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 VirConv-S 87.20 % 92.48 % 82.45 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
2 VirConv-T 86.25 % 92.54 % 81.24 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
3 TED code 85.28 % 91.61 % 80.68 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, C. Wen, W. Li, R. Yang and C. Wang: Transformation-Equivariant 3D Object Detection for Autonomous Driving. AAAI 2023.
4 LoGoNet 85.06 % 91.80 % 80.74 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
5 VirConv-L 85.05 % 91.41 % 80.22 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
6 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++)
7 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.
8 VoCo 84.76 % 91.99 % 79.81 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
9 ACF-Net 84.67 % 90.80 % 80.14 % n/a s 1 core @ 2.5 Ghz (C/C++)
10 NSAW code 84.30 % 90.57 % 77.46 % 0.1 s 1 core @ 2.5 Ghz (Python)
11 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.
12 Anonymous 83.51 % 89.08 % 78.94 % n/a s 1 core @ 2.5 Ghz (C/C++)
13 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.
14 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.
15 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.
16 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.
17 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.
18 BiProDet 82.97 % 89.13 % 80.05 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
19 HRNet++ 82.91 % 91.82 % 78.07 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
20 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.
21 HRNet 82.81 % 91.36 % 79.81 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
22 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.
23 GT3D 82.76 % 91.45 % 79.74 % 0.1 s 1 core @ 2.5 Ghz (Python)
24 3SNet 82.70 % 89.41 % 78.03 % 0.07 s GPU @ 2.5 Ghz (Python)
25 CAD 82.68 % 88.96 % 77.91 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
26 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.
27 VoxelGraphRCNN 82.66 % 91.33 % 77.93 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
28 SGFusion 82.64 % 91.13 % 77.53 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
29 OcTr 82.64 % 90.88 % 77.77 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
30 HCPVF 82.63 % 89.34 % 77.72 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
31 PA3DNet 82.57 % 90.49 % 77.88 % 0.05 s GPU @ 2.5 Ghz (Python)
32 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.
33 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. WACV 2023.
34 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.
35 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. WACV 2023.
36 3D Dual-Fusion 82.40 % 91.01 % 79.39 % 0.1 s 1 core @ 2.5 Ghz (Python)
37 NIV-SSD 82.37 % 89.74 % 75.06 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
38 VGT-RCNN 82.36 % 91.24 % 79.34 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
39 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.
40 PVT-SSD 82.29 % 90.65 % 76.85 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
41 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.
42 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.
43 CityBrainLab 82.22 % 90.54 % 77.19 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
44 SPT 82.18 % 90.52 % 77.62 % 0.1 s GPU @ 2.5 Ghz (Python)
45 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.
46 LightCPC code 82.15 % 88.93 % 77.04 % 0.02 s 1 core @ 2.5 Ghz (Python + C/C++)
47 ImpDet 82.14 % 88.39 % 76.98 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
48 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.
49 SPNet code 82.11 % 88.53 % 77.41 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
50 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.
51 PV-DT3D 82.09 % 90.07 % 77.51 % 1.4 s 1 core @ 2.5 Ghz (C/C++)
52 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.
53 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.
54 LGNet 82.02 % 90.65 % 77.34 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
55 ChTR3D 82.02 % 90.43 % 77.42 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
56 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.
57 Anomynous 82.01 % 88.80 % 77.24 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
58 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++)
59 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.
60 HMFI code 81.93 % 88.90 % 77.30 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
X. Li, B. Shi, Y. Hou, X. Wu, T. Ma, Y. Li and L. He: Homogeneous Multi-modal Feature Fusion and Interaction for 3D Object Detection. ECCV 2022.
61 SGDA3D 81.86 % 88.82 % 77.26 % 0.07 s 1 core @ 2.5 Ghz (Python)
62 GLENet 81.86 % 89.87 % 77.32 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
63 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.
64 FV2P v2 81.81 % 88.17 % 77.43 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
65 ChTR3D 81.80 % 88.00 % 77.17 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
66 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.
67 GV-RCNN code 81.75 % 90.31 % 77.17 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
68 USVLab BSAODet 81.74 % 88.89 % 77.14 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
69 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.
70 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.
71 TBD 81.71 % 88.46 % 76.63 % 0.1 s 1 core @ 2.5 Ghz (Python)
72 VGRCNN++ 81.71 % 90.15 % 77.14 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
73 DCCA 81.70 % 88.42 % 77.18 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
74 Under Blind Review#2 81.70 % 88.33 % 77.20 % 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 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.
81 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.
82 Anonymous 81.55 % 87.90 % 77.03 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
83 FARP-Net code 81.53 % 88.36 % 78.98 % 0.06 s GPU @ 2.5 Ghz (Python + 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 VGA-RCNN 81.38 % 88.38 % 76.67 % 0.07 s 1 core @ 2.5 Ghz (Python)
90 DTE3D 81.37 % 88.36 % 76.71 % 0.15s 1 core @ 2.5 Ghz (C/C++)
91 VGRCNN 81.36 % 88.43 % 76.89 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
92 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.
93 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.
94 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.
95 ChTR3D 81.19 % 87.81 % 76.70 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
96 PVE 81.15 % 89.39 % 76.71 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
97 AGS-SSD[la] 81.02 % 88.38 % 76.45 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
98 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.
99 GVNet-V2 80.96 % 87.57 % 76.32 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
100 DKDet 80.94 % 87.66 % 76.23 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
101 Anonymous 80.92 % 88.02 % 76.45 % 0.03s
102 TVTr 80.85 % 89.51 % 76.46 % 0.08 s 1 core @ 2.5 Ghz (Python)
103 NV-RCNN 80.78 % 86.35 % 76.39 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
104 Sem-Aug 80.77 % 89.41 % 75.90 % 0.08 s GPU @ 2.5 Ghz (Python)
105 CSVoxel-RCNN 80.73 % 87.44 % 76.18 % 0.03 s GPU @ 1.0 Ghz (Python)
106 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.
107 SPVB-SSD 80.68 % 86.99 % 76.23 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
108 DGT-Det3D code 80.68 % 87.89 % 76.02 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
109 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.
110 U_RVRCNN_V2_1 80.67 % 87.49 % 76.21 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
111 BSConv 80.65 % 87.41 % 74.15 % 0.1 s 1 core @ 2.5 Ghz (Java)
112 PA-RCNN code 80.60 % 88.65 % 73.65 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
113 GVNet code 80.52 % 87.63 % 75.99 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
114 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.
115 SC-Voxel-RCNN 80.46 % 86.94 % 75.85 % 0.12 s GPU @ 1.0 Ghz (Python)
116 TBD 80.44 % 88.83 % 73.18 % 0.1 s 1 core @ 2.5 Ghz (Python)
117 SRDL 80.38 % 87.73 % 76.27 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
118 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.
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 IPS 80.27 % 88.94 % 76.72 % TBD s 1 core @ 2.5 Ghz (C/C++)
124 PVRCNN_8369 80.25 % 87.45 % 76.19 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
125 GS-FPS 80.23 % 88.61 % 75.17 % TBD s 1 core @ 2.5 Ghz (C/C++)
126 ATT_SSD 80.23 % 88.74 % 75.10 % 0.01 s 1 core @ 2.5 Ghz (Python)
127 TTT_SSD 80.19 % 88.41 % 76.77 % TBD s 1 core @ 2.5 Ghz (C/C++)
128 PVTr 80.16 % 86.90 % 75.98 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
129 SWA code 80.16 % 88.45 % 76.77 % 0.18 s 1 core @ 2.5 Ghz (C/C++)
130 GEO_LOC 80.15 % 88.79 % 75.03 % TBD s 1 core @ 2.5 Ghz (C/C++)
131 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.
132 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.
133 TBD 80.12 % 86.50 % 75.72 % 0.06 s GPU @ 2.5 Ghz (Python)
134 GS-FPS-LT 80.07 % 88.62 % 74.98 % TBD s 1 core @ 2.5 Ghz (C/C++)
135 KPSCC code 80.06 % 88.75 % 74.84 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
136 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.
137 SIF 79.88 % 86.84 % 75.89 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
138 DCGNN 79.80 % 89.65 % 74.52 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
139 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.
140 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.
141 DGT-Det3D 79.78 % 86.76 % 75.73 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
142 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.
143 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.
144 mbdf-netv1 code 79.66 % 90.19 % 74.76 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
145 U_PVRCNN_V2 79.65 % 86.36 % 75.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
146 PTA-RCNN 79.61 % 87.84 % 74.43 % 0.08 s 1 core @ 2.5 Ghz (Python)
147 TBD code 79.59 % 88.39 % 74.22 % 0.1 s GPU @ 2.5 Ghz (Python)
148 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.
149 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.
150 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.
151 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.
152 HPV-RCNN 79.47 % 87.71 % 74.26 % 0.15 s 1 core @ 2.5 Ghz (Python)
153 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.
154 BASA 79.43 % 87.91 % 74.29 % 1s 1 core @ 2.5 Ghz (python)
155 DCAN-Second code 79.40 % 88.57 % 75.05 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
156 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.
157 CZY_PPF_Net 79.35 % 88.39 % 76.14 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
158 USVLab BSAODet (S) 79.30 % 88.02 % 76.05 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
159 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.
160 VPNet 79.28 % 87.66 % 76.40 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
161 WGVRF 79.25 % 88.47 % 74.60 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
162 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.
163 PSA-SSD 79.12 % 87.35 % 74.25 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
164 ITCA-SSD code 79.11 % 88.66 % 72.12 % 0.05 s 1 core @ 2.5 Ghz (Python)
165 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.
166 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.
167 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.
168 MVMM code 78.87 % 87.59 % 73.78 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
169 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.
170 PSA-Det3D 78.80 % 87.46 % 74.47 % 0.1 s GPU @ 2.5 Ghz (Python)
171 CSNet8306 code 78.74 % 89.57 % 72.09 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
172 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.
173 CenterFuse 78.70 % 86.92 % 73.87 % 0.059 sec/frame 2 x V100
174 FSFNet 78.67 % 89.69 % 72.01 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
175 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.
176 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.
177 CSNet 78.42 % 87.39 % 71.75 % 0.1 s 1 core @ 2.5 Ghz (Python)
178 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.
179 CZY 78.36 % 87.00 % 73.84 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
180 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.
181 Sem-Aug-PointRCNN++ 78.06 % 86.69 % 73.85 % 0.1 s 8 cores @ 3.0 Ghz (Python)
182 CF-cd-io-tv 78.05 % 86.38 % 73.29 % 1 s 1 core @ 2.5 Ghz (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 TBD 77.56 % 85.38 % 72.32 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
190 CFF-tv 77.53 % 85.01 % 74.01 % 1 s 1 core @ 2.5 Ghz (C/C++)
191 TCDVF 77.49 % 85.55 % 72.29 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
192 CFF-ep25 77.48 % 84.84 % 72.96 % 1 s 1 core @ 2.5 Ghz (C/C++)
193 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.
194 CFF-tv-v2 77.41 % 85.18 % 72.81 % 1 s 1 core @ 2.5 Ghz (C/C++)
195 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.
196 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.
197 cff-tv-v2-ep25 77.38 % 84.44 % 72.82 % 1 s 1 core @ 2.5 Ghz (C/C++)
198 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.
199 cp-tv-kp-io-sc 77.25 % 85.41 % 72.42 % 1 s 1 core @ 2.5 Ghz (C/C++)
200 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.
201 CF-ctdep-tv 77.12 % 84.71 % 74.02 % 1 s 1 core @ 2.5 Ghz (C/C++)
202 CF-base-tv 77.09 % 83.72 % 73.71 % 1 s 1 core @ 2.5 Ghz (C/C++)
203 KeyFuse2B 76.95 % 84.86 % 72.53 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
204 KeyPoint-IoUHead 76.81 % 84.61 % 72.16 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
205 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.
206 DKAnet 76.70 % 84.57 % 71.54 % 0.05 s 1 core @ 2.0 Ghz (Python)
207 cff-tv-t 76.68 % 85.58 % 70.69 % 1 s 1 core @ 2.5 Ghz (C/C++)
208 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.
209 SECOND_7862 76.60 % 85.29 % 71.77 % 1 s 1 core @ 2.5 Ghz (Python)
210 Anonymous 76.60 % 85.29 % 71.77 % 1 1 core @ 2.5 Ghz (Python)
211 DTFI 76.59 % 85.29 % 71.78 % 0.03 s 1 core @ 2.5 Ghz (Python)
212 CSNet8299 code 76.55 % 86.49 % 71.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
213 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.
214 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.
215 variance_point 76.27 % 87.44 % 72.03 % 0.05 s 1 core @ 2.5 Ghz (Python)
216 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.
217 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.
218 KPP3D code 76.00 % 86.66 % 71.07 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
219 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.
220 CF-base-train 75.93 % 83.47 % 71.22 % 1 s 1 core @ 2.5 Ghz (C/C++)
221 Anonymous 75.85 % 84.53 % 70.54 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
222 cp-tv-kp 75.85 % 83.50 % 72.54 % 1 s 1 core @ 2.5 Ghz (C/C++)
223 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.
224 cp-tv 75.67 % 83.31 % 72.24 % 1 s 1 core @ 2.5 Ghz (C/C++)
225 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.
226 Self-Calib Conv 75.59 % 83.54 % 71.96 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
227 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.
228 CF-ctdep-train 75.43 % 83.03 % 71.31 % 1 s 1 core @ 2.5 Ghz (C/C++)
229 Anonymous 75.39 % 85.38 % 71.70 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
230 Anonymous 75.33 % 84.42 % 70.10 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
231 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.
232 CAT 75.22 % 84.84 % 70.05 % 1 s 1 core @ 2.5 Ghz (Python)
233 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.
234 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.
235 T_PVRCNN 74.93 % 84.79 % 69.95 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
236 T_PVRCNN_V2 74.90 % 84.74 % 69.60 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
237 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.
238 MF 74.70 % 83.42 % 66.51 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
239 LazyTorch-CP-Infer-O 74.57 % 81.82 % 70.24 % 1 s 1 core @ 2.5 Ghz (C/C++)
240 LazyTorch-CP-Small-P 74.44 % 81.73 % 70.14 % 1 s 1 core @ 2.5 Ghz (C/C++)
241 City-CF-fixed 74.37 % 83.23 % 69.65 % 1 s 1 core @ 2.5 Ghz (C/C++)
242 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.
243 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.
244 Harmonic PointPillar code 73.96 % 82.26 % 69.21 % 0.01 s 1 core @ 2.5 Ghz (Python)
H. Zhang, M. Mekala, Z. Nain, D. Yang, J. Park and H. Jung: Harmonic 3D: Time-friendly and Task- consistent LiDAR-based 3D Object Detection on Edge. will submit to IEEE Transactions on Vehicle Technology 2022.
245 CenterPoint (pcdet) 73.96 % 81.17 % 69.48 % 0.051 sec/frame 2 x V100
246 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.
247 ZMMPP 73.78 % 82.48 % 68.80 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
248 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.
249 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.
250 Dune-DCF-e11 73.51 % 80.89 % 68.89 % 1 s 1 core @ 2.5 Ghz (C/C++)
251 CrazyTensor-CP 73.50 % 81.04 % 69.87 % 1 s 1 core @ 2.5 Ghz (Python)
252 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.
253 City-CF 73.48 % 80.85 % 69.07 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
254 PSM_stereo 73.43 % 81.28 % 66.98 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
255 Dune-DCF-e15 73.29 % 80.34 % 68.54 % 1 s 1 core @ 2.5 Ghz (C/C++)
256 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.
257 Dune-DCF-e09 73.15 % 80.40 % 68.57 % 1 s 1 core @ 2.5 Ghz (C/C++)
258 AFTD 73.12 % 82.71 % 68.09 % 1 s 1 core @ 2.5 Ghz (Python + C/C++)
259 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++)
260 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.
261 CrazyTensor-CF 72.92 % 79.87 % 68.41 % 1 s 1 core @ 2.5 Ghz (C/C++)
262 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.
263 SSL_PP code 72.02 % 83.74 % 64.95 % 16ms GPU @ 1.5 Ghz (Python)
264 TBD 71.94 % 83.20 % 66.83 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
265 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.
266 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.
267 new_stereo 70.79 % 80.05 % 66.04 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
268 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.
269 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.
270 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.
271 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.
272 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.
273 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.
274 Anonymous 66.97 % 83.77 % 58.41 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
275 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.
276 StereoDistill 66.39 % 81.66 % 57.39 % 0.4 s 1 core @ 2.5 Ghz (Python)
277 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.
278 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.
279 CZY 63.68 % 77.56 % 57.82 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
280 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.
281 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.
282 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.
283 Anonymous 58.57 % 77.81 % 52.13 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
284 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.
285 FD 56.40 % 73.05 % 52.25 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
286 Pseudo-Stereo++ 55.28 % 74.80 % 46.70 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
287 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.
288 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.
289 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.
290 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.
291 PS 52.88 % 74.41 % 44.38 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
292 UPF_3D
This method uses stereo information.
52.83 % 78.24 % 46.12 % 0.29 s 1 core @ 2.5 Ghz (Python)
293 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.
294 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.
295 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.
296 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.
297 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.
298 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.
299 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.
300 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.
301 ART 42.42 % 63.38 % 36.44 % 20ms s 1 core @ 2.5 Ghz (C/C++)
302 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.
303 DSC3D
This method uses stereo information.
40.69 % 67.80 % 29.95 % 0.06 s GPU @ 2.5 Ghz (Python)
304 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.
305 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.
306 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.
307 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.
308 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.
309 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.
310 SparseLiDAR_fusion 28.93 % 38.06 % 24.14 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
311 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.
312 CIE + DM3D 25.02 % 35.96 % 21.47 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
313 GCDR 23.92 % 34.89 % 19.59 % 0.28 s 1 core @ 2.5 Ghz (Python)
314 Anonymous 23.79 % 33.89 % 20.33 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
315 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.
316 CIE 20.95 % 31.55 % 17.83 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
317 Anonymous 20.23 % 28.29 % 17.55 % 40 s 1 core @ 2.5 Ghz (C/C++)
318 SARM3D 19.70 % 25.20 % 17.35 % 0.03 s GPU @ 2.5 Ghz (Python)
319 AMNet 19.26 % 26.26 % 17.05 % 0.03 s GPU @ 1.0 Ghz (Python)
320 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.
321 MonoXiver 19.04 % 25.24 % 16.39 % 0.03s GPU @ 2.5 Ghz (Python)
322 BSM3D 18.87 % 25.66 % 16.50 % 0.03 s 1 core @ 2.5 Ghz (Python)
323 CMKD code 18.69 % 28.55 % 16.77 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Y. Hong, H. Dai and Y. Ding: Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection. ECCV 2022.
324 MonoASS 18.54 % 27.25 % 15.65 % 0.04 s 1 core @ 2.5 Ghz (Python)
325 NeurOCS 18.46 % 28.68 % 15.63 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
326 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.
327 MDS-Mono3D 18.20 % 28.47 % 14.95 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
328 BAIR 18.03 % 25.76 % 15.34 % 0.04 s 1 core @ 2.5 Ghz (Python)
329 SSAL-Mono 17.99 % 22.89 % 16.20 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
330 MoGDE 17.88 % 27.07 % 15.66 % 0.03 s GPU @ 2.5 Ghz (Python)
331 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.
332 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.
333 DD3Dv2 code 17.61 % 26.36 % 15.32 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
334 MonoA^2(new) 17.55 % 23.24 % 15.26 % na s 1 core @ 2.5 Ghz (C/C++)
335 MonoATT code 17.37 % 24.72 % 15.00 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
336 MonoAD 17.28 % 25.35 % 14.58 % 0.03 s GPU @ 2.5 Ghz (Python)
337 Anonymous 17.18 % 25.51 % 14.56 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
338 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.
339 MonoNeRD 17.13 % 22.75 % 15.63 % na s 1 core @ 2.5 Ghz (C/C++)
340 Anonymous 17.08 % 24.43 % 15.25 % 40 s 1 core @ 2.5 Ghz (C/C++)
341 MonoA^2 17.07 % 23.71 % 15.36 % na s 1 core @ 2.5 Ghz (C/C++)
342 OPA-3D code 17.05 % 24.60 % 14.25 % 0.04 s 1 core @ 3.5 Ghz (Python)
343 TempM3D 17.05 % 25.29 % 14.86 % 0.07 s 1 core @ 2.5 Ghz (Python)
344 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.
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 SAD 16.41 % 26.05 % 13.60 % 0.05 s 1 core @ 2.5 Ghz (python)
349 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.
350 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.
351 MonoPPM code 16.21 % 22.43 % 13.73 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
352 Lite-FPN-GUPNet 16.20 % 23.58 % 13.56 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
353 SAD 16.14 % 25.55 % 13.29 % 0.05 s 1 core @ 2.5 Ghz (python)
354 zongmuDistill 16.08 % 25.11 % 13.69 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
355 MDNet 16.01 % 24.59 % 13.49 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
356 DCD code 15.90 % 23.81 % 13.21 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
Y. Li, Y. Chen, J. He and Z. Zhang: Densely Constrained Depth Estimator for Monocular 3D Object Detection. European Conference on Computer Vision 2022.
357 monopd code 15.72 % 23.51 % 13.07 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
358 OBMO_GUPNet 15.70 % 22.71 % 13.23 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
359 Anonymous 15.67 % 22.34 % 12.92 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
360 3DSeMoDLE code 15.58 % 23.11 % 13.58 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
361 MonoInsight 15.45 % 21.45 % 13.21 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
362 GPENet code 15.44 % 22.41 % 12.84 % 0.02 s GPU @ 2.5 Ghz (Python)
363 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.
364 Mono3DMethod 15.25 % 23.55 % 13.24 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
365 MM3D 15.23 % 23.39 % 12.87 % NA s 1 core @ 2.5 Ghz (C/C++)
366 HBD 15.17 % 21.71 % 13.06 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
367 EW code 15.13 % 21.16 % 12.81 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
368 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.
369 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.
370 Anonymous 14.87 % 23.93 % 12.45 % 40 s 1 core @ 2.5 Ghz (C/C++)
371 Anonymous 14.84 % 22.73 % 13.08 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
372 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.
373 BCA 14.54 % 21.82 % 11.99 % 0.17 s GPU @ 2.5 Ghz (Python)
374 MDSNet 14.46 % 24.30 % 11.12 % 0.05 s 1 core @ 2.5 Ghz (Python)
Z. Xie, Y. Song, J. Wu, Z. Li, C. Song and Z. Xu: MDS-Net: Multi-Scale Depth Stratification 3D Object Detection from Monocular Images. Sensors 2022.
375 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.
376 MonoEdge-RCNN 14.35 % 19.74 % 11.94 % 0.05 s 1 core @ 2.5 Ghz (Python)
377 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.
378 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.
379 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.
380 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.
381 MonoAug 13.85 % 20.06 % 11.43 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
382 DEPT 13.83 % 20.43 % 11.66 % 0.03 s 1 core @ 2.5 Ghz (Python)
383 MonoPCNS 13.74 % 20.31 % 12.31 % 0.14 s GPU @ 2.5 Ghz (Python)
384 MonoRCNN++ code 13.72 % 20.08 % 11.34 % 0.07 s GPU @ 2.5 Ghz (Python)
X. Shi, Z. Chen and T. Kim: Multivariate Probabilistic Monocular 3D Object Detection. WACV 2023.
385 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.
386 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.
387 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.
388 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.
389 MK3D 13.19 % 20.48 % 11.10 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
390 FMF-occlusion-net 13.12 % 20.28 % 9.56 % 0.16 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Liu, H. Liu, Y. Wang, F. Sun and W. Huang: Fine-grained Multi-level Fusion for Anti- occlusion Monocular 3D Object Detection. IEEE Transactions on Image Processing 2022.
391 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.
392 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.
393 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.
394 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 .
395 M3DGAF 12.66 % 19.48 % 10.99 % 0.07 s 1 core @ 2.5 Ghz (Python)
396 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.
397 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.
398 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.
399 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 .
400 LT-M3OD 12.26 % 18.15 % 10.05 % 0.03 s 1 core @ 2.5 Ghz (Python)
401 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.
402 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.
403 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.
404 CMAN 11.87 % 17.77 % 9.16 % 0.15 s 1 core @ 2.5 Ghz (Python)
C. Yuanzhouhan Cao: CMAN: Leaning Global Structure Correlation for Monocular 3D Object Detection. IEEE Trans. Intell. Transport. Syst. 2022.
405 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.
406 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.
407 MonoAug 11.47 % 16.40 % 9.26 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
408 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.
409 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.
410 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.
411 MDT code 11.11 % 15.95 % 8.80 % 0.01 s 1 core @ 2.5 Ghz (Python)
412 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.
413 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.
414 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.
415 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.
416 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.
417 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.
418 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 .
419 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.
420 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.
421 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.
422 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.
423 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.
424 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.
425 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.
426 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.
427 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.
428 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.
429 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.
430 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.
431 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.
432 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.
433 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.
434 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.
435 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.
436 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.
437 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.
438 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.
439 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.
440 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.
441 CDTrack3D code 1.92 % 3.20 % 1.63 % 0.0106 s NVIDIA RTX 3090 GPU, i9 10850k CPU
442 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.
443 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.
444 test code 0.03 % 0.01 % 0.03 % 50 s 1 core @ 2.5 Ghz (Python)
445 MonoDET code 0.01 % 0.03 % 0.01 % 0.04 s 1 core @ 2.5 Ghz (Python)
446 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 code 49.21 % 55.85 % 46.52 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, C. Wen, W. Li, R. Yang and C. Wang: Transformation-Equivariant 3D Object Detection for Autonomous Driving. AAAI 2023.
3 BiProDet 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 LoGoNet 47.43 % 53.07 % 45.22 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
7 DCAN-Second code 47.38 % 55.12 % 44.59 % 0.05 s 1 core @ 2.5 Ghz (Python + 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 ACF-Net 46.36 % 54.62 % 42.57 % n/a s 1 core @ 2.5 Ghz (C/C++)
12 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.
13 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.
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 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.
18 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.
19 CFF-tv 44.33 % 52.72 % 41.11 % 1 s 1 core @ 2.5 Ghz (C/C++)
20 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.
21 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.
22 USVLab BSAODet 43.63 % 51.71 % 41.09 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
23 CFF-ep25 43.47 % 51.85 % 40.69 % 1 s 1 core @ 2.5 Ghz (C/C++)
24 FV2P v2 43.47 % 50.64 % 40.09 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
25 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.
26 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.
27 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.
28 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.
29 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.
30 cff-tv-v2-ep25 43.25 % 51.40 % 40.23 % 1 s 1 core @ 2.5 Ghz (C/C++)
31 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.
32 KeyFuse2B 43.18 % 51.49 % 40.70 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
33 CF-ctdep-tv-ta 43.11 % 50.40 % 40.51 % 1 s 1 core @ 2.5 Ghz (C/C++)
34 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.
35 Reprod-Two-Branch 43.07 % 52.07 % 40.40 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
36 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.
37 PSA-Det3D 42.81 % 49.72 % 39.58 % 0.1 s GPU @ 2.5 Ghz (Python)
38 CFF-tv-v2 42.77 % 51.08 % 40.15 % 1 s 1 core @ 2.5 Ghz (C/C++)
39 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.
40 CF-base-tv 42.66 % 50.01 % 39.76 % 1 s 1 core @ 2.5 Ghz (C/C++)
41 HMFI code 42.65 % 50.88 % 39.78 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
X. Li, B. Shi, Y. Hou, X. Wu, T. Ma, Y. Li and L. He: Homogeneous Multi-modal Feature Fusion and Interaction for 3D Object Detection. ECCV 2022.
42 TBD 42.65 % 50.88 % 39.78 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
43 USVLab BSAODet (S) 42.62 % 49.52 % 39.12 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
44 PA-RCNN code 42.49 % 49.11 % 39.58 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
45 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.
46 VoCo 42.32 % 47.96 % 39.69 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
47 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.
48 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++)
49 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.
50 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.
51 3SNet 42.02 % 48.91 % 39.39 % 0.07 s GPU @ 2.5 Ghz (Python)
52 CF-ctdep-tv 41.98 % 49.14 % 39.03 % 1 s 1 core @ 2.5 Ghz (C/C++)
53 DTE3D 41.97 % 49.91 % 39.27 % 0.15s 1 core @ 2.5 Ghz (C/C++)
54 Self-Calib Conv 41.95 % 48.88 % 39.52 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
55 CZY_PPF_Net2 41.93 % 47.18 % 40.08 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
56 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.
57 CenterFuse 41.80 % 49.77 % 38.49 % 0.059 sec/frame 2 x V100
58 cp-tv-kp 41.70 % 48.77 % 39.41 % 1 s 1 core @ 2.5 Ghz (C/C++)
59 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.
60 TCDVF 41.47 % 49.44 % 38.41 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
61 DGT-Det3D 41.40 % 49.06 % 38.43 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
62 SGDA3D 41.39 % 47.59 % 38.37 % 0.07 s 1 core @ 2.5 Ghz (Python)
63 CZY_3917 41.38 % 46.09 % 38.64 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
64 DGT-Det3D code 41.07 % 48.79 % 38.09 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
65 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.
66 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.
67 Anonymous 40.92 % 50.07 % 38.18 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
68 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.
69 IoU-2B 40.62 % 50.33 % 36.94 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
70 TBD 40.57 % 47.65 % 38.33 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
71 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.
72 cp-tv 40.55 % 47.71 % 38.23 % 1 s 1 core @ 2.5 Ghz (C/C++)
73 MVMM code 40.49 % 47.54 % 38.36 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
74 Under Blind Review#2 40.47 % 46.61 % 38.60 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
75 cff-tv-t 40.41 % 49.46 % 37.02 % 1 s 1 core @ 2.5 Ghz (C/C++)
76 U_SECOND_V4 40.40 % 48.46 % 37.40 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
77 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.
78 CZY_PPF_Net 40.28 % 46.03 % 38.19 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
79 U_PVRCNN_V2 40.26 % 47.10 % 37.42 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
80 Semantical PVRCNN 40.18 % 45.94 % 37.28 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
81 KeyPoint-IoUHead 40.15 % 47.86 % 37.01 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
82 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.
83 cp-tv-kp-io-sc 39.92 % 48.06 % 37.68 % 1 s 1 core @ 2.5 Ghz (C/C++)
84 CF-cd-io-tv 39.82 % 48.67 % 36.45 % 1 s 1 core @ 2.5 Ghz (C/C++)
85 Anonymous 39.74 % 47.97 % 37.23 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
86 Anonymous 39.73 % 48.68 % 36.46 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
87 VPNet 39.67 % 47.55 % 36.66 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
88 IKT3D
This method makes use of Velodyne laser scans.
39.53 % 45.34 % 37.14 % 0.05 s 1 core @ 2.5 Ghz (Python)
89 WGVRF 39.52 % 45.98 % 37.56 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
90 U_RVRCNN_V2_1 39.50 % 46.42 % 37.29 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
91 VGA-RCNN 39.48 % 47.80 % 36.99 % 0.07 s 1 core @ 2.5 Ghz (Python)
92 AFTD 39.45 % 48.28 % 36.07 % 1 s 1 core @ 2.5 Ghz (Python + C/C++)
93 KPSCC code 39.45 % 47.08 % 37.12 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
94 Dune-DCF-e09 39.43 % 47.29 % 36.74 % 1 s 1 core @ 2.5 Ghz (C/C++)
95 LazyTorch-CP-Infer-O 39.43 % 47.38 % 36.96 % 1 s 1 core @ 2.5 Ghz (C/C++)
96 SRDL 39.43 % 47.30 % 36.99 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
97 PVRCNN_8369 39.41 % 47.30 % 36.96 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
98 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.
99 LazyTorch-CP-Small-P 39.33 % 47.27 % 36.89 % 1 s 1 core @ 2.5 Ghz (C/C++)
100 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.
101 CenterPoint (pcdet) 39.28 % 47.25 % 36.78 % 0.051 sec/frame 2 x V100
102 CZY 39.26 % 45.08 % 36.38 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
103 Dune-DCF-e11 39.26 % 47.32 % 36.63 % 1 s 1 core @ 2.5 Ghz (C/C++)
104 City-CF-fixed 39.22 % 47.68 % 36.55 % 1 s 1 core @ 2.5 Ghz (C/C++)
105 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.
106 GS-FPS 38.90 % 45.25 % 35.87 % TBD s 1 core @ 2.5 Ghz (C/C++)
107 BASA 38.90 % 46.74 % 36.24 % 1s 1 core @ 2.5 Ghz (python)
108 PSA-SSD 38.87 % 46.21 % 36.85 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
109 IPS 38.82 % 46.37 % 36.63 % TBD s 1 core @ 2.5 Ghz (C/C++)
110 SIF 38.74 % 46.23 % 36.06 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
111 CrazyTensor-CP 38.67 % 46.58 % 36.15 % 1 s 1 core @ 2.5 Ghz (Python)
112 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.
113 Dune-DCF-e15 38.61 % 46.41 % 36.02 % 1 s 1 core @ 2.5 Ghz (C/C++)
114 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.
115 AGS-SSD[la] 38.53 % 46.10 % 35.40 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
116 HPV-RCNN 38.46 % 46.37 % 35.10 % 0.15 s 1 core @ 2.5 Ghz (Python)
117 CF-base-train 38.44 % 45.89 % 35.55 % 1 s 1 core @ 2.5 Ghz (C/C++)
118 GEO_LOC 38.31 % 45.87 % 35.34 % TBD s 1 core @ 2.5 Ghz (C/C++)
119 TBD 38.27 % 46.35 % 36.39 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
120 GS-FPS-LT 38.10 % 44.05 % 35.75 % TBD s 1 core @ 2.5 Ghz (C/C++)
121 City-CF 38.04 % 45.42 % 35.40 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
122 CF-ctdep-train 38.03 % 44.75 % 35.49 % 1 s 1 core @ 2.5 Ghz (C/C++)
123 NV-RCNN 37.82 % 44.38 % 35.55 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
124 SWA code 37.76 % 44.59 % 34.82 % 0.18 s 1 core @ 2.5 Ghz (C/C++)
125 PVTr 37.58 % 43.99 % 35.48 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
126 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.
127 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.
128 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.
129 T_PVRCNN 37.12 % 45.20 % 34.04 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
130 ATT_SSD 37.03 % 44.14 % 34.94 % 0.01 s 1 core @ 2.5 Ghz (Python)
131 T_PVRCNN_V2 36.79 % 44.81 % 33.83 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
132 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.
133 TTT_SSD 36.26 % 43.22 % 34.31 % TBD s 1 core @ 2.5 Ghz (C/C++)
134 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.
135 SECOND_7862 35.92 % 43.04 % 33.56 % 1 s 1 core @ 2.5 Ghz (Python)
136 CrazyTensor-CF 35.83 % 43.50 % 33.23 % 1 s 1 core @ 2.5 Ghz (C/C++)
137 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.
138 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.
139 LightCPC code 34.10 % 39.59 % 31.47 % 0.02 s 1 core @ 2.5 Ghz (Python + C/C++)
140 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++)
141 KPP3D code 32.91 % 41.34 % 30.45 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
142 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.
143 ZMMPP 32.38 % 39.54 % 30.25 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
144 StereoDistill 32.23 % 44.12 % 28.95 % 0.4 s 1 core @ 2.5 Ghz (Python)
145 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.
146 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.
147 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.
148 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.
149 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.
150 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.
151 Pseudo-Stereo++ 27.45 % 36.89 % 24.01 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
152 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.
153 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.
154 PS 26.01 % 35.52 % 23.24 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
155 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.
156 CZY 25.47 % 32.33 % 23.60 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
157 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.
158 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.
159 DSC3D
This method uses stereo information.
20.67 % 30.18 % 18.22 % 0.06 s GPU @ 2.5 Ghz (Python)
160 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.
161 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.
162 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.
163 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.
164 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.
165 Anonymous 11.69 % 17.79 % 10.09 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
166 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.
167 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) .
168 DD3Dv2 code 10.82 % 16.25 % 9.24 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
169 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.
170 DEPT 10.81 % 16.28 % 9.15 % 0.03 s 1 core @ 2.5 Ghz (Python)
171 CIE 10.53 % 16.19 % 8.97 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
172 OPA-3D code 10.49 % 15.65 % 8.80 % 0.04 s 1 core @ 3.5 Ghz (Python)
173 BAIR 10.34 % 15.71 % 8.60 % 0.04 s 1 core @ 2.5 Ghz (Python)
174 MonoASS 10.30 % 15.97 % 8.68 % 0.04 s 1 core @ 2.5 Ghz (Python)
175 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.
176 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.
177 MonoInsight 10.01 % 15.17 % 9.06 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
178 LT-M3OD 9.99 % 14.85 % 8.52 % 0.03 s 1 core @ 2.5 Ghz (Python)
179 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.
180 BSM3D 9.37 % 14.05 % 7.92 % 0.03 s 1 core @ 2.5 Ghz (Python)
181 GPENet code 9.36 % 14.61 % 7.91 % 0.02 s GPU @ 2.5 Ghz (Python)
182 Lite-FPN-GUPNet 9.32 % 14.13 % 7.93 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
183 BCA 8.85 % 13.60 % 8.05 % 0.17 s GPU @ 2.5 Ghz (Python)
184 MonoAD 8.84 % 13.85 % 7.38 % 0.03 s GPU @ 2.5 Ghz (Python)
185 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.
186 CMKD code 8.79 % 13.94 % 7.42 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Y. Hong, H. Dai and Y. Ding: Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection. ECCV 2022.
187 AMNet 8.67 % 13.18 % 7.43 % 0.03 s GPU @ 1.0 Ghz (Python)
188 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.
189 MonoPCNS 8.63 % 14.16 % 7.30 % 0.14 s GPU @ 2.5 Ghz (Python)
190 MonoA^2 8.51 % 12.95 % 7.56 % na s 1 core @ 2.5 Ghz (C/C++)
191 SARM3D 8.48 % 12.95 % 7.25 % 0.03 s GPU @ 2.5 Ghz (Python)
192 MM3D 8.47 % 13.65 % 7.05 % NA s 1 core @ 2.5 Ghz (C/C++)
193 Mono3DMethod 8.37 % 13.38 % 6.91 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
194 HBD 8.33 % 13.47 % 6.99 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
195 MonoXiver 8.32 % 12.70 % 7.04 % 0.03s GPU @ 2.5 Ghz (Python)
196 GCDR 8.27 % 11.50 % 7.37 % 0.28 s 1 core @ 2.5 Ghz (Python)
197 MonoNeRD 8.26 % 13.20 % 7.02 % na s 1 core @ 2.5 Ghz (C/C++)
198 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.
199 Anonymous 8.04 % 12.18 % 6.58 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
200 MonoRCNN++ code 7.90 % 12.26 % 6.62 % 0.07 s GPU @ 2.5 Ghz (Python)
X. Shi, Z. Chen and T. Kim: Multivariate Probabilistic Monocular 3D Object Detection. WACV 2023.
201 SparseLiDAR_fusion 7.71 % 11.41 % 6.38 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
202 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.
203 M3DGAF 7.42 % 11.68 % 6.65 % 0.07 s 1 core @ 2.5 Ghz (Python)
204 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.
205 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.
206 MonoAug 7.31 % 11.31 % 6.12 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
207 3DSeMoDLE code 7.26 % 10.78 % 6.05 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
208 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.
209 Shape-Aware 7.16 % 10.40 % 5.93 % 0.05 s 1 core @ 2.5 Ghz (Python)
210 MDSNet 7.09 % 10.68 % 6.06 % 0.05 s 1 core @ 2.5 Ghz (Python)
Z. Xie, Y. Song, J. Wu, Z. Li, C. Song and Z. Xu: MDS-Net: Multi-Scale Depth Stratification 3D Object Detection from Monocular Images. Sensors 2022.
211 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.
212 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.
213 DCD code 6.73 % 10.37 % 6.28 % 1 s 1 core @ 2.5 Ghz (C/C++)
214 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.
215 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 .
216 MonoAug 6.36 % 9.59 % 5.24 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
217 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.
218 MDNet 5.66 % 8.24 % 4.74 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
219 FMF-occlusion-net 5.23 % 7.62 % 4.28 % 0.16 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Liu, H. Liu, Y. Wang, F. Sun and W. Huang: Fine-grained Multi-level Fusion for Anti- occlusion Monocular 3D Object Detection. IEEE Transactions on Image Processing 2022.
220 MK3D 5.00 % 7.29 % 4.54 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
221 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.
222 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.
223 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.
224 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.
225 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.
226 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 .
227 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.
228 MoGDE 3.42 % 5.57 % 2.73 % 0.03 s GPU @ 2.5 Ghz (Python)
229 CMAN 3.41 % 4.62 % 2.87 % 0.15 s 1 core @ 2.5 Ghz (Python)
C. Yuanzhouhan Cao: CMAN: Leaning Global Structure Correlation for Monocular 3D Object Detection. IEEE Trans. Intell. Transport. Syst. 2022.
230 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.
231 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.
232 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.
233 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.
234 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.
235 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.
236 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.
237 SSAL-Mono 1.32 % 1.65 % 1.03 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
238 CDTrack3D code 1.01 % 1.48 % 0.69 % 0.0106 s NVIDIA RTX 3090 GPU, i9 10850k CPU
239 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 BiProDet 74.32 % 86.74 % 67.45 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
2 TED code 74.12 % 88.82 % 66.84 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, C. Wen, W. Li, R. Yang and C. Wang: Transformation-Equivariant 3D Object Detection for Autonomous Driving. AAAI 2023.
3 CasA++ code 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 LoGoNet 71.70 % 84.47 % 64.67 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
6 HMFI code 70.37 % 84.02 % 62.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
X. Li, B. Shi, Y. Hou, X. Wu, T. Ma, Y. Li and L. He: Homogeneous Multi-modal Feature Fusion and Interaction for 3D Object Detection. ECCV 2022.
7 CAD 69.94 % 84.68 % 62.21 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
8 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.
9 VoCo 69.00 % 82.74 % 62.46 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
10 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.
11 CZY_PPF_Net2 68.79 % 82.21 % 61.13 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
12 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.
13 SPT 68.60 % 84.90 % 61.69 % 0.1 s GPU @ 2.5 Ghz (Python)
14 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.
15 ACF-Net 68.37 % 84.29 % 62.08 % n/a s 1 core @ 2.5 Ghz (C/C++)
16 CZY_PPF_Net 68.23 % 83.46 % 62.05 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
17 Semantical PVRCNN 68.21 % 83.46 % 61.17 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
18 Under Blind Review#2 68.03 % 81.55 % 60.51 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
19 PA-RCNN code 67.97 % 82.95 % 61.15 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
20 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.
21 USVLab BSAODet 67.79 % 82.65 % 60.26 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
22 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.
23 SGDA3D 67.55 % 82.10 % 60.70 % 0.07 s 1 core @ 2.5 Ghz (Python)
24 3SNet 67.52 % 81.09 % 60.34 % 0.07 s GPU @ 2.5 Ghz (Python)
25 DCAN-Second code 67.50 % 84.90 % 60.78 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
26 USVLab BSAODet (S) 67.25 % 81.94 % 59.19 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
27 CF-ctdep-tv-ta 67.08 % 85.49 % 59.86 % 1 s 1 core @ 2.5 Ghz (C/C++)
28 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.
29 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.
30 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.
31 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++)
32 FV2P v2 66.38 % 83.53 % 59.92 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
33 Reprod-Two-Branch 66.28 % 82.71 % 58.88 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
34 CFF-tv 66.26 % 82.09 % 58.54 % 1 s 1 core @ 2.5 Ghz (C/C++)
35 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.
36 cff-tv-v2-ep25 66.14 % 82.40 % 58.96 % 1 s 1 core @ 2.5 Ghz (C/C++)
37 CenterFuse 66.10 % 85.19 % 58.95 % 0.059 sec/frame 2 x V100
38 CFF-ep25 65.99 % 81.91 % 58.22 % 1 s 1 core @ 2.5 Ghz (C/C++)
39 CZY 65.97 % 82.86 % 58.33 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
40 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.
41 CZY_3917 65.64 % 80.45 % 58.48 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
42 TBD 65.48 % 79.90 % 57.95 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
43 CFF-tv-v2 65.47 % 81.67 % 58.22 % 1 s 1 core @ 2.5 Ghz (C/C++)
44 CF-ctdep-tv 65.43 % 82.29 % 57.86 % 1 s 1 core @ 2.5 Ghz (C/C++)
45 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.
46 VGA-RCNN 65.19 % 79.70 % 58.52 % 0.07 s 1 core @ 2.5 Ghz (Python)
47 TCDVF 65.19 % 79.41 % 58.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
48 IKT3D
This method makes use of Velodyne laser scans.
65.17 % 79.88 % 58.09 % 0.05 s 1 core @ 2.5 Ghz (Python)
49 TBD 65.13 % 83.80 % 58.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
50 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.
51 MVMM code 64.81 % 77.82 % 58.79 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
52 DGT-Det3D code 64.80 % 78.06 % 58.08 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
53 IPS 64.62 % 80.78 % 58.09 % TBD s 1 core @ 2.5 Ghz (C/C++)
54 PVTr 64.51 % 81.09 % 57.50 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
55 HPV-RCNN 64.50 % 79.17 % 57.16 % 0.15 s 1 core @ 2.5 Ghz (Python)
56 DGT-Det3D 64.38 % 78.27 % 57.79 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
57 GS-FPS 64.37 % 79.17 % 57.47 % TBD s 1 core @ 2.5 Ghz (C/C++)
58 cff-tv-t 64.16 % 83.46 % 57.21 % 1 s 1 core @ 2.5 Ghz (C/C++)
59 KPSCC code 64.12 % 77.56 % 57.95 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
60 TBD 64.12 % 79.27 % 57.48 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
61 KeyFuse2B 64.10 % 82.28 % 57.19 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
62 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.
63 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.
64 CF-base-tv 63.97 % 79.52 % 56.61 % 1 s 1 core @ 2.5 Ghz (C/C++)
65 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.
66 IoU-2B 63.75 % 82.21 % 56.43 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
67 U_RVRCNN_V2_1 63.74 % 77.85 % 57.06 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
68 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.
69 LightCPC code 63.71 % 80.15 % 56.66 % 0.02 s 1 core @ 2.5 Ghz (Python + C/C++)
70 CF-cd-io-tv 63.69 % 82.60 % 56.66 % 1 s 1 core @ 2.5 Ghz (C/C++)
71 KeyPoint-IoUHead 63.65 % 81.44 % 56.94 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
72 WGVRF 63.58 % 78.81 % 57.27 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
73 NV-RCNN 63.57 % 80.12 % 56.18 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
74 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.
75 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.
76 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.
77 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.
78 Anonymous 63.29 % 79.02 % 56.59 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
79 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.
80 cp-tv-kp-io-sc 63.03 % 79.30 % 56.66 % 1 s 1 core @ 2.5 Ghz (C/C++)
81 PSA-SSD 62.87 % 76.36 % 56.99 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
82 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.
83 U_PVRCNN_V2 62.50 % 75.08 % 55.32 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
84 TTT_SSD 62.42 % 76.07 % 56.39 % TBD s 1 core @ 2.5 Ghz (C/C++)
85 VPNet 62.38 % 77.56 % 55.92 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
86 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.
87 AGS-SSD[la] 62.15 % 77.40 % 56.14 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
88 Anonymous 62.06 % 76.51 % 55.50 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
89 SRDL 62.02 % 77.35 % 55.52 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
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90 cp-tv 62.01 % 77.26 % 55.09 % 1 s 1 core @ 2.5 Ghz (C/C++)
91 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.
92 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.
93 PVRCNN_8369 61.99 % 77.33 % 55.51 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
94 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.
95 KPP3D code 61.85 % 76.43 % 55.78 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
96 Self-Calib Conv 61.84 % 77.26 % 55.37 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
97 PSA-Det3D 61.79 % 75.82 % 55.12 % 0.1 s GPU @ 2.5 Ghz (Python)
98 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.
99 SIF 61.61 % 77.13 % 55.11 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
100 ATT_SSD 61.61 % 77.19 % 55.62 % 0.01 s 1 core @ 2.5 Ghz (Python)
101 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.
102 Anonymous 61.43 % 76.62 % 54.77 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
103 GEO_LOC 61.37 % 75.64 % 55.22 % TBD s 1 core @ 2.5 Ghz (C/C++)
104 GS-FPS-LT 61.15 % 76.16 % 54.65 % TBD s 1 core @ 2.5 Ghz (C/C++)
105 SWA code 61.12 % 76.47 % 55.51 % 0.18 s 1 core @ 2.5 Ghz (C/C++)
106 Dune-DCF-e11 61.03 % 80.38 % 54.07 % 1 s 1 core @ 2.5 Ghz (C/C++)
107 City-CF 60.84 % 79.32 % 53.83 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
108 Dune-DCF-e15 60.53 % 78.68 % 53.73 % 1 s 1 core @ 2.5 Ghz (C/C++)
109 BASA 60.43 % 76.46 % 54.47 % 1s 1 core @ 2.5 Ghz (python)
110 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.
111 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.
112 cp-tv-kp 59.68 % 75.54 % 53.34 % 1 s 1 core @ 2.5 Ghz (C/C++)
113 City-CF-fixed 59.56 % 77.39 % 53.32 % 1 s 1 core @ 2.5 Ghz (C/C++)
114 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.
115 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.
116 CF-ctdep-train 59.35 % 77.73 % 52.26 % 1 s 1 core @ 2.5 Ghz (C/C++)
117 DTE3D 59.12 % 76.99 % 52.97 % 0.15s 1 core @ 2.5 Ghz (C/C++)
118 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.
119 CF-base-train 58.80 % 76.64 % 51.15 % 1 s 1 core @ 2.5 Ghz (C/C++)
120 CrazyTensor-CF 58.72 % 78.24 % 51.39 % 1 s 1 core @ 2.5 Ghz (C/C++)
121 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.
122 variance_point 58.45 % 75.33 % 51.51 % 0.05 s 1 core @ 2.5 Ghz (Python)
123 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.
124 ZMMPP 58.03 % 71.72 % 51.89 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
125 Dune-DCF-e09 57.82 % 74.49 % 51.53 % 1 s 1 core @ 2.5 Ghz (C/C++)
126 AFTD 57.44 % 75.50 % 51.12 % 1 s 1 core @ 2.5 Ghz (Python + C/C++)
127 U_SECOND_V4 57.10 % 73.91 % 50.91 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
128 LazyTorch-CP-Small-P 56.82 % 73.06 % 50.70 % 1 s 1 core @ 2.5 Ghz (C/C++)
129 LazyTorch-CP-Infer-O 56.77 % 73.03 % 50.73 % 1 s 1 core @ 2.5 Ghz (C/C++)
130 CenterPoint (pcdet) 56.67 % 73.04 % 50.60 % 0.051 sec/frame 2 x V100
131 T_PVRCNN 56.26 % 70.51 % 49.90 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
132 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.
133 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.
134 SECOND_7862 55.64 % 71.05 % 49.83 % 1 s 1 core @ 2.5 Ghz (Python)
135 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++)
136 CrazyTensor-CP 55.31 % 72.10 % 49.40 % 1 s 1 core @ 2.5 Ghz (Python)
137 T_PVRCNN_V2 55.29 % 69.58 % 49.22 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
138 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.
139 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.
140 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.
141 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.
142 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.
143 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.
144 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.
145 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.
146 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.
147 CZY 45.32 % 59.97 % 40.44 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
148 StereoDistill 44.02 % 63.96 % 39.19 % 0.4 s 1 core @ 2.5 Ghz (Python)
149 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.
150 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.
151 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.
152 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.
153 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.
154 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.
155 Pseudo-Stereo++ 30.75 % 47.77 % 26.67 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
156 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.
157 PS 26.77 % 41.22 % 23.76 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
158 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.
159 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.
160 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.
161 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.
162 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.
163 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.
164 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.
165 CMKD code 6.67 % 12.52 % 6.34 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Y. Hong, H. Dai and Y. Ding: Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection. ECCV 2022.
166 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.
167 DD3Dv2 code 5.68 % 8.79 % 4.75 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
168 BSM3D 5.61 % 9.45 % 4.81 % 0.03 s 1 core @ 2.5 Ghz (Python)
169 Anonymous 5.24 % 9.60 % 4.50 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
170 SSAL-Mono 5.01 % 7.67 % 4.36 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
171 BAIR 4.97 % 8.17 % 4.62 % 0.04 s 1 core @ 2.5 Ghz (Python)
172 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) .
173 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.
174 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.
175 LT-M3OD 4.52 % 7.87 % 4.38 % 0.03 s 1 core @ 2.5 Ghz (Python)
176 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.
177 DEPT 4.29 % 7.67 % 3.33 % 0.03 s 1 core @ 2.5 Ghz (Python)
178 MonoASS 4.28 % 7.37 % 4.08 % 0.04 s 1 core @ 2.5 Ghz (Python)
179 3DSeMoDLE code 4.24 % 7.04 % 3.56 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
180 MonoAD 4.22 % 6.59 % 3.52 % 0.03 s GPU @ 2.5 Ghz (Python)
181 Lite-FPN-GUPNet 4.19 % 6.22 % 3.59 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
182 MM3D 3.99 % 7.46 % 3.22 % NA s 1 core @ 2.5 Ghz (C/C++)
183 Anonymous 3.94 % 6.49 % 3.25 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
184 MDNet 3.88 % 6.93 % 3.10 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
185 Shape-Aware 3.78 % 6.26 % 3.35 % 0.05 s 1 core @ 2.5 Ghz (Python)
186 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.
187 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.
188 BCA 3.54 % 5.89 % 3.34 % 0.17 s GPU @ 2.5 Ghz (Python)
189 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.
190 OPA-3D code 3.45 % 5.16 % 2.86 % 0.04 s 1 core @ 3.5 Ghz (Python)
191 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.
192 SARM3D 3.37 % 4.93 % 2.95 % 0.03 s GPU @ 2.5 Ghz (Python)
193 MonoInsight 3.37 % 5.94 % 3.22 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
194 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.
195 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.
196 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.
197 MonoAug 3.15 % 5.22 % 2.67 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
198 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.
199 CIE 3.09 % 5.62 % 2.80 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
200 GPENet code 3.05 % 5.45 % 2.56 % 0.02 s GPU @ 2.5 Ghz (Python)
201 SparseLiDAR_fusion 3.02 % 5.89 % 2.50 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
202 MoGDE 2.94 % 5.08 % 2.74 % 0.03 s GPU @ 2.5 Ghz (Python)
203 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.
204 AMNet 2.79 % 4.30 % 2.51 % 0.03 s GPU @ 1.0 Ghz (Python)
205 DCD code 2.74 % 4.72 % 2.41 % 1 s 1 core @ 2.5 Ghz (C/C++)
206 MDSNet 2.68 % 5.37 % 2.22 % 0.05 s 1 core @ 2.5 Ghz (Python)
Z. Xie, Y. Song, J. Wu, Z. Li, C. Song and Z. Xu: MDS-Net: Multi-Scale Depth Stratification 3D Object Detection from Monocular Images. Sensors 2022.
207 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 .
208 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.
209 MonoNeRD 2.48 % 4.73 % 2.16 % na s 1 core @ 2.5 Ghz (C/C++)
210 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.
211 MonoXiver 2.41 % 3.62 % 2.04 % 0.03s GPU @ 2.5 Ghz (Python)
212 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.
213 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.
214 M3DGAF 2.30 % 4.28 % 2.12 % 0.07 s 1 core @ 2.5 Ghz (Python)
215 Mono3DMethod 2.30 % 3.79 % 2.01 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
216 MonoA^2 2.28 % 4.39 % 2.31 % na s 1 core @ 2.5 Ghz (C/C++)
217 MonoAug 2.23 % 3.68 % 1.69 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
218 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.
219 MonoPCNS 2.09 % 4.07 % 2.12 % 0.14 s GPU @ 2.5 Ghz (Python)
220 MK3D 2.02 % 3.75 % 1.96 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
221 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.
222 MonoRCNN++ code 1.81 % 3.17 % 1.75 % 0.07 s GPU @ 2.5 Ghz (Python)
X. Shi, Z. Chen and T. Kim: Multivariate Probabilistic Monocular 3D Object Detection. WACV 2023.
223 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.
224 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.
225 FMF-occlusion-net 1.60 % 1.87 % 1.66 % 0.16 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Liu, H. Liu, Y. Wang, F. Sun and W. Huang: Fine-grained Multi-level Fusion for Anti- occlusion Monocular 3D Object Detection. IEEE Transactions on Image Processing 2022.
226 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.
227 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.
228 HBD 1.24 % 2.45 % 1.22 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
229 GCDR 1.17 % 1.96 % 1.02 % 0.28 s 1 core @ 2.5 Ghz (Python)
230 CMAN 1.05 % 1.59 % 1.11 % 0.15 s 1 core @ 2.5 Ghz (Python)
C. Yuanzhouhan Cao: CMAN: Leaning Global Structure Correlation for Monocular 3D Object Detection. IEEE Trans. Intell. Transport. Syst. 2022.
231 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.
232 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 .
233 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.
234 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.
235 CDTrack3D code 0.06 % 0.06 % 0.07 % 0.0106 s NVIDIA RTX 3090 GPU, i9 10850k CPU
236 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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