3D Object Detection Evaluation 2017


The 3D object detection 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 3D object 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 an 3D bounding box overlap of 70%, while for pedestrians and cyclists we require a 3D bounding box 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 TED 85.28 % 91.61 % 80.68 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
2 LIVOX_Det
This method makes use of Velodyne laser scans.
84.94 % 91.72 % 80.10 % n/a s 1 core @ 2.5 Ghz (Python + C/C++)
3 SFD code 84.76 % 91.73 % 77.92 % 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.
4 VoCo 84.76 % 91.99 % 79.81 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
5 NSAW code 84.30 % 90.57 % 77.46 % 0.1 s 1 core @ 2.5 Ghz (Python)
6 CasA++ code 84.04 % 90.68 % 79.69 % 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.
7 DGDNH 83.88 % 90.69 % 79.50 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
8 Anonymous 83.51 % 89.08 % 78.94 % n/a s 1 core @ 2.5 Ghz (C/C++)
9 GraR-VoI code 83.27 % 91.89 % 77.78 % 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.
10 GLENet-VR 83.23 % 91.67 % 78.43 % 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.
11 VPFNet code 83.21 % 91.02 % 78.20 % 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.
12 GraR-Po code 83.18 % 91.79 % 77.98 % 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.
13 CasA code 83.06 % 91.58 % 80.08 % 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.
14 Anonymous 82.97 % 89.13 % 80.05 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
15 BtcDet
This method makes use of Velodyne laser scans.
code 82.86 % 90.64 % 78.09 % 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.
16 GraR-Vo code 82.77 % 91.29 % 77.20 % 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.
17 3SNet 82.70 % 89.41 % 78.03 % 0.07 s GPU @ 2.5 Ghz (Python)
18 PE-RCVN 82.69 % 91.51 % 77.75 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
19 CAD 82.68 % 88.96 % 77.91 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
20 SPG_mini
This method makes use of Velodyne laser scans.
code 82.66 % 90.64 % 77.91 % 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.
21 SGFusion 82.64 % 91.13 % 77.53 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
22 HCPVF 82.63 % 89.34 % 77.72 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
23 DSASNet 82.63 % 89.48 % 77.94 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
24 PA3DNet 82.57 % 90.49 % 77.88 % 0.05 s GPU @ 2.5 Ghz (Python)
25 SE-SSD
This method makes use of Velodyne laser scans.
code 82.54 % 91.49 % 77.15 % 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.
26 SPD-Net 82.46 % 89.10 % 77.78 % 0.1 s 2 cores @ 3.0 Ghz (Python)
27 DVF-V 82.45 % 89.40 % 77.56 % 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.
28 GraR-Pi code 82.42 % 90.94 % 77.00 % 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.
29 DVF-PV 82.40 % 90.99 % 77.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
A. Mahmoud, J. Hu and S. Waslander: Dense Voxel Fusion for 3D Object Detection. 2022.
30 3D Dual-Fusion 82.40 % 91.01 % 79.39 % 0.1 s 1 core @ 2.5 Ghz (Python)
31 NIV-SSD 82.37 % 89.74 % 75.06 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
32 RDIoU code 82.30 % 90.65 % 77.26 % 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.
33 PVT-SSD 82.29 % 90.65 % 76.85 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
34 Focals Conv code 82.28 % 90.55 % 77.59 % 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.
35 CLOCs code 82.28 % 89.16 % 77.23 % 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.
36 CityBrainLab 82.22 % 90.54 % 77.19 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
37 SPT 82.18 % 90.52 % 77.62 % 0.1 s GPU @ 2.5 Ghz (Python)
38 SASA
This method makes use of Velodyne laser scans.
code 82.16 % 88.76 % 77.16 % 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.
39 ImpDet 82.14 % 88.39 % 76.98 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
40 SPG
This method makes use of Velodyne laser scans.
code 82.13 % 90.50 % 78.90 % 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.
41 SPNet code 82.11 % 88.53 % 77.41 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
42 VoTr-TSD code 82.09 % 89.90 % 79.14 % 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.
43 PV-DT3D 82.09 % 90.07 % 77.51 % 1.4 s 1 core @ 2.5 Ghz (C/C++)
44 TBD 82.09 % 89.50 % 79.29 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
45 Pyramid R-CNN 82.08 % 88.39 % 77.49 % 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.
46 FS-Net
This method makes use of Velodyne laser scans.
82.07 % 88.68 % 77.42 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
47 VoxSeT code 82.06 % 88.53 % 77.46 % 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.
48 VoxelGraphRCNN 82.06 % 90.55 % 77.52 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
49 SRIF-RCNN 82.04 % 88.45 % 77.54 % 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.
50 LGNet 82.02 % 90.65 % 77.34 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
51 ChTR3D 82.02 % 90.43 % 77.42 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
52 EQ-PVRCNN code 82.01 % 90.13 % 77.53 % 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.
53 Anonymous
This method makes use of Velodyne laser scans.
82.00 % 90.78 % 77.44 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
54 anonymous 81.99 % 88.82 % 77.26 % 0.09 s GPU @ 2.5 Ghz (Python)
55 EPNet++ 81.96 % 91.37 % 76.71 % 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.
56 HMFI code 81.93 % 88.90 % 77.30 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
57 PV-RCNN++ code 81.88 % 90.14 % 77.15 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
58 SGDA3D 81.86 % 88.82 % 77.26 % 0.07 s 1 core @ 2.5 Ghz (Python)
59 GLENet 81.86 % 89.87 % 77.32 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
60 PDV code 81.86 % 90.43 % 77.36 % 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.
61 SGNet 81.85 % 88.83 % 77.47 % 0.09 s GPU @ 2.5 Ghz (Python)
62 FV2P v2 81.81 % 88.17 % 77.43 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
63 ChTR3D 81.80 % 88.00 % 77.17 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
64 CityBrainLab-CT3D code 81.77 % 87.83 % 77.16 % 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.
65 GV-RCNN code 81.75 % 90.31 % 77.17 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
66 USVLab BSAODet 81.74 % 88.89 % 77.14 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
67 TBD 81.73 % 89.48 % 79.05 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
68 M3DeTR code 81.73 % 90.28 % 76.96 % 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.
69 SIENet code 81.71 % 88.22 % 77.22 % 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.
70 TBD 81.71 % 88.46 % 76.63 % 0.1 s 1 core @ 2.5 Ghz (Python)
71 VGRCNN++ 81.71 % 90.15 % 77.14 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
72 DCCA 81.70 % 88.42 % 77.18 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
73 E^2-PV-RCNN 81.70 % 88.33 % 77.20 % 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.
74 VCRCNN 81.68 % 90.52 % 77.26 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
75 IKT3D
This method makes use of Velodyne laser scans.
81.65 % 90.00 % 77.02 % 0.05 s 1 core @ 2.5 Ghz (Python)
76 Voxel R-CNN code 81.62 % 90.90 % 77.06 % 0.04 s GPU @ 3.0 Ghz (C/C++)
J. Deng, S. Shi, P. Li, W. Zhou, Y. Zhang and H. Li: Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection . AAAI 2021.
77 BADet code 81.61 % 89.28 % 76.58 % 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.
78 Semantical PVRCNN 81.60 % 90.53 % 77.07 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
79 VG-RCNN 81.60 % 88.55 % 77.13 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
80 SARFE 81.59 % 88.88 % 76.74 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
81 FromVoxelToPoint code 81.58 % 88.53 % 77.37 % 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.
82 H^23D R-CNN code 81.55 % 90.43 % 77.22 % 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.
83 Anonymous 81.55 % 87.90 % 77.03 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
84 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
code 81.46 % 88.25 % 76.96 % 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.
85 P2V-RCNN 81.45 % 88.34 % 77.20 % 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.
86 CZY_3917 81.45 % 90.11 % 77.00 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
87 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 81.43 % 90.25 % 76.82 % 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.
88 CZY_PPF_Net2 81.39 % 90.44 % 77.02 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
89 DDet 81.38 % 89.63 % 78.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
90 VGA-RCNN 81.38 % 88.38 % 76.67 % 0.07 s 1 core @ 2.5 Ghz (Python)
91 DTE3D 81.37 % 88.36 % 76.71 % 0.19 s 1 core @ 2.5 Ghz (C/C++)
92 VGRCNN 81.36 % 88.43 % 76.89 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
93 XView 81.35 % 89.21 % 76.87 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
94 RangeRCNN
This method makes use of Velodyne laser scans.
81.33 % 88.47 % 77.09 % 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.
95 CAT-Det 81.32 % 89.87 % 76.68 % 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.
96 GT3D 81.24 % 87.61 % 74.96 % 0.1 s 1 core @ 2.5 Ghz (Python)
97 ChTR3D 81.19 % 87.81 % 76.70 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
98 PVE 81.15 % 89.39 % 76.71 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
99 AGS-SSD[la] 81.02 % 88.38 % 76.45 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
100 VPFNet code 80.97 % 88.51 % 76.74 % 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.
101 GVNet-V2 80.96 % 87.57 % 76.32 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
102 DKDet 80.94 % 87.66 % 76.23 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
103 MVTr 80.85 % 89.51 % 76.46 % 0.08 s 1 core @ 2.5 Ghz (Python)
104 NV-RCNN 80.78 % 86.35 % 76.39 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
105 Sem-Aug 80.77 % 89.41 % 75.90 % 0.08 s GPU @ 2.5 Ghz (Python)
106 CSVoxel-RCNN 80.73 % 87.44 % 76.18 % 0.03 s GPU @ 1.0 Ghz (Python)
107 StructuralIF 80.69 % 87.15 % 76.26 % 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.
108 SPVB-SSD 80.68 % 86.99 % 76.23 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
109 DGT-Det3D code 80.68 % 87.89 % 76.02 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
110 CLOCs_PVCas code 80.67 % 88.94 % 77.15 % 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.
111 PA-RCNN code 80.60 % 88.65 % 73.65 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
112 GVNet code 80.52 % 87.63 % 75.99 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
113 SVGA-Net 80.47 % 87.33 % 75.91 % 0.03s 1 core @ 2.5 Ghz (Python + C/C++)
Q. He, Z. Wang, H. Zeng, Y. Zeng and Y. Liu: SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds. AAAI 2022.
114 SC-Voxel-RCNN 80.46 % 86.94 % 75.85 % 0.12 s GPU @ 1.0 Ghz (Python)
115 TBD 80.44 % 88.83 % 73.18 % 0.1 s 1 core @ 2.5 Ghz (Python)
116 SRDL 80.38 % 87.73 % 76.27 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
117 Fast-CLOCs 80.35 % 89.10 % 76.99 % 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.
118 FPV-SSD 80.34 % 87.72 % 75.40 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
119 SPANet 80.34 % 91.05 % 74.89 % 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.
120 IA-SSD (single) code 80.32 % 88.87 % 75.10 % 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.
121 TBD 80.29 % 87.37 % 73.05 % 0.1 s 1 core @ 2.5 Ghz (Python)
122 CIA-SSD
This method makes use of Velodyne laser scans.
code 80.28 % 89.59 % 72.87 % 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.
123 FusionDetv1 80.28 % 87.45 % 76.21 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
124 DVF 80.21 % 88.97 % 75.22 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
125 TBD 80.19 % 88.41 % 76.77 % TBD s 1 core @ 2.5 Ghz (C/C++)
126 PVTr 80.16 % 86.90 % 75.98 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
127 SWA code 80.16 % 88.45 % 76.77 % 0.18 s 1 core @ 2.5 Ghz (C/C++)
128 IA-SSD (multi) code 80.13 % 88.34 % 75.04 % 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.
129 EBM3DOD code 80.12 % 91.05 % 72.78 % 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.
130 TBD 80.12 % 86.50 % 75.72 % 0.06 s GPU @ 2.5 Ghz (Python)
131 ATT_SSD 80.11 % 88.94 % 74.91 % 0.01 s 1 core @ 2.5 Ghz (Python)
132 TBD code 80.06 % 88.75 % 74.84 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
133 3D-CVF at SPA
This method makes use of Velodyne laser scans.
80.05 % 89.20 % 73.11 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
J. Yoo, Y. Kim, J. Kim and J. Choi: 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection. ECCV 2020.
134 SIF 79.88 % 86.84 % 75.89 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
135 DCGNN 79.80 % 89.65 % 74.52 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
136 RangeIoUDet
This method makes use of Velodyne laser scans.
79.80 % 88.60 % 76.76 % 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.
137 SA-SSD code 79.79 % 88.75 % 74.16 % 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.
138 DGT-Det3D 79.78 % 86.76 % 75.73 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
139 KpNet 79.75 % 88.92 % 72.17 % 0.42 s 1 core @ 2.5 Ghz (C/C++)
140 STD code 79.71 % 87.95 % 75.09 % 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.
141 MGAF-3DSSD code 79.68 % 88.16 % 72.39 % 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.
142 mbdf-netv1 code 79.66 % 90.19 % 74.76 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
143 PTA-RCNN 79.61 % 87.84 % 74.43 % 0.08 s 1 core @ 2.5 Ghz (Python)
144 TBD code 79.59 % 88.39 % 74.22 % 0.1 s GPU @ 2.5 Ghz (Python)
145 Struc info fusion II 79.59 % 88.97 % 72.51 % 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.
146 3DSSD code 79.57 % 88.36 % 74.55 % 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.
147 EBM3DOD baseline code 79.52 % 88.80 % 72.30 % 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.
148 Struc info fusion I 79.49 % 88.70 % 74.25 % 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.
149 HPV-RCNN 79.47 % 87.71 % 74.26 % 0.15 s 1 core @ 2.5 Ghz (Python)
150 Point-GNN
This method makes use of Velodyne laser scans.
code 79.47 % 88.33 % 72.29 % 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.
151 BASA 79.43 % 87.91 % 74.29 % 1s 1 core @ 2.5 Ghz (python)
152 DCAN-Second code 79.40 % 88.57 % 75.05 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
153 SSL-PointGNN code 79.36 % 87.78 % 74.15 % 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.
154 CZY_PPF_Net 79.35 % 88.39 % 76.14 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
155 USVLab BSAODet (S) 79.30 % 88.02 % 76.05 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
156 EPNet code 79.28 % 89.81 % 74.59 % 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.
157 VPNet 79.28 % 87.66 % 76.40 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
158 WGVRF 79.25 % 88.47 % 74.60 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
159 DVFENet 79.18 % 86.20 % 74.58 % 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.
160 PSA-SSD 79.12 % 87.35 % 74.25 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
161 ITCA-SSD code 79.11 % 88.66 % 72.12 % 0.05 s 1 core @ 2.5 Ghz (Python)
162 Faraway-Frustum
This method makes use of Velodyne laser scans.
code 79.05 % 87.45 % 76.14 % 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.
163 3D IoU-Net 79.03 % 87.96 % 72.78 % 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.
164 SERCNN
This method makes use of Velodyne laser scans.
78.96 % 87.74 % 74.30 % 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.
165 MVMM code 78.87 % 87.59 % 73.78 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
166 B-FPS 78.87 % 87.53 % 74.23 % 0.1 s 1 core @ 2.5 Ghz (Java)
167 ACDet code 78.85 % 88.47 % 73.86 % 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.
168 MSADet 78.81 % 88.31 % 73.82 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
169 PSA-Det3D 78.80 % 87.46 % 74.47 % 0.1 s GPU @ 2.5 Ghz (Python)
170 CSNet8306 code 78.74 % 89.57 % 72.09 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
171 MVAF-Net code 78.71 % 87.87 % 75.48 % 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.
172 CenterFuse 78.70 % 86.92 % 73.87 % 0.059 sec/frame 2 x V100
173 FSFNet 78.67 % 89.69 % 72.01 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
174 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 78.49 % 87.81 % 73.51 % 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.
175 CLOCs_SecCas 78.45 % 86.38 % 72.45 % 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.
176 CSNet 78.42 % 87.39 % 71.75 % 0.1 s 1 core @ 2.5 Ghz (Python)
177 Patches - EMP
This method makes use of Velodyne laser scans.
78.41 % 89.84 % 73.15 % 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.
178 CZY 78.36 % 87.00 % 73.84 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
179 HotSpotNet 78.31 % 87.60 % 73.34 % 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.
180 Sem-Aug-PointRCNN++ 78.06 % 86.69 % 73.85 % 0.1 s 8 cores @ 3.0 Ghz (Python)
181 CF-cd-io-tv 78.05 % 86.38 % 73.29 % 1 s 1 core @ 2.5 Ghz (C/C++)
182 VPN 77.93 % 85.02 % 72.97 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
183 CenterNet3D 77.90 % 86.20 % 73.03 % 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.
184 U_SECOND_V4 77.87 % 86.69 % 73.03 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
185 TBD 77.85 % 86.46 % 72.39 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
186 CF-ctdep-tv-ta 77.75 % 85.27 % 74.83 % 1 s 1 core @ 2.5 Ghz (C/C++)
187 IoU-2B 77.74 % 85.65 % 71.30 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
188 Reprod-Two-Branch 77.73 % 85.60 % 74.24 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
189 Anonymous 77.58 % 87.10 % 73.13 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
190 TBD 77.56 % 85.38 % 72.32 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
191 CFF-tv 77.53 % 85.01 % 74.01 % 1 s 1 core @ 2.5 Ghz (C/C++)
192 TCDVF 77.49 % 85.55 % 72.29 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
193 CFF-ep25 77.48 % 84.84 % 72.96 % 1 s 1 core @ 2.5 Ghz (C/C++)
194 UberATG-MMF
This method makes use of Velodyne laser scans.
77.43 % 88.40 % 70.22 % 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.
195 CFF-tv-v2 77.41 % 85.18 % 72.81 % 1 s 1 core @ 2.5 Ghz (C/C++)
196 Associate-3Ddet code 77.40 % 85.99 % 70.53 % 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.
197 Fast Point R-CNN
This method makes use of Velodyne laser scans.
77.40 % 85.29 % 70.24 % 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.
198 cff-tv-v2-ep25 77.38 % 84.44 % 72.82 % 1 s 1 core @ 2.5 Ghz (C/C++)
199 RangeDet (Official) code 77.36 % 85.41 % 72.60 % 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.
200 3D_att
This method makes use of Velodyne laser scans.
77.27 % 88.46 % 70.11 % 0.17 s GPU @ 2.5 Ghz (Python)
201 cp-tv-kp-io-sc 77.25 % 85.41 % 72.42 % 1 s 1 core @ 2.5 Ghz (C/C++)
202 Patches
This method makes use of Velodyne laser scans.
77.20 % 88.67 % 71.82 % 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.
203 CF-ctdep-tv 77.12 % 84.71 % 74.02 % 1 s 1 core @ 2.5 Ghz (C/C++)
204 CF-base-tv 77.09 % 83.72 % 73.71 % 1 s 1 core @ 2.5 Ghz (C/C++)
205 KeyFuse2B 76.95 % 84.86 % 72.53 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
206 KeyPoint-IoUHead 76.81 % 84.61 % 72.16 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
207 HRI-VoxelFPN 76.70 % 85.64 % 69.44 % 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.
208 DKAnet 76.70 % 84.57 % 71.54 % 0.05 s 1 core @ 2.0 Ghz (Python)
209 cff-tv-t 76.68 % 85.58 % 70.69 % 1 s 1 core @ 2.5 Ghz (C/C++)
210 SARPNET 76.64 % 85.63 % 71.31 % 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.
211 SECOND_7862 76.60 % 85.29 % 71.77 % 1 s 1 core @ 2.5 Ghz (Python)
212 Anonymous 76.60 % 85.29 % 71.77 % 1 1 core @ 2.5 Ghz (Python)
213 DTFI 76.59 % 85.29 % 71.78 % 0.03 s 1 core @ 2.5 Ghz (Python)
214 CSNet8299 code 76.55 % 86.49 % 71.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
215 3D IoU Loss
This method makes use of Velodyne laser scans.
76.50 % 86.16 % 71.39 % 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.
216 F-ConvNet
This method makes use of Velodyne laser scans.
code 76.39 % 87.36 % 66.69 % 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.
217 variance_point 76.27 % 87.44 % 72.03 % 0.05 s 1 core @ 2.5 Ghz (Python)
218 SegVoxelNet 76.13 % 86.04 % 70.76 % 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.
219 S-AT GCN 76.04 % 83.20 % 71.17 % 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.
220 KPP3D code 76.00 % 86.66 % 71.07 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
221 TANet code 75.94 % 84.39 % 68.82 % 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.
222 CF-base-train 75.93 % 83.47 % 71.22 % 1 s 1 core @ 2.5 Ghz (C/C++)
223 cp-tv-kp 75.85 % 83.50 % 72.54 % 1 s 1 core @ 2.5 Ghz (C/C++)
224 PointRGCN 75.73 % 85.97 % 70.60 % 0.26 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
225 cp-tv 75.67 % 83.31 % 72.24 % 1 s 1 core @ 2.5 Ghz (C/C++)
226 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 75.64 % 86.96 % 70.70 % 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.
227 Self-Calib Conv 75.59 % 83.54 % 71.96 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
228 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 75.43 % 86.10 % 68.88 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
229 CF-ctdep-train 75.43 % 83.03 % 71.31 % 1 s 1 core @ 2.5 Ghz (C/C++)
230 MFANet 75.39 % 85.38 % 71.70 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
231 Anonymous 75.33 % 84.42 % 70.10 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
232 R-GCN 75.26 % 83.42 % 68.73 % 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.
233 CAT 75.22 % 84.84 % 70.05 % 1 s 1 core @ 2.5 Ghz (Python)
234 epBRM
This method makes use of Velodyne laser scans.
code 75.15 % 85.00 % 69.84 % 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.
235 MAFF-Net(DAF-Pillar) 75.04 % 85.52 % 67.61 % 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.
236 T_PVRCNN 74.93 % 84.79 % 69.95 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
237 T_PVRCNN_V2 74.90 % 84.74 % 69.60 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
238 PI-RCNN 74.82 % 84.37 % 70.03 % 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.
239 MF 74.70 % 83.42 % 66.51 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
240 LazyTorch-CP-Infer-O 74.57 % 81.82 % 70.24 % 1 s 1 core @ 2.5 Ghz (C/C++)
241 LazyTorch-CP-Small-P 74.44 % 81.73 % 70.14 % 1 s 1 core @ 2.5 Ghz (C/C++)
242 City-CF-fixed 74.37 % 83.23 % 69.65 % 1 s 1 core @ 2.5 Ghz (C/C++)
243 PointPillars
This method makes use of Velodyne laser scans.
code 74.31 % 82.58 % 68.99 % 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.
244 ARPNET 74.04 % 84.69 % 68.64 % 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.
245 Harmonic PointPillar code 73.96 % 82.26 % 69.21 % 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.
246 CenterPoint (pcdet) 73.96 % 81.17 % 69.48 % 0.051 sec/frame 2 x V100
247 PC-CNN-V2
This method makes use of Velodyne laser scans.
73.79 % 85.57 % 65.65 % 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.
248 ZMMPP 73.78 % 82.48 % 68.80 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
249 C-GCN 73.62 % 83.49 % 67.01 % 0.147 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
250 3DBN
This method makes use of Velodyne laser scans.
73.53 % 83.77 % 66.23 % 0.13s 1080Ti (Python+C/C++)
X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.
251 Dune-DCF-e11 73.51 % 80.89 % 68.89 % 1 s 1 core @ 2.5 Ghz (C/C++)
252 CrazyTensor-CP 73.50 % 81.04 % 69.87 % 1 s 1 core @ 2.5 Ghz (Python)
253 PointRGBNet 73.49 % 83.99 % 68.56 % 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.
254 City-CF 73.48 % 80.85 % 69.07 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
255 PSM_stereo 73.43 % 81.28 % 66.98 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
256 Dune-DCF-e15 73.29 % 80.34 % 68.54 % 1 s 1 core @ 2.5 Ghz (C/C++)
257 SCNet
This method makes use of Velodyne laser scans.
73.17 % 83.34 % 67.93 % 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.
258 Dune-DCF-e09 73.15 % 80.40 % 68.57 % 1 s 1 core @ 2.5 Ghz (C/C++)
259 AFTD 73.12 % 82.71 % 68.09 % 1 s 1 core @ 2.5 Ghz (Python + C/C++)
260 PP-PCdet code 73.07 % 83.32 % 68.18 % 0.01 s 1 core @ 2.5 Ghz (Python)
261 CAD
This method uses stereo information.
This method makes use of Velodyne laser scans.
73.04 % 81.48 % 66.63 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
262 PFF3D
This method makes use of Velodyne laser scans.
code 72.93 % 81.11 % 67.24 % 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.
263 CrazyTensor-CF 72.92 % 79.87 % 68.41 % 1 s 1 core @ 2.5 Ghz (C/C++)
264 DASS 72.31 % 81.85 % 65.99 % 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.
265 HS3D code 72.25 % 83.57 % 67.49 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
266 TBD_BD code 72.16 % 83.36 % 66.87 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
267 SSL_PP code 72.02 % 83.74 % 64.95 % 16ms GPU @ 1.5 Ghz (Python)
268 TBD 71.94 % 83.20 % 66.83 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
269 AVOD-FPN
This method makes use of Velodyne laser scans.
code 71.76 % 83.07 % 65.73 % 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.
270 PointPainting
This method makes use of Velodyne laser scans.
71.70 % 82.11 % 67.08 % 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.
271 Contrastive PP code 71.64 % 84.80 % 66.49 % 0.01 s 1 core @ 2.5 Ghz (Python)
272 new_stereo 70.79 % 80.05 % 66.04 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
273 WS3D
This method makes use of Velodyne laser scans.
70.59 % 80.99 % 64.23 % 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.
274 F-PointNet
This method makes use of Velodyne laser scans.
code 69.79 % 82.19 % 60.59 % 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.
275 UberATG-ContFuse
This method makes use of Velodyne laser scans.
68.78 % 83.68 % 61.67 % 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.
276 MLOD
This method makes use of Velodyne laser scans.
code 67.76 % 77.24 % 62.05 % 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.
277 DSGN++
This method uses stereo information.
code 67.37 % 83.21 % 59.91 % 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.
278 DMF
This method uses stereo information.
67.33 % 77.55 % 62.44 % 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.
279 Anonymous 66.97 % 83.77 % 58.41 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
280 AVOD
This method makes use of Velodyne laser scans.
code 66.47 % 76.39 % 60.23 % 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.
281 StereoDistill 66.39 % 81.66 % 57.39 % 0.4 s 1 core @ 2.5 Ghz (Python)
282 MMLAB LIGA-Stereo
This method uses stereo information.
code 64.66 % 81.39 % 57.22 % 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.
283 BirdNet+
This method makes use of Velodyne laser scans.
code 64.04 % 76.15 % 59.79 % 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.
284 CZY 63.68 % 77.56 % 57.82 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
285 MV3D
This method makes use of Velodyne laser scans.
63.63 % 74.97 % 54.00 % 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.
286 SD3DOD 62.00 % 76.09 % 55.46 % 0.04 s GPU @ 2.5 Ghz (Python)
287 SNVC
This method uses stereo information.
code 61.34 % 78.54 % 54.23 % 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.
288 RCD 60.56 % 70.54 % 55.58 % 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.
289 Anonymous 58.57 % 77.81 % 52.13 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
290 A3DODWTDA
This method makes use of Velodyne laser scans.
code 56.82 % 62.84 % 48.12 % 0.08 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
291 FD 56.40 % 73.05 % 52.25 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
292 CGPS-3DV code 55.28 % 74.80 % 46.70 % CGPS-3DV 1 core @ 2.5 Ghz (C/C++)
293 PL++ (SDN+GDC)
This method uses stereo information.
This method makes use of Velodyne laser scans.
code 54.88 % 68.38 % 49.16 % 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.
294 MV3D (LIDAR)
This method makes use of Velodyne laser scans.
54.54 % 68.35 % 49.16 % 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.
295 CDN
This method uses stereo information.
code 54.22 % 74.52 % 46.36 % 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.
296 CG-Stereo
This method uses stereo information.
53.58 % 74.39 % 46.50 % 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.
297 PS code 52.88 % 74.41 % 44.38 % PS s 1 core @ 2.5 Ghz (C/C++)
298 UPF_3D
This method uses stereo information.
52.83 % 78.24 % 46.12 % 0.29 s 1 core @ 2.5 Ghz (Python)
299 DSGN
This method uses stereo information.
code 52.18 % 73.50 % 45.14 % 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.
300 BirdNet+ (legacy)
This method makes use of Velodyne laser scans.
code 51.85 % 70.14 % 50.03 % 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.
301 ppt 50.41 % 54.19 % 45.14 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
302 Complexer-YOLO
This method makes use of Velodyne laser scans.
47.34 % 55.93 % 42.60 % 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.
303 ESGN
This method uses stereo information.
46.39 % 65.80 % 38.42 % 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.
304 Disp R-CNN (velo)
This method uses stereo information.
code 45.78 % 68.21 % 37.73 % 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.
305 CDN-PL++
This method uses stereo information.
44.86 % 64.31 % 38.11 % 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.
306 Disp R-CNN
This method uses stereo information.
code 43.27 % 67.02 % 36.43 % 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.
307 Pseudo-LiDAR++
This method uses stereo information.
code 42.43 % 61.11 % 36.99 % 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.
308 ART 42.42 % 63.38 % 36.44 % 20ms s 1 core @ 2.5 Ghz (C/C++)
309 YOLOStereo3D
This method uses stereo information.
code 41.25 % 65.68 % 30.42 % 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.
310 RT3D-GMP
This method uses stereo information.
38.76 % 45.79 % 30.00 % 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.
311 ZoomNet
This method uses stereo information.
code 38.64 % 55.98 % 30.97 % 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.
312 OC Stereo
This method uses stereo information.
code 37.60 % 55.15 % 30.25 % 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.
313 Pseudo-Lidar
This method uses stereo information.
code 34.05 % 54.53 % 28.25 % 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.
314 Stereo CenterNet
This method uses stereo information.
31.30 % 49.94 % 25.62 % 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.
315 Stereo R-CNN
This method uses stereo information.
code 30.23 % 47.58 % 23.72 % 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.
316 SparseLiDAR_fusion 28.93 % 38.06 % 24.14 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
317 BirdNet
This method makes use of Velodyne laser scans.
27.26 % 40.99 % 25.32 % 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.
318 VMDet_Boost 25.02 % 35.96 % 21.47 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
319 GCDR 23.92 % 34.89 % 19.59 % 0.28 s 1 core @ 2.5 Ghz (Python)
320 Anonymous 23.79 % 33.89 % 20.33 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
321 RT3DStereo
This method uses stereo information.
23.28 % 29.90 % 18.96 % 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.
322 Digging_M3D 21.24 % 29.15 % 19.18 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
323 VMDet 20.95 % 31.55 % 17.83 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
324 Anonymous 20.23 % 28.29 % 17.55 % 40 s 1 core @ 2.5 Ghz (C/C++)
325 SARM3D 19.70 % 25.20 % 17.35 % 0.03 s GPU @ 2.5 Ghz (Python)
326 RT3D
This method makes use of Velodyne laser scans.
19.14 % 23.74 % 18.86 % 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.
327 BSM3D 18.87 % 25.66 % 16.50 % 0.03 s 1 core @ 2.5 Ghz (Python)
328 CMKD* 18.69 % 28.55 % 16.77 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
329 Mix-Teaching 18.54 % 26.89 % 15.79 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
330 StereoFENet
This method uses stereo information.
18.41 % 29.14 % 14.20 % 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.
331 MDS-Mono3D 18.20 % 28.47 % 14.95 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
332 anonymity 18.00 % 28.10 % 15.60 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
333 MoGDE 17.88 % 27.07 % 15.66 % 0.03 s GPU @ 2.5 Ghz (Python)
334 LPCG-Monoflex code 17.80 % 25.56 % 15.38 % 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.
335 PS-fld code 17.74 % 23.74 % 15.14 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
Y. Chen, H. Dai and Y. Ding: Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
336 DD3Dv2 code 17.61 % 26.36 % 15.32 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
337 MonoAD 17.28 % 25.35 % 14.58 % 0.03 s GPU @ 2.5 Ghz (Python)
338 Anonymous 17.18 % 25.51 % 14.56 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
339 MonoDDE 17.14 % 24.93 % 15.10 % 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.
340 Anonymous 17.08 % 24.43 % 15.25 % 40 s 1 core @ 2.5 Ghz (C/C++)
341 OPA-3D code 17.05 % 24.60 % 14.25 % 0.04 s 1 core @ 3.5 Ghz (Python)
342 TempM3D 17.05 % 25.29 % 14.86 % 0.05 s 1 core @ 2.5 Ghz (Python)
343 Mobile Stereo R-CNN
This method uses stereo information.
17.04 % 26.97 % 13.26 % 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.
344 anonymity 16.99 % 27.20 % 15.08 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
345 DD3D code 16.87 % 23.19 % 14.36 % 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) .
346 ADD code 16.81 % 25.61 % 13.79 % 0.1 s 1 core @ 2.5 Ghz (Python)
347 Shape-Aware 16.52 % 23.84 % 13.88 % 0.05 s 1 core @ 2.5 Ghz (Python)
348 MonoCon code 16.46 % 22.50 % 13.95 % 0.02 s GPU @ 2.5 Ghz (Python)
T. Xianpeng Liu: Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection. AAAI 2022.
349 SAD 16.41 % 26.05 % 13.60 % 0.05 s 1 core @ 2.5 Ghz (python)
350 DID-M3D code 16.29 % 24.40 % 13.75 % 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.
351 MonoDETR code 16.26 % 24.52 % 13.93 % 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.
352 MonoPPM code 16.21 % 22.43 % 13.73 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
353 Lite-FPN-GUPNet 16.20 % 23.58 % 13.56 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
354 SAD 16.14 % 25.55 % 13.29 % 0.05 s 1 core @ 2.5 Ghz (python)
355 gupnet_se 16.10 % 23.62 % 13.41 % 0.03s 1 core @ 2.5 Ghz (C/C++)
356 zongmuDistill 16.08 % 25.11 % 13.69 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
357 MonoDistill 16.03 % 22.97 % 13.60 % 0.04 s 1 core @ 2.5 Ghz (Python)
358 MDNet 16.01 % 24.59 % 13.49 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
359 Slime 15.99 % 24.99 % 13.30 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
360 DDS code 15.90 % 23.81 % 13.21 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
361 mono3d code 15.73 % 23.96 % 13.35 % TBD TBD
362 monopd code 15.72 % 23.51 % 13.07 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
363 OBMO_GUPNet 15.70 % 22.71 % 13.23 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
364 Anonymous 15.67 % 22.34 % 12.92 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
365 MonoInsight 15.45 % 21.45 % 13.21 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
366 GPENet code 15.44 % 22.41 % 12.84 % 0.02 s GPU @ 2.5 Ghz (Python)
367 MonoDTR 15.39 % 21.99 % 12.73 % 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.
368 mono3d 15.26 % 23.41 % 12.80 % 0.03 s GPU @ 2.5 Ghz (Python)
369 HBD 15.17 % 21.71 % 13.06 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
370 EW code 15.13 % 21.16 % 12.81 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
371 ZongmuMono3d code 15.08 % 23.79 % 13.25 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
372 GUPNet code 15.02 % 22.26 % 13.12 % 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.
373 HomoLoss(monoflex) code 14.94 % 21.75 % 13.07 % 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.
374 Anonymous 14.87 % 23.93 % 12.45 % 40 s 1 core @ 2.5 Ghz (C/C++)
375 Anonymous 14.84 % 22.73 % 13.08 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
376 SGM3D code 14.65 % 22.46 % 12.97 % 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.
377 SAIC_ADC_Mono3D code 14.54 % 18.98 % 12.86 % 50 s GPU @ 2.5 Ghz (Python)
378 MonoEdge 14.47 % 21.08 % 12.73 % 0.05 s GPU @ 2.5 Ghz (Python)
379 MDSNet 14.46 % 24.30 % 11.12 % 0.07 s 1 core @ 2.5 Ghz (Python)
380 DEVIANT code 14.46 % 21.88 % 11.89 % 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.
381 MonoGround 14.36 % 21.37 % 12.62 % 0.03 s 1 core @ 2.5 Ghz (Python)
382 MonoEdge-RCNN 14.35 % 19.74 % 11.94 % 0.05 s 1 core @ 2.5 Ghz (Python)
383 DLE code 14.33 % 24.23 % 10.30 % 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.
384 AutoShape code 14.17 % 22.47 % 11.36 % 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.
385 MonoEdge-Rotate 14.13 % 21.60 % 12.27 % 0.05 s GPU @ 2.5 Ghz (Python)
386 EM code 14.00 % 22.93 % 11.26 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
387 MonoFlex 13.89 % 19.94 % 12.07 % 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.
388 MonoEF 13.87 % 21.29 % 11.71 % 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.
389 MonoAug 13.85 % 20.06 % 11.43 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
390 DEPT 13.83 % 20.43 % 11.66 % 0.03 s 1 core @ 2.5 Ghz (Python)
391 MonoPCNS 13.74 % 20.31 % 12.31 % 0.14 s GPU @ 2.5 Ghz (Python)
392 Anonymous code 13.72 % 20.08 % 11.34 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
393 DFR-Net 13.63 % 19.40 % 10.35 % 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.
394 CaDDN code 13.41 % 19.17 % 11.46 % 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.
395 PCT code 13.37 % 21.00 % 11.31 % 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.
396 Ground-Aware code 13.25 % 21.65 % 9.91 % 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.
397 MK3D 13.19 % 20.48 % 11.10 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
398 Aug3D-RPN 12.99 % 17.82 % 9.78 % 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.
399 HomoLoss(imvoxelnet) code 12.99 % 20.10 % 10.50 % 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.
400 DDMP-3D 12.78 % 19.71 % 9.80 % 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.
401 Kinematic3D code 12.72 % 19.07 % 9.17 % 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 .
402 M3DGAF 12.66 % 19.48 % 10.99 % 0.07 s 1 core @ 2.5 Ghz (Python)
403 MonoRCNN code 12.65 % 18.36 % 10.03 % 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.
404 GrooMeD-NMS code 12.32 % 18.10 % 9.65 % 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.
405 MonoRUn code 12.30 % 19.65 % 10.58 % 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.
406 monodle code 12.26 % 17.23 % 10.29 % 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 .
407 LT-M3OD 12.26 % 18.15 % 10.05 % 0.03 s 1 core @ 2.5 Ghz (Python)
408 YoloMono3D code 12.06 % 18.28 % 8.42 % 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.
409 IAFA 12.01 % 17.81 % 10.61 % 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.
410 GAC3D 12.00 % 17.75 % 9.15 % 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.
411 CMAN 11.87 % 17.77 % 9.16 % 0.15 s 1 core @ 2.5 Ghz (Python)
412 PGD-FCOS3D code 11.76 % 19.05 % 9.39 % 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.
413 D4LCN code 11.72 % 16.65 % 9.51 % 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.
414 MonoAug 11.47 % 16.40 % 9.26 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
415 KM3D code 11.45 % 16.73 % 9.92 % 0.03 s 1 core @ 2.5 Ghz (Python)
P. Li: Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training. 2020.
416 RefinedMPL 11.14 % 18.09 % 8.94 % 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.
417 PatchNet code 11.12 % 15.68 % 10.17 % 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.
418 MDT code 11.11 % 15.95 % 8.80 % 0.01 s 1 core @ 2.5 Ghz (Python)
419 ImVoxelNet code 10.97 % 17.15 % 9.15 % 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.
420 MM 10.74 % 15.80 % 8.64 % 1 s 1 core @ 2.5 Ghz (C/C++)
421 AM3D 10.74 % 16.50 % 9.52 % 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.
422 Keypoint-3D 10.42 % 15.97 % 7.91 % 14 s 1 core @ 2.5 Ghz (C/C++)
423 RTM3D code 10.34 % 14.41 % 8.77 % 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.
424 MonoPair 9.99 % 13.04 % 8.65 % 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.
425 Neighbor-Vote 9.90 % 15.57 % 8.89 % 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.
426 SMOKE code 9.76 % 14.03 % 7.84 % 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.
427 M3D-RPN code 9.71 % 14.76 % 7.42 % 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 .
428 QD-3DT
This is an online method (no batch processing).
code 9.33 % 12.81 % 7.86 % 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.
429 TopNet-HighRes
This method makes use of Velodyne laser scans.
9.28 % 12.67 % 7.95 % 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.
430 MonoCInIS 7.94 % 15.82 % 6.68 % 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.
431 SS3D 7.68 % 10.78 % 6.51 % 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.
432 MonoCInIS 7.66 % 15.21 % 6.24 % 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.
433 Mono3D_PLiDAR code 7.50 % 10.76 % 6.10 % 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.
434 MonoPSR code 7.25 % 10.76 % 5.85 % 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.
435 Decoupled-3D 7.02 % 11.08 % 5.63 % 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.
436 VoxelJones code 6.35 % 7.39 % 5.80 % .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.
437 MonoGRNet code 5.74 % 9.61 % 4.25 % 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.
438 A3DODWTDA (image) code 5.27 % 6.88 % 4.45 % 0.8 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
439 MonoFENet 5.14 % 8.35 % 4.10 % 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.
440 TLNet (Stereo)
This method uses stereo information.
code 4.37 % 7.64 % 3.74 % 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.
441 CSoR
This method makes use of Velodyne laser scans.
4.06 % 5.61 % 3.17 % 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.
442 Shift R-CNN (mono) code 3.87 % 6.88 % 2.83 % 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.
443 MVRA + I-FRCNN+ 3.27 % 5.19 % 2.49 % 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.
444 SparVox3D 3.20 % 5.27 % 2.56 % 0.05 s GPU @ 2.0 Ghz (Python)
E. Balatkan and F. Kıraç: Improving Regression Performance on Monocular 3D Object Detection Using Bin-Mixing and Sparse Voxel Data. 2021 6th International Conference on Computer Science and Engineering (UBMK) 2021.
445 TopNet-UncEst
This method makes use of Velodyne laser scans.
3.02 % 3.24 % 2.26 % 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.
446 GS3D 2.90 % 4.47 % 2.47 % 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.
447 3D-GCK 2.52 % 3.27 % 2.11 % 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.
448 WeakM3D code 2.26 % 5.03 % 1.63 % 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.
449 ROI-10D 2.02 % 4.32 % 1.46 % 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.
450 CDTrack3D code 1.92 % 3.20 % 1.63 % 0.0106 s NVIDIA RTX 3090 GPU, i9 10850k CPU
451 FQNet 1.51 % 2.77 % 1.01 % 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.
452 3D-SSMFCNN code 1.41 % 1.88 % 1.11 % 0.1 s GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.
453 test code 0.03 % 0.01 % 0.03 % 50 s 1 core @ 2.5 Ghz (Python)
454 MonoDET code 0.01 % 0.03 % 0.01 % 0.04 s 1 core @ 2.5 Ghz (Python)
455 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 CasA++ code 49.29 % 56.33 % 46.70 % 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.
2 TED 49.21 % 55.85 % 46.52 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
3 Anonymous 48.77 % 55.59 % 46.12 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
4 VPFNet code 48.36 % 54.65 % 44.98 % 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.
5 CAD 47.91 % 55.98 % 44.63 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
6 DCAN-Second code 47.38 % 55.12 % 44.59 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
7 PV-RCNN++ code 47.19 % 54.29 % 43.49 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
8 CasA code 47.09 % 54.04 % 44.56 % 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.
9 EQ-PVRCNN code 47.02 % 55.84 % 42.94 % 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 PiFeNet code 46.71 % 56.39 % 42.71 % 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.
11 CAT-Det 45.44 % 54.26 % 41.94 % 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.
12 HotSpotNet 45.37 % 53.10 % 41.47 % 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.
13 PE-RCVN 45.01 % 50.29 % 41.85 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
14 variance_point 44.89 % 53.72 % 41.21 % 0.05 s 1 core @ 2.5 Ghz (Python)
15 ACDet code 44.79 % 53.41 % 41.96 % 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.
16 SPT 44.72 % 51.35 % 41.38 % 0.1 s GPU @ 2.5 Ghz (Python)
17 VPN 44.56 % 54.13 % 41.73 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
18 EPNet++ 44.38 % 52.79 % 41.29 % 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.
19 TANet code 44.34 % 53.72 % 40.49 % 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.
20 CFF-tv 44.33 % 52.72 % 41.11 % 1 s 1 core @ 2.5 Ghz (C/C++)
21 3DSSD code 44.27 % 54.64 % 40.23 % 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.
22 Point-GNN
This method makes use of Velodyne laser scans.
code 43.77 % 51.92 % 40.14 % 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.
23 USVLab BSAODet 43.63 % 51.71 % 41.09 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
24 CFF-ep25 43.47 % 51.85 % 40.69 % 1 s 1 core @ 2.5 Ghz (C/C++)
25 FV2P v2 43.47 % 50.64 % 40.09 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
26 F-ConvNet
This method makes use of Velodyne laser scans.
code 43.38 % 52.16 % 38.80 % 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.
27 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 43.35 % 53.10 % 40.06 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
28 FS-Net
This method makes use of Velodyne laser scans.
43.31 % 49.82 % 40.89 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
29 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 43.29 % 52.17 % 40.29 % 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.
30 FromVoxelToPoint code 43.28 % 51.80 % 40.71 % 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.
31 VMVS
This method makes use of Velodyne laser scans.
43.27 % 53.44 % 39.51 % 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.
32 cff-tv-v2-ep25 43.25 % 51.40 % 40.23 % 1 s 1 core @ 2.5 Ghz (C/C++)
33 P2V-RCNN 43.19 % 50.91 % 40.81 % 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.
34 KeyFuse2B 43.18 % 51.49 % 40.70 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
35 CF-ctdep-tv-ta 43.11 % 50.40 % 40.51 % 1 s 1 core @ 2.5 Ghz (C/C++)
36 MGAF-3DSSD code 43.09 % 50.65 % 39.65 % 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.
37 Reprod-Two-Branch 43.07 % 52.07 % 40.40 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
38 SGNet 43.00 % 49.68 % 40.45 % 0.09 s GPU @ 2.5 Ghz (Python)
39 Frustum-PointPillars code 42.89 % 51.22 % 39.28 % 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.
40 PSA-Det3D 42.81 % 49.72 % 39.58 % 0.1 s GPU @ 2.5 Ghz (Python)
41 CFF-tv-v2 42.77 % 51.08 % 40.15 % 1 s 1 core @ 2.5 Ghz (C/C++)
42 Fast-CLOCs 42.72 % 52.10 % 39.08 % 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.
43 CF-base-tv 42.66 % 50.01 % 39.76 % 1 s 1 core @ 2.5 Ghz (C/C++)
44 HMFI code 42.65 % 50.88 % 39.78 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
45 TBD 42.65 % 50.88 % 39.78 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
46 USVLab BSAODet (S) 42.62 % 49.52 % 39.12 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
47 PA-RCNN code 42.49 % 49.11 % 39.58 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
48 STD code 42.47 % 53.29 % 38.35 % 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.
49 VoCo 42.32 % 47.96 % 39.69 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
50 AVOD-FPN
This method makes use of Velodyne laser scans.
code 42.27 % 50.46 % 39.04 % 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.
51 Anonymous
This method makes use of Velodyne laser scans.
42.25 % 49.06 % 40.02 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
52 SemanticVoxels 42.19 % 50.90 % 39.52 % 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.
53 TBD 42.19 % 49.89 % 39.34 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
54 TBD 42.19 % 49.89 % 39.34 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
55 F-PointNet
This method makes use of Velodyne laser scans.
code 42.15 % 50.53 % 38.08 % 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.
56 3SNet 42.02 % 48.91 % 39.39 % 0.07 s GPU @ 2.5 Ghz (Python)
57 CF-ctdep-tv 41.98 % 49.14 % 39.03 % 1 s 1 core @ 2.5 Ghz (C/C++)
58 DTE3D 41.97 % 49.91 % 39.27 % 0.19 s 1 core @ 2.5 Ghz (C/C++)
59 Self-Calib Conv 41.95 % 48.88 % 39.52 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
60 CZY_PPF_Net2 41.93 % 47.18 % 40.08 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
61 PointPillars
This method makes use of Velodyne laser scans.
code 41.92 % 51.45 % 38.89 % 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.
62 CenterFuse 41.80 % 49.77 % 38.49 % 0.059 sec/frame 2 x V100
63 cp-tv-kp 41.70 % 48.77 % 39.41 % 1 s 1 core @ 2.5 Ghz (C/C++)
64 epBRM
This method makes use of Velodyne laser scans.
code 41.52 % 49.17 % 39.08 % 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.
65 TCDVF 41.47 % 49.44 % 38.41 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
66 DGT-Det3D 41.40 % 49.06 % 38.43 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
67 SGDA3D 41.39 % 47.59 % 38.37 % 0.07 s 1 core @ 2.5 Ghz (Python)
68 CZY_3917 41.38 % 46.09 % 38.64 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
69 tbd 41.10 % 50.56 % 37.49 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
70 DGT-Det3D code 41.07 % 48.79 % 38.09 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
71 IA-SSD (single) code 41.03 % 47.90 % 37.98 % 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.
72 PointPainting
This method makes use of Velodyne laser scans.
40.97 % 50.32 % 37.87 % 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.
73 Anonymous 40.92 % 50.07 % 38.18 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
74 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
code 40.89 % 46.97 % 38.80 % 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.
75 SARFE 40.79 % 47.29 % 38.25 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
76 IoU-2B 40.62 % 50.33 % 36.94 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
77 MSADet 40.58 % 49.54 % 38.19 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
78 TBD 40.57 % 47.65 % 38.33 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
79 PDV code 40.56 % 47.80 % 38.46 % 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.
80 cp-tv 40.55 % 47.71 % 38.23 % 1 s 1 core @ 2.5 Ghz (C/C++)
81 MVMM code 40.49 % 47.54 % 38.36 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
82 E^2-PV-RCNN 40.47 % 46.61 % 38.60 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
X. Li, Y. Zhang and D. Kong: E^2-PV-RCNN: improving 3D object detection via enhancing keypoint features. Multimedia Tools and Applications 2022.
83 cff-tv-t 40.41 % 49.46 % 37.02 % 1 s 1 core @ 2.5 Ghz (C/C++)
84 U_SECOND_V4 40.40 % 48.46 % 37.40 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
85 SVGA-Net 40.39 % 48.48 % 37.92 % 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.
86 CZY_PPF_Net 40.28 % 46.03 % 38.19 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
87 Semantical PVRCNN 40.18 % 45.94 % 37.28 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
88 KeyPoint-IoUHead 40.15 % 47.86 % 37.01 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
89 TBD 40.07 % 46.11 % 37.87 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
90 M3DeTR code 39.94 % 45.70 % 37.66 % 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.
91 cp-tv-kp-io-sc 39.92 % 48.06 % 37.68 % 1 s 1 core @ 2.5 Ghz (C/C++)
92 DDet 39.87 % 45.82 % 38.00 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
93 CF-cd-io-tv 39.82 % 48.67 % 36.45 % 1 s 1 core @ 2.5 Ghz (C/C++)
94 MFANet 39.73 % 48.68 % 36.46 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
95 VPNet 39.67 % 47.55 % 36.66 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
96 DSASNet 39.65 % 47.14 % 37.05 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
97 VCRCNN 39.64 % 45.19 % 37.55 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
98 IKT3D
This method makes use of Velodyne laser scans.
39.53 % 45.34 % 37.14 % 0.05 s 1 core @ 2.5 Ghz (Python)
99 WGVRF 39.52 % 45.98 % 37.56 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
100 VGA-RCNN 39.48 % 47.80 % 36.99 % 0.07 s 1 core @ 2.5 Ghz (Python)
101 AFTD 39.45 % 48.28 % 36.07 % 1 s 1 core @ 2.5 Ghz (Python + C/C++)
102 TBD code 39.45 % 47.08 % 37.12 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
103 Dune-DCF-e09 39.43 % 47.29 % 36.74 % 1 s 1 core @ 2.5 Ghz (C/C++)
104 LazyTorch-CP-Infer-O 39.43 % 47.38 % 36.96 % 1 s 1 core @ 2.5 Ghz (C/C++)
105 SRDL 39.43 % 47.30 % 36.99 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
106 FusionDetv1 39.42 % 47.30 % 36.97 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
107 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 39.37 % 47.98 % 36.01 % 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.
108 LazyTorch-CP-Small-P 39.33 % 47.27 % 36.89 % 1 s 1 core @ 2.5 Ghz (C/C++)
109 ARPNET 39.31 % 48.32 % 35.93 % 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.
110 CenterPoint (pcdet) 39.28 % 47.25 % 36.78 % 0.051 sec/frame 2 x V100
111 CZY 39.26 % 45.08 % 36.38 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
112 Dune-DCF-e11 39.26 % 47.32 % 36.63 % 1 s 1 core @ 2.5 Ghz (C/C++)
113 ATT_SSD 39.24 % 45.92 % 36.35 % 0.01 s 1 core @ 2.5 Ghz (Python)
114 City-CF-fixed 39.22 % 47.68 % 36.55 % 1 s 1 core @ 2.5 Ghz (C/C++)
115 IA-SSD (multi) code 39.03 % 46.51 % 35.61 % 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.
116 BASA 38.90 % 46.74 % 36.24 % 1s 1 core @ 2.5 Ghz (python)
117 PSA-SSD 38.87 % 46.21 % 36.85 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
118 SIF 38.74 % 46.23 % 36.06 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
119 CrazyTensor-CP 38.67 % 46.58 % 36.15 % 1 s 1 core @ 2.5 Ghz (Python)
120 SCNet
This method makes use of Velodyne laser scans.
38.66 % 47.83 % 35.70 % 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.
121 Dune-DCF-e15 38.61 % 46.41 % 36.02 % 1 s 1 core @ 2.5 Ghz (C/C++)
122 Faraway-Frustum
This method makes use of Velodyne laser scans.
code 38.58 % 46.33 % 35.71 % 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.
123 AGS-SSD[la] 38.53 % 46.10 % 35.40 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
124 HPV-RCNN 38.46 % 46.37 % 35.10 % 0.15 s 1 core @ 2.5 Ghz (Python)
125 FPV-SSD 38.45 % 45.83 % 36.03 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
126 CF-base-train 38.44 % 45.89 % 35.55 % 1 s 1 core @ 2.5 Ghz (C/C++)
127 TBD 38.27 % 46.35 % 36.39 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
128 City-CF 38.04 % 45.42 % 35.40 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
129 CF-ctdep-train 38.03 % 44.75 % 35.49 % 1 s 1 core @ 2.5 Ghz (C/C++)
130 NV-RCNN 37.82 % 44.38 % 35.55 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
131 SWA code 37.76 % 44.59 % 34.82 % 0.18 s 1 core @ 2.5 Ghz (C/C++)
132 PVTr 37.58 % 43.99 % 35.48 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
133 DVFENet 37.50 % 43.55 % 35.33 % 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.
134 MLOD
This method makes use of Velodyne laser scans.
code 37.47 % 47.58 % 35.07 % 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.
135 S-AT GCN 37.37 % 44.63 % 34.92 % 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.
136 T_PVRCNN 37.12 % 45.20 % 34.04 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
137 HS3D code 36.86 % 45.62 % 33.67 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
138 T_PVRCNN_V2 36.79 % 44.81 % 33.83 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
139 XView 36.79 % 42.44 % 34.96 % 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.
140 TBD 36.26 % 43.22 % 34.31 % TBD s 1 core @ 2.5 Ghz (C/C++)
141 PFF3D
This method makes use of Velodyne laser scans.
code 36.07 % 43.93 % 32.86 % 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.
142 SECOND_7862 35.92 % 43.04 % 33.56 % 1 s 1 core @ 2.5 Ghz (Python)
143 CrazyTensor-CF 35.83 % 43.50 % 33.23 % 1 s 1 core @ 2.5 Ghz (C/C++)
144 BirdNet+
This method makes use of Velodyne laser scans.
code 35.06 % 41.55 % 32.93 % 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.
145 TBD_BD code 34.86 % 42.56 % 32.49 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
146 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 34.59 % 42.27 % 31.37 % 0.0047s 1 core @ 2.5 Ghz (python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
147 CAD
This method uses stereo information.
This method makes use of Velodyne laser scans.
33.67 % 41.35 % 31.28 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
148 GT3D 33.56 % 42.96 % 29.93 % 0.1 s 1 core @ 2.5 Ghz (Python)
149 KPP3D code 32.91 % 41.34 % 30.45 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
150 DSGN++
This method uses stereo information.
code 32.74 % 43.05 % 29.54 % 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.
151 ZMMPP 32.38 % 39.54 % 30.25 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
152 StereoDistill 32.23 % 44.12 % 28.95 % 0.4 s 1 core @ 2.5 Ghz (Python)
153 PP-PCdet code 32.04 % 39.23 % 29.79 % 0.01 s 1 core @ 2.5 Ghz (Python)
154 Contrastive PP code 31.64 % 38.47 % 29.30 % 0.01 s 1 core @ 2.5 Ghz (Python)
155 BirdNet+ (legacy)
This method makes use of Velodyne laser scans.
code 31.46 % 37.99 % 29.46 % 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.
156 SparsePool code 30.38 % 37.84 % 26.94 % 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.
157 MMLAB LIGA-Stereo
This method uses stereo information.
code 30.00 % 40.46 % 27.07 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
158 DMF
This method uses stereo information.
29.77 % 37.21 % 27.62 % 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 27.92 % 35.52 % 25.87 % 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 AVOD
This method makes use of Velodyne laser scans.
code 27.86 % 36.10 % 25.76 % 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.
161 CGPS-3DV code 27.45 % 36.89 % 24.01 % CGPS-3DV 1 core @ 2.5 Ghz (C/C++)
162 CSW3D
This method makes use of Velodyne laser scans.
26.64 % 33.75 % 23.34 % 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.
163 PointRGBNet 26.40 % 34.77 % 24.03 % 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.
164 PS code 26.01 % 35.52 % 23.24 % PS s 1 core @ 2.5 Ghz (C/C++)
165 Disp R-CNN (velo)
This method uses stereo information.
code 25.80 % 37.12 % 22.04 % 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.
166 CZY 25.47 % 32.33 % 23.60 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
167 Disp R-CNN
This method uses stereo information.
code 25.40 % 35.75 % 21.79 % 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.
168 CG-Stereo
This method uses stereo information.
24.31 % 33.22 % 20.95 % 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.
169 YOLOStereo3D
This method uses stereo information.
code 19.75 % 28.49 % 16.48 % 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.
170 OC Stereo
This method uses stereo information.
code 17.58 % 24.48 % 15.60 % 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.
171 BirdNet
This method makes use of Velodyne laser scans.
17.08 % 22.04 % 15.82 % 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.
172 DSGN
This method uses stereo information.
code 15.55 % 20.53 % 14.15 % 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.
173 Complexer-YOLO
This method makes use of Velodyne laser scans.
13.96 % 17.60 % 12.70 % 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.
174 Anonymous 11.69 % 17.79 % 10.09 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
175 RT3D-GMP
This method uses stereo information.
11.41 % 16.23 % 10.12 % 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.
176 DD3D code 11.04 % 16.64 % 9.38 % 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) .
177 DD3Dv2 code 10.82 % 16.25 % 9.24 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
178 PS-fld code 10.82 % 16.95 % 9.26 % 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.
179 DEPT 10.81 % 16.28 % 9.15 % 0.03 s 1 core @ 2.5 Ghz (Python)
180 OPA-3D code 10.49 % 15.65 % 8.80 % 0.04 s 1 core @ 3.5 Ghz (Python)
181 anonymity 10.39 % 16.89 % 9.29 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
182 ESGN
This method uses stereo information.
10.27 % 14.05 % 9.02 % 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.
183 MonoDTR 10.18 % 15.33 % 8.61 % 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.
184 MonoInsight 10.01 % 15.17 % 9.06 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
185 LT-M3OD 9.99 % 14.85 % 8.52 % 0.03 s 1 core @ 2.5 Ghz (Python)
186 GUPNet code 9.76 % 14.95 % 8.41 % 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.
187 BSM3D 9.37 % 14.05 % 7.92 % 0.03 s 1 core @ 2.5 Ghz (Python)
188 GPENet code 9.36 % 14.61 % 7.91 % 0.02 s GPU @ 2.5 Ghz (Python)
189 Lite-FPN-GUPNet 9.32 % 14.13 % 7.93 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
190 mono3d code 9.20 % 14.53 % 7.82 % TBD TBD
191 ZongmuMono3d code 9.18 % 14.23 % 7.82 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
192 MonoAD 8.84 % 13.85 % 7.38 % 0.03 s GPU @ 2.5 Ghz (Python)
193 SGM3D code 8.81 % 13.99 % 7.26 % 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.
194 CMKD* 8.79 % 13.94 % 7.42 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
195 DEVIANT code 8.65 % 13.43 % 7.69 % 0.04 s 1 GPU (Python)
A. Kumar, G. Brazil, E. Corona, A. Parchami and X. Liu: DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection. European Conference on Computer Vision (ECCV) 2022.
196 gupnet_se 8.65 % 13.40 % 7.78 % 0.03s 1 core @ 2.5 Ghz (C/C++)
197 MonoPCNS 8.63 % 14.16 % 7.30 % 0.14 s GPU @ 2.5 Ghz (Python)
198 SARM3D 8.48 % 12.95 % 7.25 % 0.03 s GPU @ 2.5 Ghz (Python)
199 MonoCon code 8.41 % 13.10 % 6.94 % 0.02 s GPU @ 2.5 Ghz (Python)
T. Xianpeng Liu: Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection. AAAI 2022.
200 HBD 8.33 % 13.47 % 6.99 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
201 MonoEdge 8.33 % 12.11 % 7.03 % 0.05 s GPU @ 2.5 Ghz (Python)
202 GCDR 8.27 % 11.50 % 7.37 % 0.28 s 1 core @ 2.5 Ghz (Python)
203 CaDDN code 8.14 % 12.87 % 6.76 % 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 Anonymous 8.04 % 12.18 % 6.58 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
205 Anonymous code 7.90 % 12.26 % 6.62 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
206 MonoGround 7.89 % 12.37 % 7.13 % 0.03 s 1 core @ 2.5 Ghz (Python)
207 SparseLiDAR_fusion 7.71 % 11.41 % 6.38 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
208 HomoLoss(monoflex) code 7.66 % 11.87 % 6.82 % 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.
209 SAIC_ADC_Mono3D code 7.54 % 12.06 % 6.41 % 50 s GPU @ 2.5 Ghz (Python)
210 Mix-Teaching 7.47 % 11.67 % 6.61 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
211 M3DGAF 7.42 % 11.68 % 6.65 % 0.07 s 1 core @ 2.5 Ghz (Python)
212 LPCG-Monoflex code 7.33 % 10.82 % 6.18 % 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.
213 MonoDDE 7.32 % 11.13 % 6.67 % 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 MonoAug 7.31 % 11.31 % 6.12 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
215 RefinedMPL 7.18 % 11.14 % 5.84 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
216 Shape-Aware 7.16 % 10.40 % 5.93 % 0.05 s 1 core @ 2.5 Ghz (Python)
217 MDSNet 7.09 % 10.68 % 6.06 % 0.07 s 1 core @ 2.5 Ghz (Python)
218 MonoEdge-Rotate 7.02 % 10.47 % 5.84 % 0.05 s GPU @ 2.5 Ghz (Python)
219 TopNet-HighRes
This method makes use of Velodyne laser scans.
6.92 % 10.40 % 6.63 % 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.
220 MonoRUn code 6.78 % 10.88 % 5.83 % 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.
221 DCD code 6.73 % 10.37 % 6.28 % 1 s 1 core @ 2.5 Ghz (C/C++)
222 MonoPair 6.68 % 10.02 % 5.53 % 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.
223 mono3d 6.62 % 10.10 % 5.46 % 0.03 s GPU @ 2.5 Ghz (Python)
224 monodle code 6.55 % 9.64 % 5.44 % 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 .
225 MonoAug 6.36 % 9.59 % 5.24 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
226 MonoFlex 6.31 % 9.43 % 5.26 % 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.
227 MDNet 5.66 % 8.24 % 4.74 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
228 MK3D 5.00 % 7.29 % 4.54 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
229 Aug3D-RPN 4.71 % 6.01 % 3.87 % 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.
230 MM 4.70 % 7.81 % 4.01 % 1 s 1 core @ 2.5 Ghz (C/C++)
231 Shift R-CNN (mono) code 4.66 % 7.95 % 4.16 % 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.
232 MonoPSR code 4.00 % 6.12 % 3.30 % 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.
233 DFR-Net 3.62 % 6.09 % 3.39 % 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.
234 DDMP-3D 3.55 % 4.93 % 3.01 % 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.
235 M3D-RPN code 3.48 % 4.92 % 2.94 % 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 .
236 D4LCN code 3.42 % 4.55 % 2.83 % 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.
237 MoGDE 3.42 % 5.57 % 2.73 % 0.03 s GPU @ 2.5 Ghz (Python)
238 CMAN 3.41 % 4.62 % 2.87 % 0.15 s 1 core @ 2.5 Ghz (Python)
239 QD-3DT
This is an online method (no batch processing).
code 3.37 % 5.53 % 3.02 % 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 MonoEF 2.79 % 4.27 % 2.21 % 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.
241 RT3DStereo
This method uses stereo information.
2.45 % 3.28 % 2.35 % 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.
242 TopNet-UncEst
This method makes use of Velodyne laser scans.
1.87 % 3.42 % 1.73 % 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.
243 SS3D 1.78 % 2.31 % 1.48 % 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.
244 PGD-FCOS3D code 1.49 % 2.28 % 1.38 % 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.
245 SparVox3D 1.35 % 1.93 % 1.04 % 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.
246 EM code 1.18 % 1.09 % 0.80 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
247 CDTrack3D code 1.01 % 1.48 % 0.69 % 0.0106 s NVIDIA RTX 3090 GPU, i9 10850k CPU
248 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 74.32 % 86.74 % 67.45 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
2 TED 74.12 % 88.82 % 66.84 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
3 CasA++ code 73.79 % 87.76 % 66.84 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.
4 CasA code 73.47 % 87.91 % 66.17 % 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 SGNet 70.40 % 86.75 % 62.73 % 0.09 s GPU @ 2.5 Ghz (Python)
6 HMFI code 70.37 % 84.02 % 62.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
7 CAD 69.94 % 84.68 % 62.21 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
8 SARFE 69.67 % 84.88 % 62.26 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
9 EQ-PVRCNN code 69.10 % 85.41 % 62.30 % 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 VoCo 69.00 % 82.74 % 62.46 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
11 CAT-Det 68.81 % 83.68 % 61.45 % 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.
12 CZY_PPF_Net2 68.79 % 82.21 % 61.13 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
13 BtcDet
This method makes use of Velodyne laser scans.
code 68.68 % 82.81 % 61.81 % 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.
14 SPT 68.60 % 84.90 % 61.69 % 0.1 s GPU @ 2.5 Ghz (Python)
15 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
code 68.54 % 82.19 % 61.33 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
16 FS-Net
This method makes use of Velodyne laser scans.
68.35 % 81.81 % 60.96 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
17 CZY_PPF_Net 68.23 % 83.46 % 62.05 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
18 Semantical PVRCNN 68.21 % 83.46 % 61.17 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
19 PE-RCVN 68.13 % 84.96 % 60.34 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
20 E^2-PV-RCNN 68.03 % 81.55 % 60.51 % 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.
21 PA-RCNN code 67.97 % 82.95 % 61.15 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
22 PDV code 67.81 % 83.04 % 60.46 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Hu, T. Kuai and S. Waslander: Point Density-Aware Voxels for LiDAR 3D Object Detection. CVPR 2022.
23 USVLab BSAODet 67.79 % 82.65 % 60.26 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
24 RangeIoUDet
This method makes use of Velodyne laser scans.
67.77 % 83.12 % 60.26 % 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.
25 SGDA3D 67.55 % 82.10 % 60.70 % 0.07 s 1 core @ 2.5 Ghz (Python)
26 DDet 67.55 % 82.03 % 60.23 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
27 3SNet 67.52 % 81.09 % 60.34 % 0.07 s GPU @ 2.5 Ghz (Python)
28 DCAN-Second code 67.50 % 84.90 % 60.78 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
29 PV-RCNN++ code 67.33 % 82.22 % 60.04 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
30 USVLab BSAODet (S) 67.25 % 81.94 % 59.19 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
31 CF-ctdep-tv-ta 67.08 % 85.49 % 59.86 % 1 s 1 core @ 2.5 Ghz (C/C++)
32 SPG_mini
This method makes use of Velodyne laser scans.
code 66.96 % 80.21 % 60.50 % 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.
33 VCRCNN 66.78 % 81.29 % 59.32 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
34 M3DeTR code 66.74 % 83.83 % 59.03 % n/a s GPU @ 1.0 Ghz (Python)
T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis and D. Manocha: M3DeTR: Multi-representation, Multi- scale, Mutual-relation 3D Object Detection with Transformers. 2021.
35 DSASNet 66.71 % 81.82 % 59.37 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
36 ACDet code 66.61 % 83.80 % 59.99 % 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.
37 MSADet 66.49 % 84.21 % 59.73 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
38 Anonymous
This method makes use of Velodyne laser scans.
66.46 % 82.62 % 60.09 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
39 FV2P v2 66.38 % 83.53 % 59.92 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
40 Reprod-Two-Branch 66.28 % 82.71 % 58.88 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
41 CFF-tv 66.26 % 82.09 % 58.54 % 1 s 1 core @ 2.5 Ghz (C/C++)
42 IA-SSD (single) code 66.25 % 82.36 % 59.70 % 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.
43 cff-tv-v2-ep25 66.14 % 82.40 % 58.96 % 1 s 1 core @ 2.5 Ghz (C/C++)
44 CenterFuse 66.10 % 85.19 % 58.95 % 0.059 sec/frame 2 x V100
45 CFF-ep25 65.99 % 81.91 % 58.22 % 1 s 1 core @ 2.5 Ghz (C/C++)
46 CZY 65.97 % 82.86 % 58.33 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
47 HotSpotNet 65.95 % 82.59 % 59.00 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
48 CZY_3917 65.64 % 80.45 % 58.48 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
49 TBD 65.48 % 79.90 % 57.95 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
50 CFF-tv-v2 65.47 % 81.67 % 58.22 % 1 s 1 core @ 2.5 Ghz (C/C++)
51 CF-ctdep-tv 65.43 % 82.29 % 57.86 % 1 s 1 core @ 2.5 Ghz (C/C++)
52 Fast-CLOCs 65.31 % 82.83 % 57.43 % 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.
53 VGA-RCNN 65.19 % 79.70 % 58.52 % 0.07 s 1 core @ 2.5 Ghz (Python)
54 TCDVF 65.19 % 79.41 % 58.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
55 IKT3D
This method makes use of Velodyne laser scans.
65.17 % 79.88 % 58.09 % 0.05 s 1 core @ 2.5 Ghz (Python)
56 TBD 65.13 % 83.80 % 58.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
57 F-ConvNet
This method makes use of Velodyne laser scans.
code 65.07 % 81.98 % 56.54 % 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.
58 TBD 64.92 % 76.57 % 58.43 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
59 MVMM code 64.81 % 77.82 % 58.79 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
60 DGT-Det3D code 64.80 % 78.06 % 58.08 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
61 PVTr 64.51 % 81.09 % 57.50 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
62 HPV-RCNN 64.50 % 79.17 % 57.16 % 0.15 s 1 core @ 2.5 Ghz (Python)
63 FPV-SSD 64.40 % 78.36 % 56.72 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
64 DGT-Det3D 64.38 % 78.27 % 57.79 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
65 cff-tv-t 64.16 % 83.46 % 57.21 % 1 s 1 core @ 2.5 Ghz (C/C++)
66 TBD code 64.12 % 77.56 % 57.95 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
67 TBD 64.12 % 79.27 % 57.48 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
68 KeyFuse2B 64.10 % 82.28 % 57.19 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
69 3DSSD code 64.10 % 82.48 % 56.90 % 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.
70 VPFNet code 64.10 % 77.64 % 58.00 % 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.
71 CF-base-tv 63.97 % 79.52 % 56.61 % 1 s 1 core @ 2.5 Ghz (C/C++)
72 PointPainting
This method makes use of Velodyne laser scans.
63.78 % 77.63 % 55.89 % 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.
73 IoU-2B 63.75 % 82.21 % 56.43 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
74 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 63.71 % 78.60 % 57.65 % 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.
75 CF-cd-io-tv 63.69 % 82.60 % 56.66 % 1 s 1 core @ 2.5 Ghz (C/C++)
76 KeyPoint-IoUHead 63.65 % 81.44 % 56.94 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
77 WGVRF 63.58 % 78.81 % 57.27 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
78 NV-RCNN 63.57 % 80.12 % 56.18 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
79 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 63.52 % 79.17 % 56.93 % 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.
80 Point-GNN
This method makes use of Velodyne laser scans.
code 63.48 % 78.60 % 57.08 % 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.
81 MGAF-3DSSD code 63.43 % 80.64 % 55.15 % 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.
82 FromVoxelToPoint code 63.41 % 81.49 % 56.40 % 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.
83 P2V-RCNN 63.13 % 78.62 % 56.81 % 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.
84 cp-tv-kp-io-sc 63.03 % 79.30 % 56.66 % 1 s 1 core @ 2.5 Ghz (C/C++)
85 PSA-SSD 62.87 % 76.36 % 56.99 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
86 H^23D R-CNN code 62.74 % 78.67 % 55.78 % 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.
87 TBD 62.42 % 76.07 % 56.39 % TBD s 1 core @ 2.5 Ghz (C/C++)
88 VPNet 62.38 % 77.56 % 55.92 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
89 SVGA-Net 62.28 % 78.58 % 54.88 % 0.03s 1 core @ 2.5 Ghz (Python + C/C++)
Q. He, Z. Wang, H. Zeng, Y. Zeng and Y. Liu: SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds. AAAI 2022.
90 AGS-SSD[la] 62.15 % 77.40 % 56.14 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
91 ATT_SSD 62.07 % 76.56 % 55.87 % 0.01 s 1 core @ 2.5 Ghz (Python)
92 Anonymous 62.06 % 76.51 % 55.50 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
93 SRDL 62.02 % 77.35 % 55.52 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
94 FusionDetv1 62.02 % 77.33 % 55.52 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
95 cp-tv 62.01 % 77.26 % 55.09 % 1 s 1 core @ 2.5 Ghz (C/C++)
96 Faraway-Frustum
This method makes use of Velodyne laser scans.
code 62.00 % 77.36 % 55.40 % 0.1 s GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-frustum: Dealing with lidar sparsity for 3D object detection using fusion. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) 2021.
97 DVFENet 62.00 % 78.73 % 55.18 % 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.
98 IA-SSD (multi) code 61.94 % 78.35 % 55.70 % 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.
99 KPP3D code 61.85 % 76.43 % 55.78 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
100 Self-Calib Conv 61.84 % 77.26 % 55.37 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
101 VPN 61.82 % 77.81 % 55.33 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
102 PSA-Det3D 61.79 % 75.82 % 55.12 % 0.1 s GPU @ 2.5 Ghz (Python)
103 S-AT GCN 61.70 % 75.24 % 55.32 % 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.
104 SIF 61.61 % 77.13 % 55.11 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
105 STD code 61.59 % 78.69 % 55.30 % 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.
106 MFANet 61.43 % 76.62 % 54.77 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
107 SWA code 61.12 % 76.47 % 55.51 % 0.18 s 1 core @ 2.5 Ghz (C/C++)
108 Dune-DCF-e11 61.03 % 80.38 % 54.07 % 1 s 1 core @ 2.5 Ghz (C/C++)
109 City-CF 60.84 % 79.32 % 53.83 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
110 Dune-DCF-e15 60.53 % 78.68 % 53.73 % 1 s 1 core @ 2.5 Ghz (C/C++)
111 BASA 60.43 % 76.46 % 54.47 % 1s 1 core @ 2.5 Ghz (python)
112 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 60.30 % 75.42 % 53.81 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
113 EPNet++ 59.71 % 76.15 % 53.67 % 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.
114 cp-tv-kp 59.68 % 75.54 % 53.34 % 1 s 1 core @ 2.5 Ghz (C/C++)
115 City-CF-fixed 59.56 % 77.39 % 53.32 % 1 s 1 core @ 2.5 Ghz (C/C++)
116 XView 59.55 % 77.24 % 53.47 % 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.
117 TANet code 59.44 % 75.70 % 52.53 % 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.
118 TBD_BD code 59.42 % 77.20 % 53.38 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
119 CF-ctdep-train 59.35 % 77.73 % 52.26 % 1 s 1 core @ 2.5 Ghz (C/C++)
120 DTE3D 59.12 % 76.99 % 52.97 % 0.19 s 1 core @ 2.5 Ghz (C/C++)
121 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 58.82 % 74.96 % 52.53 % 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.
122 CF-base-train 58.80 % 76.64 % 51.15 % 1 s 1 core @ 2.5 Ghz (C/C++)
123 CrazyTensor-CF 58.72 % 78.24 % 51.39 % 1 s 1 core @ 2.5 Ghz (C/C++)
124 HS3D code 58.65 % 74.75 % 52.98 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
125 PointPillars
This method makes use of Velodyne laser scans.
code 58.65 % 77.10 % 51.92 % 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.
126 variance_point 58.45 % 75.33 % 51.51 % 0.05 s 1 core @ 2.5 Ghz (Python)
127 ARPNET 58.20 % 74.21 % 52.13 % 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.
128 ZMMPP 58.03 % 71.72 % 51.89 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
129 Dune-DCF-e09 57.82 % 74.49 % 51.53 % 1 s 1 core @ 2.5 Ghz (C/C++)
130 AFTD 57.44 % 75.50 % 51.12 % 1 s 1 core @ 2.5 Ghz (Python + C/C++)
131 U_SECOND_V4 57.10 % 73.91 % 50.91 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
132 LazyTorch-CP-Small-P 56.82 % 73.06 % 50.70 % 1 s 1 core @ 2.5 Ghz (C/C++)
133 LazyTorch-CP-Infer-O 56.77 % 73.03 % 50.73 % 1 s 1 core @ 2.5 Ghz (C/C++)
134 CenterPoint (pcdet) 56.67 % 73.04 % 50.60 % 0.051 sec/frame 2 x V100
135 T_PVRCNN 56.26 % 70.51 % 49.90 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
136 Contrastive PP code 56.24 % 71.38 % 49.15 % 0.01 s 1 core @ 2.5 Ghz (Python)
137 epBRM
This method makes use of Velodyne laser scans.
code 56.13 % 72.08 % 49.91 % 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.
138 F-PointNet
This method makes use of Velodyne laser scans.
code 56.12 % 72.27 % 49.01 % 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.
139 SECOND_7862 55.64 % 71.05 % 49.83 % 1 s 1 core @ 2.5 Ghz (Python)
140 CAD
This method uses stereo information.
This method makes use of Velodyne laser scans.
55.39 % 70.98 % 48.81 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
141 CrazyTensor-CP 55.31 % 72.10 % 49.40 % 1 s 1 core @ 2.5 Ghz (Python)
142 T_PVRCNN_V2 55.29 % 69.58 % 49.22 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
143 PP-PCdet code 54.25 % 68.87 % 48.06 % 0.01 s 1 core @ 2.5 Ghz (Python)
144 GT3D 54.08 % 73.71 % 47.95 % 0.1 s 1 core @ 2.5 Ghz (Python)
145 TBD 53.95 % 70.44 % 47.22 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
146 TBD 53.95 % 70.44 % 47.22 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
147 BirdNet+
This method makes use of Velodyne laser scans.
code 53.84 % 65.67 % 49.06 % 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.
148 tbd 53.00 % 68.71 % 46.32 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
149 PointRGBNet 52.15 % 67.05 % 46.78 % 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.
150 DMF
This method uses stereo information.
51.33 % 65.51 % 45.05 % 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.
151 PiFeNet code 51.10 % 67.50 % 44.66 % 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.
152 SCNet
This method makes use of Velodyne laser scans.
50.79 % 67.98 % 45.15 % 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.
153 AVOD-FPN
This method makes use of Velodyne laser scans.
code 50.55 % 63.76 % 44.93 % 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.
154 MLOD
This method makes use of Velodyne laser scans.
code 49.43 % 68.81 % 42.84 % 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.
155 BirdNet+ (legacy)
This method makes use of Velodyne laser scans.
code 47.72 % 67.38 % 42.89 % 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.
156 PFF3D
This method makes use of Velodyne laser scans.
code 46.78 % 63.27 % 41.37 % 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 CZY 45.32 % 59.97 % 40.44 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
158 StereoDistill 44.02 % 63.96 % 39.19 % 0.4 s 1 core @ 2.5 Ghz (Python)
159 DSGN++
This method uses stereo information.
code 43.90 % 62.82 % 39.21 % 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 AVOD
This method makes use of Velodyne laser scans.
code 42.08 % 57.19 % 38.29 % 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.
161 SparsePool code 37.33 % 52.61 % 33.39 % 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.
162 MMLAB LIGA-Stereo
This method uses stereo information.
code 36.86 % 54.44 % 32.06 % 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.
163 SparsePool code 32.61 % 40.87 % 29.05 % 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 CG-Stereo
This method uses stereo information.
30.89 % 47.40 % 27.23 % 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.
165 CGPS-3DV code 30.75 % 47.77 % 26.67 % CGPS-3DV 1 core @ 2.5 Ghz (C/C++)
166 BirdNet
This method makes use of Velodyne laser scans.
30.25 % 43.98 % 27.21 % 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.
167 PS code 26.77 % 41.22 % 23.76 % PS s 1 core @ 2.5 Ghz (C/C++)
168 Disp R-CNN (velo)
This method uses stereo information.
code 24.40 % 40.05 % 21.12 % 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
This method uses stereo information.
code 24.40 % 40.04 % 21.12 % 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 Complexer-YOLO
This method makes use of Velodyne laser scans.
18.53 % 24.27 % 17.31 % 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.
171 DSGN
This method uses stereo information.
code 18.17 % 27.76 % 16.21 % 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.
172 OC Stereo
This method uses stereo information.
code 16.63 % 29.40 % 14.72 % 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.
173 RT3D-GMP
This method uses stereo information.
12.99 % 18.31 % 10.63 % 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.
174 ESGN
This method uses stereo information.
7.69 % 13.84 % 6.75 % 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.
175 CMKD* 6.67 % 12.52 % 6.34 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
176 PS-fld code 6.18 % 11.22 % 5.21 % 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.
177 DD3Dv2 code 5.68 % 8.79 % 4.75 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
178 anonymity 5.68 % 9.27 % 4.99 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
179 BSM3D 5.61 % 9.45 % 4.81 % 0.03 s 1 core @ 2.5 Ghz (Python)
180 mono3d code 5.27 % 10.08 % 4.12 % TBD TBD
181 Anonymous 5.24 % 9.60 % 4.50 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
182 anonymity 5.22 % 9.08 % 4.67 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
183 Mix-Teaching 4.91 % 8.04 % 4.15 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
184 DD3D code 4.79 % 7.52 % 4.22 % 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) .
185 MonoPSR code 4.74 % 8.37 % 3.68 % 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.
186 TopNet-UncEst
This method makes use of Velodyne laser scans.
4.54 % 7.13 % 3.81 % 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.
187 LT-M3OD 4.52 % 7.87 % 4.38 % 0.03 s 1 core @ 2.5 Ghz (Python)
188 LPCG-Monoflex code 4.38 % 6.98 % 3.56 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
L. Peng, F. Liu, Z. Yu, S. Yan, D. Deng, Z. Yang, H. Liu and D. Cai: Lidar Point Cloud Guided Monocular 3D Object Detection. ECCV 2022.
189 DEPT 4.29 % 7.67 % 3.33 % 0.03 s 1 core @ 2.5 Ghz (Python)
190 MonoAD 4.22 % 6.59 % 3.52 % 0.03 s GPU @ 2.5 Ghz (Python)
191 Lite-FPN-GUPNet 4.19 % 6.22 % 3.59 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
192 Anonymous 3.94 % 6.49 % 3.25 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
193 MDNet 3.88 % 6.93 % 3.10 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
194 SAIC_ADC_Mono3D code 3.81 % 6.73 % 3.03 % 50 s GPU @ 2.5 Ghz (Python)
195 Shape-Aware 3.78 % 6.26 % 3.35 % 0.05 s 1 core @ 2.5 Ghz (Python)
196 MonoDDE 3.78 % 5.94 % 3.33 % 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.
197 ZongmuMono3d code 3.77 % 7.21 % 3.15 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
198 DFR-Net 3.58 % 5.69 % 3.10 % 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.
199 HomoLoss(monoflex) code 3.50 % 5.48 % 2.99 % 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.
200 OPA-3D code 3.45 % 5.16 % 2.86 % 0.04 s 1 core @ 3.5 Ghz (Python)
201 CaDDN code 3.41 % 7.00 % 3.30 % 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.
202 SARM3D 3.37 % 4.93 % 2.95 % 0.03 s GPU @ 2.5 Ghz (Python)
203 MonoInsight 3.37 % 5.94 % 3.22 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
204 RT3DStereo
This method uses stereo information.
3.37 % 5.29 % 2.57 % 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.
205 MonoDTR 3.27 % 5.05 % 3.19 % 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 GUPNet code 3.21 % 5.58 % 2.66 % NA s 1 core @ 2.5 Ghz (Python + C/C++)
Y. Lu, X. Ma, L. Yang, T. Zhang, Y. Liu, Q. Chu, J. Yan and W. Ouyang: Geometry Uncertainty Projection Network for Monocular 3D Object Detection. arXiv preprint arXiv:2107.13774 2021.
207 MonoAug 3.15 % 5.22 % 2.67 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
208 DEVIANT code 3.13 % 5.05 % 2.59 % 0.04 s 1 GPU (Python)
A. Kumar, G. Brazil, E. Corona, A. Parchami and X. Liu: DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection. European Conference on Computer Vision (ECCV) 2022.
209 GPENet code 3.05 % 5.45 % 2.56 % 0.02 s GPU @ 2.5 Ghz (Python)
210 SparseLiDAR_fusion 3.02 % 5.89 % 2.50 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
211 MoGDE 2.94 % 5.08 % 2.74 % 0.03 s GPU @ 2.5 Ghz (Python)
212 SGM3D code 2.92 % 5.49 % 2.64 % 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.
213 DCD code 2.74 % 4.72 % 2.41 % 1 s 1 core @ 2.5 Ghz (C/C++)
214 MonoGround 2.68 % 4.62 % 2.53 % 0.03 s 1 core @ 2.5 Ghz (Python)
215 MDSNet 2.68 % 5.37 % 2.22 % 0.07 s 1 core @ 2.5 Ghz (Python)
216 monodle code 2.66 % 4.59 % 2.45 % 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 .
217 mono3d 2.53 % 4.71 % 2.22 % 0.03 s GPU @ 2.5 Ghz (Python)
218 MonoEdge-Rotate 2.51 % 4.28 % 2.13 % 0.05 s GPU @ 2.5 Ghz (Python)
219 DDMP-3D 2.50 % 4.18 % 2.32 % 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.
220 Aug3D-RPN 2.43 % 4.36 % 2.55 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth. 2021.
221 QD-3DT
This is an online method (no batch processing).
code 2.39 % 4.16 % 1.85 % 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.
222 MonoFlex 2.35 % 4.17 % 2.04 % 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.
223 M3DGAF 2.30 % 4.28 % 2.12 % 0.07 s 1 core @ 2.5 Ghz (Python)
224 MonoAug 2.23 % 3.68 % 1.69 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
225 MonoEdge 2.19 % 3.15 % 1.77 % 0.05 s GPU @ 2.5 Ghz (Python)
226 gupnet_se 2.13 % 3.84 % 2.13 % 0.03s 1 core @ 2.5 Ghz (C/C++)
227 MonoPair 2.12 % 3.79 % 1.83 % 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 MonoPCNS 2.09 % 4.07 % 2.12 % 0.14 s GPU @ 2.5 Ghz (Python)
229 MK3D 2.02 % 3.75 % 1.96 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
230 MonoCon code 1.92 % 2.80 % 1.55 % 0.02 s GPU @ 2.5 Ghz (Python)
T. Xianpeng Liu: Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection. AAAI 2022.
231 RefinedMPL 1.82 % 3.23 % 1.77 % 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.
232 Anonymous code 1.81 % 3.17 % 1.75 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
233 TopNet-HighRes
This method makes use of Velodyne laser scans.
1.67 % 2.49 % 1.88 % 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.
234 D4LCN code 1.67 % 2.45 % 1.36 % 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.
235 SS3D 1.45 % 2.80 % 1.35 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
236 PGD-FCOS3D code 1.38 % 2.81 % 1.20 % 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.
237 HBD 1.24 % 2.45 % 1.22 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
238 GCDR 1.17 % 1.96 % 1.02 % 0.28 s 1 core @ 2.5 Ghz (Python)
239 CMAN 1.05 % 1.59 % 1.11 % 0.15 s 1 core @ 2.5 Ghz (Python)
240 MonoEF 0.92 % 1.80 % 0.71 % 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.
241 M3D-RPN code 0.65 % 0.94 % 0.47 % 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 .
242 MonoRUn code 0.61 % 1.01 % 0.48 % 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.
243 Shift R-CNN (mono) code 0.29 % 0.48 % 0.31 % 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.
244 MM 0.27 % 0.48 % 0.24 % 1 s 1 core @ 2.5 Ghz (C/C++)
245 CDTrack3D code 0.06 % 0.06 % 0.07 % 0.0106 s NVIDIA RTX 3090 GPU, i9 10850k CPU
246 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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