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 code 92.12 % 95.79 % 87.11 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph. ECCV 2022.
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 code 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. IEEE Transactions on Multimedia 2022.
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 code 91.72 % 95.27 % 86.51 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph. ECCV 2022.
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 code 91.54 % 95.19 % 86.82 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.
12 GraR-Pi code 91.52 % 95.06 % 86.42 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph. ECCV 2022.
13 Anonymous 91.36 % 92.96 % 86.80 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
14 DGDNH 91.36 % 95.03 % 88.79 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
15 NSAW code 91.35 % 94.51 % 86.42 % 0.1 s 1 core @ 2.5 Ghz (Python)
16 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.
17 VoCo 91.32 % 95.42 % 88.38 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
18 CasA++ code 91.22 % 94.57 % 88.43 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.
19 Anonymous 91.14 % 94.04 % 86.33 % n/a s 1 core @ 2.5 Ghz (C/C++)
20 SGFusion 91.11 % 94.76 % 86.27 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
21 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.
22 anonymous 90.90 % 92.96 % 86.34 % 0.09 s GPU @ 2.5 Ghz (Python)
23 3D Dual-Fusion 90.86 % 93.08 % 86.44 % 0.1 s 1 core @ 2.5 Ghz (Python)
24 VoxelGraphRCNN 90.84 % 94.84 % 86.35 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
25 Anonymous
This method makes use of Velodyne laser scans.
90.82 % 94.89 % 86.39 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
26 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.
27 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.
28 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.
29 VCRCNN 90.42 % 94.55 % 86.11 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
30 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.
31 TBD 90.37 % 93.82 % 87.72 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
32 SGDA3D 90.36 % 92.53 % 86.09 % 0.07 s 1 core @ 2.5 Ghz (Python)
33 DDet 90.34 % 94.16 % 86.01 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
34 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.
35 E^2-PV-RCNN 90.27 % 92.51 % 86.01 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
X. Li, Y. Zhang and D. Kong: E^2-PV-RCNN: improving 3D object detection via enhancing keypoint features. Multimedia Tools and Applications 2022.
36 IKT3D
This method makes use of Velodyne laser scans.
90.23 % 94.22 % 85.94 % 0.05 s 1 core @ 2.5 Ghz (Python)
37 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.
38 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.
39 GraR-VoI code 90.10 % 95.69 % 86.85 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph. ECCV 2022.
40 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.
41 FPV-SSD 89.93 % 91.45 % 85.21 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
42 NIV-SSD 89.92 % 95.59 % 84.58 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
43 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.
44 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.
45 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.
46 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.
47 PE-RCVN 89.79 % 95.55 % 84.78 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
48 GLENet-VR 89.76 % 93.48 % 84.89 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Zhang, Z. Zhu, J. Hou and Y. Yuan: GLENet: Boosting 3D Object Detectors with Generative Label Uncertainty Estimation. arXiv preprint arXiv:2207.02466 2022.
49 RDIoU code 89.75 % 94.90 % 84.67 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Sheng, S. Cai, N. Zhao, B. Deng, J. Huang, X. Hua, M. Zhao and G. Lee: Rethinking IoU-based Optimization for Single- stage 3D Object Detection. ECCV 2022.
50 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.
51 HCPVF 89.62 % 93.20 % 86.72 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
52 DSASNet 89.59 % 93.41 % 84.81 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
53 3SNet 89.58 % 93.26 % 84.80 % 0.07 s GPU @ 2.5 Ghz (Python)
54 CAD 89.57 % 93.03 % 84.71 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
55 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.
56 ImpDet 89.55 % 92.74 % 84.41 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
57 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.
58 KpNet 89.53 % 93.34 % 81.95 % 0.42 s 1 core @ 2.5 Ghz (C/C++)
59 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.
60 PA-RCNN code 89.51 % 92.95 % 82.42 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
61 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.
62 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.
63 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.
64 PA3DNet 89.46 % 93.11 % 84.60 % 0.05 s GPU @ 2.5 Ghz (Python)
65 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.
66 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.
67 SWA code 89.36 % 92.82 % 86.21 % 0.18 s 1 core @ 2.5 Ghz (C/C++)
68 DCGNN 89.36 % 94.57 % 84.13 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
69 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.
70 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.
71 Anonymous 89.27 % 92.79 % 86.53 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
72 TBD 89.24 % 92.59 % 85.99 % 0.1 s 1 core @ 2.5 Ghz (Python)
73 ATT_SSD 89.22 % 92.82 % 85.90 % 0.01 s 1 core @ 2.5 Ghz (Python)
74 TBD code 89.21 % 92.88 % 85.87 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
75 ACDet code 89.21 % 92.87 % 85.80 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
J. Xu, G. Wang, X. Zhang and G. Wan: ACDet: Attentive Cross-view Fusion for LiDAR-based 3D Object Detection. 3DV 2022.
76 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.
77 TBD 89.20 % 92.55 % 86.07 % TBD s 1 core @ 2.5 Ghz (C/C++)
78 SPD-Net 89.19 % 93.10 % 84.41 % 0.1 s 2 cores @ 3.0 Ghz (Python)
79 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.
80 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++)
81 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.
82 HMFI code 89.17 % 93.04 % 86.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
83 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.
84 SGNet 89.14 % 93.04 % 86.54 % 0.09 s GPU @ 2.5 Ghz (Python)
85 USVLab BSAODet 89.13 % 92.92 % 86.41 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
86 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.
87 HPV-RCNN 89.12 % 92.49 % 83.98 % 0.15 s 1 core @ 2.5 Ghz (Python)
88 ITCA-SSD code 89.12 % 93.19 % 83.99 % 0.05 s 1 core @ 2.5 Ghz (Python)
89 PV-DT3D 89.10 % 92.65 % 86.43 % 1.4 s 1 core @ 2.5 Ghz (C/C++)
90 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.
91 SPT 89.09 % 94.87 % 84.38 % 0.1 s GPU @ 2.5 Ghz (Python)
92 TBD code 89.09 % 92.61 % 83.85 % 0.1 s GPU @ 2.5 Ghz (Python)
93 MSADet 89.08 % 92.76 % 85.66 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
94 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.
95 ChTR3D 89.04 % 92.72 % 86.29 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
96 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.
97 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.
98 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.
99 LGNet 88.98 % 92.83 % 86.26 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
100 ChTR3D 88.98 % 92.35 % 86.17 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
101 VGRCNN++ 88.96 % 92.96 % 86.25 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
102 PTA-RCNN 88.94 % 92.32 % 85.63 % 0.08 s 1 core @ 2.5 Ghz (Python)
103 GV-RCNN code 88.94 % 94.52 % 86.24 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
104 TBD 88.94 % 92.03 % 86.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
105 SPNet code 88.92 % 92.29 % 86.16 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
106 AGS-SSD[la] 88.90 % 92.51 % 85.96 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
107 CZY_PPF_Net2 88.88 % 94.68 % 86.15 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
108 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.
109 ChTR3D 88.85 % 92.58 % 85.98 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
110 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.
111 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.
112 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.
113 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.
114 VG-RCNN 88.81 % 92.75 % 86.12 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
115 GLENet 88.81 % 92.22 % 84.13 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
116 FV2P v2 88.80 % 92.22 % 84.24 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
117 mbdf-netv1 code 88.77 % 94.45 % 83.90 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
118 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.
119 BASA 88.76 % 92.72 % 83.71 % 1s 1 core @ 2.5 Ghz (python)
120 PV-RCNN++ code 88.74 % 92.66 % 85.97 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
121 CZY_3917 88.71 % 94.23 % 86.01 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
122 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.
123 MVMM code 88.70 % 92.17 % 85.47 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
124 VGRCNN 88.69 % 92.58 % 86.02 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
125 DTE3D 88.69 % 92.61 % 85.77 % 0.19 s 1 core @ 2.5 Ghz (C/C++)
126 DCAN-Second code 88.68 % 92.76 % 85.32 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
127 PSA-SSD 88.65 % 92.21 % 83.75 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
128 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.
129 CZY_PPF_Net 88.65 % 92.78 % 85.83 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
130 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.
131 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.
132 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.
133 WGVRF 88.56 % 92.45 % 85.69 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
134 DCCA 88.55 % 92.29 % 85.85 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
135 GVNet-V2 88.54 % 92.26 % 85.71 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
136 VGA-RCNN 88.53 % 92.37 % 85.77 % 0.07 s 1 core @ 2.5 Ghz (Python)
137 MVTr 88.51 % 94.30 % 85.80 % 0.08 s 1 core @ 2.5 Ghz (Python)
138 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.
139 GT3D 88.46 % 92.22 % 83.81 % 0.1 s 1 core @ 2.5 Ghz (Python)
140 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.
141 GVNet code 88.43 % 92.19 % 85.63 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
142 USVLab BSAODet (S) 88.42 % 92.19 % 85.55 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
143 DGT-Det3D code 88.41 % 92.57 % 85.50 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
144 Semantical PVRCNN 88.41 % 92.71 % 85.86 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
145 PVE 88.40 % 92.49 % 85.79 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
146 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.
147 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.
148 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.
149 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.
150 CSVoxel-RCNN 88.37 % 92.07 % 85.51 % 0.03 s GPU @ 1.0 Ghz (Python)
151 VPNet 88.37 % 92.11 % 85.63 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
152 NV-RCNN 88.36 % 91.41 % 85.72 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
153 FSFNet 88.35 % 94.88 % 83.58 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
154 DKDet 88.32 % 92.21 % 85.46 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
155 CenterFuse 88.31 % 91.54 % 83.39 % 0.059 sec/frame 2 x V100
156 SARFE 88.28 % 92.35 % 85.50 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
157 TBD 88.26 % 91.44 % 85.44 % 0.06 s GPU @ 2.5 Ghz (Python)
158 KPP3D code 88.25 % 93.93 % 83.26 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
159 B-FPS 88.23 % 92.15 % 85.01 % 0.1 s 1 core @ 2.5 Ghz (Java)
160 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.
161 SPVB-SSD 88.23 % 91.82 % 85.46 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
162 U_SECOND_V4 88.22 % 91.95 % 85.03 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
163 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.
164 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.
165 SRDL 88.17 % 92.01 % 85.43 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
166 CF-cd-io-tv 88.16 % 91.32 % 83.26 % 1 s 1 core @ 2.5 Ghz (C/C++)
167 FusionDetv1 88.13 % 91.91 % 85.40 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
168 PSA-Det3D 88.13 % 92.08 % 85.35 % 0.1 s GPU @ 2.5 Ghz (Python)
169 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.
170 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.
171 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.
172 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.
173 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.
174 VPN 88.06 % 90.94 % 83.24 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
175 TBD 88.04 % 91.31 % 84.79 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
176 SC-Voxel-RCNN 88.02 % 91.45 % 85.22 % 0.12 s GPU @ 1.0 Ghz (Python)
177 CZY 88.00 % 91.85 % 85.22 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
178 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.
179 MFANet 87.96 % 91.52 % 82.99 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
180 TCDVF 87.94 % 91.21 % 84.66 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
181 DGT-Det3D 87.88 % 91.70 % 85.14 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
182 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.
183 CSNet 87.84 % 92.23 % 82.93 % 0.1 s 1 core @ 2.5 Ghz (Python)
184 CF-ctdep-tv-ta 87.81 % 90.73 % 84.97 % 1 s 1 core @ 2.5 Ghz (C/C++)
185 Anonymous 87.80 % 91.58 % 82.86 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
186 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.
187 cp-tv-kp-io-sc 87.78 % 90.98 % 84.04 % 1 s 1 core @ 2.5 Ghz (C/C++)
188 SIF 87.76 % 91.44 % 85.15 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
189 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.
190 Reprod-Two-Branch 87.69 % 90.69 % 84.72 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
191 DKAnet 87.68 % 91.07 % 84.03 % 0.05 s 1 core @ 2.0 Ghz (Python)
192 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.
193 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.
194 TBD 87.67 % 91.02 % 82.42 % 0.1 s 1 core @ 2.5 Ghz (Python)
195 CFF-tv-v2 87.67 % 90.70 % 84.58 % 1 s 1 core @ 2.5 Ghz (C/C++)
196 RangeDet (Official) code 87.67 % 90.93 % 82.92 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
L. Fan, X. Xiong, F. Wang, N. Wang and Z. Zhang: RangeDet: In Defense of Range View for LiDAR-Based 3D Object Detection. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
197 CFF-ep25 87.66 % 90.60 % 84.71 % 1 s 1 core @ 2.5 Ghz (C/C++)
198 TBD 87.62 % 90.86 % 82.29 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
199 CF-base-tv 87.60 % 90.28 % 84.52 % 1 s 1 core @ 2.5 Ghz (C/C++)
200 KeyFuse2B 87.59 % 90.70 % 84.58 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
201 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.
202 CFF-tv 87.55 % 90.56 % 84.59 % 1 s 1 core @ 2.5 Ghz (C/C++)
203 cff-tv-v2-ep25 87.55 % 90.26 % 84.53 % 1 s 1 core @ 2.5 Ghz (C/C++)
204 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.
205 TBD 87.51 % 90.76 % 80.15 % 0.1 s 1 core @ 2.5 Ghz (Python)
206 DTFI 87.51 % 91.01 % 84.25 % 0.03 s 1 core @ 2.5 Ghz (Python)
207 CF-ctdep-tv 87.50 % 90.56 % 84.65 % 1 s 1 core @ 2.5 Ghz (C/C++)
208 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.
209 Anonymous 87.48 % 90.98 % 84.22 % 1 1 core @ 2.5 Ghz (Python)
210 SECOND_7862 87.48 % 90.98 % 84.22 % 1 s 1 core @ 2.5 Ghz (Python)
211 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.
212 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.
213 HS3D code 87.40 % 91.97 % 82.85 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
214 PVTr 87.39 % 91.21 % 84.77 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
215 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.
216 Sem-Aug 87.37 % 93.35 % 82.43 % 0.08 s GPU @ 2.5 Ghz (Python)
217 Anonymous 87.36 % 91.72 % 82.82 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
218 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.
219 KeyPoint-IoUHead 87.32 % 90.36 % 83.23 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
220 Harmonic PointPillar code 87.28 % 90.89 % 82.54 % 0.01 s 1 core @ 2.5 Ghz (Python)
H. Zhang, M. Mekala, Z. Nain, J. Park and H. Jung: Harmonic 3D: Time-friendly and Task- consistent LiDAR-based 3D Object Detection on Edge. will submit to IEEE Transactions on Intelligent Transportation Systems 2022.
221 ZMMPP 87.25 % 90.47 % 82.42 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
222 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.
223 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.
224 3D_att
This method makes use of Velodyne laser scans.
87.09 % 93.14 % 81.92 % 0.17 s GPU @ 2.5 Ghz (Python)
225 Contrastive PP code 87.06 % 92.99 % 81.96 % 0.01 s 1 core @ 2.5 Ghz (Python)
226 DVF 87.05 % 92.76 % 84.13 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
227 T_PVRCNN 86.97 % 91.63 % 82.20 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
228 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.
229 cff-tv-t 86.92 % 91.04 % 80.46 % 1 s 1 core @ 2.5 Ghz (C/C++)
230 CF-base-train 86.88 % 90.03 % 83.16 % 1 s 1 core @ 2.5 Ghz (C/C++)
231 Self-Calib Conv 86.86 % 90.00 % 83.88 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
232 T_PVRCNN_V2 86.85 % 91.54 % 81.82 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
233 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.
234 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.
235 IoU-2B 86.74 % 90.92 % 80.40 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
236 cp-tv-kp 86.58 % 89.58 % 83.64 % 1 s 1 core @ 2.5 Ghz (C/C++)
237 CAD
This method uses stereo information.
This method makes use of Velodyne laser scans.
86.56 % 90.00 % 81.62 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
238 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.
239 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.
240 cp-tv 86.52 % 89.55 % 83.45 % 1 s 1 core @ 2.5 Ghz (C/C++)
241 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.
242 CF-ctdep-train 86.46 % 89.57 % 82.03 % 1 s 1 core @ 2.5 Ghz (C/C++)
243 CSNet8306 code 86.44 % 92.57 % 81.36 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
244 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.
245 Dune-DCF-e09 86.36 % 89.33 % 81.77 % 1 s 1 core @ 2.5 Ghz (C/C++)
246 Dune-DCF-e11 86.32 % 89.32 % 81.78 % 1 s 1 core @ 2.5 Ghz (C/C++)
247 PP-PCdet code 86.32 % 89.86 % 81.62 % 0.01 s 1 core @ 2.5 Ghz (Python)
248 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.
249 Dune-DCF-e15 86.21 % 88.99 % 81.62 % 1 s 1 core @ 2.5 Ghz (C/C++)
250 TBD_BD code 86.12 % 91.00 % 81.66 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
251 CrazyTensor-CF 86.10 % 89.13 % 81.61 % 1 s 1 core @ 2.5 Ghz (C/C++)
252 City-CF-fixed 86.09 % 89.94 % 81.73 % 1 s 1 core @ 2.5 Ghz (C/C++)
253 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.
254 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.
255 CAT 85.97 % 91.48 % 80.93 % 1 s 1 core @ 2.5 Ghz (Python)
256 SSL_PP code 85.93 % 92.19 % 80.40 % 16ms GPU @ 1.5 Ghz (Python)
257 CSNet8299 code 85.91 % 91.64 % 80.95 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
258 Sem-Aug-PointRCNN++ 85.88 % 91.68 % 83.37 % 0.1 s 8 cores @ 3.0 Ghz (Python)
259 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.
260 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.
261 City-CF 85.83 % 89.20 % 81.61 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
262 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.
263 LazyTorch-CP-Infer-O 85.74 % 89.19 % 81.35 % 1 s 1 core @ 2.5 Ghz (C/C++)
264 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.
265 AFTD 85.63 % 90.61 % 82.28 % 1 s 1 core @ 2.5 Ghz (Python + C/C++)
266 LazyTorch-CP-Small-P 85.63 % 89.10 % 81.27 % 1 s 1 core @ 2.5 Ghz (C/C++)
267 CrazyTensor-CP 85.55 % 87.94 % 82.63 % 1 s 1 core @ 2.5 Ghz (Python)
268 variance_point 85.39 % 91.90 % 81.13 % 0.05 s 1 core @ 2.5 Ghz (Python)
269 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.
270 new_stereo 85.24 % 90.74 % 82.10 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
271 PSM_stereo 85.12 % 90.26 % 80.21 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
272 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.
273 CenterPoint (pcdet) 85.05 % 88.47 % 81.19 % 0.051 sec/frame 2 x V100
274 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.
275 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.
276 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.
277 MF 84.72 % 88.58 % 78.17 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
278 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.
279 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.
280 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.
281 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.
282 TBD 81.53 % 87.90 % 74.26 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
283 FD 81.47 % 88.34 % 75.07 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
284 CZY 81.21 % 89.10 % 76.13 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
285 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.
286 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.
287 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.
288 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.
289 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.
290 StereoDistill 78.59 % 89.03 % 69.34 % 0.4 s 1 core @ 2.5 Ghz (Python)
291 Anonymous 77.40 % 90.76 % 70.00 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
292 SD3DOD 76.96 % 86.82 % 70.05 % 0.04 s GPU @ 2.5 Ghz (Python)
293 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.
294 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.
295 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.
296 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.
297 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.
298 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.
299 Anonymous 71.23 % 86.67 % 64.08 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
300 ppt 70.21 % 72.17 % 65.26 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
301 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.
302 CGPS-3DV code 68.36 % 84.64 % 59.01 % CGPS-3DV 1 core @ 2.5 Ghz (C/C++)
303 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.
304 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.
305 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.
306 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.
307 PS code 65.33 % 83.75 % 56.14 % PS s 1 core @ 2.5 Ghz (C/C++)
308 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.
309 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.
310 UPF_3D
This method uses stereo information.
63.58 % 85.53 % 56.56 % 0.29 s 1 core @ 2.5 Ghz (Python)
311 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.
312 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.
313 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.
314 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.
315 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.
316 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.
317 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.
318 ESGN
This method uses stereo information.
58.12 % 78.10 % 49.28 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
A. Gao, Y. Pang, J. Nie, Z. Shao, J. Cao, Y. Guo and X. Li: ESGN: Efficient Stereo Geometry Network for Fast 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2022.
319 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.
320 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.
321 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.
322 ART 54.23 % 75.05 % 48.19 % 20ms s 1 core @ 2.5 Ghz (C/C++)
323 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.
324 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.
325 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.
326 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.
327 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.
328 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.
329 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.
330 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.
331 SparseLiDAR_fusion 41.51 % 54.10 % 34.14 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
332 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.
333 GCDR 37.34 % 50.85 % 30.51 % 0.28 s 1 core @ 2.5 Ghz (Python)
334 VMDet_Boost 33.13 % 46.17 % 28.80 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
335 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.
336 Anonymous 30.81 % 43.11 % 26.81 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
337 Digging_M3D 28.84 % 39.74 % 26.08 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
338 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.
339 VMDet 28.50 % 41.41 % 23.88 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
340 Anonymous 27.70 % 37.81 % 24.61 % 40 s 1 core @ 2.5 Ghz (C/C++)
341 SARM3D 26.81 % 34.17 % 23.68 % 0.03 s GPU @ 2.5 Ghz (Python)
342 MDS-Mono3D 26.33 % 41.07 % 21.22 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
343 CMKD* 25.82 % 38.98 % 22.80 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
344 MoGDE 25.60 % 38.38 % 22.91 % 0.03 s GPU @ 2.5 Ghz (Python)
345 BSM3D 25.23 % 34.82 % 22.37 % 0.03 s 1 core @ 2.5 Ghz (Python)
346 LPCG-Monoflex code 24.81 % 35.96 % 21.86 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
L. Peng, F. Liu, Z. Yu, S. Yan, D. Deng, Z. Yang, H. Liu and D. Cai: Lidar Point Cloud Guided Monocular 3D Object Detection. ECCV 2022.
347 Anonymous 24.78 % 33.38 % 22.00 % 40 s 1 core @ 2.5 Ghz (C/C++)
348 DD3Dv2 code 24.67 % 35.70 % 21.73 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
349 Mix-Teaching 24.23 % 35.74 % 20.80 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
350 anonymity 23.92 % 36.92 % 21.68 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
351 Anonymous 23.82 % 34.35 % 20.80 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
352 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.
353 TempM3D 23.71 % 33.86 % 20.31 % 0.05 s 1 core @ 2.5 Ghz (Python)
354 anonymity 23.61 % 36.80 % 21.31 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
355 ADD code 23.58 % 35.20 % 20.08 % 0.1 s 1 core @ 2.5 Ghz (Python)
356 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.
357 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) .
358 SAD 22.81 % 34.34 % 19.44 % 0.05 s 1 core @ 2.5 Ghz (python)
359 DID-M3D code 22.76 % 32.95 % 19.83 % 0.04 s 1 core @ 2.5 Ghz (Python)
L. Peng, X. Wu, Z. Yang, H. Liu and D. Cai: DID-M3D: Decoupling Instance Depth for Monocular 3D Object Detection. ECCV 2022.
360 MonoAD 22.70 % 33.33 % 20.48 % 0.03 s GPU @ 2.5 Ghz (Python)
361 MonoDistill 22.59 % 31.87 % 19.72 % 0.04 s 1 core @ 2.5 Ghz (Python)
362 zongmuDistill 22.56 % 33.48 % 19.88 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
363 OPA-3D code 22.53 % 33.54 % 19.22 % 0.04 s 1 core @ 3.5 Ghz (Python)
364 Shape-Aware 22.13 % 32.55 % 18.94 % 0.05 s 1 core @ 2.5 Ghz (Python)
365 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.
366 Anonymous 22.05 % 31.75 % 19.44 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
367 gupnet_se 21.98 % 32.82 % 18.70 % 0.03s 1 core @ 2.5 Ghz (C/C++)
368 ZongmuMono3d code 21.78 % 33.18 % 18.71 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
369 Anonymous 21.74 % 32.44 % 18.38 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
370 MDNet 21.71 % 33.31 % 18.49 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
371 MonoPPM code 21.66 % 30.54 % 18.64 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
372 SAD 21.56 % 33.90 % 19.08 % 0.05 s 1 core @ 2.5 Ghz (python)
373 Lite-FPN-GUPNet 21.53 % 31.68 % 18.38 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
374 DDS code 21.50 % 32.55 % 18.25 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
375 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.
376 OBMO_GUPNet 21.41 % 30.81 % 18.37 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
377 M3DGAF 21.39 % 31.34 % 19.28 % 0.07 s 1 core @ 2.5 Ghz (Python)
378 mono3d code 21.39 % 32.17 % 18.47 % TBD TBD
379 SGM3D code 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, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection. RA-L 2022.
380 monopd code 21.29 % 32.12 % 18.08 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
381 DEPT 21.22 % 30.85 % 18.47 % 0.03 s 1 core @ 2.5 Ghz (Python)
382 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.
383 Slime 21.17 % 32.31 % 18.01 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
384 MonoInsight 21.06 % 29.65 % 18.22 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
385 HBD 20.91 % 29.87 % 18.22 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
386 GPENet code 20.79 % 30.31 % 18.21 % 0.02 s GPU @ 2.5 Ghz (Python)
387 mono3d 20.75 % 31.58 % 17.66 % 0.03 s GPU @ 2.5 Ghz (Python)
388 LT-M3OD 20.74 % 29.40 % 17.83 % 0.03 s 1 core @ 2.5 Ghz (Python)
389 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.
390 Anonymous 20.47 % 33.17 % 17.31 % 40 s 1 core @ 2.5 Ghz (C/C++)
391 MonoGround 20.47 % 30.07 % 17.74 % 0.03 s 1 core @ 2.5 Ghz (Python)
392 DEVIANT code 20.44 % 29.65 % 17.43 % 0.04 s 1 GPU (Python)
A. Kumar, G. Brazil, E. Corona, A. Parchami and X. Liu: DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection. European Conference on Computer Vision (ECCV) 2022.
393 EW code 20.38 % 28.88 % 17.59 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
394 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.
395 MonoEdge 20.35 % 28.80 % 17.57 % 0.05 s GPU @ 2.5 Ghz (Python)
396 SAIC_ADC_Mono3D code 20.20 % 27.09 % 18.78 % 50 s GPU @ 2.5 Ghz (Python)
397 MonoEdge-Rotate 20.16 % 31.19 % 17.35 % 0.05 s GPU @ 2.5 Ghz (Python)
398 MDSNet 20.14 % 32.81 % 15.77 % 0.07 s 1 core @ 2.5 Ghz (Python)
399 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.
400 MonoEdge-RCNN 20.07 % 27.62 % 16.34 % 0.05 s 1 core @ 2.5 Ghz (Python)
401 MonoPCNS 19.89 % 28.27 % 17.96 % 0.14 s GPU @ 2.5 Ghz (Python)
402 EM code 19.80 % 30.61 % 16.55 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
403 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.
404 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.
405 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.
406 MonoAug 19.19 % 28.20 % 16.15 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
407 MK3D 19.18 % 29.11 % 15.78 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
408 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.
409 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.
410 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.
411 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.
412 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 .
413 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.
414 Anonymous code 18.62 % 27.20 % 15.69 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
415 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.
416 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.
417 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.
418 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.
419 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.
420 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.
421 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.
422 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 .
423 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.
424 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.
425 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.
426 CMAN 17.04 % 25.89 % 12.88 % 0.15 s 1 core @ 2.5 Ghz (Python)
427 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.
428 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.
429 MonoAug 16.71 % 24.39 % 13.83 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
430 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.
431 MDT code 16.47 % 24.22 % 13.42 % 0.01 s 1 core @ 2.5 Ghz (Python)
432 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.
433 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.
434 MM 16.09 % 24.65 % 13.99 % 1 s 1 core @ 2.5 Ghz (C/C++)
435 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.
436 Keypoint-3D 15.54 % 23.16 % 11.83 % 14 s 1 core @ 2.5 Ghz (C/C++)
437 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.
438 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.
439 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.
440 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.
441 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.
442 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.
443 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 .
444 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.
445 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.
446 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.
447 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.
448 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.
449 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.
450 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.
451 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.
452 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.
453 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.
454 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.
455 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.
456 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.
457 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.
458 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.
459 CDTrack3D code 4.61 % 7.02 % 3.73 % 0.0106 s NVIDIA RTX 3090 GPU, i9 10850k CPU
460 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.
461 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.
462 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.
463 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 .
464 MonoDET code 0.14 % 0.25 % 0.10 % 0.04 s 1 core @ 2.5 Ghz (Python)
465 test code 0.09 % 0.04 % 0.11 % 50 s 1 core @ 2.5 Ghz (Python)
466 Yolo5x6_Ghost 0.00 % 0.00 % 0.00 % 0.03 s GPU @ 2.5 Ghz (Python)
467 Yolo5x6_Ghost 0.00 % 0.00 % 0.00 % 0.03 s GPU @ 2.5 Ghz (Python)
468 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.
469 Ghost3D object detec 0.00 % 0.00 % 0.00 % 0.03 s 1 core @ 2.5 Ghz (Python)
470 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 code 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: Accurate and Real-time 3D Pedestrian Detection Using an Efficient Attentive Pillar Network. arXiv preprint arXiv:2112.15458 2022.
2 CasA++ code 53.84 % 60.14 % 51.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.
3 TED 53.48 % 60.13 % 50.89 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
4 Anonymous 53.32 % 58.91 % 50.82 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
5 DCAN-Second code 53.18 % 60.92 % 50.56 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
6 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.
7 PV-RCNN++ code 52.43 % 59.73 % 48.73 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
8 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.
9 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.
10 CAD 52.20 % 60.23 % 49.54 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
11 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.
12 CasA code 51.37 % 57.95 % 49.08 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.
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 ACDet code 49.82 % 58.35 % 47.17 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
J. Xu, G. Wang, X. Zhang and G. Wan: ACDet: Attentive Cross-view Fusion for LiDAR-based 3D Object Detection. 3DV 2022.
23 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.
24 TBD 49.59 % 58.17 % 47.08 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
25 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.
26 TBD 49.56 % 58.10 % 47.05 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
27 CFF-tv 49.29 % 57.83 % 46.70 % 1 s 1 core @ 2.5 Ghz (C/C++)
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 tbd 47.84 % 57.69 % 43.99 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
48 HMFI code 47.77 % 55.61 % 45.17 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
49 TBD 47.77 % 55.61 % 45.17 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
50 CFF-tv-v2 47.59 % 55.46 % 45.09 % 1 s 1 core @ 2.5 Ghz (C/C++)
51 VoCo 47.47 % 52.94 % 45.41 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
52 CF-ctdep-tv-ta 47.46 % 54.36 % 45.07 % 1 s 1 core @ 2.5 Ghz (C/C++)
53 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.
54 SGNet 47.29 % 53.84 % 44.10 % 0.09 s GPU @ 2.5 Ghz (Python)
55 CF-base-tv 47.28 % 54.77 % 44.81 % 1 s 1 core @ 2.5 Ghz (C/C++)
56 CZY_PPF_Net2 47.22 % 51.95 % 45.46 % 0.1 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 SGDA3D 46.66 % 52.65 % 44.62 % 0.07 s 1 core @ 2.5 Ghz (Python)
64 Anonymous
This method makes use of Velodyne laser scans.
46.65 % 52.20 % 44.61 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
65 DGT-Det3D code 46.59 % 54.25 % 44.15 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
66 PSA-Det3D 46.36 % 53.26 % 43.73 % 0.1 s GPU @ 2.5 Ghz (Python)
67 CF-ctdep-tv 46.36 % 53.50 % 44.01 % 1 s 1 core @ 2.5 Ghz (C/C++)
68 CZY_3917 46.31 % 51.01 % 44.44 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
69 PA-RCNN code 46.30 % 53.60 % 44.33 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
70 MSADet 46.27 % 55.91 % 43.83 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
71 3SNet 46.25 % 52.22 % 42.89 % 0.07 s GPU @ 2.5 Ghz (Python)
72 DGT-Det3D 46.22 % 53.98 % 43.85 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
73 DTE3D 46.18 % 53.38 % 43.52 % 0.19 s 1 core @ 2.5 Ghz (C/C++)
74 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.
75 Anonymous 46.13 % 55.51 % 43.60 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
76 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.
77 E^2-PV-RCNN 45.85 % 52.35 % 44.00 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
X. Li, Y. Zhang and D. Kong: E^2-PV-RCNN: improving 3D object detection via enhancing keypoint features. Multimedia Tools and Applications 2022.
78 CenterFuse 45.84 % 55.20 % 43.46 % 0.059 sec/frame 2 x V100
79 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.
80 U_SECOND_V4 45.79 % 53.57 % 43.52 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
81 cp-tv 45.75 % 52.90 % 43.49 % 1 s 1 core @ 2.5 Ghz (C/C++)
82 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.
83 SARFE 45.60 % 51.45 % 43.49 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
84 TBD 45.57 % 52.08 % 42.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
85 MFANet 45.50 % 54.84 % 42.71 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
86 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.
87 TBD code 45.46 % 52.72 % 42.53 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
88 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.
89 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.
90 cp-tv-kp-io-sc 45.30 % 53.84 % 42.12 % 1 s 1 core @ 2.5 Ghz (C/C++)
91 VPNet 45.12 % 52.68 % 42.05 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
92 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.
93 TBD 44.99 % 50.41 % 42.85 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
94 FusionDetv1 44.85 % 52.42 % 42.56 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
95 SRDL 44.84 % 52.42 % 42.56 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
96 CZY_PPF_Net 44.80 % 49.97 % 42.11 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
97 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.
98 WGVRF 44.75 % 50.80 % 42.78 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
99 Semantical PVRCNN 44.75 % 49.40 % 41.94 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
100 AFTD 44.74 % 53.94 % 42.36 % 1 s 1 core @ 2.5 Ghz (Python + C/C++)
101 Dune-DCF-e11 44.58 % 52.44 % 41.75 % 1 s 1 core @ 2.5 Ghz (C/C++)
102 ATT_SSD 44.57 % 51.26 % 42.33 % 0.01 s 1 core @ 2.5 Ghz (Python)
103 CF-cd-io-tv 44.54 % 53.64 % 41.21 % 1 s 1 core @ 2.5 Ghz (C/C++)
104 Dune-DCF-e09 44.50 % 52.64 % 41.86 % 1 s 1 core @ 2.5 Ghz (C/C++)
105 SIF 44.28 % 52.05 % 42.03 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
106 LazyTorch-CP-Infer-O 44.27 % 51.92 % 41.99 % 1 s 1 core @ 2.5 Ghz (C/C++)
107 KeyPoint-IoUHead 44.27 % 53.12 % 41.83 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
108 LazyTorch-CP-Small-P 44.25 % 51.84 % 41.97 % 1 s 1 core @ 2.5 Ghz (C/C++)
109 DDet 44.24 % 50.01 % 42.38 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
110 IoU-2B 44.19 % 55.31 % 40.33 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
111 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.
112 VCRCNN 44.09 % 48.82 % 42.19 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
113 CenterPoint (pcdet) 44.08 % 51.76 % 41.80 % 0.051 sec/frame 2 x V100
114 CrazyTensor-CP 44.06 % 51.25 % 41.50 % 1 s 1 core @ 2.5 Ghz (Python)
115 cff-tv-t 44.00 % 54.42 % 41.46 % 1 s 1 core @ 2.5 Ghz (C/C++)
116 DSASNet 43.98 % 50.55 % 40.63 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
117 CF-base-train 43.90 % 51.40 % 41.24 % 1 s 1 core @ 2.5 Ghz (C/C++)
118 VGA-RCNN 43.89 % 51.80 % 41.57 % 0.07 s 1 core @ 2.5 Ghz (Python)
119 IKT3D
This method makes use of Velodyne laser scans.
43.88 % 49.25 % 41.79 % 0.05 s 1 core @ 2.5 Ghz (Python)
120 City-CF-fixed 43.86 % 51.92 % 41.33 % 1 s 1 core @ 2.5 Ghz (C/C++)
121 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.
122 PSA-SSD 43.77 % 50.26 % 41.75 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
123 BASA 43.67 % 50.82 % 40.91 % 1s 1 core @ 2.5 Ghz (python)
124 Dune-DCF-e15 43.63 % 51.18 % 41.11 % 1 s 1 core @ 2.5 Ghz (C/C++)
125 AGS-SSD[la] 43.60 % 51.06 % 40.37 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
126 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.
127 CF-ctdep-train 43.20 % 50.14 % 40.69 % 1 s 1 core @ 2.5 Ghz (C/C++)
128 FPV-SSD 43.19 % 50.37 % 40.95 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
129 HPV-RCNN 42.99 % 50.53 % 39.54 % 0.15 s 1 core @ 2.5 Ghz (Python)
130 City-CF 42.95 % 49.91 % 40.61 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
131 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.
132 CZY 42.80 % 49.42 % 40.83 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
133 TBD 42.76 % 50.17 % 39.75 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
134 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.
135 HS3D code 42.60 % 51.58 % 39.27 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
136 NV-RCNN 42.58 % 49.00 % 40.39 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
137 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.
138 PVTr 42.26 % 48.79 % 40.27 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
139 T_PVRCNN_V2 42.21 % 50.58 % 39.81 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
140 T_PVRCNN 41.87 % 49.87 % 39.44 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
141 SWA code 41.57 % 48.98 % 39.32 % 0.18 s 1 core @ 2.5 Ghz (C/C++)
142 TBD 41.19 % 47.42 % 39.19 % TBD s 1 core @ 2.5 Ghz (C/C++)
143 SECOND_7862 40.96 % 47.55 % 38.85 % 1 s 1 core @ 2.5 Ghz (Python)
144 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.
145 CAD
This method uses stereo information.
This method makes use of Velodyne laser scans.
40.93 % 48.07 % 38.43 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
146 CrazyTensor-CF 40.78 % 48.79 % 38.16 % 1 s 1 core @ 2.5 Ghz (C/C++)
147 TBD_BD code 40.41 % 48.27 % 38.45 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
148 ZMMPP 39.11 % 46.50 % 37.04 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
149 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.
150 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.
151 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.
152 PP-PCdet code 38.21 % 45.14 % 36.04 % 0.01 s 1 core @ 2.5 Ghz (Python)
153 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.
154 KPP3D code 37.82 % 45.25 % 35.36 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
155 StereoDistill 37.75 % 50.79 % 34.28 % 0.4 s 1 core @ 2.5 Ghz (Python)
156 Contrastive PP code 37.68 % 44.10 % 35.84 % 0.01 s 1 core @ 2.5 Ghz (Python)
157 GT3D 36.37 % 46.02 % 32.62 % 0.1 s 1 core @ 2.5 Ghz (Python)
158 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.
159 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.
160 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.
161 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.
162 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.
163 CGPS-3DV code 32.38 % 43.37 % 28.66 % CGPS-3DV 1 core @ 2.5 Ghz (C/C++)
164 CZY 32.05 % 39.50 % 29.90 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
165 PS code 31.13 % 41.55 % 27.50 % PS s 1 core @ 2.5 Ghz (C/C++)
166 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.
167 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.
168 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.
169 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.
170 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.
171 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.
172 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.
173 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.
174 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.
175 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.
176 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.
177 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.
178 Anonymous 13.47 % 20.42 % 11.64 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
179 ESGN
This method uses stereo information.
13.03 % 17.94 % 11.54 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
A. Gao, Y. Pang, J. Nie, Z. Shao, J. Cao, Y. Guo and X. Li: ESGN: Efficient Stereo Geometry Network for Fast 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2022.
180 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) .
181 DEPT 12.29 % 18.05 % 10.50 % 0.03 s 1 core @ 2.5 Ghz (Python)
182 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.
183 DD3Dv2 code 12.16 % 17.74 % 10.49 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
184 anonymity 12.00 % 18.98 % 10.21 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
185 MonoInsight 11.28 % 16.08 % 9.69 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
186 OPA-3D code 11.01 % 17.14 % 9.94 % 0.04 s 1 core @ 3.5 Ghz (Python)
187 GCDR 10.92 % 15.65 % 9.86 % 0.28 s 1 core @ 2.5 Ghz (Python)
188 LT-M3OD 10.89 % 16.63 % 9.20 % 0.03 s 1 core @ 2.5 Ghz (Python)
189 ZongmuMono3d code 10.65 % 16.19 % 9.04 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
190 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.
191 mono3d code 10.41 % 16.66 % 9.22 % TBD TBD
192 BSM3D 10.41 % 15.30 % 8.89 % 0.03 s 1 core @ 2.5 Ghz (Python)
193 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.
194 CMKD* 10.28 % 16.03 % 8.85 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
195 Lite-FPN-GUPNet 10.08 % 15.73 % 8.52 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
196 GPENet code 9.96 % 15.47 % 8.55 % 0.02 s GPU @ 2.5 Ghz (Python)
197 gupnet_se 9.85 % 14.65 % 8.32 % 0.03s 1 core @ 2.5 Ghz (C/C++)
198 DEVIANT code 9.77 % 14.49 % 8.28 % 0.04 s 1 GPU (Python)
A. Kumar, G. Brazil, E. Corona, A. Parchami and X. Liu: DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection. European Conference on Computer Vision (ECCV) 2022.
199 HBD 9.66 % 15.26 % 8.17 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
200 MonoPCNS 9.65 % 15.56 % 8.27 % 0.14 s GPU @ 2.5 Ghz (Python)
201 MonoAD 9.44 % 14.65 % 8.60 % 0.03 s GPU @ 2.5 Ghz (Python)
202 SARM3D 9.42 % 14.32 % 8.15 % 0.03 s GPU @ 2.5 Ghz (Python)
203 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.
204 SGM3D code 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, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection. RA-L 2022.
205 MonoGround 9.11 % 13.67 % 7.68 % 0.03 s 1 core @ 2.5 Ghz (Python)
206 Anonymous 9.08 % 13.35 % 7.63 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
207 Anonymous code 9.04 % 13.45 % 7.74 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
208 M3DGAF 8.93 % 13.42 % 7.58 % 0.07 s 1 core @ 2.5 Ghz (Python)
209 SAIC_ADC_Mono3D code 8.87 % 13.92 % 7.55 % 50 s GPU @ 2.5 Ghz (Python)
210 MonoEdge 8.87 % 13.33 % 7.50 % 0.05 s GPU @ 2.5 Ghz (Python)
211 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.
212 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.
213 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.
214 Mix-Teaching 8.40 % 12.34 % 7.06 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
215 SparseLiDAR_fusion 8.23 % 12.59 % 6.82 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
216 MDSNet 8.18 % 12.05 % 7.03 % 0.07 s 1 core @ 2.5 Ghz (Python)
217 DCD code 8.08 % 11.76 % 6.61 % 1 s 1 core @ 2.5 Ghz (C/C++)
218 mono3d 7.95 % 11.89 % 6.75 % 0.03 s GPU @ 2.5 Ghz (Python)
219 MonoAug 7.94 % 12.66 % 6.64 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
220 LPCG-Monoflex code 7.92 % 12.11 % 6.61 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
L. Peng, F. Liu, Z. Yu, S. Yan, D. Deng, Z. Yang, H. Liu and D. Cai: Lidar Point Cloud Guided Monocular 3D Object Detection. ECCV 2022.
221 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.
222 Shape-Aware 7.65 % 11.69 % 6.35 % 0.05 s 1 core @ 2.5 Ghz (Python)
223 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.
224 MonoEdge-Rotate 7.53 % 11.62 % 6.79 % 0.05 s GPU @ 2.5 Ghz (Python)
225 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.
226 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.
227 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 .
228 MonoAug 6.87 % 10.81 % 5.66 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
229 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.
230 MDNet 6.18 % 9.48 % 5.63 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
231 MK3D 6.15 % 8.76 % 5.14 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
232 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.
233 MM 5.63 % 9.20 % 4.78 % 1 s 1 core @ 2.5 Ghz (C/C++)
234 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.
235 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.
236 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.
237 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.
238 MoGDE 4.51 % 7.22 % 3.83 % 0.03 s GPU @ 2.5 Ghz (Python)
239 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.
240 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 .
241 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.
242 CMAN 3.96 % 5.24 % 3.18 % 0.15 s 1 core @ 2.5 Ghz (Python)
243 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.
244 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.
245 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.
246 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.
247 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.
248 CDTrack3D code 1.91 % 2.56 % 1.49 % 0.0106 s NVIDIA RTX 3090 GPU, i9 10850k CPU
249 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.
250 EM code 1.25 % 1.18 % 0.89 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
251 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 Anonymous 78.19 % 89.65 % 71.13 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
2 CasA++ code 76.99 % 88.93 % 70.10 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.
3 TED 76.95 % 89.54 % 70.31 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
4 CasA code 75.74 % 88.99 % 68.47 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.
5 HMFI code 74.06 % 85.69 % 67.11 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
6 SGNet 73.88 % 88.03 % 66.84 % 0.09 s GPU @ 2.5 Ghz (Python)
7 SARFE 73.84 % 85.63 % 66.31 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
8 CZY_PPF_Net2 73.64 % 85.39 % 66.01 % 0.1 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 Semantical PVRCNN 73.14 % 86.75 % 64.87 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
11 VoCo 73.08 % 85.29 % 66.46 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
12 SPT 72.90 % 86.10 % 65.13 % 0.1 s GPU @ 2.5 Ghz (Python)
13 CAD 72.87 % 87.09 % 65.78 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
14 DCAN-Second code 72.74 % 88.62 % 65.89 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
15 CZY_PPF_Net 72.73 % 86.92 % 65.30 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
16 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++)
17 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.
18 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.
19 Reprod-Two-Branch 72.16 % 87.50 % 64.41 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
20 CFF-tv 72.02 % 86.54 % 64.25 % 1 s 1 core @ 2.5 Ghz (C/C++)
21 CFF-ep25 71.99 % 86.78 % 64.18 % 1 s 1 core @ 2.5 Ghz (C/C++)
22 PA-RCNN code 71.98 % 86.09 % 64.02 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
23 VCRCNN 71.93 % 84.06 % 64.07 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
24 SGDA3D 71.90 % 84.81 % 64.88 % 0.07 s 1 core @ 2.5 Ghz (Python)
25 E^2-PV-RCNN 71.89 % 84.41 % 65.15 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
X. Li, Y. Zhang and D. Kong: E^2-PV-RCNN: improving 3D object detection via enhancing keypoint features. Multimedia Tools and Applications 2022.
26 PV-RCNN++ code 71.86 % 84.60 % 63.84 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
27 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.
28 CF-ctdep-tv-ta 71.74 % 87.38 % 64.30 % 1 s 1 core @ 2.5 Ghz (C/C++)
29 cff-tv-v2-ep25 71.70 % 85.61 % 64.15 % 1 s 1 core @ 2.5 Ghz (C/C++)
30 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.
31 CFF-tv-v2 71.53 % 85.70 % 63.77 % 1 s 1 core @ 2.5 Ghz (C/C++)
32 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.
33 ACDet code 71.48 % 87.76 % 64.69 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
J. Xu, G. Wang, X. Zhang and G. Wan: ACDet: Attentive Cross-view Fusion for LiDAR-based 3D Object Detection. 3DV 2022.
34 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.
35 3SNet 71.44 % 84.55 % 64.79 % 0.07 s GPU @ 2.5 Ghz (Python)
36 Anonymous
This method makes use of Velodyne laser scans.
71.43 % 84.75 % 64.89 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
37 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.
38 PE-RCVN 71.18 % 85.95 % 64.38 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
39 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.
40 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.
41 USVLab BSAODet 70.85 % 85.28 % 64.09 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
42 DDet 70.76 % 84.81 % 63.98 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
43 CZY_3917 70.73 % 83.46 % 63.16 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
44 CenterFuse 70.59 % 86.53 % 62.18 % 0.059 sec/frame 2 x V100
45 TBD code 70.59 % 83.06 % 63.07 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
46 MSADet 70.38 % 86.58 % 63.63 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
47 CZY 70.32 % 86.42 % 63.32 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
48 TCDVF 70.28 % 82.85 % 63.54 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
49 USVLab BSAODet (S) 70.24 % 84.38 % 63.24 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
50 MVMM code 70.17 % 81.84 % 63.84 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
51 CF-ctdep-tv 70.16 % 86.31 % 62.63 % 1 s 1 core @ 2.5 Ghz (C/C++)
52 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.
53 TBD 70.09 % 82.60 % 63.39 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
54 TBD 69.97 % 79.75 % 63.46 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
55 VGA-RCNN 69.86 % 80.95 % 62.16 % 0.07 s 1 core @ 2.5 Ghz (Python)
56 FV2P v2 69.82 % 86.88 % 63.09 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
57 KeyFuse2B 69.76 % 84.95 % 62.16 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
58 IKT3D
This method makes use of Velodyne laser scans.
69.74 % 81.92 % 62.59 % 0.05 s 1 core @ 2.5 Ghz (Python)
59 CF-base-tv 69.49 % 84.12 % 61.85 % 1 s 1 core @ 2.5 Ghz (C/C++)
60 DGT-Det3D code 69.47 % 81.26 % 61.88 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
61 PVTr 69.46 % 84.62 % 62.33 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
62 HPV-RCNN 69.43 % 82.51 % 61.87 % 0.15 s 1 core @ 2.5 Ghz (Python)
63 IoU-2B 69.24 % 86.64 % 60.57 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
64 DSASNet 69.12 % 82.32 % 62.68 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
65 TBD 69.09 % 82.53 % 62.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
66 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.
67 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.
68 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.
69 WGVRF 68.71 % 82.04 % 62.04 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
70 CF-cd-io-tv 68.52 % 83.71 % 60.12 % 1 s 1 core @ 2.5 Ghz (C/C++)
71 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.
72 TBD 68.33 % 85.17 % 61.38 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
73 BASA 68.22 % 81.97 % 61.48 % 1s 1 core @ 2.5 Ghz (python)
74 FPV-SSD 68.15 % 80.32 % 60.51 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
75 ATT_SSD 68.14 % 81.49 % 61.31 % 0.01 s 1 core @ 2.5 Ghz (Python)
76 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.
77 KPP3D code 67.97 % 81.23 % 60.72 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
78 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.
79 Anonymous 67.83 % 81.75 % 60.92 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
80 Self-Calib Conv 67.73 % 82.11 % 60.57 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
81 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.
82 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.
83 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.
84 NV-RCNN 67.54 % 82.53 % 60.97 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
85 DGT-Det3D 67.44 % 80.73 % 60.71 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
86 cff-tv-t 67.41 % 85.91 % 60.15 % 1 s 1 core @ 2.5 Ghz (C/C++)
87 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.
88 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.
89 AGS-SSD[la] 67.35 % 81.70 % 60.41 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
90 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.
91 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.
92 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.
93 MFANet 67.06 % 80.51 % 60.13 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
94 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.
95 PSA-SSD 66.79 % 79.56 % 59.94 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
96 KeyPoint-IoUHead 66.72 % 83.32 % 59.93 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
97 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.
98 T_PVRCNN_V2 66.49 % 80.88 % 58.51 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
99 cp-tv-kp-io-sc 66.40 % 82.88 % 58.53 % 1 s 1 core @ 2.5 Ghz (C/C++)
100 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.
101 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.
102 T_PVRCNN 66.17 % 79.84 % 59.04 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
103 cp-tv 66.08 % 80.65 % 58.98 % 1 s 1 core @ 2.5 Ghz (C/C++)
104 SWA code 66.08 % 78.96 % 60.18 % 0.18 s 1 core @ 2.5 Ghz (C/C++)
105 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.
106 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.
107 U_SECOND_V4 65.84 % 80.94 % 58.31 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
108 VPN 65.60 % 82.20 % 58.96 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
109 PSA-Det3D 65.51 % 79.21 % 59.06 % 0.1 s GPU @ 2.5 Ghz (Python)
110 TBD 65.31 % 78.56 % 59.27 % TBD s 1 core @ 2.5 Ghz (C/C++)
111 ZMMPP 65.23 % 77.62 % 58.08 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
112 VPNet 64.95 % 79.83 % 58.33 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
113 cp-tv-kp 64.87 % 79.91 % 58.22 % 1 s 1 core @ 2.5 Ghz (C/C++)
114 TBD_BD code 64.60 % 82.19 % 58.01 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
115 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.
116 FusionDetv1 64.53 % 79.62 % 57.91 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
117 Dune-DCF-e11 64.52 % 82.14 % 57.40 % 1 s 1 core @ 2.5 Ghz (C/C++)
118 SRDL 64.52 % 79.64 % 57.90 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
119 Dune-DCF-e15 64.42 % 81.10 % 57.35 % 1 s 1 core @ 2.5 Ghz (C/C++)
120 City-CF-fixed 64.39 % 81.11 % 57.74 % 1 s 1 core @ 2.5 Ghz (C/C++)
121 CF-ctdep-train 64.33 % 81.02 % 56.17 % 1 s 1 core @ 2.5 Ghz (C/C++)
122 City-CF 64.25 % 81.33 % 57.01 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
123 SIF 64.13 % 79.32 % 57.38 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
124 AFTD 64.03 % 82.99 % 55.93 % 1 s 1 core @ 2.5 Ghz (Python + C/C++)
125 SECOND_7862 63.95 % 78.30 % 57.28 % 1 s 1 core @ 2.5 Ghz (Python)
126 variance_point 63.90 % 78.49 % 56.51 % 0.05 s 1 core @ 2.5 Ghz (Python)
127 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.
128 CF-base-train 63.63 % 80.31 % 55.56 % 1 s 1 core @ 2.5 Ghz (C/C++)
129 HS3D code 63.56 % 78.53 % 58.03 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
130 DTE3D 63.10 % 79.79 % 56.94 % 0.19 s 1 core @ 2.5 Ghz (C/C++)
131 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.
132 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.
133 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.
134 Dune-DCF-e09 62.23 % 77.53 % 55.46 % 1 s 1 core @ 2.5 Ghz (C/C++)
135 Contrastive PP code 62.10 % 75.71 % 54.84 % 0.01 s 1 core @ 2.5 Ghz (Python)
136 CAD
This method uses stereo information.
This method makes use of Velodyne laser scans.
62.05 % 77.71 % 54.56 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
137 CrazyTensor-CF 61.95 % 80.59 % 55.16 % 1 s 1 core @ 2.5 Ghz (C/C++)
138 PP-PCdet code 61.81 % 75.56 % 55.06 % 0.01 s 1 core @ 2.5 Ghz (Python)
139 LazyTorch-CP-Infer-O 61.40 % 76.40 % 54.73 % 1 s 1 core @ 2.5 Ghz (C/C++)
140 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.
141 CenterPoint (pcdet) 61.25 % 76.38 % 54.68 % 0.051 sec/frame 2 x V100
142 LazyTorch-CP-Small-P 61.07 % 76.37 % 54.73 % 1 s 1 core @ 2.5 Ghz (C/C++)
143 TBD 60.58 % 76.98 % 53.49 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
144 TBD 60.58 % 76.98 % 53.49 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
145 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.
146 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.
147 CrazyTensor-CP 59.54 % 75.40 % 53.21 % 1 s 1 core @ 2.5 Ghz (Python)
148 GT3D 58.64 % 76.60 % 52.07 % 0.1 s 1 core @ 2.5 Ghz (Python)
149 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.
150 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.
151 tbd 57.15 % 72.89 % 50.29 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
152 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.
153 PiFeNet code 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: Accurate and Real-time 3D Pedestrian Detection Using an Efficient Attentive Pillar Network. arXiv preprint arXiv:2112.15458 2022.
154 CZY 56.71 % 70.64 % 50.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
155 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.
156 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.
157 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.
158 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.
159 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.
160 StereoDistill 48.37 % 69.46 % 42.69 % 0.4 s 1 core @ 2.5 Ghz (Python)
161 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.
162 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.
163 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.
164 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.
165 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.
166 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.
167 CGPS-3DV code 35.75 % 54.06 % 31.17 % CGPS-3DV 1 core @ 2.5 Ghz (C/C++)
168 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.
169 PS code 32.16 % 49.23 % 27.73 % PS s 1 core @ 2.5 Ghz (C/C++)
170 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.
171 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.
172 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.
173 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.
174 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.
175 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.
176 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.
177 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.
178 ESGN
This method uses stereo information.
9.02 % 15.78 % 7.96 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
A. Gao, Y. Pang, J. Nie, Z. Shao, J. Cao, Y. Guo and X. Li: ESGN: Efficient Stereo Geometry Network for Fast 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2022.
179 CMKD* 8.15 % 14.66 % 7.23 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
180 anonymity 7.39 % 12.38 % 6.65 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
181 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.
182 Anonymous 7.24 % 12.53 % 6.21 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
183 DD3Dv2 code 7.02 % 10.67 % 5.78 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
184 anonymity 6.84 % 10.90 % 5.72 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
185 mono3d code 6.52 % 11.40 % 5.19 % TBD TBD
186 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.
187 BSM3D 6.42 % 10.59 % 5.50 % 0.03 s 1 core @ 2.5 Ghz (Python)
188 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.
189 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) .
190 LT-M3OD 5.53 % 9.17 % 4.84 % 0.03 s 1 core @ 2.5 Ghz (Python)
191 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.
192 Mix-Teaching 5.36 % 8.56 % 4.62 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
193 Anonymous 5.17 % 7.71 % 4.31 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
194 LPCG-Monoflex code 4.90 % 8.14 % 3.86 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
L. Peng, F. Liu, Z. Yu, S. Yan, D. Deng, Z. Yang, H. Liu and D. Cai: Lidar Point Cloud Guided Monocular 3D Object Detection. ECCV 2022.
195 MonoAD 4.85 % 8.13 % 4.71 % 0.03 s GPU @ 2.5 Ghz (Python)
196 MDNet 4.74 % 8.10 % 4.19 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
197 DEPT 4.71 % 8.82 % 4.15 % 0.03 s 1 core @ 2.5 Ghz (Python)
198 Lite-FPN-GUPNet 4.70 % 7.67 % 4.61 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
199 ZongmuMono3d code 4.63 % 8.72 % 3.94 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
200 Shape-Aware 4.60 % 8.00 % 4.50 % 0.05 s 1 core @ 2.5 Ghz (Python)
201 SAIC_ADC_Mono3D code 4.55 % 7.90 % 3.73 % 50 s GPU @ 2.5 Ghz (Python)
202 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.
203 GPENet code 4.28 % 7.06 % 3.68 % 0.02 s GPU @ 2.5 Ghz (Python)
204 SparseLiDAR_fusion 4.26 % 7.77 % 3.45 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
205 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.
206 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.
207 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.
208 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.
209 MonoInsight 3.99 % 6.56 % 3.49 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
210 DEVIANT code 3.97 % 6.42 % 3.51 % 0.04 s 1 GPU (Python)
A. Kumar, G. Brazil, E. Corona, A. Parchami and X. Liu: DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection. European Conference on Computer Vision (ECCV) 2022.
211 SARM3D 3.85 % 5.59 % 3.28 % 0.03 s GPU @ 2.5 Ghz (Python)
212 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.
213 MoGDE 3.76 % 6.04 % 3.09 % 0.03 s GPU @ 2.5 Ghz (Python)
214 OPA-3D code 3.75 % 6.01 % 3.56 % 0.04 s 1 core @ 3.5 Ghz (Python)
215 MonoAug 3.71 % 5.66 % 3.00 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
216 SGM3D code 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, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection. RA-L 2022.
217 DCD code 3.62 % 5.84 % 3.33 % 1 s 1 core @ 2.5 Ghz (C/C++)
218 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.
219 mono3d 3.30 % 5.84 % 2.68 % 0.03 s GPU @ 2.5 Ghz (Python)
220 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 .
221 MonoGround 3.22 % 4.81 % 2.75 % 0.03 s 1 core @ 2.5 Ghz (Python)
222 MDSNet 3.22 % 5.99 % 2.62 % 0.07 s 1 core @ 2.5 Ghz (Python)
223 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.
224 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.
225 M3DGAF 3.02 % 5.33 % 2.87 % 0.07 s 1 core @ 2.5 Ghz (Python)
226 MonoEdge-Rotate 3.01 % 5.36 % 2.83 % 0.05 s GPU @ 2.5 Ghz (Python)
227 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.
228 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.
229 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.
230 gupnet_se 2.61 % 4.38 % 2.34 % 0.03s 1 core @ 2.5 Ghz (C/C++)
231 MK3D 2.55 % 4.17 % 2.24 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
232 MonoEdge 2.54 % 4.02 % 2.43 % 0.05 s GPU @ 2.5 Ghz (Python)
233 MonoAug 2.46 % 4.31 % 2.21 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
234 MonoPCNS 2.46 % 4.65 % 2.42 % 0.14 s GPU @ 2.5 Ghz (Python)
235 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.
236 Anonymous code 2.31 % 3.50 % 2.01 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
237 GCDR 2.11 % 3.74 % 1.99 % 0.28 s 1 core @ 2.5 Ghz (Python)
238 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.
239 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.
240 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.
241 HBD 1.64 % 3.15 % 1.76 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
242 CMAN 1.48 % 1.76 % 1.17 % 0.15 s 1 core @ 2.5 Ghz (Python)
243 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.
244 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 .
245 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.
246 MM 0.40 % 0.71 % 0.35 % 1 s 1 core @ 2.5 Ghz (C/C++)
247 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.
248 CDTrack3D code 0.10 % 0.24 % 0.11 % 0.0106 s NVIDIA RTX 3090 GPU, i9 10850k CPU
249 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

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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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