390 Aug3D-RPN 17.89 % 26.00 % 14.18 % 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.
391 DDMP-3D 17.89 % 28.08 % 13.44 % 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.
392 IAFA 17.88 % 25.88 % 15.35 % 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.
393 RefinedMPL 17.60 % 28.08 % 13.95 % 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.
394 Lite-FPN 17.58 % 26.67 % 14.61 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
395 Kinematic3D code 17.52 % 26.69 % 13.10 % 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 .
396 MonoRUn code 17.34 % 27.94 % 15.24 % 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.
397 RetinaMono 17.33 % 26.12 % 15.26 % 0.02 s 1 core @ 2.5 Ghz (Python)
398 AM3D 17.32 % 25.03 % 14.91 % 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.
399 YoloMono3D code 17.15 % 26.79 % 12.56 % 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.
400 CMAN 17.04 % 25.89 % 12.88 % 0.15 s 1 core @ 2.5 Ghz (Python)
401 GAC3D 16.93 % 25.80 % 12.50 % 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.
402 PatchNet code 16.86 % 22.97 % 14.97 % 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.
403 MonoAug 16.71 % 24.39 % 13.83 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
404 PGD-FCOS3D code 16.51 % 26.89 % 13.49 % 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.
405 ImVoxelNet code 16.37 % 25.19 % 13.58 % 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.
406 KM3D code 16.20 % 23.44 % 14.47 % 0.03 s 1 core @ 2.5 Ghz (Python)
P. Li: Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training. 2020.
407 MM 16.09 % 24.65 % 13.99 % 1 s 1 core @ 2.5 Ghz (C/C++)
408 D4LCN code 16.02 % 22.51 % 12.55 % 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.
409 Keypoint-3D 15.54 % 23.16 % 11.83 % 14 s 1 core @ 2.5 Ghz (C/C++)
410 COF3D 15.39 % 25.36 % 11.34 % 200 s 1 core @ 2.5 Ghz (Python + C/C++)
411 MonoPair 14.83 % 19.28 % 12.89 % 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.
412 Decoupled-3D 14.82 % 23.16 % 11.25 % 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.
413 QD-3DT
This is an online method (no batch processing).
code 14.71 % 20.16 % 12.76 % 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.
414 SMOKE code 14.49 % 20.83 % 12.75 % 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.
415 RTM3D code 14.20 % 19.17 % 11.99 % 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.
416 Mono3D_PLiDAR code 13.92 % 21.27 % 11.25 % 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.
417 M3D-RPN code 13.67 % 21.02 % 10.23 % 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 .
418 CSoR
This method makes use of Velodyne laser scans.
13.07 % 18.67 % 10.34 % 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.
419 MonoPSR code 12.58 % 18.33 % 9.91 % 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.
420 MonoCInIS 11.64 % 22.28 % 9.95 % 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.
421 SS3D 11.52 % 16.33 % 9.93 % 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.
422 MonoGRNet code 11.17 % 18.19 % 8.73 % 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.
423 MonoFENet 11.03 % 17.03 % 9.05 % 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.
424 MonoCInIS 10.96 % 20.42 % 9.23 % 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.
425 A3DODWTDA (image) code 8.66 % 10.37 % 7.06 % 0.8 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
426 TLNet (Stereo)
This method uses stereo information.
code 7.69 % 13.71 % 6.73 % 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.
427 Shift R-CNN (mono) code 6.82 % 11.84 % 5.27 % 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.
428 SparVox3D 6.39 % 10.20 % 5.06 % 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.
429 GS3D 6.08 % 8.41 % 4.94 % 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.
430 MVRA + I-FRCNN+ 5.84 % 9.05 % 4.50 % 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.
431 WeakM3D code 5.66 % 11.82 % 4.08 % 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.
432 ROI-10D 4.91 % 9.78 % 3.74 % 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.
433 CDTrack3D code 4.61 % 7.02 % 3.73 % 0.0106 s NVIDIA RTX 3090 GPU, i9 10850k CPU
434 3D-GCK 4.57 % 5.79 % 3.64 % 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.
435 FQNet 3.23 % 5.40 % 2.46 % 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.
436 3D-SSMFCNN code 2.63 % 3.20 % 2.40 % 0.1 s GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.
437 VeloFCN
This method makes use of Velodyne laser scans.
0.14 % 0.02 % 0.21 % 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 .
438 MonoDET code 0.14 % 0.25 % 0.10 % 0.04 s 1 core @ 2.5 Ghz (Python)
439 test code 0.09 % 0.04 % 0.11 % 50 s 1 core @ 2.5 Ghz (Python)
440 multi-task CNN 0.00 % 0.00 % 0.00 % 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.
441 mBoW
This method makes use of Velodyne laser scans.
0.00 % 0.00 % 0.00 % 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.
Table as LaTeX | Only published Methods

Pedestrian


Method Setting Code Moderate Easy Hard Runtime Environment
1 PiFeNet 53.92 % 63.25 % 50.53 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
D. Le, H. Shi, H. Rezatofighi and J. Cai: PiFeNet: Pillar-Feature Network for Real-Time 3D Pedestrian Detection from Point Cloud. 2021.
2 CasA++ 53.84 % 60.14 % 51.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
3 TED 53.48 % 60.13 % 50.89 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
4 DCAN-Second code 53.18 % 60.92 % 50.56 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
5 EQ-PVRCNN code 52.81 % 61.73 % 49.87 % 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.
6 PV-RCNN++ code 52.43 % 59.73 % 48.73 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
7 VPFNet code 52.41 % 60.07 % 50.28 % 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.
8 Frustum-PointPillars code 52.23 % 60.98 % 48.30 % 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.
9 CAD 52.20 % 60.23 % 49.54 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
10 TANet code 51.38 % 60.85 % 47.54 % 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.
11 CasA 51.37 % 57.95 % 49.08 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
12 ISE-RCNN 51.06 % 55.64 % 47.76 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
13 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 50.57 % 59.86 % 46.74 % 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.
14 HotSpotNet 50.53 % 57.39 % 46.65 % 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.
15 VMVS
This method makes use of Velodyne laser scans.
50.34 % 60.34 % 46.45 % 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.
16 AVOD-FPN
This method makes use of Velodyne laser scans.
code 50.32 % 58.49 % 46.98 % 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.
17 SPT 50.22 % 56.54 % 46.72 % 0.1 s GPU @ 2.5 Ghz (Python)
18 variance_point 50.03 % 57.72 % 46.27 % 0.05 s 1 core @ 2.5 Ghz (Python)
19 3DSSD code 49.94 % 60.54 % 45.73 % 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.
20 PointPainting
This method makes use of Velodyne laser scans.
49.93 % 58.70 % 46.29 % 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.
21 SemanticVoxels 49.93 % 58.91 % 47.31 % 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.
22 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 49.81 % 59.04 % 45.92 % 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.
23 TBD 49.59 % 58.17 % 47.08 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
24 F-PointNet
This method makes use of Velodyne laser scans.
code 49.57 % 57.13 % 45.48 % 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.
25 TBD 49.56 % 58.10 % 47.05 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
26 CFF-tv 49.29 % 57.83 % 46.70 % 1 s 1 core @ 2.5 Ghz (C/C++)
27 AutoAlign 49.27 % 59.28 % 46.14 % 0.1 s 1 core @ 2.5 Ghz (Python)
28 VPN 49.19 % 57.98 % 45.26 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
29 F-ConvNet
This method makes use of Velodyne laser scans.
code 48.96 % 57.04 % 44.33 % 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.
30 HVNet 48.86 % 54.84 % 46.33 % 0.03 s GPU @ 2.0 Ghz (Python)
M. Ye, S. Xu and T. Cao: HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection. CVPR 2020.
31 CAT-Det 48.78 % 57.13 % 45.56 % 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.
32 PE-RCVN 48.72 % 54.09 % 46.71 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
33 STD code 48.72 % 60.02 % 44.55 % 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.
34 Reprod-Two-Branch 48.71 % 57.25 % 45.75 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
35 KeyFuse2B 48.64 % 56.16 % 46.20 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
36 PointPillars
This method makes use of Velodyne laser scans.
code 48.64 % 57.60 % 45.78 % 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.
37 USVLab BSAODet 48.61 % 55.76 % 46.08 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
38 FV2P v2 48.58 % 54.90 % 45.11 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
39 FS-Net
This method makes use of Velodyne laser scans.
48.50 % 54.91 % 46.29 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
40 EPNet++ 48.47 % 56.24 % 45.73 % 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.
41 MGAF-3DSSD code 48.46 % 56.09 % 44.90 % 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.
42 CFF-ep25 48.31 % 56.34 % 45.58 % 1 s 1 core @ 2.5 Ghz (C/C++)
43 Fast-CLOCs 48.27 % 57.19 % 44.55 % 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.
44 FromVoxelToPoint code 48.15 % 56.54 % 45.63 % 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.
45 cff-tv-v2-ep25 48.13 % 56.48 % 45.66 % 1 s 1 core @ 2.5 Ghz (C/C++)
46 USVLab BSAODet (S) 48.10 % 54.96 % 45.65 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
47 ISE-RCNN-PV 47.85 % 55.63 % 45.80 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
48 tbd 47.84 % 57.69 % 43.99 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
49 HMFI code 47.77 % 55.61 % 45.17 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
50 TBD 47.77 % 55.61 % 45.17 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
51 CFF-tv-v2 47.59 % 55.46 % 45.09 % 1 s 1 core @ 2.5 Ghz (C/C++)
52 Anonymous 47.47 % 52.94 % 45.41 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
53 CF-ctdep-tv-ta 47.46 % 54.36 % 45.07 % 1 s 1 core @ 2.5 Ghz (C/C++)
54 P2V-RCNN 47.36 % 54.15 % 45.10 % 0.1 s 2 cores @ 2.5 Ghz (Python)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.
55 SGNet 47.29 % 53.84 % 44.10 % 0.09 s GPU @ 2.5 Ghz (Python)
56 CF-base-tv 47.28 % 54.77 % 44.81 % 1 s 1 core @ 2.5 Ghz (C/C++)
57 Self-Calib Conv 47.17 % 54.20 % 44.84 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
58 TCDVF 47.11 % 55.26 % 44.53 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
59 Point-GNN
This method makes use of Velodyne laser scans.
code 47.07 % 55.36 % 44.61 % 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.
60 MVMM code 46.84 % 53.75 % 44.87 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
61 SCNet
This method makes use of Velodyne laser scans.
46.73 % 56.87 % 42.74 % 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.
62 cp-tv-kp 46.71 % 53.73 % 44.45 % 1 s 1 core @ 2.5 Ghz (C/C++)
63 PSA-Det3D 46.36 % 53.26 % 43.73 % 0.1 s GPU @ 2.5 Ghz (Python)
64 CF-ctdep-tv 46.36 % 53.50 % 44.01 % 1 s 1 core @ 2.5 Ghz (C/C++)
65 MSADet 46.27 % 55.91 % 43.83 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
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66 3SNet 46.25 % 52.22 % 42.89 % 0.07 s GPU @ 2.5 Ghz (Python)
67 DGT-Det3D 46.22 % 53.98 % 43.85 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
68 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 46.13 % 54.77 % 42.84 % 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.
69 ARPNET 45.92 % 55.48 % 42.54 % 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.
70 CenterFuse 45.84 % 55.20 % 43.46 % 0.059 sec/frame 2 x V100
71 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
code 45.82 % 52.03 % 43.81 % 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.
72 cp-tv 45.75 % 52.90 % 43.49 % 1 s 1 core @ 2.5 Ghz (C/C++)
73 SVGA-Net 45.68 % 53.09 % 43.30 % 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.
74 SARFE 45.60 % 51.45 % 43.49 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
75 TBD 45.57 % 52.08 % 42.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
76 epBRM
This method makes use of Velodyne laser scans.
code 45.49 % 52.48 % 42.75 % 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.
77 TBD code 45.46 % 52.72 % 42.53 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
78 PDV code 45.45 % 51.95 % 43.33 % 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.
79 MLOD
This method makes use of Velodyne laser scans.
code 45.40 % 55.09 % 41.42 % 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.
80 cp-tv-kp-io-sc 45.30 % 53.84 % 42.12 % 1 s 1 core @ 2.5 Ghz (C/C++)
81 IA-SSD (single) code 45.07 % 52.73 % 42.75 % 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.
82 TBD 44.99 % 50.41 % 42.85 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
83 FusionDetv1 44.85 % 52.42 % 42.56 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
84 SRDL 44.84 % 52.42 % 42.56 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
85 M3DeTR code 44.78 % 50.63 % 42.57 % 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.
86 WGVRF 44.75 % 50.80 % 42.78 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
87 AFTD 44.74 % 53.94 % 42.36 % 1 s 1 core @ 2.5 Ghz (Python + C/C++)
88 AGS-SSD[la] 44.65 % 51.26 % 41.57 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
89 FusionDetv2-v5 44.64 % 51.44 % 42.32 % 0.05 s 1 core @ 2.5 Ghz (Java + C/C++)
90 Dune-DCF-e11 44.58 % 52.44 % 41.75 % 1 s 1 core @ 2.5 Ghz (C/C++)
91 ATT_SSD 44.57 % 51.26 % 42.33 % 0.01 s 1 core @ 2.5 Ghz (Python)
92 CF-cd-io-tv 44.54 % 53.64 % 41.21 % 1 s 1 core @ 2.5 Ghz (C/C++)
93 Dune-DCF-e09 44.50 % 52.64 % 41.86 % 1 s 1 core @ 2.5 Ghz (C/C++)
94 FusionDetv2-v4 44.47 % 50.88 % 42.18 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
95 IKT3D
This method makes use of Velodyne laser scans.
44.45 % 49.50 % 42.58 % 0.05 s 1 core @ 2.5 Ghz (Python)
96 SIF 44.28 % 52.05 % 42.03 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
97 LazyTorch-CP-Infer-O 44.27 % 51.92 % 41.99 % 1 s 1 core @ 2.5 Ghz (C/C++)
98 KeyPoint-IoUHead 44.27 % 53.12 % 41.83 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
99 LazyTorch-CP-Small-P 44.25 % 51.84 % 41.97 % 1 s 1 core @ 2.5 Ghz (C/C++)
100 DDet 44.24 % 50.01 % 42.38 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
101 IoU-2B 44.19 % 55.31 % 40.33 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
102 ST-RCNN
This method makes use of Velodyne laser scans.
44.14 % 49.78 % 41.95 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
103 DVFENet 44.12 % 50.98 % 41.62 % 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.
104 VCRCNN 44.09 % 48.82 % 42.19 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
105 CenterPoint (pcdet) 44.08 % 51.76 % 41.80 % 0.051 sec/frame 2 x V100
106 CrazyTensor-CP 44.06 % 51.25 % 41.50 % 1 s 1 core @ 2.5 Ghz (Python)
107 cff-tv-t 44.00 % 54.42 % 41.46 % 1 s 1 core @ 2.5 Ghz (C/C++)
108 DSASNet 43.98 % 50.55 % 40.63 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
109 CF-base-train 43.90 % 51.40 % 41.24 % 1 s 1 core @ 2.5 Ghz (C/C++)
110 City-CF-fixed 43.86 % 51.92 % 41.33 % 1 s 1 core @ 2.5 Ghz (C/C++)
111 Faraway-Frustum
This method makes use of Velodyne laser scans.
code 43.85 % 52.15 % 41.68 % 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.
112 Dune-DCF-e15 43.63 % 51.18 % 41.11 % 1 s 1 core @ 2.5 Ghz (C/C++)
113 S-AT GCN 43.43 % 50.63 % 41.58 % 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 CF-ctdep-train 43.20 % 50.14 % 40.69 % 1 s 1 core @ 2.5 Ghz (C/C++)
115 FPV-SSD 43.19 % 50.37 % 40.95 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
116 City-CF 42.95 % 49.91 % 40.61 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
117 BirdNet+
This method makes use of Velodyne laser scans.
code 42.87 % 48.90 % 40.59 % 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.
118 CZY 42.80 % 49.42 % 40.83 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
119 TBD 42.76 % 50.17 % 39.75 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
120 IA-SSD (multi) code 42.61 % 51.76 % 40.51 % 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.
121 HS3D code 42.60 % 51.58 % 39.27 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
122 NV-RCNN 42.58 % 49.00 % 40.39 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
123 FusionDetv2-baseline 42.53 % 47.08 % 40.71 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
124 XView 42.42 % 47.24 % 39.96 % 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.
125 PVTr 42.26 % 48.79 % 40.27 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
126 T_PVRCNN_V2 42.21 % 50.58 % 39.81 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
127 T_PVRCNN 41.87 % 49.87 % 39.44 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
128 PFF3D
This method makes use of Velodyne laser scans.
code 40.94 % 48.74 % 38.54 % 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.
129 CrazyTensor-CF 40.78 % 48.79 % 38.16 % 1 s 1 core @ 2.5 Ghz (C/C++)
130 NV2P-RCNN 40.71 % 46.83 % 38.86 % 0.1 s GPU @ 2.5 Ghz (Python)
131 TBD_BD code 40.41 % 48.27 % 38.45 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
132 ZMMPP 39.11 % 46.50 % 37.04 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
133 DSGN++
This method uses stereo information.
code 38.92 % 50.26 % 35.12 % 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.
134 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 38.79 % 47.51 % 35.85 % 0.0047s 1 core @ 2.5 Ghz (python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
135 BirdNet+ (legacy)
This method makes use of Velodyne laser scans.
code 38.28 % 45.53 % 35.37 % 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.
136 PP-PCdet code 38.21 % 45.14 % 36.04 % 0.01 s 1 core @ 2.5 Ghz (Python)
137 CSW3D
This method makes use of Velodyne laser scans.
37.96 % 49.27 % 33.83 % 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.
138 KPP3D code 37.82 % 45.25 % 35.36 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
139 StereoDistill 37.75 % 50.79 % 34.28 % 0.4 s 1 core @ 2.5 Ghz (Python)
140 Contrastive PP code 37.68 % 44.10 % 35.84 % 0.01 s 1 core @ 2.5 Ghz (Python)
141 CZY_3917 37.26 % 44.66 % 34.56 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
142 GT3D 36.37 % 46.02 % 32.62 % 0.1 s 1 core @ 2.5 Ghz (Python)
143 DMF
This method uses stereo information.
34.92 % 42.08 % 32.69 % 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.
144 SparsePool code 34.15 % 43.33 % 31.78 % 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.
145 MMLAB LIGA-Stereo
This method uses stereo information.
code 34.13 % 44.71 % 30.42 % 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.
146 AVOD
This method makes use of Velodyne laser scans.
code 33.57 % 42.58 % 30.14 % 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.
147 SparsePool code 33.22 % 41.55 % 29.66 % 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.
148 PS++ code 32.38 % 43.37 % 28.66 % PS++ s 1 core @ 2.5 Ghz (C/C++)
149 CZY 32.05 % 39.50 % 29.90 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
150 PS code 31.13 % 41.55 % 27.50 % PS s 1 core @ 2.5 Ghz (C/C++)
151 CG-Stereo
This method uses stereo information.
29.56 % 39.24 % 25.87 % 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.
152 PointRGBNet 29.32 % 38.07 % 26.94 % 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.
153 Disp R-CNN
This method uses stereo information.
code 29.12 % 42.72 % 25.09 % 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.
154 Disp R-CNN (velo)
This method uses stereo information.
code 28.34 % 40.21 % 24.46 % 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.
155 BirdNet
This method makes use of Velodyne laser scans.
23.06 % 28.20 % 21.65 % 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.
156 AEC3D 22.40 % 28.59 % 20.67 % 18 ms GPU @ 2.5 Ghz (Python)
157 OC Stereo
This method uses stereo information.
code 20.80 % 29.79 % 18.62 % 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.
158 YOLOStereo3D
This method uses stereo information.
code 20.76 % 31.01 % 18.41 % 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.
159 DSGN
This method uses stereo information.
code 20.75 % 26.61 % 18.86 % 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.
160 Complexer-YOLO
This method makes use of Velodyne laser scans.
18.26 % 21.42 % 17.06 % 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.
161 TopNet-Retina
This method makes use of Velodyne laser scans.
14.57 % 18.04 % 12.48 % 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.
162 RT3D-GMP
This method uses stereo information.
14.22 % 19.92 % 12.83 % 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.
163 TopNet-HighRes
This method makes use of Velodyne laser scans.
13.50 % 19.43 % 11.93 % 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.
164 Anonymous 13.47 % 20.42 % 11.64 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
165 ESGN
This method uses stereo information.
13.03 % 17.94 % 11.54 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
166 DD3D code 12.51 % 18.58 % 10.65 % 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) .
167 DEPT 12.29 % 18.05 % 10.50 % 0.03 s 1 core @ 2.5 Ghz (Python)
168 PS-fld code 12.23 % 19.03 % 10.53 % 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.
169 DD3Dv2 code 12.16 % 17.74 % 10.49 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
170 anonymity 12.00 % 18.98 % 10.21 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
171 OPA-3D code 11.01 % 17.14 % 9.94 % 0.04 s 1 core @ 3.5 Ghz (Python)
172 GCDR 10.92 % 15.65 % 9.86 % 0.28 s 1 core @ 2.5 Ghz (Python)
173 LT-M3OD 10.89 % 16.63 % 9.20 % 0.03 s 1 core @ 2.5 Ghz (Python)
174 ZongmuMono3d code 10.65 % 16.19 % 9.04 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
175 MonoDTR 10.59 % 16.66 % 9.00 % 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.
176 mono3d code 10.41 % 16.66 % 9.22 % TBD TBD
177 GUPNet code 10.37 % 15.62 % 8.79 % 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.
178 CMKD* 10.28 % 16.03 % 8.85 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
179 Lite-FPN-GUPNet 10.08 % 15.73 % 8.52 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
180 MonoInsight 9.98 % 15.20 % 8.90 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
181 GPENet code 9.96 % 15.47 % 8.55 % 0.02 s GPU @ 2.5 Ghz (Python)
182 gupnet_se 9.85 % 14.65 % 8.32 % 0.03s 1 core @ 2.5 Ghz (C/C++)
183 SwinMono3D 9.82 % 14.55 % 8.31 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
184 HBD 9.66 % 15.26 % 8.17 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
185 SCSTSV-MonoFlex 9.62 % 14.45 % 8.14 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
186 SARM3D 9.42 % 14.32 % 8.15 % 0.03 s GPU @ 2.5 Ghz (Python)
187 CaDDN code 9.41 % 14.72 % 8.17 % 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.
188 SGM3D 9.39 % 15.39 % 8.61 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
Z. Zhou, L. Du, X. Ye, Z. Zou, X. Tan, E. Ding, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection. 2021.
189 MonoGround 9.11 % 13.67 % 7.68 % 0.03 s 1 core @ 2.5 Ghz (Python)
190 K3D 9.06 % 14.56 % 7.59 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
191 M3DGAF 8.93 % 13.42 % 7.58 % 0.07 s 1 core @ 2.5 Ghz (Python)
192 MonoFlex 8.91 % 13.26 % 7.38 % 0.03 s 1 core @ 2.5 Ghz (Python)
193 MonoFar 8.89 % 12.73 % 7.72 % 0.04 s 1 core @ 2.5 Ghz (Python)
194 SAIC_ADC_Mono3D code 8.87 % 13.92 % 7.55 % 50 s GPU @ 2.5 Ghz (Python)
195 MonoEdge 8.87 % 13.33 % 7.50 % 0.05 s GPU @ 2.5 Ghz (Python)
196 HomoLoss(monoflex) code 8.81 % 13.26 % 7.41 % 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.
197 MonoCon code 8.73 % 13.55 % 7.83 % 0.02 s GPU @ 2.5 Ghz (Python)
T. Xianpeng Liu: Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection. AAAI 2022.
198 MonoDDE 8.41 % 12.38 % 7.16 % 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.
199 Mix-Teaching 8.40 % 12.34 % 7.06 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
200 ANM 8.28 % 12.83 % 7.59 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
201 MDSNet 8.18 % 12.05 % 7.03 % 0.07 s 1 core @ 2.5 Ghz (Python)
202 DCD code 8.08 % 11.76 % 6.61 % 1 s 1 core @ 2.5 Ghz (C/C++)
203 mono3d 7.95 % 11.89 % 6.75 % 0.03 s GPU @ 2.5 Ghz (Python)
204 MonoAug 7.94 % 12.66 % 6.64 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
205 LPCG-Monoflex 7.92 % 12.11 % 6.61 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
206 RefinedMPL 7.92 % 13.09 % 7.25 % 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.
207 Shape-Aware 7.65 % 11.69 % 6.35 % 0.05 s 1 core @ 2.5 Ghz (Python)
208 MonoRUn code 7.59 % 11.70 % 6.34 % 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.
209 MonoEdge-Rotate 7.53 % 11.62 % 6.79 % 0.05 s GPU @ 2.5 Ghz (Python)
210 MonoFlex 7.36 % 10.36 % 6.29 % 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.
211 MonoPair 7.04 % 10.99 % 6.29 % 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.
212 monodle code 6.96 % 10.73 % 6.20 % 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 .
213 MonoAug 6.87 % 10.81 % 5.66 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
214 TopNet-DecayRate
This method makes use of Velodyne laser scans.
6.59 % 8.78 % 6.25 % 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.
215 M3DSSD++ code 6.19 % 9.24 % 5.54 % 0.16s 1 core @ 2.5 Ghz (C/C++)
216 MDNet 6.18 % 9.48 % 5.63 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
217 MK3D 6.15 % 8.76 % 5.14 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
218 Shift R-CNN (mono) code 5.66 % 8.58 % 4.49 % 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.
219 MM 5.63 % 9.20 % 4.78 % 1 s 1 core @ 2.5 Ghz (C/C++)
220 Aug3D-RPN 5.22 % 7.14 % 4.21 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth. 2021.
221 Lite-FPN 4.79 % 7.13 % 4.26 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
222 COF3D 4.78 % 7.20 % 4.55 % 200 s 1 core @ 2.5 Ghz (Python + C/C++)
223 MAOLoss code 4.74 % 6.63 % 4.19 % 0.05 s 1 core @ 2.5 Ghz (Python)
224 TopNet-UncEst
This method makes use of Velodyne laser scans.
4.60 % 6.88 % 3.79 % 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.
225 MP-Mono 4.59 % 6.04 % 3.96 % 0.16 s GPU @ 2.5 Ghz (Python)
226 MonoPSR code 4.56 % 7.24 % 4.11 % 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.
227 DFR-Net 4.52 % 6.66 % 3.71 % 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.
228 MoGDE 4.51 % 7.22 % 3.83 % 0.03 s GPU @ 2.5 Ghz (Python)
229 QD-3DT
This is an online method (no batch processing).
code 4.23 % 6.62 % 3.39 % 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.
230 M3D-RPN code 4.05 % 5.65 % 3.29 % 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 .
231 DDMP-3D 4.02 % 5.53 % 3.36 % 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.
232 CMAN 3.96 % 5.24 % 3.18 % 0.15 s 1 core @ 2.5 Ghz (Python)
233 D4LCN code 3.86 % 5.06 % 3.59 % 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.
234 RT3DStereo
This method uses stereo information.
3.65 % 4.72 % 3.00 % 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.
235 MonoEF 3.05 % 4.61 % 2.85 % 0.03 s 1 core @ 2.5 Ghz (Python)
Y. Zhou, Y. He, H. Zhu, C. Wang, H. Li and Q. Jiang: Monocular 3D Object Detection: An Extrinsic Parameter Free Approach. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
236 SS3D 2.09 % 2.48 % 1.61 % 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.
237 SparVox3D 2.05 % 2.90 % 1.69 % 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.
238 CDTrack3D code 1.91 % 2.56 % 1.49 % 0.0106 s NVIDIA RTX 3090 GPU, i9 10850k CPU
239 PGD-FCOS3D code 1.88 % 2.82 % 1.54 % 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.
240 EM code 1.25 % 1.18 % 0.89 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
241 mBoW
This method makes use of Velodyne laser scans.
0.00 % 0.00 % 0.00 % 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.
Table as LaTeX | Only published Methods

Cyclist


Method Setting Code Moderate Easy Hard Runtime Environment
1 CasA++ 76.99 % 88.93 % 70.10 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
2 TED 76.95 % 89.54 % 70.31 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
3 CasA 75.74 % 88.99 % 68.47 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
4 ISE-RCNN-PV 75.40 % 86.08 % 68.58 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
5 ISE-RCNN 74.49 % 85.93 % 67.96 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
6 HMFI code 74.06 % 85.69 % 67.11 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
7 SGNet 73.88 % 88.03 % 66.84 % 0.09 s GPU @ 2.5 Ghz (Python)
8 SARFE 73.84 % 85.63 % 66.31 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
9 EQ-PVRCNN code 73.30 % 86.25 % 65.49 % 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.
10 Anonymous 73.08 % 85.29 % 66.46 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
11 SPT 72.90 % 86.10 % 65.13 % 0.1 s GPU @ 2.5 Ghz (Python)
12 CAD 72.87 % 87.09 % 65.78 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
13 DCAN-Second code 72.74 % 88.62 % 65.89 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
14 FS-Net
This method makes use of Velodyne laser scans.
72.61 % 84.43 % 65.88 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
15 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
code 72.61 % 83.93 % 65.82 % 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.
16 CAT-Det 72.51 % 85.35 % 65.55 % 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.
17 Reprod-Two-Branch 72.16 % 87.50 % 64.41 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
18 IKT3D
This method makes use of Velodyne laser scans.
72.12 % 83.56 % 64.12 % 0.05 s 1 core @ 2.5 Ghz (Python)
19 CFF-tv 72.02 % 86.54 % 64.25 % 1 s 1 core @ 2.5 Ghz (C/C++)
20 CFF-ep25 71.99 % 86.78 % 64.18 % 1 s 1 core @ 2.5 Ghz (C/C++)
21 VCRCNN 71.93 % 84.06 % 64.07 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
22 PV-RCNN++ code 71.86 % 84.60 % 63.84 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
23 BtcDet
This method makes use of Velodyne laser scans.
code 71.76 % 84.48 % 64.70 % 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.
24 CF-ctdep-tv-ta 71.74 % 87.38 % 64.30 % 1 s 1 core @ 2.5 Ghz (C/C++)
25 cff-tv-v2-ep25 71.70 % 85.61 % 64.15 % 1 s 1 core @ 2.5 Ghz (C/C++)
26 PointPainting
This method makes use of Velodyne laser scans.
71.54 % 83.91 % 62.97 % 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.
27 CFF-tv-v2 71.53 % 85.70 % 63.77 % 1 s 1 core @ 2.5 Ghz (C/C++)
28 RangeIoUDet
This method makes use of Velodyne laser scans.
71.49 % 85.99 % 63.62 % 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.
29 IA-SSD (single) code 71.44 % 85.91 % 63.41 % 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.
30 3SNet 71.44 % 84.55 % 64.79 % 0.07 s GPU @ 2.5 Ghz (Python)
31 PDV code 71.31 % 85.54 % 64.40 % 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.
32 PE-RCVN 71.18 % 85.95 % 64.38 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
33 HVNet 71.17 % 83.97 % 63.65 % 0.03 s GPU @ 2.0 Ghz (Python)
M. Ye, S. Xu and T. Cao: HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection. CVPR 2020.
34 M3DeTR code 70.89 % 85.03 % 63.14 % 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.
35 USVLab BSAODet 70.85 % 85.28 % 64.09 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
36 DDet 70.76 % 84.81 % 63.98 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
37 CenterFuse 70.59 % 86.53 % 62.18 % 0.059 sec/frame 2 x V100
38 TBD code 70.59 % 83.06 % 63.07 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
39 AutoAlign 70.55 % 85.98 % 63.39 % 0.1 s 1 core @ 2.5 Ghz (Python)
40 MSADet 70.38 % 86.58 % 63.63 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
41 CZY 70.32 % 86.42 % 63.32 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
42 TCDVF 70.28 % 82.85 % 63.54 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
43 USVLab BSAODet (S) 70.24 % 84.38 % 63.24 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
44 MVMM code 70.17 % 81.84 % 63.84 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
45 CF-ctdep-tv 70.16 % 86.31 % 62.63 % 1 s 1 core @ 2.5 Ghz (C/C++)
46 SPG_mini
This method makes use of Velodyne laser scans.
code 70.09 % 82.66 % 63.61 % 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.
47 TBD 70.09 % 82.60 % 63.39 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
48 TBD 69.97 % 79.75 % 63.46 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
49 FV2P v2 69.82 % 86.88 % 63.09 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
50 KeyFuse2B 69.76 % 84.95 % 62.16 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
51 CF-base-tv 69.49 % 84.12 % 61.85 % 1 s 1 core @ 2.5 Ghz (C/C++)
52 PVTr 69.46 % 84.62 % 62.33 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
53 ST-RCNN
This method makes use of Velodyne laser scans.
69.42 % 80.69 % 62.63 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
54 IoU-2B 69.24 % 86.64 % 60.57 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
55 DSASNet 69.12 % 82.32 % 62.68 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
56 TBD 69.09 % 82.53 % 62.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
57 JPVNet 69.07 % 83.46 % 62.73 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
58 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 68.89 % 82.49 % 62.41 % 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.
59 F-ConvNet
This method makes use of Velodyne laser scans.
code 68.88 % 84.16 % 60.05 % 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.
60 FusionDetv2-v5 68.84 % 80.42 % 61.90 % 0.05 s 1 core @ 2.5 Ghz (Java + C/C++)
61 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 68.73 % 83.43 % 61.85 % 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.
62 WGVRF 68.71 % 82.04 % 62.04 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
63 CF-cd-io-tv 68.52 % 83.71 % 60.12 % 1 s 1 core @ 2.5 Ghz (C/C++)
64 HotSpotNet 68.51 % 83.29 % 61.84 % 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.
65 TBD 68.33 % 85.17 % 61.38 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
66 NV2P-RCNN 68.31 % 78.63 % 61.08 % 0.1 s GPU @ 2.5 Ghz (Python)
67 FPV-SSD 68.15 % 80.32 % 60.51 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
68 ATT_SSD 68.14 % 81.49 % 61.31 % 0.01 s 1 core @ 2.5 Ghz (Python)
69 P2V-RCNN 68.06 % 81.09 % 60.73 % 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.
70 KPP3D code 67.97 % 81.23 % 60.72 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
71 H^23D R-CNN code 67.90 % 82.76 % 60.49 % 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.
72 Self-Calib Conv 67.73 % 82.11 % 60.57 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
73 VPFNet code 67.66 % 80.83 % 61.36 % 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.
74 3DSSD code 67.62 % 85.04 % 61.14 % 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.
75 Fast-CLOCs 67.55 % 83.34 % 59.61 % 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.
76 NV-RCNN 67.54 % 82.53 % 60.97 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
77 DGT-Det3D 67.44 % 80.73 % 60.71 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
78 cff-tv-t 67.41 % 85.91 % 60.15 % 1 s 1 core @ 2.5 Ghz (C/C++)
79 DVFENet 67.40 % 82.29 % 60.71 % 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.
80 FromVoxelToPoint code 67.36 % 82.68 % 59.15 % 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.
81 Point-GNN
This method makes use of Velodyne laser scans.
code 67.28 % 81.17 % 59.67 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
82 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 67.24 % 82.56 % 60.28 % 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.
83 STD code 67.23 % 81.36 % 59.35 % 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.
84 SVGA-Net 66.82 % 81.25 % 59.37 % 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.
85 KeyPoint-IoUHead 66.72 % 83.32 % 59.93 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
86 S-AT GCN 66.71 % 78.53 % 60.19 % 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.
87 FusionDetv2-v4 66.54 % 82.50 % 59.78 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
88 T_PVRCNN_V2 66.49 % 80.88 % 58.51 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
89 cp-tv-kp-io-sc 66.40 % 82.88 % 58.53 % 1 s 1 core @ 2.5 Ghz (C/C++)
90 ARPNET 66.39 % 82.32 % 58.80 % 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.
91 IA-SSD (multi) code 66.29 % 81.30 % 59.58 % 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.
92 T_PVRCNN 66.17 % 79.84 % 59.04 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
93 cp-tv 66.08 % 80.65 % 58.98 % 1 s 1 core @ 2.5 Ghz (C/C++)
94 MGAF-3DSSD code 66.00 % 83.03 % 57.57 % 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.
95 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 65.85 % 80.00 % 58.69 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
96 VPN 65.60 % 82.20 % 58.96 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
97 PSA-Det3D 65.51 % 79.21 % 59.06 % 0.1 s GPU @ 2.5 Ghz (Python)
98 ZMMPP 65.23 % 77.62 % 58.08 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
99 cp-tv-kp 64.87 % 79.91 % 58.22 % 1 s 1 core @ 2.5 Ghz (C/C++)
100 TBD_BD code 64.60 % 82.19 % 58.01 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
101 Faraway-Frustum
This method makes use of Velodyne laser scans.
code 64.54 % 79.65 % 57.84 % 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 FusionDetv1 64.53 % 79.62 % 57.91 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
103 Dune-DCF-e11 64.52 % 82.14 % 57.40 % 1 s 1 core @ 2.5 Ghz (C/C++)
104 SRDL 64.52 % 79.64 % 57.90 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
105 Dune-DCF-e15 64.42 % 81.10 % 57.35 % 1 s 1 core @ 2.5 Ghz (C/C++)
106 City-CF-fixed 64.39 % 81.11 % 57.74 % 1 s 1 core @ 2.5 Ghz (C/C++)
107 CF-ctdep-train 64.33 % 81.02 % 56.17 % 1 s 1 core @ 2.5 Ghz (C/C++)
108 City-CF 64.25 % 81.33 % 57.01 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
109 CZY_3917 64.21 % 78.18 % 57.64 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
110 AGS-SSD[la] 64.19 % 76.17 % 57.48 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
111 SIF 64.13 % 79.32 % 57.38 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
112 AFTD 64.03 % 82.99 % 55.93 % 1 s 1 core @ 2.5 Ghz (Python + C/C++)
113 variance_point 63.90 % 78.49 % 56.51 % 0.05 s 1 core @ 2.5 Ghz (Python)
114 FusionDetv2-baseline 63.77 % 76.64 % 57.01 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
115 TANet code 63.77 % 79.16 % 56.21 % 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.
116 CF-base-train 63.63 % 80.31 % 55.56 % 1 s 1 core @ 2.5 Ghz (C/C++)
117 HS3D code 63.56 % 78.53 % 58.03 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
118 XView 63.06 % 81.32 % 56.65 % 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.
119 EPNet++ 62.94 % 78.57 % 56.62 % 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.
120 PointPillars
This method makes use of Velodyne laser scans.
code 62.73 % 79.90 % 55.58 % 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.
121 Dune-DCF-e09 62.23 % 77.53 % 55.46 % 1 s 1 core @ 2.5 Ghz (C/C++)
122 Contrastive PP code 62.10 % 75.71 % 54.84 % 0.01 s 1 core @ 2.5 Ghz (Python)
123 CrazyTensor-CF 61.95 % 80.59 % 55.16 % 1 s 1 core @ 2.5 Ghz (C/C++)
124 PP-PCdet code 61.81 % 75.56 % 55.06 % 0.01 s 1 core @ 2.5 Ghz (Python)
125 LazyTorch-CP-Infer-O 61.40 % 76.40 % 54.73 % 1 s 1 core @ 2.5 Ghz (C/C++)
126 F-PointNet
This method makes use of Velodyne laser scans.
code 61.37 % 77.26 % 53.78 % 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.
127 CenterPoint (pcdet) 61.25 % 76.38 % 54.68 % 0.051 sec/frame 2 x V100
128 LazyTorch-CP-Small-P 61.07 % 76.37 % 54.73 % 1 s 1 core @ 2.5 Ghz (C/C++)
129 TBD 60.58 % 76.98 % 53.49 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
130 TBD 60.58 % 76.98 % 53.49 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
131 epBRM
This method makes use of Velodyne laser scans.
code 59.79 % 75.13 % 53.36 % 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.
132 BirdNet+
This method makes use of Velodyne laser scans.
code 59.58 % 70.84 % 54.20 % 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.
133 CrazyTensor-CP 59.54 % 75.40 % 53.21 % 1 s 1 core @ 2.5 Ghz (Python)
134 GT3D 58.64 % 76.60 % 52.07 % 0.1 s 1 core @ 2.5 Ghz (Python)
135 DMF
This method uses stereo information.
57.99 % 71.92 % 51.55 % 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.
136 PointRGBNet 57.59 % 73.09 % 51.78 % 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.
137 tbd 57.15 % 72.89 % 50.29 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
138 AVOD-FPN
This method makes use of Velodyne laser scans.
code 57.12 % 69.39 % 51.09 % 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.
139 PiFeNet 56.94 % 72.80 % 50.04 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
D. Le, H. Shi, H. Rezatofighi and J. Cai: PiFeNet: Pillar-Feature Network for Real-Time 3D Pedestrian Detection from Point Cloud. 2021.
140 CZY 56.71 % 70.64 % 50.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
141 SCNet
This method makes use of Velodyne laser scans.
56.39 % 73.73 % 49.99 % 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.
142 PFF3D
This method makes use of Velodyne laser scans.
code 55.71 % 72.67 % 49.58 % 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.
143 MLOD
This method makes use of Velodyne laser scans.
code 55.06 % 73.03 % 48.21 % 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.
144 BirdNet+ (legacy)
This method makes use of Velodyne laser scans.
code 52.15 % 72.45 % 46.57 % 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.
145 DSGN++
This method uses stereo information.
code 49.37 % 68.29 % 43.79 % 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.
146 StereoDistill 48.37 % 69.46 % 42.69 % 0.4 s 1 core @ 2.5 Ghz (Python)
147 AVOD
This method makes use of Velodyne laser scans.
code 48.15 % 64.11 % 42.37 % 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.
148 BirdNet
This method makes use of Velodyne laser scans.
41.56 % 58.64 % 36.94 % 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.
149 SparsePool code 40.74 % 56.52 % 36.68 % 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.
150 MMLAB LIGA-Stereo
This method uses stereo information.
code 40.60 % 58.95 % 35.27 % 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.
151 TopNet-Retina
This method makes use of Velodyne laser scans.
36.83 % 47.48 % 33.58 % 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.
152 CG-Stereo
This method uses stereo information.
36.25 % 55.33 % 32.17 % 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.
153 PS++ code 35.75 % 54.06 % 31.17 % PS++ s 1 core @ 2.5 Ghz (C/C++)
154 SparsePool code 35.24 % 43.55 % 30.15 % 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.
155 PS code 32.16 % 49.23 % 27.73 % PS s 1 core @ 2.5 Ghz (C/C++)
156 Disp R-CNN (velo)
This method uses stereo information.
code 27.04 % 44.19 % 23.58 % 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.
157 Disp R-CNN
This method uses stereo information.
code 27.04 % 44.19 % 23.58 % 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.
158 AEC3D 26.17 % 36.57 % 25.21 % 18 ms GPU @ 2.5 Ghz (Python)
159 Complexer-YOLO
This method makes use of Velodyne laser scans.
25.43 % 32.00 % 22.88 % 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.
160 DSGN
This method uses stereo information.
code 21.04 % 31.23 % 18.93 % 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.
161 OC Stereo
This method uses stereo information.
code 19.23 % 32.47 % 17.11 % 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.
162 TopNet-DecayRate
This method makes use of Velodyne laser scans.
16.00 % 23.02 % 13.24 % 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.
163 RT3D-GMP
This method uses stereo information.
13.92 % 20.59 % 12.74 % 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.
164 TopNet-UncEst
This method makes use of Velodyne laser scans.
9.18 % 12.31 % 8.14 % 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.
165 ESGN
This method uses stereo information.
9.02 % 15.78 % 7.96 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
166 CMKD* 8.15 % 14.66 % 7.23 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
167 anonymity 7.39 % 12.38 % 6.65 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
168 PS-fld code 7.29 % 12.80 % 6.05 % 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.
169 Anonymous 7.24 % 12.53 % 6.21 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
170 DD3Dv2 code 7.02 % 10.67 % 5.78 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
171 anonymity 6.84 % 10.90 % 5.72 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
172 mono3d code 6.52 % 11.40 % 5.19 % TBD TBD
173 TopNet-HighRes
This method makes use of Velodyne laser scans.
6.48 % 9.99 % 6.76 % 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.
174 MonoPSR code 5.78 % 9.87 % 4.57 % 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.
175 DD3D code 5.69 % 9.20 % 5.20 % 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) .
176 LT-M3OD 5.53 % 9.17 % 4.84 % 0.03 s 1 core @ 2.5 Ghz (Python)
177 CaDDN code 5.38 % 9.67 % 4.75 % 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.
178 Mix-Teaching 5.36 % 8.56 % 4.62 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
179 MonoInsight 4.97 % 7.40 % 4.09 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
180 LPCG-Monoflex 4.90 % 8.14 % 3.86 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
181 MDNet 4.74 % 8.10 % 4.19 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
182 DEPT 4.71 % 8.82 % 4.15 % 0.03 s 1 core @ 2.5 Ghz (Python)
183 Lite-FPN-GUPNet 4.70 % 7.67 % 4.61 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
184 ZongmuMono3d code 4.63 % 8.72 % 3.94 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
185 Shape-Aware 4.60 % 8.00 % 4.50 % 0.05 s 1 core @ 2.5 Ghz (Python)
186 SAIC_ADC_Mono3D code 4.55 % 7.90 % 3.73 % 50 s GPU @ 2.5 Ghz (Python)
187 SCSTSV-MonoFlex 4.50 % 7.40 % 3.66 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
188 MAOLoss code 4.49 % 7.28 % 3.60 % 0.05 s 1 core @ 2.5 Ghz (Python)
189 MonoDDE 4.36 % 6.68 % 3.76 % 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.
190 GPENet code 4.28 % 7.06 % 3.68 % 0.02 s GPU @ 2.5 Ghz (Python)
191 MonoDTR 4.11 % 5.84 % 3.48 % 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.
192 RT3DStereo
This method uses stereo information.
4.10 % 7.03 % 3.88 % 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.
193 HomoLoss(monoflex) code 4.09 % 6.81 % 3.78 % 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.
194 DFR-Net 4.00 % 5.99 % 3.95 % 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.
195 SARM3D 3.85 % 5.59 % 3.28 % 0.03 s GPU @ 2.5 Ghz (Python)
196 GUPNet code 3.85 % 6.94 % 3.64 % 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.
197 MoGDE 3.76 % 6.04 % 3.09 % 0.03 s GPU @ 2.5 Ghz (Python)
198 OPA-3D code 3.75 % 6.01 % 3.56 % 0.04 s 1 core @ 3.5 Ghz (Python)
199 MonoAug 3.71 % 5.66 % 3.00 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
200 SGM3D 3.63 % 7.05 % 3.33 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
Z. Zhou, L. Du, X. Ye, Z. Zou, X. Tan, E. Ding, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection. 2021.
201 DCD code 3.62 % 5.84 % 3.33 % 1 s 1 core @ 2.5 Ghz (C/C++)
202 K3D 3.39 % 6.16 % 3.13 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
203 MonoFar 3.33 % 5.27 % 2.90 % 0.04 s 1 core @ 2.5 Ghz (Python)
204 Aug3D-RPN 3.33 % 5.44 % 2.82 % 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.
205 mono3d 3.30 % 5.84 % 2.68 % 0.03 s GPU @ 2.5 Ghz (Python)
206 monodle code 3.28 % 5.34 % 2.83 % 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 .
207 ANM 3.26 % 4.90 % 2.87 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
208 MonoGround 3.22 % 4.81 % 2.75 % 0.03 s 1 core @ 2.5 Ghz (Python)
209 MDSNet 3.22 % 5.99 % 2.62 % 0.07 s 1 core @ 2.5 Ghz (Python)
210 DDMP-3D 3.14 % 4.92 % 2.44 % 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.
211 M3DSSD++ code 3.14 % 5.73 % 3.03 % 0.16s 1 core @ 2.5 Ghz (C/C++)
212 QD-3DT
This is an online method (no batch processing).
code 3.02 % 5.71 % 2.73 % 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.
213 M3DGAF 3.02 % 5.33 % 2.87 % 0.07 s 1 core @ 2.5 Ghz (Python)
214 MonoEdge-Rotate 3.01 % 5.36 % 2.83 % 0.05 s GPU @ 2.5 Ghz (Python)
215 MonoPair 2.87 % 4.76 % 2.42 % 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.
216 SwinMono3D 2.78 % 4.50 % 2.53 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
217 MonoCon code 2.68 % 3.87 % 2.24 % 0.02 s GPU @ 2.5 Ghz (Python)
T. Xianpeng Liu: Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection. AAAI 2022.
218 MonoFlex 2.67 % 4.41 % 2.50 % 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.
219 gupnet_se 2.61 % 4.38 % 2.34 % 0.03s 1 core @ 2.5 Ghz (C/C++)
220 MK3D 2.55 % 4.17 % 2.24 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
221 MonoEdge 2.54 % 4.02 % 2.43 % 0.05 s GPU @ 2.5 Ghz (Python)
222 MonoFlex 2.51 % 4.36 % 2.38 % 0.03 s 1 core @ 2.5 Ghz (Python)
223 MonoAug 2.46 % 4.31 % 2.21 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
224 RefinedMPL 2.42 % 4.23 % 2.14 % 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.
225 GCDR 2.11 % 3.74 % 1.99 % 0.28 s 1 core @ 2.5 Ghz (Python)
226 SS3D 1.89 % 3.45 % 1.44 % 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.
227 D4LCN code 1.82 % 2.72 % 1.79 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
228 PGD-FCOS3D code 1.79 % 3.54 % 1.56 % 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.
229 MP-Mono 1.74 % 2.78 % 1.86 % 0.16 s GPU @ 2.5 Ghz (Python)
230 HBD 1.64 % 3.15 % 1.76 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
231 COF3D 1.60 % 2.70 % 1.55 % 200 s 1 core @ 2.5 Ghz (Python + C/C++)
232 CMAN 1.48 % 1.76 % 1.17 % 0.15 s 1 core @ 2.5 Ghz (Python)
233 MonoEF 1.18 % 2.36 % 1.15 % 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.
234 M3D-RPN code 0.81 % 1.25 % 0.78 % 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 .
235 MonoRUn code 0.73 % 1.14 % 0.66 % 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.
236 Lite-FPN 0.44 % 0.52 % 0.27 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
237 MM 0.40 % 0.71 % 0.35 % 1 s 1 core @ 2.5 Ghz (C/C++)
238 Shift R-CNN (mono) code 0.38 % 0.76 % 0.41 % 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.
239 CDTrack3D code 0.10 % 0.24 % 0.11 % 0.0106 s NVIDIA RTX 3090 GPU, i9 10850k CPU
240 mBoW
This method makes use of Velodyne laser scans.
0.00 % 0.00 % 0.00 % 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.
Table as LaTeX | Only published Methods

Related Datasets

Citation

When using this dataset in your research, we will be happy if you cite us:
@INPROCEEDINGS{Geiger2012CVPR,
  author = {Andreas Geiger and Philip Lenz and Raquel Urtasun},
  title = {Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite},
  booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2012}
}



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