Bird's Eye View Evaluation 2017


The bird's eye view benchmark consists of 7481 training images and 7518 test images as well as the corresponding point clouds, comprising a total of 80.256 labeled objects. For evaluation, we compute precision-recall curves. To rank the methods we compute average precision. We require that all methods use the same parameter set for all test pairs. Our development kit provides details about the data format as well as MATLAB / C++ utility functions for reading and writing the label files.

We evaluate bird's eye view detection performance using the PASCAL criteria also used for 2D object detection. Far objects are thus filtered based on their bounding box height in the image plane. As only objects also appearing on the image plane are labeled, objects in don't car areas do not count as false positives. We note that the evaluation does not take care of ignoring detections that are not visible on the image plane — these detections might give rise to false positives. For cars we require a bounding box overlap of 70% in bird's eye view, while for pedestrians and cyclists we require an overlap of 50%. Difficulties are defined as follows:

  • Easy: Min. bounding box height: 40 Px, Max. occlusion level: Fully visible, Max. truncation: 15 %
  • Moderate: Min. bounding box height: 25 Px, Max. occlusion level: Partly occluded, Max. truncation: 30 %
  • Hard: Min. bounding box height: 25 Px, Max. occlusion level: Difficult to see, Max. truncation: 50 %

All methods are ranked based on the moderately difficult results.

Note 2: On 08.10.2019, we have followed the suggestions of the Mapillary team in their paper Disentangling Monocular 3D Object Detection and use 40 recall positions instead of the 11 recall positions proposed in the original Pascal VOC benchmark. This results in a more fair comparison of the results, please check their paper. The last leaderboards right before this change can be found here: Object Detection Evaluation, 3D Object Detection Evaluation, Bird's Eye View Evaluation.
Important Policy Update: As more and more non-published work and re-implementations of existing work is submitted to KITTI, we have established a new policy: from now on, only submissions with significant novelty that are leading to a peer-reviewed paper in a conference or journal are allowed. Minor modifications of existing algorithms or student research projects are not allowed. Such work must be evaluated on a split of the training set. To ensure that our policy is adopted, new users must detail their status, describe their work and specify the targeted venue during registration. Furthermore, we will regularly delete all entries that are 6 months old but are still anonymous or do not have a paper associated with them. For conferences, 6 month is enough to determine if a paper has been accepted and to add the bibliography information. For longer review cycles, you need to resubmit your results.
Additional information used by the methods
  • Stereo: Method uses left and right (stereo) images
  • Flow: Method uses optical flow (2 temporally adjacent images)
  • Multiview: Method uses more than 2 temporally adjacent images
  • Laser Points: Method uses point clouds from Velodyne laser scanner
  • Additional training data: Use of additional data sources for training (see details)

Car


Method Setting Code Moderate Easy Hard Runtime Environment
1 VirConv-S 93.52 % 95.99 % 90.38 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
2 VirConv-T 92.65 % 96.11 % 89.69 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
3 GraR-Po code 92.12 % 95.79 % 87.11 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph. ECCV 2022.
4 TED code 92.05 % 95.44 % 87.30 % 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.
5 LIVOX_Det
This method makes use of Velodyne laser scans.
92.05 % 95.60 % 89.22 % n/a s 1 core @ 2.5 Ghz (Python + C/C++)
6 VirConv-L 91.95 % 95.53 % 87.07 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
7 VPFNet code 91.86 % 93.02 % 86.94 % 0.06 s 2 cores @ 2.5 Ghz (Python)
H. Zhu, J. Deng, Y. Zhang, J. Ji, Q. Mao, H. Li and Y. Zhang: VPFNet: Improving 3D Object Detection with Virtual Point based LiDAR and Stereo Data Fusion. IEEE Transactions on Multimedia 2022.
8 SFD code 91.85 % 95.64 % 86.83 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
X. Wu, L. Peng, H. Yang, L. Xie, C. Huang, C. Deng, H. Liu and D. Cai: Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completion. CVPR 2022.
9 SE-SSD
This method makes use of Velodyne laser scans.
code 91.84 % 95.68 % 86.72 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, L. Jiang and C. Fu: SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud. CVPR 2021.
10 ACF-Net 91.78 % 92.91 % 87.06 % n/a s 1 core @ 2.5 Ghz (C/C++)
11 GraR-Vo code 91.72 % 95.27 % 86.51 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph. ECCV 2022.
12 PVT-SSD 91.63 % 95.23 % 86.43 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
13 CityBrainLab 91.62 % 94.78 % 86.68 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
14 SPANet 91.59 % 95.59 % 86.53 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
Y. Ye: SPANet: Spatial and Part-Aware Aggregation Network for 3D Object Detection. Pacific Rim International Conference on Artificial Intelligence 2021.
15 CasA code 91.54 % 95.19 % 86.82 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.
16 LoGoNet 91.52 % 95.48 % 87.09 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
17 GraR-Pi code 91.52 % 95.06 % 86.42 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph. ECCV 2022.
18 HRNet 91.42 % 95.18 % 86.73 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
19 BiProDet 91.36 % 92.96 % 86.80 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
20 NSAW code 91.35 % 94.51 % 86.42 % 0.1 s 1 core @ 2.5 Ghz (Python)
21 BADet code 91.32 % 95.23 % 86.48 % 0.14 s 1 core @ 2.5 Ghz (C/C++)
R. Qian, X. Lai and X. Li: BADet: Boundary-Aware 3D Object Detection from Point Clouds. Pattern Recognition 2022.
22 VoCo 91.32 % 95.42 % 88.38 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
23 GT3D 91.31 % 95.05 % 86.67 % 0.1 s 1 core @ 2.5 Ghz (Python)
24 CasA++ code 91.22 % 94.57 % 88.43 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.
25 Anonymous 91.14 % 94.04 % 86.33 % n/a s 1 core @ 2.5 Ghz (C/C++)
26 SGFusion 91.11 % 94.76 % 86.27 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
27 SA-SSD code 91.03 % 95.03 % 85.96 % 0.04 s 1 core @ 2.5 Ghz (Python)
C. He, H. Zeng, J. Huang, X. Hua and L. Zhang: Structure Aware Single-stage 3D Object Detection from Point Cloud. CVPR 2020.
28 VGT-RCNN 90.89 % 94.59 % 86.36 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
29 3D Dual-Fusion 90.86 % 93.08 % 86.44 % 0.1 s 1 core @ 2.5 Ghz (Python)
30 Anonymous
This method makes use of Velodyne laser scans.
90.82 % 94.89 % 86.39 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
31 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 90.65 % 94.98 % 86.14 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
32 VPFNet code 90.52 % 93.94 % 86.25 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
C. Wang, H. Chen and L. Fu: VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection. 2021.
33 PDV code 90.48 % 94.56 % 86.23 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Hu, T. Kuai and S. Waslander: Point Density-Aware Voxels for LiDAR 3D Object Detection. CVPR 2022.
34 M3DeTR code 90.37 % 94.41 % 85.98 % n/a s GPU @ 1.0 Ghz (Python)
T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis and D. Manocha: M3DeTR: Multi-representation, Multi- scale, Mutual-relation 3D Object Detection with Transformers. 2021.
35 SGDA3D 90.36 % 92.53 % 86.09 % 0.07 s 1 core @ 2.5 Ghz (Python)
36 VoTr-TSD code 90.34 % 94.03 % 86.14 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
J. Mao, Y. Xue, M. Niu, H. Bai, J. Feng, X. Liang, H. Xu and C. Xu: Voxel Transformer for 3D Object Detection. ICCV 2021.
37 Under Blind Review#2 90.27 % 92.51 % 86.01 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
38 IKT3D
This method makes use of Velodyne laser scans.
90.23 % 94.22 % 85.94 % 0.05 s 1 core @ 2.5 Ghz (Python)
39 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
code 90.13 % 92.42 % 85.93 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
40 XView 90.12 % 92.27 % 85.94 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
41 GraR-VoI code 90.10 % 95.69 % 86.85 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph. ECCV 2022.
42 CAT-Det 90.07 % 92.59 % 85.82 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Zhang, J. Chen and D. Huang: CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection. CVPR 2022.
43 NIV-SSD 89.92 % 95.59 % 84.58 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
44 SVGA-Net 89.88 % 92.07 % 85.59 % 0.03s 1 core @ 2.5 Ghz (Python + C/C++)
Q. He, Z. Wang, H. Zeng, Y. Zeng and Y. Liu: SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds. AAAI 2022.
45 EBM3DOD code 89.86 % 95.64 % 84.56 % 0.12 s 1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.
46 CIA-SSD
This method makes use of Velodyne laser scans.
code 89.84 % 93.74 % 82.39 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, S. Chen, L. Jiang and C. Fu: CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud. AAAI 2021.
47 CLOCs_PVCas code 89.80 % 93.05 % 86.57 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection . 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
48 GLENet-VR 89.76 % 93.48 % 84.89 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Zhang, Z. Zhu, J. Hou and Y. Yuan: GLENet: Boosting 3D Object Detectors with Generative Label Uncertainty Estimation. arXiv preprint arXiv:2207.02466 2022.
49 RDIoU code 89.75 % 94.90 % 84.67 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Sheng, S. Cai, N. Zhao, B. Deng, J. Huang, X. Hua, M. Zhao and G. Lee: Rethinking IoU-based Optimization for Single- stage 3D Object Detection. ECCV 2022.
50 HRNet++ 89.69 % 95.38 % 84.75 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
51 EBM3DOD baseline code 89.63 % 95.44 % 84.34 % 0.05 s 1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.
52 HCPVF 89.62 % 93.20 % 86.72 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
53 LightCPC code 89.62 % 92.99 % 86.51 % 0.02 s 1 core @ 2.5 Ghz (Python + C/C++)
54 3SNet 89.58 % 93.26 % 84.80 % 0.07 s GPU @ 2.5 Ghz (Python)
55 CAD 89.57 % 93.03 % 84.71 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
56 3D-CVF at SPA
This method makes use of Velodyne laser scans.
89.56 % 93.52 % 82.45 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
J. Yoo, Y. Kim, J. Kim and J. Choi: 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection. ECCV 2020.
57 OcTr 89.56 % 93.08 % 86.74 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
58 ImpDet 89.55 % 92.74 % 84.41 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
59 Struc info fusion II 89.54 % 95.26 % 82.31 % 0.05 s GPU @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.
60 SASA
This method makes use of Velodyne laser scans.
code 89.51 % 92.87 % 86.35 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
C. Chen, Z. Chen, J. Zhang and D. Tao: SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object Detection. arXiv preprint arXiv:2201.01976 2022.
61 PA-RCNN code 89.51 % 92.95 % 82.42 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
62 Fast-CLOCs 89.49 % 93.03 % 86.40 % 0.1 s GPU @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: Fast-CLOCs: Fast Camera-LiDAR Object Candidates Fusion for 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022.
63 IA-SSD (single) code 89.48 % 93.14 % 84.42 % 0.013 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
64 VoxelGraphRCNN 89.48 % 93.35 % 86.68 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
65 CLOCs code 89.48 % 92.91 % 86.42 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection . 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
66 PA3DNet 89.46 % 93.11 % 84.60 % 0.05 s GPU @ 2.5 Ghz (Python)
67 DVF-V 89.42 % 93.12 % 86.50 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
A. Mahmoud, J. Hu and S. Waslander: Dense Voxel Fusion for 3D Object Detection. WACV 2023.
68 GS-FPS 89.39 % 92.69 % 86.16 % TBD s 1 core @ 2.5 Ghz (C/C++)
69 Struc info fusion I 89.38 % 94.91 % 84.29 % 0.05 s 1 core @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.
70 SWA code 89.36 % 92.82 % 86.21 % 0.18 s 1 core @ 2.5 Ghz (C/C++)
71 IPS 89.36 % 92.78 % 86.08 % TBD s 1 core @ 2.5 Ghz (C/C++)
72 DCGNN 89.36 % 94.57 % 84.13 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
73 ATT_SSD 89.34 % 92.58 % 86.08 % 0.01 s 1 core @ 2.5 Ghz (Python)
74 BtcDet
This method makes use of Velodyne laser scans.
code 89.34 % 92.81 % 84.55 % 0.09 s GPU @ 2.5 Ghz (Python + C/C++)
Q. Xu, Y. Zhong and U. Neumann: Behind the Curtain: Learning Occluded Shapes for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2022.
75 IA-SSD (multi) code 89.33 % 92.79 % 84.35 % 0.014 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
76 Anomynous 89.29 % 92.99 % 86.49 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
77 Anonymous 89.27 % 92.79 % 86.53 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
78 GEO_LOC 89.25 % 92.80 % 84.23 % TBD s 1 core @ 2.5 Ghz (C/C++)
79 TBD 89.24 % 92.59 % 85.99 % 0.1 s 1 core @ 2.5 Ghz (Python)
80 KPSCC code 89.21 % 92.88 % 85.87 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
81 ACDet code 89.21 % 92.87 % 85.80 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
J. Xu, G. Wang, X. Zhang and G. Wan: ACDet: Attentive Cross-view Fusion for LiDAR-based 3D Object Detection. 3DV 2022.
82 DVF-PV 89.20 % 93.08 % 86.28 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
A. Mahmoud, J. Hu and S. Waslander: Dense Voxel Fusion for 3D Object Detection. WACV 2023.
83 TTT_SSD 89.20 % 92.55 % 86.07 % TBD s 1 core @ 2.5 Ghz (C/C++)
84 STD code 89.19 % 94.74 % 86.42 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
85 GS-FPS-LT 89.18 % 92.74 % 84.17 % TBD s 1 core @ 2.5 Ghz (C/C++)
86 Point-GNN
This method makes use of Velodyne laser scans.
code 89.17 % 93.11 % 83.90 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
87 HMFI code 89.17 % 93.04 % 86.37 % 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.
88 SSL-PointGNN code 89.16 % 92.92 % 83.99 % 0.56 s GPU @ 1.5 Ghz (Python)
E. Erçelik, E. Yurtsever, M. Liu, Z. Yang, H. Zhang, P. Topçam, M. Listl, Y. Çaylı and A. Knoll: 3D Object Detection with a Self-supervised Lidar Scene Flow Backbone. arXiv preprint arXiv:2205.00705 2022.
89 USVLab BSAODet 89.13 % 92.92 % 86.41 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
90 SPG_mini
This method makes use of Velodyne laser scans.
code 89.12 % 92.80 % 86.27 % 0.09 s GPU @ 2.5 Ghz (Python)
Q. Xu, Y. Zhou, W. Wang, C. Qi and D. Anguelov: SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation. Proceedings of the IEEE conference on computer vision and pattern recognition (ICCV) 2021.
91 HPV-RCNN 89.12 % 92.49 % 83.98 % 0.15 s 1 core @ 2.5 Ghz (Python)
92 ITCA-SSD code 89.12 % 93.19 % 83.99 % 0.05 s 1 core @ 2.5 Ghz (Python)
93 PV-DT3D 89.10 % 92.65 % 86.43 % 1.4 s 1 core @ 2.5 Ghz (C/C++)
94 EQ-PVRCNN code 89.09 % 94.55 % 86.42 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, L. Jiang, Y. Sun, B. Schiele and J. Jia: A Unified Query-based Paradigm for Point Cloud Understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.
95 SPT 89.09 % 94.87 % 84.38 % 0.1 s GPU @ 2.5 Ghz (Python)
96 TBD code 89.09 % 92.61 % 83.85 % 0.1 s GPU @ 2.5 Ghz (Python)
97 VoxSeT code 89.07 % 92.70 % 86.29 % 33 ms 1 core @ 2.5 Ghz (C/C++)
C. He, R. Li, S. Li and L. Zhang: Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds. CVPR 2022.
98 ChTR3D 89.04 % 92.72 % 86.29 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
99 3DSSD code 89.02 % 92.66 % 85.86 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
100 EPNet++ 89.00 % 95.41 % 85.73 % 0.1 s GPU @ 2.5 Ghz (Python)
Z. Liu, H. tengteng, B. Li, X. Chen, X. Wang and X. Bai: EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object Detection. arXiv preprint arXiv:2112.11088 2021.
101 Focals Conv code 89.00 % 92.67 % 86.33 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Y. Chen, Y. Li, X. Zhang, J. Sun and J. Jia: Focal Sparse Convolutional Networks for 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.
102 LGNet 88.98 % 92.83 % 86.26 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
103 ChTR3D 88.98 % 92.35 % 86.17 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
104 VGRCNN++ 88.96 % 92.96 % 86.25 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
105 PTA-RCNN 88.94 % 92.32 % 85.63 % 0.08 s 1 core @ 2.5 Ghz (Python)
106 GV-RCNN code 88.94 % 94.52 % 86.24 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
107 SPNet code 88.92 % 92.29 % 86.16 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
108 AGS-SSD[la] 88.90 % 92.51 % 85.96 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
109 BSConv 88.88 % 92.49 % 85.65 % 0.1 s 1 core @ 2.5 Ghz (Java)
110 CZY_PPF_Net2 88.88 % 94.68 % 86.15 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
111 H^23D R-CNN code 88.87 % 92.85 % 86.07 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
J. Deng, W. Zhou, Y. Zhang and H. Li: From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2021.
112 ChTR3D 88.85 % 92.58 % 85.98 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
113 Pyramid R-CNN 88.84 % 92.19 % 86.21 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
J. Mao, M. Niu, H. Bai, X. Liang, H. Xu and C. Xu: Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection. ICCV 2021.
114 CityBrainLab-CT3D code 88.83 % 92.36 % 84.07 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Sheng, S. Cai, Y. Liu, B. Deng, J. Huang, X. Hua and M. Zhao: Improving 3D Object Detection with Channel- wise Transformer. ICCV 2021.
115 Voxel R-CNN code 88.83 % 94.85 % 86.13 % 0.04 s GPU @ 3.0 Ghz (C/C++)
J. Deng, S. Shi, P. Li, W. Zhou, Y. Zhang and H. Li: Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection . AAAI 2021.
116 HVNet 88.82 % 92.83 % 83.38 % 0.03 s GPU @ 2.0 Ghz (Python)
M. Ye, S. Xu and T. Cao: HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection. CVPR 2020.
117 VG-RCNN 88.81 % 92.75 % 86.12 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
118 GLENet 88.81 % 92.22 % 84.13 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
119 FV2P v2 88.80 % 92.22 % 84.24 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
120 mbdf-netv1 code 88.77 % 94.45 % 83.90 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
121 BASA 88.76 % 92.72 % 83.71 % 1s 1 core @ 2.5 Ghz (python)
122 CZY_3917 88.71 % 94.23 % 86.01 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
123 SPG
This method makes use of Velodyne laser scans.
code 88.70 % 94.33 % 85.98 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Xu, Y. Zhou, W. Wang, C. Qi and D. Anguelov: SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation. Proceedings of the IEEE conference on computer vision and pattern recognition (ICCV) 2021.
124 MVMM code 88.70 % 92.17 % 85.47 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
125 VGRCNN 88.69 % 92.58 % 86.02 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
126 DTE3D 88.69 % 92.61 % 85.77 % 0.15s 1 core @ 2.5 Ghz (C/C++)
127 DCAN-Second code 88.68 % 92.76 % 85.32 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
128 PSA-SSD 88.65 % 92.21 % 83.75 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
129 SIENet code 88.65 % 92.38 % 86.03 % 0.08 s 1 core @ 2.5 Ghz (Python)
Z. Li, Y. Yao, Z. Quan, W. Yang and J. Xie: SIENet: Spatial Information Enhancement Network for 3D Object Detection from Point Cloud. 2021.
130 CZY_PPF_Net 88.65 % 92.78 % 85.83 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
131 P2V-RCNN 88.63 % 92.72 % 86.14 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.
132 FromVoxelToPoint code 88.61 % 92.23 % 86.11 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to- Point Decoder. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
133 RangeIoUDet
This method makes use of Velodyne laser scans.
88.59 % 92.28 % 85.83 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, Z. Zhang, M. Zhang, X. Zhao and S. Pu: RangeIoUDet: Range Image Based Real-Time 3D Object Detector Optimized by Intersection Over Union. CVPR 2021.
134 WGVRF 88.56 % 92.45 % 85.69 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
135 DCCA 88.55 % 92.29 % 85.85 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
136 GVNet-V2 88.54 % 92.26 % 85.71 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
137 VGA-RCNN 88.53 % 92.37 % 85.77 % 0.07 s 1 core @ 2.5 Ghz (Python)
138 TVTr 88.51 % 94.30 % 85.80 % 0.08 s 1 core @ 2.5 Ghz (Python)
139 Anonymous 88.49 % 92.40 % 85.77 % 0.03s
140 EPNet code 88.47 % 94.22 % 83.69 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
T. Huang, Z. Liu, X. Chen and X. Bai: EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection. ECCV 2020.
141 CenterNet3D 88.46 % 91.80 % 83.62 % 0.04 s GPU @ 1.5 Ghz (Python)
G. Wang, B. Tian, Y. Ai, T. Xu, L. Chen and D. Cao: CenterNet3D:An Anchor free Object Detector for Autonomous Driving. 2020.
142 FARP-Net code 88.45 % 91.20 % 86.01 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
143 GVNet code 88.43 % 92.19 % 85.63 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
144 USVLab BSAODet (S) 88.42 % 92.19 % 85.55 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
145 DGT-Det3D code 88.41 % 92.57 % 85.50 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
146 Semantical PVRCNN 88.41 % 92.71 % 85.86 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
147 PVE 88.40 % 92.49 % 85.79 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
148 RangeRCNN
This method makes use of Velodyne laser scans.
88.40 % 92.15 % 85.74 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, M. Zhang, Z. Zhang, X. Zhao and S. Pu: RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representation. arXiv preprint arXiv:2009.00206 2020.
149 Patches
This method makes use of Velodyne laser scans.
88.39 % 92.72 % 83.19 % 0.15 s GPU @ 2.0 Ghz
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
150 3D IoU-Net 88.38 % 94.76 % 81.93 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, S. Krylov, Y. Ding and L. Shao: 3D IoU-Net: IoU Guided 3D Object Detector for Point Clouds. arXiv preprint arXiv:2004.04962 2020.
151 StructuralIF 88.38 % 91.78 % 85.67 % 0.02 s 8 cores @ 2.5 Ghz (Python)
J. Pei An: Deep structural information fusion for 3D object detection on LiDAR-camera system. Accepted in CVIU 2021.
152 CSVoxel-RCNN 88.37 % 92.07 % 85.51 % 0.03 s GPU @ 1.0 Ghz (Python)
153 VPNet 88.37 % 92.11 % 85.63 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
154 NV-RCNN 88.36 % 91.41 % 85.72 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
155 FSFNet 88.35 % 94.88 % 83.58 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
156 DKDet 88.32 % 92.21 % 85.46 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
157 CenterFuse 88.31 % 91.54 % 83.39 % 0.059 sec/frame 2 x V100
158 TBD 88.26 % 91.44 % 85.44 % 0.06 s GPU @ 2.5 Ghz (Python)
159 KPP3D code 88.25 % 93.93 % 83.26 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
160 CLOCs_SecCas 88.23 % 91.16 % 82.63 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
161 SPVB-SSD 88.23 % 91.82 % 85.46 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
162 U_SECOND_V4 88.22 % 91.95 % 85.03 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
163 U_RVRCNN_V2_1 88.21 % 92.05 % 85.39 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
164 UberATG-MMF
This method makes use of Velodyne laser scans.
88.21 % 93.67 % 81.99 % 0.08 s GPU @ 2.5 Ghz (Python)
M. Liang*, B. Yang*, Y. Chen, R. Hu and R. Urtasun: Multi-Task Multi-Sensor Fusion for 3D Object Detection. CVPR 2019.
165 Patches - EMP
This method makes use of Velodyne laser scans.
88.17 % 94.49 % 84.75 % 0.5 s GPU @ 2.5 Ghz (Python)
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
166 SRDL 88.17 % 92.01 % 85.43 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
167 CF-cd-io-tv 88.16 % 91.32 % 83.26 % 1 s 1 core @ 2.5 Ghz (C/C++)
168 PSA-Det3D 88.13 % 92.08 % 85.35 % 0.1 s GPU @ 2.5 Ghz (Python)
169 PVRCNN_8369 88.13 % 91.91 % 85.40 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
170 PointPainting
This method makes use of Velodyne laser scans.
88.11 % 92.45 % 83.36 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
171 SERCNN
This method makes use of Velodyne laser scans.
88.10 % 94.11 % 83.43 % 0.1 s 1 core @ 2.5 Ghz (Python)
D. Zhou, J. Fang, X. Song, L. Liu, J. Yin, Y. Dai, H. Li and R. Yang: Joint 3D Instance Segmentation and Object Detection for Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020.
172 Associate-3Ddet code 88.09 % 91.40 % 82.96 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
L. Du, X. Ye, X. Tan, J. Feng, Z. Xu, E. Ding and S. Wen: Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection. The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
173 HotSpotNet 88.09 % 94.06 % 83.24 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
174 Faraway-Frustum
This method makes use of Velodyne laser scans.
code 88.08 % 91.90 % 85.35 % 0.1 s GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-frustum: Dealing with lidar sparsity for 3D object detection using fusion. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) 2021.
175 TBD 88.04 % 91.31 % 84.79 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
176 SC-Voxel-RCNN 88.02 % 91.45 % 85.22 % 0.12 s GPU @ 1.0 Ghz (Python)
177 CZY 88.00 % 91.85 % 85.22 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
178 UberATG-HDNET
This method makes use of Velodyne laser scans.
87.98 % 93.13 % 81.23 % 0.05 s GPU @ 2.5 Ghz (Python)
B. Yang, M. Liang and R. Urtasun: HDNET: Exploiting HD Maps for 3D Object Detection. 2nd Conference on Robot Learning (CoRL) 2018.
179 Anonymous 87.96 % 91.52 % 82.99 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
180 TCDVF 87.94 % 91.21 % 84.66 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
181 DGT-Det3D 87.88 % 91.70 % 85.14 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
182 Fast Point R-CNN
This method makes use of Velodyne laser scans.
87.84 % 90.87 % 80.52 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, S. Liu, X. Shen and J. Jia: Fast Point R-CNN. Proceedings of the IEEE international conference on computer vision (ICCV) 2019.
183 CSNet 87.84 % 92.23 % 82.93 % 0.1 s 1 core @ 2.5 Ghz (Python)
184 CF-ctdep-tv-ta 87.81 % 90.73 % 84.97 % 1 s 1 core @ 2.5 Ghz (C/C++)
185 Anonymous 87.80 % 91.58 % 82.86 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
186 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 87.79 % 91.70 % 84.61 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
187 cp-tv-kp-io-sc 87.78 % 90.98 % 84.04 % 1 s 1 core @ 2.5 Ghz (C/C++)
188 SIF 87.76 % 91.44 % 85.15 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
189 U_PVRCNN_V2 87.74 % 91.62 % 85.03 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
190 MVAF-Net code 87.73 % 91.95 % 85.00 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
G. Wang, B. Tian, Y. Zhang, L. Chen, D. Cao and J. Wu: Multi-View Adaptive Fusion Network for 3D Object Detection. arXiv preprint arXiv:2011.00652 2020.
191 Reprod-Two-Branch 87.69 % 90.69 % 84.72 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
192 DKAnet 87.68 % 91.07 % 84.03 % 0.05 s 1 core @ 2.0 Ghz (Python)
193 DVFENet 87.68 % 90.93 % 84.60 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection. Neurocomputing 2021.
194 S-AT GCN 87.68 % 90.85 % 84.20 % 0.02 s GPU @ 2.0 Ghz (Python)
L. Wang, C. Wang, X. Zhang, T. Lan and J. Li: S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection. CoRR 2021.
195 TBD 87.67 % 91.02 % 82.42 % 0.1 s 1 core @ 2.5 Ghz (Python)
196 CFF-tv-v2 87.67 % 90.70 % 84.58 % 1 s 1 core @ 2.5 Ghz (C/C++)
197 RangeDet (Official) code 87.67 % 90.93 % 82.92 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
L. Fan, X. Xiong, F. Wang, N. Wang and Z. Zhang: RangeDet: In Defense of Range View for LiDAR-Based 3D Object Detection. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
198 CFF-ep25 87.66 % 90.60 % 84.71 % 1 s 1 core @ 2.5 Ghz (C/C++)
199 Anonymous 87.64 % 91.40 % 82.97 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
200 TBD 87.62 % 90.86 % 82.29 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
201 CF-base-tv 87.60 % 90.28 % 84.52 % 1 s 1 core @ 2.5 Ghz (C/C++)
202 KeyFuse2B 87.59 % 90.70 % 84.58 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
203 MODet
This method makes use of Velodyne laser scans.
87.56 % 90.80 % 82.69 % 0.05 s GTX1080Ti
Y. Zhang, Z. Xiang, C. Qiao and S. Chen: Accurate and Real-Time Object Detection Based on Bird's Eye View on 3D Point Clouds. 2019 International Conference on 3D Vision (3DV) 2019.
204 CFF-tv 87.55 % 90.56 % 84.59 % 1 s 1 core @ 2.5 Ghz (C/C++)
205 cff-tv-v2-ep25 87.55 % 90.26 % 84.53 % 1 s 1 core @ 2.5 Ghz (C/C++)
206 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 87.53 % 91.99 % 81.03 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
207 TBD 87.51 % 90.76 % 80.15 % 0.1 s 1 core @ 2.5 Ghz (Python)
208 DTFI 87.51 % 91.01 % 84.25 % 0.03 s 1 core @ 2.5 Ghz (Python)
209 CF-ctdep-tv 87.50 % 90.56 % 84.65 % 1 s 1 core @ 2.5 Ghz (C/C++)
210 PointRGCN 87.49 % 91.63 % 80.73 % 0.26 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
211 Anonymous 87.48 % 90.98 % 84.22 % 1 1 core @ 2.5 Ghz (Python)
212 SECOND_7862 87.48 % 90.98 % 84.22 % 1 s 1 core @ 2.5 Ghz (Python)
213 MGAF-3DSSD code 87.47 % 92.70 % 82.19 % 0.1 s 1 core @ 2.5 Ghz (Python)
J. Li, H. Dai, L. Shao and Y. Ding: Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
214 PC-CNN-V2
This method makes use of Velodyne laser scans.
87.40 % 91.19 % 79.35 % 0.5 s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Du, M. Ang, S. Karaman and D. Rus: A General Pipeline for 3D Detection of Vehicles. 2018 IEEE International Conference on Robotics and Automation (ICRA) 2018.
215 PVTr 87.39 % 91.21 % 84.77 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
216 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 87.39 % 92.13 % 82.72 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
217 Sem-Aug 87.37 % 93.35 % 82.43 % 0.08 s GPU @ 2.5 Ghz (Python)
218 MAFF-Net(DAF-Pillar) 87.34 % 90.79 % 77.66 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Z. Zhang, Z. Liang, M. Zhang, X. Zhao, Y. Ming, T. Wenming and S. Pu: MAFF-Net: Filter False Positive for 3D Vehicle Detection with Multi-modal Adaptive Feature Fusion. arXiv preprint arXiv:2009.10945 2020.
219 KeyPoint-IoUHead 87.32 % 90.36 % 83.23 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
220 Harmonic PointPillar code 87.28 % 90.89 % 82.54 % 0.01 s 1 core @ 2.5 Ghz (Python)
H. Zhang, M. Mekala, Z. Nain, 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.
221 ZMMPP 87.25 % 90.47 % 82.42 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
222 HRI-VoxelFPN 87.21 % 92.75 % 79.82 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
H. Kuang, B. Wang, J. An, M. Zhang and Z. Zhang: Voxel-FPN:multi-scale voxel feature aggregation in 3D object detection from point clouds. sensors 2020.
223 epBRM
This method makes use of Velodyne laser scans.
code 87.13 % 90.70 % 81.92 % 0.1 s GPU @ >3.5 Ghz (Python + C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
224 T_PVRCNN 86.97 % 91.63 % 82.20 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
225 SARPNET 86.92 % 92.21 % 81.68 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. Ye, H. Chen, C. Zhang, X. Hao and Z. Zhang: SARPNET: Shape Attention Regional Proposal Network for LiDAR-based 3D Object Detection. Neurocomputing 2019.
226 cff-tv-t 86.92 % 91.04 % 80.46 % 1 s 1 core @ 2.5 Ghz (C/C++)
227 CF-base-train 86.88 % 90.03 % 83.16 % 1 s 1 core @ 2.5 Ghz (C/C++)
228 Self-Calib Conv 86.86 % 90.00 % 83.88 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
229 T_PVRCNN_V2 86.85 % 91.54 % 81.82 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
230 ARPNET 86.81 % 90.06 % 79.41 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
231 C-GCN 86.78 % 91.11 % 80.09 % 0.147 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
232 IoU-2B 86.74 % 90.92 % 80.40 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
233 cp-tv-kp 86.58 % 89.58 % 83.64 % 1 s 1 core @ 2.5 Ghz (C/C++)
234 CAD
This method uses stereo information.
This method makes use of Velodyne laser scans.
86.56 % 90.00 % 81.62 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
235 PointPillars
This method makes use of Velodyne laser scans.
code 86.56 % 90.07 % 82.81 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
236 TANet code 86.54 % 91.58 % 81.19 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
237 cp-tv 86.52 % 89.55 % 83.45 % 1 s 1 core @ 2.5 Ghz (C/C++)
238 SCNet
This method makes use of Velodyne laser scans.
86.48 % 90.07 % 81.30 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
239 CF-ctdep-train 86.46 % 89.57 % 82.03 % 1 s 1 core @ 2.5 Ghz (C/C++)
240 CSNet8306 code 86.44 % 92.57 % 81.36 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
241 SegVoxelNet 86.37 % 91.62 % 83.04 % 0.04 s 1 core @ 2.5 Ghz (Python)
H. Yi, S. Shi, M. Ding, J. Sun, K. Xu, H. Zhou, Z. Wang, S. Li and G. Wang: SegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloud. ICRA 2020.
242 Dune-DCF-e09 86.36 % 89.33 % 81.77 % 1 s 1 core @ 2.5 Ghz (C/C++)
243 Dune-DCF-e11 86.32 % 89.32 % 81.78 % 1 s 1 core @ 2.5 Ghz (C/C++)
244 3D IoU Loss
This method makes use of Velodyne laser scans.
86.22 % 91.36 % 81.20 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
D. Zhou, J. Fang, X. Song, C. Guan, J. Yin, Y. Dai and R. Yang: IoU Loss for 2D/3D Object Detection. International Conference on 3D Vision (3DV) 2019.
245 Dune-DCF-e15 86.21 % 88.99 % 81.62 % 1 s 1 core @ 2.5 Ghz (C/C++)
246 CrazyTensor-CF 86.10 % 89.13 % 81.61 % 1 s 1 core @ 2.5 Ghz (C/C++)
247 City-CF-fixed 86.09 % 89.94 % 81.73 % 1 s 1 core @ 2.5 Ghz (C/C++)
248 R-GCN 86.05 % 91.91 % 81.05 % 0.16 s GPU @ 2.5 Ghz (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
249 UberATG-PIXOR++
This method makes use of Velodyne laser scans.
86.01 % 93.28 % 80.11 % 0.035 s GPU @ 2.5 Ghz (Python)
B. Yang, M. Liang and R. Urtasun: HDNET: Exploiting HD Maps for 3D Object Detection. 2nd Conference on Robot Learning (CoRL) 2018.
250 CAT 85.97 % 91.48 % 80.93 % 1 s 1 core @ 2.5 Ghz (Python)
251 SSL_PP code 85.93 % 92.19 % 80.40 % 16ms GPU @ 1.5 Ghz (Python)
252 CSNet8299 code 85.91 % 91.64 % 80.95 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
253 Sem-Aug-PointRCNN++ 85.88 % 91.68 % 83.37 % 0.1 s 8 cores @ 3.0 Ghz (Python)
254 DASS 85.85 % 91.74 % 80.97 % 0.09 s 1 core @ 2.0 Ghz (Python)
O. Unal, L. Van Gool and D. Dai: Improving Point Cloud Semantic Segmentation by Learning 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2021.
255 F-ConvNet
This method makes use of Velodyne laser scans.
code 85.84 % 91.51 % 76.11 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
256 City-CF 85.83 % 89.20 % 81.61 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
257 PI-RCNN 85.81 % 91.44 % 81.00 % 0.1 s 1 core @ 2.5 Ghz (Python)
L. Xie, C. Xiang, Z. Yu, G. Xu, Z. Yang, D. Cai and X. He: PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module. AAAI 2020 : The Thirty-Fourth AAAI Conference on Artificial Intelligence 2020.
258 LazyTorch-CP-Infer-O 85.74 % 89.19 % 81.35 % 1 s 1 core @ 2.5 Ghz (C/C++)
259 PointRGBNet 85.73 % 91.39 % 80.68 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
P. Xie Desheng: Real-time Detection of 3D Objects Based on Multi-Sensor Information Fusion. Automotive Engineering 2022.
260 AFTD 85.63 % 90.61 % 82.28 % 1 s 1 core @ 2.5 Ghz (Python + C/C++)
261 LazyTorch-CP-Small-P 85.63 % 89.10 % 81.27 % 1 s 1 core @ 2.5 Ghz (C/C++)
262 CrazyTensor-CP 85.55 % 87.94 % 82.63 % 1 s 1 core @ 2.5 Ghz (Python)
263 variance_point 85.39 % 91.90 % 81.13 % 0.05 s 1 core @ 2.5 Ghz (Python)
264 UberATG-ContFuse
This method makes use of Velodyne laser scans.
85.35 % 94.07 % 75.88 % 0.06 s GPU @ 2.5 Ghz (Python)
M. Liang, B. Yang, S. Wang and R. Urtasun: Deep Continuous Fusion for Multi-Sensor 3D Object Detection. ECCV 2018.
265 new_stereo 85.24 % 90.74 % 82.10 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
266 PSM_stereo 85.12 % 90.26 % 80.21 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
267 PFF3D
This method makes use of Velodyne laser scans.
code 85.08 % 89.61 % 80.42 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
268 CenterPoint (pcdet) 85.05 % 88.47 % 81.19 % 0.051 sec/frame 2 x V100
269 AVOD
This method makes use of Velodyne laser scans.
code 84.95 % 89.75 % 78.32 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
270 WS3D
This method makes use of Velodyne laser scans.
84.93 % 90.96 % 77.96 % 0.1 s GPU @ 2.5 Ghz (Python)
Q. Meng, W. Wang, T. Zhou, J. Shen, L. Van Gool and D. Dai: Weakly Supervised 3D Object Detection from Lidar Point Cloud. 2020.
271 AVOD-FPN
This method makes use of Velodyne laser scans.
code 84.82 % 90.99 % 79.62 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
272 MF 84.72 % 88.58 % 78.17 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
273 F-PointNet
This method makes use of Velodyne laser scans.
code 84.67 % 91.17 % 74.77 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
274 3DBN
This method makes use of Velodyne laser scans.
83.94 % 89.66 % 76.50 % 0.13s 1080Ti (Python+C/C++)
X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.
275 MLOD
This method makes use of Velodyne laser scans.
code 82.68 % 90.25 % 77.97 % 0.12 s GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
276 BirdNet+
This method makes use of Velodyne laser scans.
code 81.85 % 87.43 % 75.36 % 0.11 s Titan Xp (PyTorch)
A. Barrera, J. Beltrán, C. Guindel, J. Iglesias and F. García: BirdNet+: Two-Stage 3D Object Detection in LiDAR through a Sparsity-Invariant Bird’s Eye View. IEEE Access 2021.
277 TBD 81.53 % 87.90 % 74.26 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
278 FD 81.47 % 88.34 % 75.07 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
279 CZY 81.21 % 89.10 % 76.13 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
280 DMF
This method uses stereo information.
80.29 % 84.64 % 76.05 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
X. J. Chen and W. Xu: Disparity-Based Multiscale Fusion Network for Transportation Detection. IEEE Transactions on Intelligent Transportation Systems 2022.
281 UberATG-PIXOR
This method makes use of Velodyne laser scans.
80.01 % 83.97 % 74.31 % 0.035 s TITAN Xp (Python)
B. Yang, W. Luo and R. Urtasun: PIXOR: Real-time 3D Object Detection from Point Clouds. CVPR 2018.
282 MV3D (LIDAR)
This method makes use of Velodyne laser scans.
78.98 % 86.49 % 72.23 % 0.24 s GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
283 DSGN++
This method uses stereo information.
code 78.94 % 88.55 % 69.74 % 0.2 s GeForce RTX 2080Ti
Y. Chen, S. Huang, S. Liu, B. Yu and J. Jia: DSGN++: Exploiting Visual-Spatial Relation for Stereo-based 3D Detectors. arXiv preprint arXiv:2204.03039 2022.
284 MV3D
This method makes use of Velodyne laser scans.
78.93 % 86.62 % 69.80 % 0.36 s GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
285 StereoDistill 78.59 % 89.03 % 69.34 % 0.4 s 1 core @ 2.5 Ghz (Python)
286 Anonymous 77.40 % 90.76 % 70.00 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
287 MMLAB LIGA-Stereo
This method uses stereo information.
code 76.78 % 88.15 % 67.40 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
288 RCD 75.83 % 82.26 % 69.61 % 0.1 s GPU @ 2.5 Ghz (Python)
A. Bewley, P. Sun, T. Mensink, D. Anguelov and C. Sminchisescu: Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection. Conference on Robot Learning (CoRL) 2020.
289 LaserNet 74.52 % 79.19 % 68.45 % 12 ms GPU @ 2.5 Ghz (C/C++)
G. Meyer, A. Laddha, E. Kee, C. Vallespi-Gonzalez and C. Wellington: LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
290 PL++ (SDN+GDC)
This method uses stereo information.
This method makes use of Velodyne laser scans.
code 73.80 % 84.61 % 65.59 % 0.6 s GPU @ 2.5 Ghz (C/C++)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.
291 SNVC
This method uses stereo information.
code 73.61 % 86.88 % 64.49 % 1 s GPU @ 1.0 Ghz (Python)
S. Li, Z. Liu, Z. Shen and K. Cheng: Stereo Neural Vernier Caliper. Proceedings of the AAAI Conference on Artificial Intelligence 2022.
292 A3DODWTDA
This method makes use of Velodyne laser scans.
code 73.26 % 79.58 % 62.77 % 0.08 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
293 Anonymous 71.23 % 86.67 % 64.08 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
294 Complexer-YOLO
This method makes use of Velodyne laser scans.
68.96 % 77.24 % 64.95 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
295 Pseudo-Stereo++ 68.36 % 84.64 % 59.01 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
296 TopNet-Retina
This method makes use of Velodyne laser scans.
68.16 % 80.16 % 63.43 % 52ms GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
297 CG-Stereo
This method uses stereo information.
66.44 % 85.29 % 58.95 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
298 PLUME
This method uses stereo information.
66.27 % 82.97 % 56.70 % 0.15 s GPU @ 2.5 Ghz (Python)
Y. Wang, B. Yang, R. Hu, M. Liang and R. Urtasun: PLUME: Efficient 3D Object Detection from Stereo Images. IROS 2021.
299 CDN
This method uses stereo information.
code 66.24 % 83.32 % 57.65 % 0.6 s GPU @ 2.5 Ghz (Python)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems (NeurIPS) 2020.
300 PS 65.33 % 83.75 % 56.14 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
301 DSGN
This method uses stereo information.
code 65.05 % 82.90 % 56.60 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
302 TopNet-DecayRate
This method makes use of Velodyne laser scans.
64.60 % 79.74 % 58.04 % 92 ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
303 UPF_3D
This method uses stereo information.
63.58 % 85.53 % 56.56 % 0.29 s 1 core @ 2.5 Ghz (Python)
304 BirdNet+ (legacy)
This method makes use of Velodyne laser scans.
code 63.33 % 84.80 % 61.23 % 0.1 s Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird’s Eye View. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
305 3D FCN
This method makes use of Velodyne laser scans.
61.67 % 70.62 % 55.61 % >5 s 1 core @ 2.5 Ghz (C/C++)
B. Li: 3D Fully Convolutional Network for Vehicle Detection in Point Cloud. IROS 2017.
306 CDN-PL++
This method uses stereo information.
61.04 % 81.27 % 52.84 % 0.4 s GPU @ 2.5 Ghz (C/C++)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems 2020.
307 BirdNet
This method makes use of Velodyne laser scans.
59.83 % 84.17 % 57.35 % 0.11 s Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
308 TopNet-UncEst
This method makes use of Velodyne laser scans.
59.67 % 72.05 % 51.67 % 0.09 s NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
309 RT3D-GMP
This method uses stereo information.
59.00 % 69.14 % 45.49 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
310 Disp R-CNN (velo)
This method uses stereo information.
code 58.62 % 79.76 % 47.73 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
311 ESGN
This method uses stereo information.
58.12 % 78.10 % 49.28 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
A. Gao, Y. Pang, J. Nie, Z. Shao, J. Cao, Y. Guo and X. Li: ESGN: Efficient Stereo Geometry Network for Fast 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2022.
312 Pseudo-LiDAR++
This method uses stereo information.
code 58.01 % 78.31 % 51.25 % 0.4 s GPU @ 2.5 Ghz (Python)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.
313 Disp R-CNN
This method uses stereo information.
code 57.98 % 79.61 % 47.09 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
314 ZoomNet
This method uses stereo information.
code 54.91 % 72.94 % 44.14 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
L. Z. Xu: ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2020.
315 ART 54.23 % 75.05 % 48.19 % 20ms s 1 core @ 2.5 Ghz (C/C++)
316 VoxelJones code 53.96 % 66.21 % 47.66 % .18 s 1 core @ 2.5 Ghz (Python + C/C++)
M. Motro and J. Ghosh: Vehicular Multi-object Tracking with Persistent Detector Failures. arXiv preprint arXiv:1907.11306 2019.
317 TopNet-HighRes
This method makes use of Velodyne laser scans.
53.05 % 67.84 % 46.99 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
318 OC Stereo
This method uses stereo information.
code 51.47 % 68.89 % 42.97 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
319 YOLOStereo3D
This method uses stereo information.
code 50.28 % 76.10 % 36.86 % 0.1 s GPU 1080Ti
Y. Liu, L. Wang and M. Liu: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
320 DSC3D
This method uses stereo information.
49.69 % 75.96 % 36.37 % 0.06 s GPU @ 2.5 Ghz (Python)
321 RT3DStereo
This method uses stereo information.
46.82 % 58.81 % 38.38 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
322 Pseudo-Lidar
This method uses stereo information.
code 45.00 % 67.30 % 38.40 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Wang, W. Chao, D. Garg, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
323 RT3D
This method makes use of Velodyne laser scans.
44.00 % 56.44 % 42.34 % 0.09 s GPU @ 1.8Ghz
Y. Zeng, Y. Hu, S. Liu, J. Ye, Y. Han, X. Li and N. Sun: RT3D: Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving. IEEE Robotics and Automation Letters 2018.
324 Stereo CenterNet
This method uses stereo information.
42.12 % 62.97 % 35.37 % 0.04 s GPU @ 2.5 Ghz (Python)
Y. Shi, Y. Guo, Z. Mi and X. Li: Stereo CenterNet-based 3D object detection for autonomous driving. Neurocomputing 2022.
325 SparseLiDAR_fusion 41.51 % 54.10 % 34.14 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
326 Stereo R-CNN
This method uses stereo information.
code 41.31 % 61.92 % 33.42 % 0.3 s GPU @ 2.5 Ghz (Python)
P. Li, X. Chen and S. Shen: Stereo R-CNN based 3D Object Detection for Autonomous Driving. CVPR 2019.
327 GCDR 37.34 % 50.85 % 30.51 % 0.28 s 1 core @ 2.5 Ghz (Python)
328 CIE + DM3D 33.13 % 46.17 % 28.80 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
329 StereoFENet
This method uses stereo information.
32.96 % 49.29 % 25.90 % 0.15 s 1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
330 Anonymous 30.81 % 43.11 % 26.81 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
331 Mobile Stereo R-CNN
This method uses stereo information.
28.78 % 44.51 % 22.30 % 1.8 s NVIDIA Jetson TX2
M. Hussein, M. Khalil and B. Abdullah: 3D Object Detection using Mobile Stereo R- CNN on Nvidia Jetson TX2. International Conference on Advanced Engineering, Technology and Applications (ICAETA) 2021.
332 CIE 28.50 % 41.41 % 23.88 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
333 Anonymous 27.70 % 37.81 % 24.61 % 40 s 1 core @ 2.5 Ghz (C/C++)
334 SARM3D 26.81 % 34.17 % 23.68 % 0.03 s GPU @ 2.5 Ghz (Python)
335 MDS-Mono3D 26.33 % 41.07 % 21.22 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
336 SSAL-Mono 26.17 % 33.15 % 23.81 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
337 CMKD code 25.82 % 38.98 % 22.80 % 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.
338 MoGDE 25.60 % 38.38 % 22.91 % 0.03 s GPU @ 2.5 Ghz (Python)
339 BAIR 25.60 % 35.41 % 22.14 % 0.04 s 1 core @ 2.5 Ghz (Python)
340 MonoASS 25.55 % 36.14 % 22.10 % 0.04 s 1 core @ 2.5 Ghz (Python)
341 AMNet 25.40 % 34.68 % 22.85 % 0.03 s GPU @ 1.0 Ghz (Python)
342 MonoXiver 25.37 % 34.14 % 22.20 % 0.03s GPU @ 2.5 Ghz (Python)
343 BSM3D 25.23 % 34.82 % 22.37 % 0.03 s 1 core @ 2.5 Ghz (Python)
344 LPCG-Monoflex code 24.81 % 35.96 % 21.86 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
L. Peng, F. Liu, Z. Yu, S. Yan, D. Deng, Z. Yang, H. Liu and D. Cai: Lidar Point Cloud Guided Monocular 3D Object Detection. ECCV 2022.
345 Anonymous 24.78 % 33.38 % 22.00 % 40 s 1 core @ 2.5 Ghz (C/C++)
346 DD3Dv2 code 24.67 % 35.70 % 21.73 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
347 MonoATT code 24.42 % 36.87 % 21.88 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
348 NeurOCS 24.41 % 37.38 % 20.95 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
349 Anonymous 23.82 % 34.35 % 20.80 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
350 PS-fld code 23.76 % 32.64 % 20.64 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
Y. Chen, H. Dai and Y. Ding: Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
351 TempM3D 23.71 % 33.86 % 20.31 % 0.07 s 1 core @ 2.5 Ghz (Python)
352 ADD code 23.58 % 35.20 % 20.08 % 0.1 s 1 core @ 2.5 Ghz (Python)
353 MonoNeRD 23.46 % 31.13 % 20.97 % na s 1 core @ 2.5 Ghz (C/C++)
354 MonoDDE 23.46 % 33.58 % 20.37 % 0.04 s 1 core @ 2.5 Ghz (Python)
Z. Li, Z. Qu, Y. Zhou, J. Liu, H. Wang and L. Jiang: Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection. CVPR 2022.
355 MonoA^2 23.45 % 32.35 % 20.71 % na s 1 core @ 2.5 Ghz (C/C++)
356 DD3D code 23.41 % 32.35 % 20.42 % n/a s 1 core @ 2.5 Ghz (C/C++)
D. Park, R. Ambrus, V. Guizilini, J. Li and A. Gaidon: Is Pseudo-Lidar needed for Monocular 3D Object detection?. IEEE/CVF International Conference on Computer Vision (ICCV) .
357 MonoA^2(new) 23.14 % 31.71 % 20.45 % na s 1 core @ 2.5 Ghz (C/C++)
358 SAD 22.81 % 34.34 % 19.44 % 0.05 s 1 core @ 2.5 Ghz (python)
359 DID-M3D code 22.76 % 32.95 % 19.83 % 0.04 s 1 core @ 2.5 Ghz (Python)
L. Peng, X. Wu, Z. Yang, H. Liu and D. Cai: DID-M3D: Decoupling Instance Depth for Monocular 3D Object Detection. ECCV 2022.
360 MonoAD 22.70 % 33.33 % 20.48 % 0.03 s GPU @ 2.5 Ghz (Python)
361 zongmuDistill 22.56 % 33.48 % 19.88 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
362 OPA-3D code 22.53 % 33.54 % 19.22 % 0.04 s 1 core @ 3.5 Ghz (Python)
363 Shape-Aware 22.13 % 32.55 % 18.94 % 0.05 s 1 core @ 2.5 Ghz (Python)
364 Anonymous 22.05 % 31.75 % 19.44 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
365 3DSeMoDLE code 21.78 % 30.99 % 18.64 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
366 Anonymous 21.74 % 32.44 % 18.38 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
367 MDNet 21.71 % 33.31 % 18.49 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
368 MonoPPM code 21.66 % 30.54 % 18.64 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
369 SAD 21.56 % 33.90 % 19.08 % 0.05 s 1 core @ 2.5 Ghz (python)
370 Lite-FPN-GUPNet 21.53 % 31.68 % 18.38 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
371 DCD code 21.50 % 32.55 % 18.25 % 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.
372 MonoDETR code 21.45 % 32.20 % 18.68 % 0.04 s 1 core @ 2.5 Ghz (Python)
R. Zhang, H. Qiu, T. Wang, X. Xu, Z. Guo, Y. Qiao, P. Gao and H. Li: MonoDETR: Depth-aware Transformer for Monocular 3D Object Detection. arXiv preprint arXiv:2203.13310 2022.
373 OBMO_GUPNet 21.41 % 30.81 % 18.37 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
374 M3DGAF 21.39 % 31.34 % 19.28 % 0.07 s 1 core @ 2.5 Ghz (Python)
375 SGM3D code 21.37 % 31.49 % 18.43 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
Z. Zhou, L. Du, X. Ye, Z. Zou, X. Tan, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection. RA-L 2022.
376 monopd code 21.29 % 32.12 % 18.08 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
377 DEPT 21.22 % 30.85 % 18.47 % 0.03 s 1 core @ 2.5 Ghz (Python)
378 Mono3DMethod 21.21 % 32.57 % 18.07 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
379 GUPNet code 21.19 % 30.29 % 18.20 % NA s 1 core @ 2.5 Ghz (Python + C/C++)
Y. Lu, X. Ma, L. Yang, T. Zhang, Y. Liu, Q. Chu, J. Yan and W. Ouyang: Geometry Uncertainty Projection Network for Monocular 3D Object Detection. arXiv preprint arXiv:2107.13774 2021.
380 MonoInsight 21.06 % 29.65 % 18.22 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
381 MM3D 20.93 % 31.44 % 18.72 % NA s 1 core @ 2.5 Ghz (C/C++)
382 HBD 20.91 % 29.87 % 18.22 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
383 GPENet code 20.79 % 30.31 % 18.21 % 0.02 s GPU @ 2.5 Ghz (Python)
384 BCA 20.75 % 30.03 % 17.60 % 0.17 s GPU @ 2.5 Ghz (Python)
385 LT-M3OD 20.74 % 29.40 % 17.83 % 0.03 s 1 core @ 2.5 Ghz (Python)
386 HomoLoss(monoflex) code 20.68 % 29.60 % 17.81 % 0.04 s 1 core @ 2.5 Ghz (Python)
J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homography Loss for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
387 Anonymous 20.47 % 33.17 % 17.31 % 40 s 1 core @ 2.5 Ghz (C/C++)
388 DEVIANT code 20.44 % 29.65 % 17.43 % 0.04 s 1 GPU (Python)
A. Kumar, G. Brazil, E. Corona, A. Parchami and X. Liu: DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection. European Conference on Computer Vision (ECCV) 2022.
389 EW code 20.38 % 28.88 % 17.59 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
390 MonoDTR 20.38 % 28.59 % 17.14 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
K. Huang, T. Wu, H. Su and W. Hsu: MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer. CVPR 2022.
391 MDSNet 20.14 % 32.81 % 15.77 % 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.
392 AutoShape code 20.08 % 30.66 % 15.95 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
Z. Liu, D. Zhou, F. Lu, J. Fang and L. Zhang: AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.
393 MonoEdge-RCNN 20.07 % 27.62 % 16.34 % 0.05 s 1 core @ 2.5 Ghz (Python)
394 MonoPCNS 19.89 % 28.27 % 17.96 % 0.14 s GPU @ 2.5 Ghz (Python)
395 MonoFlex 19.75 % 28.23 % 16.89 % 0.03 s GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.
396 MonoEF 19.70 % 29.03 % 17.26 % 0.03 s 1 core @ 2.5 Ghz (Python)
Y. Zhou, Y. He, H. Zhu, C. Wang, H. Li and Q. Jiang: Monocular 3D Object Detection: An Extrinsic Parameter Free Approach. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
397 HomoLoss(imvoxelnet) code 19.25 % 29.18 % 16.21 % 0.20 s 1 core @ 2.5 Ghz (Python)
J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homogrpahy Loss for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
398 MonoAug 19.19 % 28.20 % 16.15 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
399 MK3D 19.18 % 29.11 % 15.78 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
400 DFR-Net 19.17 % 28.17 % 14.84 % 0.18 s 1080 Ti (Pytorch)
Z. Zou, X. Ye, L. Du, X. Cheng, X. Tan, L. Zhang, J. Feng, X. Xue and E. Ding: The devil is in the task: Exploiting reciprocal appearance-localization features for monocular 3d object detection . ICCV 2021.
401 DLE code 19.05 % 31.09 % 14.13 % 0.06 s NVIDIA Tesla V100
C. Liu, S. Gu, L. Gool and R. Timofte: Deep Line Encoding for Monocular 3D Object Detection and Depth Prediction. Proceedings of the British Machine Vision Conference (BMVC) 2021.
402 PCT code 19.03 % 29.65 % 15.92 % 0.045 s 1 core @ 2.5 Ghz (C/C++)
L. Wang, L. Zhang, Y. Zhu, Z. Zhang, T. He, M. Li and X. Xue: Progressive Coordinate Transforms for Monocular 3D Object Detection. NeurIPS 2021.
403 CaDDN code 18.91 % 27.94 % 17.19 % 0.63 s GPU @ 2.5 Ghz (Python)
C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.
404 monodle code 18.89 % 24.79 % 16.00 % 0.04 s GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for Monocular 3D Object Detection. CVPR 2021 .
405 Neighbor-Vote 18.65 % 27.39 % 16.54 % 0.1 s GPU @ 2.5 Ghz (Python)
X. Chu, J. Deng, Y. Li, Z. Yuan, Y. Zhang, J. Ji and Y. Zhang: Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance Voting. ACM MM 2021.
406 MonoRCNN++ code 18.62 % 27.20 % 15.69 % 0.07 s GPU @ 2.5 Ghz (Python)
X. Shi, Z. Chen and T. Kim: Multivariate Probabilistic Monocular 3D Object Detection. WACV 2023.
407 GrooMeD-NMS code 18.27 % 26.19 % 14.05 % 0.12 s 1 core @ 2.5 Ghz (Python)
A. Kumar, G. Brazil and X. Liu: GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection. CVPR 2021.
408 MonoRCNN code 18.11 % 25.48 % 14.10 % 0.07 s GPU @ 2.5 Ghz (Python)
X. Shi, Q. Ye, X. Chen, C. Chen, Z. Chen and T. Kim: Geometry-based Distance Decomposition for Monocular 3D Object Detection. ICCV 2021.
409 Ground-Aware code 17.98 % 29.81 % 13.08 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
Y. Liu, Y. Yuan and M. Liu: Ground-aware Monocular 3D Object Detection for Autonomous Driving. IEEE Robotics and Automation Letters 2021.
410 Aug3D-RPN 17.89 % 26.00 % 14.18 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth. 2021.
411 DDMP-3D 17.89 % 28.08 % 13.44 % 0.18 s 1 core @ 2.5 Ghz (Python)
L. Wang, L. Du, X. Ye, Y. Fu, G. Guo, X. Xue, J. Feng and L. Zhang: Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection. CVPR 2020.
412 IAFA 17.88 % 25.88 % 15.35 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
D. Zhou, X. Song, Y. Dai, J. Yin, F. Lu, M. Liao, J. Fang and L. Zhang: IAFA: Instance-Aware Feature Aggregation for 3D Object Detection from a Single Image. Proceedings of the Asian Conference on Computer Vision 2020.
413 FMF-occlusion-net 17.60 % 27.39 % 13.25 % 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.
414 RefinedMPL 17.60 % 28.08 % 13.95 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
415 Kinematic3D code 17.52 % 26.69 % 13.10 % 0.12 s 1 core @ 1.5 Ghz (C/C++)
G. Brazil, G. Pons-Moll, X. Liu and B. Schiele: Kinematic 3D Object Detection in Monocular Video. ECCV 2020 .
416 MonoRUn code 17.34 % 27.94 % 15.24 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
417 AM3D 17.32 % 25.03 % 14.91 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
X. Ma, Z. Wang, H. Li, P. Zhang, W. Ouyang and X. Fan: Accurate Monocular Object Detection via Color- Embedded 3D Reconstruction for Autonomous Driving. Proceedings of the IEEE international Conference on Computer Vision (ICCV) 2019.
418 YoloMono3D code 17.15 % 26.79 % 12.56 % 0.05 s GPU @ 2.5 Ghz (Python)
Y. Liu, L. Wang and L. Ming: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
419 CMAN 17.04 % 25.89 % 12.88 % 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.
420 GAC3D 16.93 % 25.80 % 12.50 % 0.25 s 1 core @ 2.5 Ghz (Python)
M. Bui, D. Ngo, H. Pham and D. Nguyen: GAC3D: improving monocular 3D object detection with ground-guide model and adaptive convolution. 2021.
421 PatchNet code 16.86 % 22.97 % 14.97 % 0.4 s 1 core @ 2.5 Ghz (C/C++)
X. Ma, S. Liu, Z. Xia, H. Zhang, X. Zeng and W. Ouyang: Rethinking Pseudo-LiDAR Representation. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
422 MonoAug 16.71 % 24.39 % 13.83 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
423 PGD-FCOS3D code 16.51 % 26.89 % 13.49 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
T. Wang, X. Zhu, J. Pang and D. Lin: Probabilistic and Geometric Depth: Detecting Objects in Perspective. Conference on Robot Learning (CoRL) 2021.
424 MDT code 16.47 % 24.22 % 13.42 % 0.01 s 1 core @ 2.5 Ghz (Python)
425 ImVoxelNet code 16.37 % 25.19 % 13.58 % 0.2 s GPU @ 2.5 Ghz (Python)
D. Rukhovich, A. Vorontsova and A. Konushin: ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection. arXiv preprint arXiv:2106.01178 2021.
426 KM3D code 16.20 % 23.44 % 14.47 % 0.03 s 1 core @ 2.5 Ghz (Python)
P. Li: Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training. 2020.
427 D4LCN code 16.02 % 22.51 % 12.55 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
428 MonoPair 14.83 % 19.28 % 12.89 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
429 Decoupled-3D 14.82 % 23.16 % 11.25 % 0.08 s GPU @ 2.5 Ghz (C/C++)
Y. Cai, B. Li, Z. Jiao, H. Li, X. Zeng and X. Wang: Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation. AAAI 2020.
430 QD-3DT
This is an online method (no batch processing).
code 14.71 % 20.16 % 12.76 % 0.03 s GPU @ 2.5 Ghz (Python)
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.
431 SMOKE code 14.49 % 20.83 % 12.75 % 0.03 s GPU @ 2.5 Ghz (Python)
Z. Liu, Z. Wu and R. Tóth: SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation. 2020.
432 RTM3D code 14.20 % 19.17 % 11.99 % 0.05 s GPU @ 1.0 Ghz (Python)
P. Li, H. Zhao, P. Liu and F. Cao: RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving. 2020.
433 Mono3D_PLiDAR code 13.92 % 21.27 % 11.25 % 0.1 s NVIDIA GeForce 1080 (pytorch)
X. Weng and K. Kitani: Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud. arXiv:1903.09847 2019.
434 M3D-RPN code 13.67 % 21.02 % 10.23 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
435 CSoR
This method makes use of Velodyne laser scans.
13.07 % 18.67 % 10.34 % 3.5 s 4 cores @ >3.5 Ghz (Python + C/C++)
L. Plotkin: PyDriver: Entwicklung eines Frameworks für räumliche Detektion und Klassifikation von Objekten in Fahrzeugumgebung. 2015.
436 MonoPSR code 12.58 % 18.33 % 9.91 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
437 MonoCInIS 11.64 % 22.28 % 9.95 % 0,13 s GPU @ 2.5 Ghz (C/C++)
J. Heylen, M. De Wolf, B. Dawagne, M. Proesmans, L. Van Gool, W. Abbeloos, H. Abdelkawy and D. Reino: MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.
438 SS3D 11.52 % 16.33 % 9.93 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
439 MonoGRNet code 11.17 % 18.19 % 8.73 % 0.04s NVIDIA P40
Z. Qin, J. Wang and Y. Lu: MonoGRNet: A Geometric Reasoning Network for 3D Object Localization. The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) 2019.
440 MonoFENet 11.03 % 17.03 % 9.05 % 0.15 s 1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
441 MonoCInIS 10.96 % 20.42 % 9.23 % 0,14 s GPU @ 2.5 Ghz (Python)
J. Heylen, M. De Wolf, B. Dawagne, M. Proesmans, L. Van Gool, W. Abbeloos, H. Abdelkawy and D. Reino: MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.
442 A3DODWTDA (image) code 8.66 % 10.37 % 7.06 % 0.8 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
443 TLNet (Stereo)
This method uses stereo information.
code 7.69 % 13.71 % 6.73 % 0.1 s 1 core @ 2.5 Ghz (Python)
Z. Qin, J. Wang and Y. Lu: Triangulation Learning Network: from Monocular to Stereo 3D Object Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
444 Shift R-CNN (mono) code 6.82 % 11.84 % 5.27 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
445 SparVox3D 6.39 % 10.20 % 5.06 % 0.05 s GPU @ 2.0 Ghz (Python)
E. Balatkan and F. Kıraç: Improving Regression Performance on Monocular 3D Object Detection Using Bin-Mixing and Sparse Voxel Data. 2021 6th International Conference on Computer Science and Engineering (UBMK) 2021.
446 GS3D 6.08 % 8.41 % 4.94 % 2 s 1 core @ 2.5 Ghz (C/C++)
B. Li, W. Ouyang, L. Sheng, X. Zeng and X. Wang: GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
447 MVRA + I-FRCNN+ 5.84 % 9.05 % 4.50 % 0.18 s GPU @ 2.5 Ghz (Python)
H. Choi, H. Kang and Y. Hyun: Multi-View Reprojection Architecture for Orientation Estimation. The IEEE International Conference on Computer Vision (ICCV) Workshops 2019.
448 WeakM3D code 5.66 % 11.82 % 4.08 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
L. Peng, S. Yan, B. Wu, Z. Yang, X. He and D. Cai: WeakM3D: Towards Weakly Supervised Monocular 3D Object Detection. ICLR 2022.
449 ROI-10D 4.91 % 9.78 % 3.74 % 0.2 s GPU @ 3.5 Ghz (Python)
F. Manhardt, W. Kehl and A. Gaidon: ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape. Computer Vision and Pattern Recognition (CVPR) 2019.
450 CDTrack3D code 4.61 % 7.02 % 3.73 % 0.0106 s NVIDIA RTX 3090 GPU, i9 10850k CPU
451 3D-GCK 4.57 % 5.79 % 3.64 % 24 ms Tesla V100
N. Gählert, J. Wan, N. Jourdan, J. Finkbeiner, U. Franke and J. Denzler: Single-Shot 3D Detection of Vehicles from Monocular RGB Images via Geometrically Constrained Keypoints in Real-Time. 2020 IEEE Intelligent Vehicles Symposium (IV) 2020.
452 FQNet 3.23 % 5.40 % 2.46 % 0.5 s 1 core @ 2.5 Ghz (Python)
L. Liu, J. Lu, C. Xu, Q. Tian and J. Zhou: Deep Fitting Degree Scoring Network for Monocular 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
453 3D-SSMFCNN code 2.63 % 3.20 % 2.40 % 0.1 s GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.
454 VeloFCN
This method makes use of Velodyne laser scans.
0.14 % 0.02 % 0.21 % 1 s GPU @ 2.5 Ghz (Python + C/C++)
B. Li, T. Zhang and T. Xia: Vehicle Detection from 3D Lidar Using Fully Convolutional Network. RSS 2016 .
455 MonoDET code 0.14 % 0.25 % 0.10 % 0.04 s 1 core @ 2.5 Ghz (Python)
456 test code 0.09 % 0.04 % 0.11 % 50 s 1 core @ 2.5 Ghz (Python)
457 Yolo5x6_Ghost 0.00 % 0.00 % 0.00 % 0.03 s GPU @ 2.5 Ghz (Python)
458 Yolo5x6_Ghost 0.00 % 0.00 % 0.00 % 0.03 s GPU @ 2.5 Ghz (Python)
459 multi-task CNN 0.00 % 0.00 % 0.00 % 25.1 ms GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
460 Ghost3D object detec 0.00 % 0.00 % 0.00 % 0.03 s 1 core @ 2.5 Ghz (Python)
461 aliii0 0.00 % 0.00 % 0.00 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
462 Res 0.00 % 0.00 % 0.00 % 0.03 s 1 core @ 2.5 Ghz (Python)
463 GHos_3d 0.00 % 0.00 % 0.00 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
464 ALI_TRY1 0.00 % 0.00 % 0.00 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
465 BEV_GHOST 0.00 % 0.00 % 0.00 % 0.1 s 1 core @ 2.5 Ghz (Python)
466 mBoW
This method makes use of Velodyne laser scans.
0.00 % 0.00 % 0.00 % 10 s 1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
Table as LaTeX | Only published Methods

Pedestrian


Method Setting Code Moderate Easy Hard Runtime Environment
1 PiFeNet code 53.92 % 63.25 % 50.53 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
D. Le, H. Shi, H. Rezatofighi and J. Cai: Accurate and Real-time 3D Pedestrian Detection Using an Efficient Attentive Pillar Network. arXiv preprint arXiv:2112.15458 2022.
2 CasA++ code 53.84 % 60.14 % 51.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.
3 TED code 53.48 % 60.13 % 50.89 % 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 BiProDet 53.32 % 58.91 % 50.82 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
5 DCAN-Second code 53.18 % 60.92 % 50.56 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
6 EQ-PVRCNN code 52.81 % 61.73 % 49.87 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, L. Jiang, Y. Sun, B. Schiele and J. Jia: A Unified Query-based Paradigm for Point Cloud Understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.
7 VPFNet code 52.41 % 60.07 % 50.28 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
C. Wang, H. Chen and L. Fu: VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection. 2021.
8 Frustum-PointPillars code 52.23 % 60.98 % 48.30 % 0.06 s 4 cores @ 3.0 Ghz (Python)
A. Paigwar, D. Sierra-Gonzalez, \. Erkent and C. Laugier: Frustum-PointPillars: A Multi-Stage Approach for 3D Object Detection using RGB Camera and LiDAR. International Conference on Computer Vision, ICCV, Workshop on Autonomous Vehicle Vision 2021.
9 CAD 52.20 % 60.23 % 49.54 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
10 LoGoNet 52.06 % 58.24 % 49.87 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
11 TANet code 51.38 % 60.85 % 47.54 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
12 CasA code 51.37 % 57.95 % 49.08 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.
13 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 50.57 % 59.86 % 46.74 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
14 HotSpotNet 50.53 % 57.39 % 46.65 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
15 VMVS
This method makes use of Velodyne laser scans.
50.34 % 60.34 % 46.45 % 0.25 s GPU @ 2.5 Ghz (Python)
J. Ku, A. Pon, S. Walsh and S. Waslander: Improving 3D object detection for pedestrians with virtual multi-view synthesis orientation estimation. IROS 2019.
16 AVOD-FPN
This method makes use of Velodyne laser scans.
code 50.32 % 58.49 % 46.98 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
17 SPT 50.22 % 56.54 % 46.72 % 0.1 s GPU @ 2.5 Ghz (Python)
18 variance_point 50.03 % 57.72 % 46.27 % 0.05 s 1 core @ 2.5 Ghz (Python)
19 3DSSD code 49.94 % 60.54 % 45.73 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
20 PointPainting
This method makes use of Velodyne laser scans.
49.93 % 58.70 % 46.29 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
21 SemanticVoxels 49.93 % 58.91 % 47.31 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
J. Fei, W. Chen, P. Heidenreich, S. Wirges and C. Stiller: SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentation. MFI 2020.
22 ACDet code 49.82 % 58.35 % 47.17 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
J. Xu, G. Wang, X. Zhang and G. Wan: ACDet: Attentive Cross-view Fusion for LiDAR-based 3D Object Detection. 3DV 2022.
23 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 49.81 % 59.04 % 45.92 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
24 ACF-Net 49.74 % 58.07 % 47.27 % n/a s 1 core @ 2.5 Ghz (C/C++)
25 F-PointNet
This method makes use of Velodyne laser scans.
code 49.57 % 57.13 % 45.48 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
26 CFF-tv 49.29 % 57.83 % 46.70 % 1 s 1 core @ 2.5 Ghz (C/C++)
27 F-ConvNet
This method makes use of Velodyne laser scans.
code 48.96 % 57.04 % 44.33 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
28 HVNet 48.86 % 54.84 % 46.33 % 0.03 s GPU @ 2.0 Ghz (Python)
M. Ye, S. Xu and T. Cao: HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection. CVPR 2020.
29 CAT-Det 48.78 % 57.13 % 45.56 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Zhang, J. Chen and D. Huang: CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection. CVPR 2022.
30 STD code 48.72 % 60.02 % 44.55 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
31 Reprod-Two-Branch 48.71 % 57.25 % 45.75 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
32 KeyFuse2B 48.64 % 56.16 % 46.20 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
33 PointPillars
This method makes use of Velodyne laser scans.
code 48.64 % 57.60 % 45.78 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
34 USVLab BSAODet 48.61 % 55.76 % 46.08 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
35 FV2P v2 48.58 % 54.90 % 45.11 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
36 EPNet++ 48.47 % 56.24 % 45.73 % 0.1 s GPU @ 2.5 Ghz (Python)
Z. Liu, H. tengteng, B. Li, X. Chen, X. Wang and X. Bai: EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object Detection. arXiv preprint arXiv:2112.11088 2021.
37 MGAF-3DSSD code 48.46 % 56.09 % 44.90 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
38 CFF-ep25 48.31 % 56.34 % 45.58 % 1 s 1 core @ 2.5 Ghz (C/C++)
39 Fast-CLOCs 48.27 % 57.19 % 44.55 % 0.1 s GPU @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: Fast-CLOCs: Fast Camera-LiDAR Object Candidates Fusion for 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022.
40 FromVoxelToPoint code 48.15 % 56.54 % 45.63 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to- Point Decoder. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
41 cff-tv-v2-ep25 48.13 % 56.48 % 45.66 % 1 s 1 core @ 2.5 Ghz (C/C++)
42 USVLab BSAODet (S) 48.10 % 54.96 % 45.65 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
43 HMFI code 47.77 % 55.61 % 45.17 % 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.
44 TBD 47.77 % 55.61 % 45.17 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
45 CFF-tv-v2 47.59 % 55.46 % 45.09 % 1 s 1 core @ 2.5 Ghz (C/C++)
46 VoCo 47.47 % 52.94 % 45.41 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
47 CF-ctdep-tv-ta 47.46 % 54.36 % 45.07 % 1 s 1 core @ 2.5 Ghz (C/C++)
48 P2V-RCNN 47.36 % 54.15 % 45.10 % 0.1 s 2 cores @ 2.5 Ghz (Python)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.
49 CF-base-tv 47.28 % 54.77 % 44.81 % 1 s 1 core @ 2.5 Ghz (C/C++)
50 CZY_PPF_Net2 47.22 % 51.95 % 45.46 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
51 Self-Calib Conv 47.17 % 54.20 % 44.84 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
52 TCDVF 47.11 % 55.26 % 44.53 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
53 Point-GNN
This method makes use of Velodyne laser scans.
code 47.07 % 55.36 % 44.61 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
54 MVMM code 46.84 % 53.75 % 44.87 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
55 SCNet
This method makes use of Velodyne laser scans.
46.73 % 56.87 % 42.74 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
56 cp-tv-kp 46.71 % 53.73 % 44.45 % 1 s 1 core @ 2.5 Ghz (C/C++)
57 SGDA3D 46.66 % 52.65 % 44.62 % 0.07 s 1 core @ 2.5 Ghz (Python)
58 Anonymous
This method makes use of Velodyne laser scans.
46.65 % 52.20 % 44.61 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
59 DGT-Det3D code 46.59 % 54.25 % 44.15 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
60 PSA-Det3D 46.36 % 53.26 % 43.73 % 0.1 s GPU @ 2.5 Ghz (Python)
61 CF-ctdep-tv 46.36 % 53.50 % 44.01 % 1 s 1 core @ 2.5 Ghz (C/C++)
62 CZY_3917 46.31 % 51.01 % 44.44 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
63 PA-RCNN code 46.30 % 53.60 % 44.33 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
64 3SNet 46.25 % 52.22 % 42.89 % 0.07 s GPU @ 2.5 Ghz (Python)
65 DGT-Det3D 46.22 % 53.98 % 43.85 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
66 DTE3D 46.18 % 53.38 % 43.52 % 0.15s 1 core @ 2.5 Ghz (C/C++)
67 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 46.13 % 54.77 % 42.84 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
68 Anonymous 46.13 % 55.51 % 43.60 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
69 ARPNET 45.92 % 55.48 % 42.54 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
70 Under Blind Review#2 45.85 % 52.35 % 44.00 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
71 CenterFuse 45.84 % 55.20 % 43.46 % 0.059 sec/frame 2 x V100
72 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
code 45.82 % 52.03 % 43.81 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
73 U_SECOND_V4 45.79 % 53.57 % 43.52 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
74 cp-tv 45.75 % 52.90 % 43.49 % 1 s 1 core @ 2.5 Ghz (C/C++)
75 SVGA-Net 45.68 % 53.09 % 43.30 % 0.03s 1 core @ 2.5 Ghz (Python + C/C++)
Q. He, Z. Wang, H. Zeng, Y. Zeng and Y. Liu: SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds. AAAI 2022.
76 TBD 45.57 % 52.08 % 42.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
77 Anonymous 45.53 % 53.94 % 43.02 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
78 Anonymous 45.50 % 54.84 % 42.71 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
79 epBRM
This method makes use of Velodyne laser scans.
code 45.49 % 52.48 % 42.75 % 0.10 s 1 core @ 2.5 Ghz (C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
80 KPSCC code 45.46 % 52.72 % 42.53 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
81 PDV code 45.45 % 51.95 % 43.33 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Hu, T. Kuai and S. Waslander: Point Density-Aware Voxels for LiDAR 3D Object Detection. CVPR 2022.
82 MLOD
This method makes use of Velodyne laser scans.
code 45.40 % 55.09 % 41.42 % 0.12 s GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
83 cp-tv-kp-io-sc 45.30 % 53.84 % 42.12 % 1 s 1 core @ 2.5 Ghz (C/C++)
84 U_PVRCNN_V2 45.23 % 51.52 % 42.55 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
85 VPNet 45.12 % 52.68 % 42.05 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
86 IA-SSD (single) code 45.07 % 52.73 % 42.75 % 0.013 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
87 SRDL 44.84 % 52.42 % 42.56 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
88 PVRCNN_8369 44.83 % 52.41 % 42.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
89 CZY_PPF_Net 44.80 % 49.97 % 42.11 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
90 M3DeTR code 44.78 % 50.63 % 42.57 % n/a s GPU @ 1.0 Ghz (Python)
T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis and D. Manocha: M3DeTR: Multi-representation, Multi- scale, Mutual-relation 3D Object Detection with Transformers. 2021.
91 WGVRF 44.75 % 50.80 % 42.78 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
92 Semantical PVRCNN 44.75 % 49.40 % 41.94 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
93 AFTD 44.74 % 53.94 % 42.36 % 1 s 1 core @ 2.5 Ghz (Python + C/C++)
94 U_RVRCNN_V2_1 44.73 % 51.76 % 42.62 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
95 Dune-DCF-e11 44.58 % 52.44 % 41.75 % 1 s 1 core @ 2.5 Ghz (C/C++)
96 CF-cd-io-tv 44.54 % 53.64 % 41.21 % 1 s 1 core @ 2.5 Ghz (C/C++)
97 Dune-DCF-e09 44.50 % 52.64 % 41.86 % 1 s 1 core @ 2.5 Ghz (C/C++)
98 SIF 44.28 % 52.05 % 42.03 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
99 LazyTorch-CP-Infer-O 44.27 % 51.92 % 41.99 % 1 s 1 core @ 2.5 Ghz (C/C++)
100 KeyPoint-IoUHead 44.27 % 53.12 % 41.83 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
101 LazyTorch-CP-Small-P 44.25 % 51.84 % 41.97 % 1 s 1 core @ 2.5 Ghz (C/C++)
102 IoU-2B 44.19 % 55.31 % 40.33 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
103 DVFENet 44.12 % 50.98 % 41.62 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection. Neurocomputing 2021.
104 CenterPoint (pcdet) 44.08 % 51.76 % 41.80 % 0.051 sec/frame 2 x V100
105 CrazyTensor-CP 44.06 % 51.25 % 41.50 % 1 s 1 core @ 2.5 Ghz (Python)
106 cff-tv-t 44.00 % 54.42 % 41.46 % 1 s 1 core @ 2.5 Ghz (C/C++)
107 CF-base-train 43.90 % 51.40 % 41.24 % 1 s 1 core @ 2.5 Ghz (C/C++)
108 VGA-RCNN 43.89 % 51.80 % 41.57 % 0.07 s 1 core @ 2.5 Ghz (Python)
109 GS-FPS 43.88 % 50.53 % 40.93 % TBD s 1 core @ 2.5 Ghz (C/C++)
110 IKT3D
This method makes use of Velodyne laser scans.
43.88 % 49.25 % 41.79 % 0.05 s 1 core @ 2.5 Ghz (Python)
111 City-CF-fixed 43.86 % 51.92 % 41.33 % 1 s 1 core @ 2.5 Ghz (C/C++)
112 Faraway-Frustum
This method makes use of Velodyne laser scans.
code 43.85 % 52.15 % 41.68 % 0.1 s GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-frustum: Dealing with lidar sparsity for 3D object detection using fusion. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) 2021.
113 PSA-SSD 43.77 % 50.26 % 41.75 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
114 BASA 43.67 % 50.82 % 40.91 % 1s 1 core @ 2.5 Ghz (python)
115 Dune-DCF-e15 43.63 % 51.18 % 41.11 % 1 s 1 core @ 2.5 Ghz (C/C++)
116 AGS-SSD[la] 43.60 % 51.06 % 40.37 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
117 IPS 43.58 % 50.38 % 41.54 % TBD s 1 core @ 2.5 Ghz (C/C++)
118 S-AT GCN 43.43 % 50.63 % 41.58 % 0.02 s GPU @ 2.0 Ghz (Python)
L. Wang, C. Wang, X. Zhang, T. Lan and J. Li: S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection. CoRR 2021.
119 CF-ctdep-train 43.20 % 50.14 % 40.69 % 1 s 1 core @ 2.5 Ghz (C/C++)
120 GEO_LOC 43.10 % 49.74 % 41.02 % TBD s 1 core @ 2.5 Ghz (C/C++)
121 HPV-RCNN 42.99 % 50.53 % 39.54 % 0.15 s 1 core @ 2.5 Ghz (Python)
122 City-CF 42.95 % 49.91 % 40.61 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
123 GS-FPS-LT 42.89 % 49.33 % 40.63 % TBD s 1 core @ 2.5 Ghz (C/C++)
124 BirdNet+
This method makes use of Velodyne laser scans.
code 42.87 % 48.90 % 40.59 % 0.11 s Titan Xp (PyTorch)
A. Barrera, J. Beltrán, C. Guindel, J. Iglesias and F. García: BirdNet+: Two-Stage 3D Object Detection in LiDAR through a Sparsity-Invariant Bird’s Eye View. IEEE Access 2021.
125 CZY 42.80 % 49.42 % 40.83 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
126 TBD 42.76 % 50.17 % 39.75 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
127 IA-SSD (multi) code 42.61 % 51.76 % 40.51 % 0.014 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
128 NV-RCNN 42.58 % 49.00 % 40.39 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
129 XView 42.42 % 47.24 % 39.96 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
130 PVTr 42.26 % 48.79 % 40.27 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
131 ATT_SSD 42.24 % 49.55 % 40.13 % 0.01 s 1 core @ 2.5 Ghz (Python)
132 T_PVRCNN_V2 42.21 % 50.58 % 39.81 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
133 T_PVRCNN 41.87 % 49.87 % 39.44 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
134 SWA code 41.57 % 48.98 % 39.32 % 0.18 s 1 core @ 2.5 Ghz (C/C++)
135 TTT_SSD 41.19 % 47.42 % 39.19 % TBD s 1 core @ 2.5 Ghz (C/C++)
136 SECOND_7862 40.96 % 47.55 % 38.85 % 1 s 1 core @ 2.5 Ghz (Python)
137 PFF3D
This method makes use of Velodyne laser scans.
code 40.94 % 48.74 % 38.54 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
138 CAD
This method uses stereo information.
This method makes use of Velodyne laser scans.
40.93 % 48.07 % 38.43 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
139 CrazyTensor-CF 40.78 % 48.79 % 38.16 % 1 s 1 core @ 2.5 Ghz (C/C++)
140 ZMMPP 39.11 % 46.50 % 37.04 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
141 DSGN++
This method uses stereo information.
code 38.92 % 50.26 % 35.12 % 0.2 s GeForce RTX 2080Ti
Y. Chen, S. Huang, S. Liu, B. Yu and J. Jia: DSGN++: Exploiting Visual-Spatial Relation for Stereo-based 3D Detectors. arXiv preprint arXiv:2204.03039 2022.
142 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 38.79 % 47.51 % 35.85 % 0.0047s 1 core @ 2.5 Ghz (python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
143 BirdNet+ (legacy)
This method makes use of Velodyne laser scans.
code 38.28 % 45.53 % 35.37 % 0.1 s Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird’s Eye View. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
144 LightCPC code 38.17 % 44.37 % 36.04 % 0.02 s 1 core @ 2.5 Ghz (Python + C/C++)
145 CSW3D
This method makes use of Velodyne laser scans.
37.96 % 49.27 % 33.83 % 0.03 s 4 cores @ 2.5 Ghz (C/C++)
J. Hu, T. Wu, H. Fu, Z. Wang and K. Ding: Cascaded Sliding Window Based Real-Time 3D Region Proposal for Pedestrian Detection. ROBIO 2019.
146 KPP3D code 37.82 % 45.25 % 35.36 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
147 StereoDistill 37.75 % 50.79 % 34.28 % 0.4 s 1 core @ 2.5 Ghz (Python)
148 DMF
This method uses stereo information.
34.92 % 42.08 % 32.69 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
X. J. Chen and W. Xu: Disparity-Based Multiscale Fusion Network for Transportation Detection. IEEE Transactions on Intelligent Transportation Systems 2022.
149 SparsePool code 34.15 % 43.33 % 31.78 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
150 MMLAB LIGA-Stereo
This method uses stereo information.
code 34.13 % 44.71 % 30.42 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
151 AVOD
This method makes use of Velodyne laser scans.
code 33.57 % 42.58 % 30.14 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
152 SparsePool code 33.22 % 41.55 % 29.66 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
153 Pseudo-Stereo++ 32.38 % 43.37 % 28.66 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
154 CZY 32.05 % 39.50 % 29.90 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
155 PS 31.13 % 41.55 % 27.50 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
156 CG-Stereo
This method uses stereo information.
29.56 % 39.24 % 25.87 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
157 PointRGBNet 29.32 % 38.07 % 26.94 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
P. Xie Desheng: Real-time Detection of 3D Objects Based on Multi-Sensor Information Fusion. Automotive Engineering 2022.
158 Disp R-CNN
This method uses stereo information.
code 29.12 % 42.72 % 25.09 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
159 Disp R-CNN (velo)
This method uses stereo information.
code 28.34 % 40.21 % 24.46 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
160 BirdNet
This method makes use of Velodyne laser scans.
23.06 % 28.20 % 21.65 % 0.11 s Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
161 DSC3D
This method uses stereo information.
23.01 % 32.94 % 19.38 % 0.06 s GPU @ 2.5 Ghz (Python)
162 OC Stereo
This method uses stereo information.
code 20.80 % 29.79 % 18.62 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
163 YOLOStereo3D
This method uses stereo information.
code 20.76 % 31.01 % 18.41 % 0.1 s GPU 1080Ti
Y. Liu, L. Wang and M. Liu: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
164 DSGN
This method uses stereo information.
code 20.75 % 26.61 % 18.86 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
165 Complexer-YOLO
This method makes use of Velodyne laser scans.
18.26 % 21.42 % 17.06 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
166 TopNet-Retina
This method makes use of Velodyne laser scans.
14.57 % 18.04 % 12.48 % 52ms GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
167 RT3D-GMP
This method uses stereo information.
14.22 % 19.92 % 12.83 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
168 TopNet-HighRes
This method makes use of Velodyne laser scans.
13.50 % 19.43 % 11.93 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
169 Anonymous 13.47 % 20.42 % 11.64 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
170 ESGN
This method uses stereo information.
13.03 % 17.94 % 11.54 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
A. Gao, Y. Pang, J. Nie, Z. Shao, J. Cao, Y. Guo and X. Li: ESGN: Efficient Stereo Geometry Network for Fast 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2022.
171 DD3D code 12.51 % 18.58 % 10.65 % n/a s 1 core @ 2.5 Ghz (C/C++)
D. Park, R. Ambrus, V. Guizilini, J. Li and A. Gaidon: Is Pseudo-Lidar needed for Monocular 3D Object detection?. IEEE/CVF International Conference on Computer Vision (ICCV) .
172 DEPT 12.29 % 18.05 % 10.50 % 0.03 s 1 core @ 2.5 Ghz (Python)
173 PS-fld code 12.23 % 19.03 % 10.53 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
Y. Chen, H. Dai and Y. Ding: Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
174 DD3Dv2 code 12.16 % 17.74 % 10.49 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
175 CIE 11.94 % 17.90 % 10.34 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
176 MonoASS 11.39 % 17.37 % 9.73 % 0.04 s 1 core @ 2.5 Ghz (Python)
177 MonoInsight 11.28 % 16.08 % 9.69 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
178 OPA-3D code 11.01 % 17.14 % 9.94 % 0.04 s 1 core @ 3.5 Ghz (Python)
179 GCDR 10.92 % 15.65 % 9.86 % 0.28 s 1 core @ 2.5 Ghz (Python)
180 LT-M3OD 10.89 % 16.63 % 9.20 % 0.03 s 1 core @ 2.5 Ghz (Python)
181 BAIR 10.78 % 16.47 % 9.58 % 0.04 s 1 core @ 2.5 Ghz (Python)
182 MonoDTR 10.59 % 16.66 % 9.00 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
K. Huang, T. Wu, H. Su and W. Hsu: MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer. CVPR 2022.
183 BSM3D 10.41 % 15.30 % 8.89 % 0.03 s 1 core @ 2.5 Ghz (Python)
184 GUPNet code 10.37 % 15.62 % 8.79 % NA s 1 core @ 2.5 Ghz (Python + C/C++)
Y. Lu, X. Ma, L. Yang, T. Zhang, Y. Liu, Q. Chu, J. Yan and W. Ouyang: Geometry Uncertainty Projection Network for Monocular 3D Object Detection. arXiv preprint arXiv:2107.13774 2021.
185 CMKD code 10.28 % 16.03 % 8.85 % 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.
186 Lite-FPN-GUPNet 10.08 % 15.73 % 8.52 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
187 BCA 9.99 % 15.00 % 8.49 % 0.17 s GPU @ 2.5 Ghz (Python)
188 GPENet code 9.96 % 15.47 % 8.55 % 0.02 s GPU @ 2.5 Ghz (Python)
189 MM3D 9.90 % 15.37 % 8.23 % NA s 1 core @ 2.5 Ghz (C/C++)
190 DEVIANT code 9.77 % 14.49 % 8.28 % 0.04 s 1 GPU (Python)
A. Kumar, G. Brazil, E. Corona, A. Parchami and X. Liu: DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection. European Conference on Computer Vision (ECCV) 2022.
191 HBD 9.66 % 15.26 % 8.17 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
192 MonoNeRD 9.66 % 15.27 % 8.28 % na s 1 core @ 2.5 Ghz (C/C++)
193 MonoPCNS 9.65 % 15.56 % 8.27 % 0.14 s GPU @ 2.5 Ghz (Python)
194 Mono3DMethod 9.53 % 14.55 % 8.07 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
195 MonoAD 9.44 % 14.65 % 8.60 % 0.03 s GPU @ 2.5 Ghz (Python)
196 MonoA^2 9.42 % 13.82 % 7.99 % na s 1 core @ 2.5 Ghz (C/C++)
197 SARM3D 9.42 % 14.32 % 8.15 % 0.03 s GPU @ 2.5 Ghz (Python)
198 CaDDN code 9.41 % 14.72 % 8.17 % 0.63 s GPU @ 2.5 Ghz (Python)
C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.
199 SGM3D code 9.39 % 15.39 % 8.61 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
Z. Zhou, L. Du, X. Ye, Z. Zou, X. Tan, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection. RA-L 2022.
200 AMNet 9.30 % 14.10 % 8.02 % 0.03 s GPU @ 1.0 Ghz (Python)
201 Anonymous 9.08 % 13.35 % 7.63 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
202 MonoRCNN++ code 9.04 % 13.45 % 7.74 % 0.07 s GPU @ 2.5 Ghz (Python)
X. Shi, Z. Chen and T. Kim: Multivariate Probabilistic Monocular 3D Object Detection. WACV 2023.
203 M3DGAF 8.93 % 13.42 % 7.58 % 0.07 s 1 core @ 2.5 Ghz (Python)
204 MonoXiver 8.93 % 13.75 % 7.61 % 0.03s GPU @ 2.5 Ghz (Python)
205 HomoLoss(monoflex) code 8.81 % 13.26 % 7.41 % 0.04 s 1 core @ 2.5 Ghz (Python)
J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homography Loss for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
206 MonoDDE 8.41 % 12.38 % 7.16 % 0.04 s 1 core @ 2.5 Ghz (Python)
Z. Li, Z. Qu, Y. Zhou, J. Liu, H. Wang and L. Jiang: Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection. CVPR 2022.
207 SparseLiDAR_fusion 8.23 % 12.59 % 6.82 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
208 MDSNet 8.18 % 12.05 % 7.03 % 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.
209 DCD code 8.08 % 11.76 % 6.61 % 1 s 1 core @ 2.5 Ghz (C/C++)
210 MonoAug 7.94 % 12.66 % 6.64 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
211 LPCG-Monoflex code 7.92 % 12.11 % 6.61 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
L. Peng, F. Liu, Z. Yu, S. Yan, D. Deng, Z. Yang, H. Liu and D. Cai: Lidar Point Cloud Guided Monocular 3D Object Detection. ECCV 2022.
212 RefinedMPL 7.92 % 13.09 % 7.25 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
213 3DSeMoDLE code 7.71 % 11.86 % 6.43 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
214 Shape-Aware 7.65 % 11.69 % 6.35 % 0.05 s 1 core @ 2.5 Ghz (Python)
215 MonoRUn code 7.59 % 11.70 % 6.34 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
216 MonoFlex 7.36 % 10.36 % 6.29 % 0.03 s GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.
217 MonoPair 7.04 % 10.99 % 6.29 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
218 monodle code 6.96 % 10.73 % 6.20 % 0.04 s GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for Monocular 3D Object Detection. CVPR 2021 .
219 MonoAug 6.87 % 10.81 % 5.66 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
220 TopNet-DecayRate
This method makes use of Velodyne laser scans.
6.59 % 8.78 % 6.25 % 92 ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
221 MDNet 6.18 % 9.48 % 5.63 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
222 MK3D 6.15 % 8.76 % 5.14 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
223 Shift R-CNN (mono) code 5.66 % 8.58 % 4.49 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
224 FMF-occlusion-net 5.62 % 8.69 % 5.25 % 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.
225 Aug3D-RPN 5.22 % 7.14 % 4.21 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth. 2021.
226 TopNet-UncEst
This method makes use of Velodyne laser scans.
4.60 % 6.88 % 3.79 % 0.09 s NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
227 MonoPSR code 4.56 % 7.24 % 4.11 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
228 DFR-Net 4.52 % 6.66 % 3.71 % 0.18 s 1080 Ti (Pytorch)
Z. Zou, X. Ye, L. Du, X. Cheng, X. Tan, L. Zhang, J. Feng, X. Xue and E. Ding: The devil is in the task: Exploiting reciprocal appearance-localization features for monocular 3d object detection . ICCV 2021.
229 MoGDE 4.51 % 7.22 % 3.83 % 0.03 s GPU @ 2.5 Ghz (Python)
230 QD-3DT
This is an online method (no batch processing).
code 4.23 % 6.62 % 3.39 % 0.03 s GPU @ 2.5 Ghz (Python)
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.
231 M3D-RPN code 4.05 % 5.65 % 3.29 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
232 DDMP-3D 4.02 % 5.53 % 3.36 % 0.18 s 1 core @ 2.5 Ghz (Python)
L. Wang, L. Du, X. Ye, Y. Fu, G. Guo, X. Xue, J. Feng and L. Zhang: Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection. CVPR 2020.
233 CMAN 3.96 % 5.24 % 3.18 % 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.
234 D4LCN code 3.86 % 5.06 % 3.59 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
235 RT3DStereo
This method uses stereo information.
3.65 % 4.72 % 3.00 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
236 MonoEF 3.05 % 4.61 % 2.85 % 0.03 s 1 core @ 2.5 Ghz (Python)
Y. Zhou, Y. He, H. Zhu, C. Wang, H. Li and Q. Jiang: Monocular 3D Object Detection: An Extrinsic Parameter Free Approach. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
237 SS3D 2.09 % 2.48 % 1.61 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
238 SparVox3D 2.05 % 2.90 % 1.69 % 0.05 s GPU @ 2.0 Ghz (Python)
E. Balatkan and F. Kıraç: Improving Regression Performance on Monocular 3D Object Detection Using Bin-Mixing and Sparse Voxel Data. 2021 6th International Conference on Computer Science and Engineering (UBMK) 2021.
239 CDTrack3D code 1.91 % 2.56 % 1.49 % 0.0106 s NVIDIA RTX 3090 GPU, i9 10850k CPU
240 PGD-FCOS3D code 1.88 % 2.82 % 1.54 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
T. Wang, X. Zhu, J. Pang and D. Lin: Probabilistic and Geometric Depth: Detecting Objects in Perspective. Conference on Robot Learning (CoRL) 2021.
241 SSAL-Mono 1.53 % 2.18 % 1.54 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
242 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 78.19 % 89.65 % 71.13 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
2 CasA++ code 76.99 % 88.93 % 70.10 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.
3 TED code 76.95 % 89.54 % 70.31 % 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 CasA code 75.74 % 88.99 % 68.47 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.
5 LoGoNet 74.92 % 85.85 % 67.62 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
6 HMFI code 74.06 % 85.69 % 67.11 % 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 CZY_PPF_Net2 73.64 % 85.39 % 66.01 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
8 EQ-PVRCNN code 73.30 % 86.25 % 65.49 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, L. Jiang, Y. Sun, B. Schiele and J. Jia: A Unified Query-based Paradigm for Point Cloud Understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.
9 Semantical PVRCNN 73.14 % 86.75 % 64.87 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
10 VoCo 73.08 % 85.29 % 66.46 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
11 SPT 72.90 % 86.10 % 65.13 % 0.1 s GPU @ 2.5 Ghz (Python)
12 CAD 72.87 % 87.09 % 65.78 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
13 DCAN-Second code 72.74 % 88.62 % 65.89 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
14 CZY_PPF_Net 72.73 % 86.92 % 65.30 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
15 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
code 72.61 % 83.93 % 65.82 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
16 CAT-Det 72.51 % 85.35 % 65.55 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Zhang, J. Chen and D. Huang: CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection. CVPR 2022.
17 Reprod-Two-Branch 72.16 % 87.50 % 64.41 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
18 CFF-tv 72.02 % 86.54 % 64.25 % 1 s 1 core @ 2.5 Ghz (C/C++)
19 CFF-ep25 71.99 % 86.78 % 64.18 % 1 s 1 core @ 2.5 Ghz (C/C++)
20 PA-RCNN code 71.98 % 86.09 % 64.02 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
21 SGDA3D 71.90 % 84.81 % 64.88 % 0.07 s 1 core @ 2.5 Ghz (Python)
22 Under Blind Review#2 71.89 % 84.41 % 65.15 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
23 BtcDet
This method makes use of Velodyne laser scans.
code 71.76 % 84.48 % 64.70 % 0.09 s GPU @ 2.5 Ghz (Python + C/C++)
Q. Xu, Y. Zhong and U. Neumann: Behind the Curtain: Learning Occluded Shapes for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2022.
24 CF-ctdep-tv-ta 71.74 % 87.38 % 64.30 % 1 s 1 core @ 2.5 Ghz (C/C++)
25 cff-tv-v2-ep25 71.70 % 85.61 % 64.15 % 1 s 1 core @ 2.5 Ghz (C/C++)
26 ACF-Net 71.68 % 85.76 % 65.33 % n/a s 1 core @ 2.5 Ghz (C/C++)
27 PointPainting
This method makes use of Velodyne laser scans.
71.54 % 83.91 % 62.97 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
28 CFF-tv-v2 71.53 % 85.70 % 63.77 % 1 s 1 core @ 2.5 Ghz (C/C++)
29 RangeIoUDet
This method makes use of Velodyne laser scans.
71.49 % 85.99 % 63.62 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, Z. Zhang, M. Zhang, X. Zhao and S. Pu: RangeIoUDet: Range Image Based Real-Time 3D Object Detector Optimized by Intersection Over Union. CVPR 2021.
30 ACDet code 71.48 % 87.76 % 64.69 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
J. Xu, G. Wang, X. Zhang and G. Wan: ACDet: Attentive Cross-view Fusion for LiDAR-based 3D Object Detection. 3DV 2022.
31 IA-SSD (single) code 71.44 % 85.91 % 63.41 % 0.013 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
32 3SNet 71.44 % 84.55 % 64.79 % 0.07 s GPU @ 2.5 Ghz (Python)
33 Anonymous
This method makes use of Velodyne laser scans.
71.43 % 84.75 % 64.89 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
34 PDV code 71.31 % 85.54 % 64.40 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Hu, T. Kuai and S. Waslander: Point Density-Aware Voxels for LiDAR 3D Object Detection. CVPR 2022.
35 HVNet 71.17 % 83.97 % 63.65 % 0.03 s GPU @ 2.0 Ghz (Python)
M. Ye, S. Xu and T. Cao: HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection. CVPR 2020.
36 M3DeTR code 70.89 % 85.03 % 63.14 % n/a s GPU @ 1.0 Ghz (Python)
T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis and D. Manocha: M3DeTR: Multi-representation, Multi- scale, Mutual-relation 3D Object Detection with Transformers. 2021.
37 USVLab BSAODet 70.85 % 85.28 % 64.09 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
38 CZY_3917 70.73 % 83.46 % 63.16 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
39 CenterFuse 70.59 % 86.53 % 62.18 % 0.059 sec/frame 2 x V100
40 KPSCC code 70.59 % 83.06 % 63.07 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
41 CZY 70.32 % 86.42 % 63.32 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
42 TCDVF 70.28 % 82.85 % 63.54 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
43 USVLab BSAODet (S) 70.24 % 84.38 % 63.24 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
44 MVMM code 70.17 % 81.84 % 63.84 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
45 CF-ctdep-tv 70.16 % 86.31 % 62.63 % 1 s 1 core @ 2.5 Ghz (C/C++)
46 SPG_mini
This method makes use of Velodyne laser scans.
code 70.09 % 82.66 % 63.61 % 0.09 s GPU @ 2.5 Ghz (Python)
Q. Xu, Y. Zhou, W. Wang, C. Qi and D. Anguelov: SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation. Proceedings of the IEEE conference on computer vision and pattern recognition (ICCV) 2021.
47 TBD 70.09 % 82.60 % 63.39 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
48 VGA-RCNN 69.86 % 80.95 % 62.16 % 0.07 s 1 core @ 2.5 Ghz (Python)
49 FV2P v2 69.82 % 86.88 % 63.09 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
50 KeyFuse2B 69.76 % 84.95 % 62.16 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
51 IKT3D
This method makes use of Velodyne laser scans.
69.74 % 81.92 % 62.59 % 0.05 s 1 core @ 2.5 Ghz (Python)
52 CF-base-tv 69.49 % 84.12 % 61.85 % 1 s 1 core @ 2.5 Ghz (C/C++)
53 DGT-Det3D code 69.47 % 81.26 % 61.88 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
54 PVTr 69.46 % 84.62 % 62.33 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
55 HPV-RCNN 69.43 % 82.51 % 61.87 % 0.15 s 1 core @ 2.5 Ghz (Python)
56 IoU-2B 69.24 % 86.64 % 60.57 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
57 TBD 69.09 % 82.53 % 62.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
58 IPS 68.94 % 83.95 % 61.33 % TBD s 1 core @ 2.5 Ghz (C/C++)
59 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 68.89 % 82.49 % 62.41 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
60 F-ConvNet
This method makes use of Velodyne laser scans.
code 68.88 % 84.16 % 60.05 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
61 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 68.73 % 83.43 % 61.85 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
62 WGVRF 68.71 % 82.04 % 62.04 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
63 U_RVRCNN_V2_1 68.65 % 80.63 % 61.90 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
64 CF-cd-io-tv 68.52 % 83.71 % 60.12 % 1 s 1 core @ 2.5 Ghz (C/C++)
65 HotSpotNet 68.51 % 83.29 % 61.84 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
66 TBD 68.33 % 85.17 % 61.38 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
67 LightCPC code 68.24 % 84.48 % 61.82 % 0.02 s 1 core @ 2.5 Ghz (Python + C/C++)
68 BASA 68.22 % 81.97 % 61.48 % 1s 1 core @ 2.5 Ghz (python)
69 P2V-RCNN 68.06 % 81.09 % 60.73 % 0.1 s 2 cores @ 2.5 Ghz (Python)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.
70 KPP3D code 67.97 % 81.23 % 60.72 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
71 H^23D R-CNN code 67.90 % 82.76 % 60.49 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
J. Deng, W. Zhou, Y. Zhang and H. Li: From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2021.
72 Anonymous 67.83 % 81.75 % 60.92 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
73 Self-Calib Conv 67.73 % 82.11 % 60.57 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
74 Anonymous 67.73 % 81.50 % 60.87 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
75 VPFNet code 67.66 % 80.83 % 61.36 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
C. Wang, H. Chen and L. Fu: VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection. 2021.
76 3DSSD code 67.62 % 85.04 % 61.14 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
77 GS-FPS 67.57 % 80.63 % 61.45 % TBD s 1 core @ 2.5 Ghz (C/C++)
78 Fast-CLOCs 67.55 % 83.34 % 59.61 % 0.1 s GPU @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: Fast-CLOCs: Fast Camera-LiDAR Object Candidates Fusion for 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022.
79 NV-RCNN 67.54 % 82.53 % 60.97 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
80 U_PVRCNN_V2 67.51 % 79.04 % 59.98 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
81 DGT-Det3D 67.44 % 80.73 % 60.71 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
82 cff-tv-t 67.41 % 85.91 % 60.15 % 1 s 1 core @ 2.5 Ghz (C/C++)
83 DVFENet 67.40 % 82.29 % 60.71 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection. Neurocomputing 2021.
84 FromVoxelToPoint code 67.36 % 82.68 % 59.15 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to- Point Decoder. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
85 AGS-SSD[la] 67.35 % 81.70 % 60.41 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
86 Point-GNN
This method makes use of Velodyne laser scans.
code 67.28 % 81.17 % 59.67 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
87 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 67.24 % 82.56 % 60.28 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
88 STD code 67.23 % 81.36 % 59.35 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
89 Anonymous 67.06 % 80.51 % 60.13 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
90 SVGA-Net 66.82 % 81.25 % 59.37 % 0.03s 1 core @ 2.5 Ghz (Python + C/C++)
Q. He, Z. Wang, H. Zeng, Y. Zeng and Y. Liu: SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds. AAAI 2022.
91 PSA-SSD 66.79 % 79.56 % 59.94 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
92 KeyPoint-IoUHead 66.72 % 83.32 % 59.93 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
93 S-AT GCN 66.71 % 78.53 % 60.19 % 0.02 s GPU @ 2.0 Ghz (Python)
L. Wang, C. Wang, X. Zhang, T. Lan and J. Li: S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection. CoRR 2021.
94 T_PVRCNN_V2 66.49 % 80.88 % 58.51 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
95 ATT_SSD 66.41 % 80.73 % 60.16 % 0.01 s 1 core @ 2.5 Ghz (Python)
96 cp-tv-kp-io-sc 66.40 % 82.88 % 58.53 % 1 s 1 core @ 2.5 Ghz (C/C++)
97 ARPNET 66.39 % 82.32 % 58.80 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
98 IA-SSD (multi) code 66.29 % 81.30 % 59.58 % 0.014 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
99 T_PVRCNN 66.17 % 79.84 % 59.04 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
100 cp-tv 66.08 % 80.65 % 58.98 % 1 s 1 core @ 2.5 Ghz (C/C++)
101 SWA code 66.08 % 78.96 % 60.18 % 0.18 s 1 core @ 2.5 Ghz (C/C++)
102 GEO_LOC 66.08 % 79.15 % 58.56 % TBD s 1 core @ 2.5 Ghz (C/C++)
103 MGAF-3DSSD code 66.00 % 83.03 % 57.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
104 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 65.85 % 80.00 % 58.69 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
105 U_SECOND_V4 65.84 % 80.94 % 58.31 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
106 PSA-Det3D 65.51 % 79.21 % 59.06 % 0.1 s GPU @ 2.5 Ghz (Python)
107 TTT_SSD 65.31 % 78.56 % 59.27 % TBD s 1 core @ 2.5 Ghz (C/C++)
108 ZMMPP 65.23 % 77.62 % 58.08 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
109 VPNet 64.95 % 79.83 % 58.33 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
110 cp-tv-kp 64.87 % 79.91 % 58.22 % 1 s 1 core @ 2.5 Ghz (C/C++)
111 GS-FPS-LT 64.86 % 79.49 % 58.93 % TBD s 1 core @ 2.5 Ghz (C/C++)
112 PVRCNN_8369 64.56 % 79.60 % 57.94 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
113 Faraway-Frustum
This method makes use of Velodyne laser scans.
code 64.54 % 79.65 % 57.84 % 0.1 s GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-frustum: Dealing with lidar sparsity for 3D object detection using fusion. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) 2021.
114 Dune-DCF-e11 64.52 % 82.14 % 57.40 % 1 s 1 core @ 2.5 Ghz (C/C++)
115 SRDL 64.52 % 79.64 % 57.90 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
116 Dune-DCF-e15 64.42 % 81.10 % 57.35 % 1 s 1 core @ 2.5 Ghz (C/C++)
117 City-CF-fixed 64.39 % 81.11 % 57.74 % 1 s 1 core @ 2.5 Ghz (C/C++)
118 CF-ctdep-train 64.33 % 81.02 % 56.17 % 1 s 1 core @ 2.5 Ghz (C/C++)
119 City-CF 64.25 % 81.33 % 57.01 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
120 SIF 64.13 % 79.32 % 57.38 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
121 AFTD 64.03 % 82.99 % 55.93 % 1 s 1 core @ 2.5 Ghz (Python + C/C++)
122 SECOND_7862 63.95 % 78.30 % 57.28 % 1 s 1 core @ 2.5 Ghz (Python)
123 variance_point 63.90 % 78.49 % 56.51 % 0.05 s 1 core @ 2.5 Ghz (Python)
124 TANet code 63.77 % 79.16 % 56.21 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
125 CF-base-train 63.63 % 80.31 % 55.56 % 1 s 1 core @ 2.5 Ghz (C/C++)
126 DTE3D 63.10 % 79.79 % 56.94 % 0.15s 1 core @ 2.5 Ghz (C/C++)
127 XView 63.06 % 81.32 % 56.65 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
128 EPNet++ 62.94 % 78.57 % 56.62 % 0.1 s GPU @ 2.5 Ghz (Python)
Z. Liu, H. tengteng, B. Li, X. Chen, X. Wang and X. Bai: EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object Detection. arXiv preprint arXiv:2112.11088 2021.
129 PointPillars
This method makes use of Velodyne laser scans.
code 62.73 % 79.90 % 55.58 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
130 Dune-DCF-e09 62.23 % 77.53 % 55.46 % 1 s 1 core @ 2.5 Ghz (C/C++)
131 CAD
This method uses stereo information.
This method makes use of Velodyne laser scans.
62.05 % 77.71 % 54.56 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
132 CrazyTensor-CF 61.95 % 80.59 % 55.16 % 1 s 1 core @ 2.5 Ghz (C/C++)
133 LazyTorch-CP-Infer-O 61.40 % 76.40 % 54.73 % 1 s 1 core @ 2.5 Ghz (C/C++)
134 F-PointNet
This method makes use of Velodyne laser scans.
code 61.37 % 77.26 % 53.78 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
135 CenterPoint (pcdet) 61.25 % 76.38 % 54.68 % 0.051 sec/frame 2 x V100
136 LazyTorch-CP-Small-P 61.07 % 76.37 % 54.73 % 1 s 1 core @ 2.5 Ghz (C/C++)
137 epBRM
This method makes use of Velodyne laser scans.
code 59.79 % 75.13 % 53.36 % 0.10 s 1 core @ 2.5 Ghz (C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
138 BirdNet+
This method makes use of Velodyne laser scans.
code 59.58 % 70.84 % 54.20 % 0.11 s Titan Xp (PyTorch)
A. Barrera, J. Beltrán, C. Guindel, J. Iglesias and F. García: BirdNet+: Two-Stage 3D Object Detection in LiDAR through a Sparsity-Invariant Bird’s Eye View. IEEE Access 2021.
139 CrazyTensor-CP 59.54 % 75.40 % 53.21 % 1 s 1 core @ 2.5 Ghz (Python)
140 DMF
This method uses stereo information.
57.99 % 71.92 % 51.55 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
X. J. Chen and W. Xu: Disparity-Based Multiscale Fusion Network for Transportation Detection. IEEE Transactions on Intelligent Transportation Systems 2022.
141 PointRGBNet 57.59 % 73.09 % 51.78 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
P. Xie Desheng: Real-time Detection of 3D Objects Based on Multi-Sensor Information Fusion. Automotive Engineering 2022.
142 AVOD-FPN
This method makes use of Velodyne laser scans.
code 57.12 % 69.39 % 51.09 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
143 PiFeNet code 56.94 % 72.80 % 50.04 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
D. Le, H. Shi, H. Rezatofighi and J. Cai: Accurate and Real-time 3D Pedestrian Detection Using an Efficient Attentive Pillar Network. arXiv preprint arXiv:2112.15458 2022.
144 CZY 56.71 % 70.64 % 50.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
145 SCNet
This method makes use of Velodyne laser scans.
56.39 % 73.73 % 49.99 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
146 PFF3D
This method makes use of Velodyne laser scans.
code 55.71 % 72.67 % 49.58 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
147 MLOD
This method makes use of Velodyne laser scans.
code 55.06 % 73.03 % 48.21 % 0.12 s GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
148 BirdNet+ (legacy)
This method makes use of Velodyne laser scans.
code 52.15 % 72.45 % 46.57 % 0.1 s Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird’s Eye View. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
149 DSGN++
This method uses stereo information.
code 49.37 % 68.29 % 43.79 % 0.2 s GeForce RTX 2080Ti
Y. Chen, S. Huang, S. Liu, B. Yu and J. Jia: DSGN++: Exploiting Visual-Spatial Relation for Stereo-based 3D Detectors. arXiv preprint arXiv:2204.03039 2022.
150 StereoDistill 48.37 % 69.46 % 42.69 % 0.4 s 1 core @ 2.5 Ghz (Python)
151 AVOD
This method makes use of Velodyne laser scans.
code 48.15 % 64.11 % 42.37 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
152 BirdNet
This method makes use of Velodyne laser scans.
41.56 % 58.64 % 36.94 % 0.11 s Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
153 SparsePool code 40.74 % 56.52 % 36.68 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
154 MMLAB LIGA-Stereo
This method uses stereo information.
code 40.60 % 58.95 % 35.27 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
155 TopNet-Retina
This method makes use of Velodyne laser scans.
36.83 % 47.48 % 33.58 % 52ms GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
156 CG-Stereo
This method uses stereo information.
36.25 % 55.33 % 32.17 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
157 Pseudo-Stereo++ 35.75 % 54.06 % 31.17 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
158 SparsePool code 35.24 % 43.55 % 30.15 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
159 PS 32.16 % 49.23 % 27.73 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
160 Disp R-CNN (velo)
This method uses stereo information.
code 27.04 % 44.19 % 23.58 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
161 Disp R-CNN
This method uses stereo information.
code 27.04 % 44.19 % 23.58 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
162 Complexer-YOLO
This method makes use of Velodyne laser scans.
25.43 % 32.00 % 22.88 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
163 DSGN
This method uses stereo information.
code 21.04 % 31.23 % 18.93 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
164 OC Stereo
This method uses stereo information.
code 19.23 % 32.47 % 17.11 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
165 TopNet-DecayRate
This method makes use of Velodyne laser scans.
16.00 % 23.02 % 13.24 % 92 ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
166 RT3D-GMP
This method uses stereo information.
13.92 % 20.59 % 12.74 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
167 TopNet-UncEst
This method makes use of Velodyne laser scans.
9.18 % 12.31 % 8.14 % 0.09 s NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
168 ESGN
This method uses stereo information.
9.02 % 15.78 % 7.96 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
A. Gao, Y. Pang, J. Nie, Z. Shao, J. Cao, Y. Guo and X. Li: ESGN: Efficient Stereo Geometry Network for Fast 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2022.
169 CMKD code 8.15 % 14.66 % 7.23 % 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.
170 PS-fld code 7.29 % 12.80 % 6.05 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
Y. Chen, H. Dai and Y. Ding: Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
171 Anonymous 7.24 % 12.53 % 6.21 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
172 DD3Dv2 code 7.02 % 10.67 % 5.78 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
173 SSAL-Mono 6.70 % 10.55 % 5.76 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
174 TopNet-HighRes
This method makes use of Velodyne laser scans.
6.48 % 9.99 % 6.76 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
175 BSM3D 6.42 % 10.59 % 5.50 % 0.03 s 1 core @ 2.5 Ghz (Python)
176 MonoPSR code 5.78 % 9.87 % 4.57 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
177 DD3D code 5.69 % 9.20 % 5.20 % n/a s 1 core @ 2.5 Ghz (C/C++)
D. Park, R. Ambrus, V. Guizilini, J. Li and A. Gaidon: Is Pseudo-Lidar needed for Monocular 3D Object detection?. IEEE/CVF International Conference on Computer Vision (ICCV) .
178 BAIR 5.57 % 9.42 % 5.02 % 0.04 s 1 core @ 2.5 Ghz (Python)
179 LT-M3OD 5.53 % 9.17 % 4.84 % 0.03 s 1 core @ 2.5 Ghz (Python)
180 CaDDN code 5.38 % 9.67 % 4.75 % 0.63 s GPU @ 2.5 Ghz (Python)
C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.
181 Anonymous 5.17 % 7.71 % 4.31 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
182 MonoASS 4.99 % 8.65 % 4.51 % 0.04 s 1 core @ 2.5 Ghz (Python)
183 LPCG-Monoflex code 4.90 % 8.14 % 3.86 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
L. Peng, F. Liu, Z. Yu, S. Yan, D. Deng, Z. Yang, H. Liu and D. Cai: Lidar Point Cloud Guided Monocular 3D Object Detection. ECCV 2022.
184 MonoAD 4.85 % 8.13 % 4.71 % 0.03 s GPU @ 2.5 Ghz (Python)
185 MDNet 4.74 % 8.10 % 4.19 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
186 DEPT 4.71 % 8.82 % 4.15 % 0.03 s 1 core @ 2.5 Ghz (Python)
187 Lite-FPN-GUPNet 4.70 % 7.67 % 4.61 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
188 Shape-Aware 4.60 % 8.00 % 4.50 % 0.05 s 1 core @ 2.5 Ghz (Python)
189 MM3D 4.50 % 8.45 % 3.56 % NA s 1 core @ 2.5 Ghz (C/C++)
190 3DSeMoDLE code 4.47 % 7.51 % 3.84 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
191 BCA 4.42 % 6.89 % 3.91 % 0.17 s GPU @ 2.5 Ghz (Python)
192 MonoDDE 4.36 % 6.68 % 3.76 % 0.04 s 1 core @ 2.5 Ghz (Python)
Z. Li, Z. Qu, Y. Zhou, J. Liu, H. Wang and L. Jiang: Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection. CVPR 2022.
193 GPENet code 4.28 % 7.06 % 3.68 % 0.02 s GPU @ 2.5 Ghz (Python)
194 SparseLiDAR_fusion 4.26 % 7.77 % 3.45 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
195 MonoDTR 4.11 % 5.84 % 3.48 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
K. Huang, T. Wu, H. Su and W. Hsu: MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer. CVPR 2022.
196 RT3DStereo
This method uses stereo information.
4.10 % 7.03 % 3.88 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
197 HomoLoss(monoflex) code 4.09 % 6.81 % 3.78 % 0.04 s 1 core @ 2.5 Ghz (Python)
J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homography Loss for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
198 DFR-Net 4.00 % 5.99 % 3.95 % 0.18 s 1080 Ti (Pytorch)
Z. Zou, X. Ye, L. Du, X. Cheng, X. Tan, L. Zhang, J. Feng, X. Xue and E. Ding: The devil is in the task: Exploiting reciprocal appearance-localization features for monocular 3d object detection . ICCV 2021.
199 MonoInsight 3.99 % 6.56 % 3.49 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
200 DEVIANT code 3.97 % 6.42 % 3.51 % 0.04 s 1 GPU (Python)
A. Kumar, G. Brazil, E. Corona, A. Parchami and X. Liu: DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection. European Conference on Computer Vision (ECCV) 2022.
201 SARM3D 3.85 % 5.59 % 3.28 % 0.03 s GPU @ 2.5 Ghz (Python)
202 GUPNet code 3.85 % 6.94 % 3.64 % NA s 1 core @ 2.5 Ghz (Python + C/C++)
Y. Lu, X. Ma, L. Yang, T. Zhang, Y. Liu, Q. Chu, J. Yan and W. Ouyang: Geometry Uncertainty Projection Network for Monocular 3D Object Detection. arXiv preprint arXiv:2107.13774 2021.
203 MoGDE 3.76 % 6.04 % 3.09 % 0.03 s GPU @ 2.5 Ghz (Python)
204 OPA-3D code 3.75 % 6.01 % 3.56 % 0.04 s 1 core @ 3.5 Ghz (Python)
205 CIE 3.74 % 6.13 % 3.18 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
206 MonoAug 3.71 % 5.66 % 3.00 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
207 SGM3D code 3.63 % 7.05 % 3.33 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
Z. Zhou, L. Du, X. Ye, Z. Zou, X. Tan, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection. RA-L 2022.
208 DCD code 3.62 % 5.84 % 3.33 % 1 s 1 core @ 2.5 Ghz (C/C++)
209 AMNet 3.61 % 5.54 % 3.19 % 0.03 s GPU @ 1.0 Ghz (Python)
210 Aug3D-RPN 3.33 % 5.44 % 2.82 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth. 2021.
211 monodle code 3.28 % 5.34 % 2.83 % 0.04 s GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for Monocular 3D Object Detection. CVPR 2021 .
212 MDSNet 3.22 % 5.99 % 2.62 % 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.
213 MonoXiver 3.17 % 4.66 % 2.69 % 0.03s GPU @ 2.5 Ghz (Python)
214 DDMP-3D 3.14 % 4.92 % 2.44 % 0.18 s 1 core @ 2.5 Ghz (Python)
L. Wang, L. Du, X. Ye, Y. Fu, G. Guo, X. Xue, J. Feng and L. Zhang: Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection. CVPR 2020.
215 MonoA^2 3.04 % 5.41 % 2.67 % na s 1 core @ 2.5 Ghz (C/C++)
216 QD-3DT
This is an online method (no batch processing).
code 3.02 % 5.71 % 2.73 % 0.03 s GPU @ 2.5 Ghz (Python)
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.
217 M3DGAF 3.02 % 5.33 % 2.87 % 0.07 s 1 core @ 2.5 Ghz (Python)
218 MonoPair 2.87 % 4.76 % 2.42 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
219 MonoNeRD 2.80 % 5.24 % 2.55 % na s 1 core @ 2.5 Ghz (C/C++)
220 MonoFlex 2.67 % 4.41 % 2.50 % 0.03 s GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.
221 MK3D 2.55 % 4.17 % 2.24 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
222 Mono3DMethod 2.50 % 4.09 % 2.15 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
223 MonoAug 2.46 % 4.31 % 2.21 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
224 MonoPCNS 2.46 % 4.65 % 2.42 % 0.14 s GPU @ 2.5 Ghz (Python)
225 RefinedMPL 2.42 % 4.23 % 2.14 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
226 MonoRCNN++ code 2.31 % 3.50 % 2.01 % 0.07 s GPU @ 2.5 Ghz (Python)
X. Shi, Z. Chen and T. Kim: Multivariate Probabilistic Monocular 3D Object Detection. WACV 2023.
227 GCDR 2.11 % 3.74 % 1.99 % 0.28 s 1 core @ 2.5 Ghz (Python)
228 SS3D 1.89 % 3.45 % 1.44 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
229 D4LCN code 1.82 % 2.72 % 1.79 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
230 PGD-FCOS3D code 1.79 % 3.54 % 1.56 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
T. Wang, X. Zhu, J. Pang and D. Lin: Probabilistic and Geometric Depth: Detecting Objects in Perspective. Conference on Robot Learning (CoRL) 2021.
231 FMF-occlusion-net 1.65 % 1.91 % 1.75 % 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.
232 HBD 1.64 % 3.15 % 1.76 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
233 CMAN 1.48 % 1.76 % 1.17 % 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.
234 MonoEF 1.18 % 2.36 % 1.15 % 0.03 s 1 core @ 2.5 Ghz (Python)
Y. Zhou, Y. He, H. Zhu, C. Wang, H. Li and Q. Jiang: Monocular 3D Object Detection: An Extrinsic Parameter Free Approach. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
235 M3D-RPN code 0.81 % 1.25 % 0.78 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
236 MonoRUn code 0.73 % 1.14 % 0.66 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
237 Shift R-CNN (mono) code 0.38 % 0.76 % 0.41 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
238 CDTrack3D code 0.10 % 0.24 % 0.11 % 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

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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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