Bird's Eye View Evaluation 2017


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

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

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

All methods are ranked based on the moderately difficult results.

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

Car


Method Setting Code Moderate Easy Hard Runtime Environment
1 GraR-Po 92.12 % 95.79 % 87.11 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
2 VPFNet 91.86 % 93.02 % 86.94 % 0.06 s 2 cores @ 2.5 Ghz (Python)
H. Zhu, J. Deng, Y. Zhang, J. Ji, Q. Mao, H. Li and Y. Zhang: VPFNet: Improving 3D Object Detection with Virtual Point based LiDAR and Stereo Data Fusion. 2021.
3 SFD 91.85 % 95.64 % 86.83 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
4 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.
5 GraR-Vo 91.72 % 95.27 % 86.51 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
6 CityBrainLab 91.60 % 94.75 % 86.67 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
7 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.
8 CasA 91.54 % 95.19 % 86.82 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
9 Anonymous 91.53 % 95.04 % 86.69 % 0.1 s GPU @ 2.5 Ghz (Python)
10 GraR-Pi 91.52 % 95.06 % 86.42 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
11 Anonymous 91.38 % 95.23 % 86.71 % n/a s 1 core @ 2.5 Ghz (C/C++)
12 DGDNH 91.36 % 95.03 % 88.79 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
13 BADet code 91.32 % 95.23 % 86.48 % 0.11 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 (PR) 2022.
14 Anonymous 91.04 % 94.31 % 86.31 % 0.1s 1 core @ 2.5 Ghz (C/C++)
15 Anonymous 91.04 % 94.76 % 86.31 % n/a s 1 core @ 2.5 Ghz (Python + C/C++)
16 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.
17 anonymous 90.90 % 92.96 % 86.34 % 0.09 s GPU @ 2.5 Ghz (Python)
18 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.
19 PTA-RCNN 90.61 % 92.51 % 86.18 % 0.08 s 1 core @ 2.5 Ghz (Python)
20 VueronNet code 90.56 % 94.67 % 85.31 % 0.06 s 1 core @ 2.0 Ghz (Python)
21 ST-RCNN
This method makes use of Velodyne laser scans.
90.53 % 94.58 % 86.08 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
22 ST-RCNN (SNLW-RCNN)
This method makes use of Velodyne laser scans.
code 90.53 % 94.58 % 86.08 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
23 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.
24 PDV 90.48 % 94.56 % 86.23 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
25 VCRCNN 90.42 % 94.55 % 86.11 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
26 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.
27 TBD 90.37 % 93.82 % 87.72 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
28 HyBrid Feature Det 90.35 % 92.87 % 85.87 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
29 DDet 90.34 % 94.16 % 86.01 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
30 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.
31 TransCyclistNet 90.33 % 92.68 % 85.90 % 0.08 s 1 core @ 2.5 Ghz (Python)
32 Fast VP-RCNN code 90.32 % 95.09 % 85.84 % 0.05 s 1 core @ 3.5 Ghz (C/C++)
33 WHUT-iou_ssd code 90.31 % 94.22 % 85.83 % 0.045s 1 core @ 2.5 Ghz (C/C++)
34 LZY_RCNN 90.29 % 92.88 % 85.84 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
35 anonymous code 90.22 % 94.86 % 85.73 % 0.05s 1 core @ >3.5 Ghz (python)
36 MSG-PGNN 90.20 % 92.89 % 85.80 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
37 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
code 90.13 % 92.42 % 85.93 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
38 XView 90.12 % 92.27 % 85.94 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
39 GraR-VoI 90.10 % 95.69 % 86.85 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
40 Generalized-SIENet 90.09 % 92.12 % 85.88 % 0.08 s 1 core @ 2.5 Ghz (Python)
41 CAT-Det 90.07 % 92.59 % 85.82 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
42 SCIR-Net
This method makes use of Velodyne laser scans.
90.04 % 92.11 % 85.63 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
43 FPC-RCNN 90.03 % 92.74 % 85.67 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
44 TPCG 90.02 % 92.22 % 85.88 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
45 Associate-3Ddet_v2 90.00 % 95.55 % 84.72 % 0.04 s 1 core @ 2.5 Ghz (Python)
46 TransDet3D 89.98 % 92.44 % 85.71 % 0.08 s 1 core @ 2.5 Ghz (Python)
47 FPV-SSD 89.93 % 91.45 % 85.21 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
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48 SAA-PV-RCNN 89.88 % 91.54 % 86.93 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
49 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.
50 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.
51 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.
52 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.
53 PE-RCVN 89.79 % 95.55 % 84.78 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
54 Anonymous 89.76 % 95.41 % 86.42 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
55 EA-M-RCNN(BorderAtt) 89.76 % 94.67 % 86.73 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
56 sa-voxel-centernet code 89.74 % 92.02 % 85.69 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
57 AM-SSD 89.74 % 95.56 % 84.65 % 0.04 s 1 core @ 2.5 Ghz (Python)
58 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.
59 DSASNet 89.59 % 93.41 % 84.81 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
60 CAD 89.57 % 93.03 % 84.71 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
61 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.
62 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.
63 KpNet 89.53 % 93.34 % 81.95 % 0.42 s 1 core @ 2.5 Ghz (C/C++)
64 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.
65 KpNet 89.49 % 93.29 % 81.92 % 42 s 1 core @ 2.5 Ghz (C/C++)
66 IA-SSD (single) 89.48 % 93.14 % 84.42 % 0.013 s 1 core @ 2.5 Ghz (C/C++)
67 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.
68 SA3DNet
This method uses stereo information.
This method makes use of Velodyne laser scans.
89.46 % 93.11 % 84.60 % 0.05 s GPU @ 2.5 Ghz (Python)
69 DVF-V 89.42 % 93.12 % 86.50 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
70 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.
71 JPVNet 89.36 % 92.78 % 84.37 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
72 ASCNet 89.36 % 92.85 % 86.45 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
73 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.
74 IA-SSD (multi) 89.33 % 92.79 % 84.35 % 0.014 s 1 core @ 2.5 Ghz (C/C++)
75 TBD
This method makes use of Velodyne laser scans.
89.29 % 92.94 % 84.46 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
76 TBD 89.24 % 92.59 % 85.99 % 0.1 s 1 core @ 2.5 Ghz (Python)
77 TBD 89.21 % 92.74 % 84.23 % TBD GPU @ 2.5 Ghz (Python + C/C++)
78 DVF-PV 89.20 % 93.08 % 86.28 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
79 TF3D
This method makes use of Velodyne laser scans.
89.19 % 93.10 % 84.41 % 0.1 s 2 cores @ 3.0 Ghz (Python)
80 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.
81 FS-Net
This method makes use of Velodyne laser scans.
89.18 % 92.88 % 84.39 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
82 MBDF-Net 89.18 % 95.36 % 84.24 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
83 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.
84 Anonymous 89.15 % 92.47 % 85.97 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
85 SGNet 89.14 % 93.04 % 86.54 % 0.09 s GPU @ 2.5 Ghz (Python)
86 USVLab BSAODet (MM) 89.13 % 92.92 % 86.41 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
87 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.
88 TBD 89.11 % 92.42 % 84.26 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
89 EQ-PVRCNN 89.09 % 94.55 % 86.42 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
90 MSADet 89.08 % 92.76 % 85.66 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
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91 VoxSeT 89.07 % 92.70 % 86.29 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
92 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.
93 EPNet++ 89.00 % 95.41 % 85.73 % 0.1 s GPU @ 2.5 Ghz (Python)
94 Anonymous code 89.00 % 92.67 % 86.33 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
95 SECOND 88.98 % 92.01 % 83.67 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
96 LGNet 88.98 % 92.83 % 86.26 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
97 ISE-RCNN 88.97 % 92.86 % 86.28 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
98 TBD 88.94 % 92.03 % 86.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
99 Sem-Aug v1 code 88.92 % 92.59 % 84.29 % 0.04 s GPU @ 3.5 Ghz (Python)
100 MBDF-Net-1 88.90 % 94.68 % 83.98 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
101 VCT 88.90 % 93.01 % 84.23 % 0.2 s 1 core @ 2.5 Ghz (Python)
102 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.
103 MVOD 88.85 % 92.50 % 86.19 % 0.16 s 1 core @ 2.5 Ghz (C/C++)
104 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.
105 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.
106 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.
107 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.
108 SRIF-RCNN 88.77 % 92.10 % 86.06 % 0.0947 s 1 core @ 2.5 Ghz (C/C++)
109 TBD 88.75 % 92.30 % 84.01 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
110 PV-RCNN++ 88.74 % 92.66 % 85.97 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
111 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.
112 ISE-RCNN-PV 88.69 % 92.31 % 86.10 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
113 SIENet code 88.65 % 92.38 % 86.03 % 0.08 s 1 core @ 2.5 Ghz (Python)
Z. Li, Y. Yao, Z. Quan, W. Yang and J. Xie: SIENet: Spatial Information Enhancement Network for 3D Object Detection from Point Cloud. 2021.
114 P2V-RCNN 88.63 % 92.72 % 86.14 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.
115 demo 88.62 % 92.35 % 83.56 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
116 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.
117 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.
118 DCAnet 88.55 % 92.29 % 85.85 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
119 SqueezeRCNN 88.52 % 92.65 % 85.82 % 0.08 s 1 core @ 2.5 Ghz (Python)
120 FusionDetv2-v3 88.47 % 92.55 % 85.63 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
121 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.
122 CenterNet3D 88.46 % 91.80 % 83.62 % 0.04 s GPU @ 1.5 Ghz (Python)
G. Wang, B. Tian, Y. Ai, T. Xu, L. Chen and D. Cao: CenterNet3D:An Anchor free Object Detector for Autonomous Driving. 2020.
123 GVNet code 88.43 % 92.19 % 85.63 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
124 USVLab BSAODet (SM) 88.42 % 92.19 % 85.55 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
125 NV-RCNN 88.41 % 92.03 % 85.66 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
126 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.
127 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.
128 Point Image Fusion 88.39 % 92.14 % 85.78 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
129 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.
130 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.
131 CSVoxel-RCNN 88.38 % 92.09 % 85.59 % 0.03 s GPU @ 1.0 Ghz (Python)
132 SA-voxel-centernet code 88.28 % 91.80 % 85.73 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
133 SARFE 88.28 % 92.35 % 85.50 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
134 FusionDetv2-v4 88.27 % 92.05 % 85.38 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
135 TBD 88.26 % 91.44 % 85.44 % 0.06 s GPU @ 2.5 Ghz (Python)
136 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.
137 SPVB-SSD 88.23 % 91.82 % 85.46 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
138 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.
139 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.
140 SRDL 88.17 % 92.01 % 85.43 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
141 FPC3D
This method makes use of the epipolar geometry.
88.15 % 91.92 % 85.32 % 33 s 1 core @ 2.5 Ghz (C/C++)
142 3D-VDNet 88.15 % 91.72 % 84.65 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
143 SAA-SECOND 88.14 % 91.32 % 85.23 % 38m s 1 core @ 2.5 Ghz (C/C++)
144 FusionDetv1 88.13 % 91.91 % 85.40 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
145 FPCR-CNN 88.12 % 92.62 % 85.18 % 0.05 s 1 core @ 2.5 Ghz (Python)
146 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.
147 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.
148 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.
149 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.
150 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. arXiv preprint arXiv:2011.01404 2020.
151 NV2P-RCNN 88.08 % 93.44 % 85.32 % 0.1 s GPU @ 2.5 Ghz (Python)
152 VPN 88.06 % 90.94 % 83.24 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
153 TBD 88.04 % 91.31 % 84.79 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
154 FusionDetv2-v2 88.04 % 91.77 % 85.29 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
155 UberATG-HDNET
This method makes use of Velodyne laser scans.
87.98 % 93.13 % 81.23 % 0.05 s GPU @ 2.5 Ghz (Python)
B. Yang, M. Liang and R. Urtasun: HDNET: Exploiting HD Maps for 3D Object Detection. 2nd Conference on Robot Learning (CoRL) 2018.
156 TCDVF 87.94 % 91.21 % 84.66 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
157 AIMC-RUC 87.91 % 93.92 % 82.70 % 0.11 s 1 core @ 2.5 Ghz (Python)
158 DGT-Det3D 87.88 % 91.70 % 85.14 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
159 CVFNet 87.87 % 93.65 % 82.29 % 28.1ms 1 core @ 2.5 Ghz (Python)
160 FusionDetv2-v5 87.86 % 91.92 % 83.07 % 0.05 s 1 core @ 2.5 Ghz (Java + C/C++)
161 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.
162 CSNet 87.84 % 92.23 % 82.93 % 0.1 s 1 core @ 2.5 Ghz (Python)
163 YF 87.81 % 92.11 % 83.07 % 0.04 s GPU @ 2.5 Ghz (C/C++)
164 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.
165 SIF 87.76 % 91.44 % 85.15 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
166 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.
167 DKAnet 87.68 % 91.07 % 84.03 % 0.05 s 1 core @ 2.0 Ghz (Python)
168 DVFENet 87.68 % 90.93 % 84.60 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection. Neurocomputing 2021.
169 S-AT GCN 87.68 % 90.85 % 84.20 % 0.02 s GPU @ 2.0 Ghz (Python)
L. Wang, C. Wang, X. Zhang, T. Lan and J. Li: S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection. CoRR 2021.
170 TBD 87.67 % 91.02 % 82.42 % 0.1 s 1 core @ 2.5 Ghz (Python)
171 TBD 87.62 % 90.86 % 82.29 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
172 AutoAlign 87.60 % 91.72 % 84.44 % 0.1 s 1 core @ 2.5 Ghz (Python)
173 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.
174 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.
175 TBD 87.51 % 90.76 % 80.15 % 0.1 s 1 core @ 2.5 Ghz (Python)
176 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.
177 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.
178 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.
179 HS3D code 87.40 % 91.97 % 82.85 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
180 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.
181 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.
182 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.
183 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.
184 3D_att
This method makes use of Velodyne laser scans.
87.09 % 93.14 % 81.92 % 0.17 s GPU @ 2.5 Ghz (Python)
185 Contrastive PP code 87.06 % 92.99 % 81.96 % 0.01 s 1 core @ 2.5 Ghz (Python)
186 DVF 87.05 % 92.76 % 84.13 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
187 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.
188 sscl-20p 86.82 % 91.43 % 82.06 % 0.02 s 1 core @ 2.5 Ghz (Python)
189 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.
190 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.
191 HNet-3DSSD
This method makes use of Velodyne laser scans.
code 86.69 % 91.65 % 81.05 % 0.05 s GPU @ 2.5 Ghz (Python)
192 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.
193 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.
194 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.
195 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.
196 Dune-DCF-e09 86.36 % 89.33 % 81.77 % 1 s 1 core @ 2.5 Ghz (C/C++)
197 Dune-DCF-e11 86.32 % 89.32 % 81.78 % 1 s 1 core @ 2.5 Ghz (C/C++)
198 PP-PCdet code 86.32 % 89.86 % 81.62 % 0.01 s 1 core @ 2.5 Ghz (Python)
199 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.
200 Dune-DCF-e15 86.21 % 88.99 % 81.62 % 1 s 1 core @ 2.5 Ghz (C/C++)
201 APL-Second 86.16 % 91.45 % 81.08 % 0.05 s 1 core @ 2.5 Ghz (Python)
202 TBD_BD code 86.12 % 91.00 % 81.66 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
203 CrazyTensor-CF 86.10 % 89.13 % 81.61 % 1 s 1 core @ 2.5 Ghz (C/C++)
204 City-CF-fixed 86.09 % 89.94 % 81.73 % 1 s 1 core @ 2.5 Ghz (C/C++)
205 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.
206 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.
207 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.
208 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.
209 City-CF 85.83 % 89.20 % 81.61 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
210 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.
211 LazyTorch-CP-Infer-O 85.74 % 89.19 % 81.35 % 1 s 1 core @ 2.5 Ghz (C/C++)
212 PointRGBNet 85.73 % 91.39 % 80.68 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
213 LazyTorch-CP-Small-P 85.63 % 89.10 % 81.27 % 1 s 1 core @ 2.5 Ghz (C/C++)
214 CrazyTensor-CP 85.55 % 87.94 % 82.63 % 1 s 1 core @ 2.5 Ghz (Python)
215 Sem-Aug-PointRCNN code 85.50 % 89.75 % 83.13 % 0.1 s GPU @ 3.5 Ghz (C/C++)
216 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.
217 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.
218 RangeDet code 85.06 % 89.88 % 80.23 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
219 LazyTorch-CP 85.05 % 88.47 % 81.19 % 1 s 1 core @ 2.5 Ghz (C/C++)
220 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.
221 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.
222 FPC3D_all
This method makes use of Velodyne laser scans.
84.85 % 91.05 % 80.23 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
223 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.
224 MF 84.72 % 88.58 % 78.17 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
225 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.
226 FusionDetv2-v1 84.45 % 89.64 % 79.73 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
227 FusionDetv2-baseline 84.31 % 90.38 % 79.23 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
228 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.
229 KMC code 83.90 % 88.87 % 76.87 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
230 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.
231 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.
232 TBD 81.53 % 87.90 % 74.26 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
233 AEC3D 80.37 % 86.81 % 74.26 % 18 ms GPU @ 2.5 Ghz (Python)
234 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.
235 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.
236 DSGN++
This method uses stereo information.
78.94 % 88.55 % 69.74 % 0.4 s NVIDIA Tesla V100
237 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.
238 VN3D 77.45 % 86.35 % 71.59 % 0.02 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
239 SD3DOD 76.96 % 86.82 % 70.05 % 0.04 s GPU @ 2.5 Ghz (Python)
240 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.
241 R-AGNO-Net 76.24 % 80.10 % 70.38 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
242 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.
243 LIGA-Stereo-old
This method uses stereo information.
74.76 % 88.33 % 65.31 % 0.375 s Titan Xp
244 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.
245 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.
246 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.
247 ppt 70.21 % 72.17 % 65.26 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
248 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.
249 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.
250 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.
251 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.
252 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.
253 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.
254 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.
255 UPF_3D
This method uses stereo information.
63.58 % 85.53 % 56.56 % 0.29 s 1 core @ 2.5 Ghz (Python)
256 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.
257 BEVC 61.89 % 69.00 % 56.32 % 35ms GPU @ 1.5 Ghz (Python)
258 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.
259 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.
260 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.
261 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.
262 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.
263 OSE+ 58.65 % 79.80 % 50.52 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
264 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.
265 SOD 58.50 % 81.25 % 49.08 % 0.1 s 1 core @ 2.5 Ghz (Python)
266 EGFN
This method uses stereo information.
58.12 % 78.10 % 49.28 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
267 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.
268 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.
269 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.
270 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.
271 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.
272 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.
273 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.
274 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.
275 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.
276 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.
277 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.
278 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.
279 GCDR 37.34 % 50.85 % 30.51 % 0.28 s 1 core @ 2.5 Ghz (Python)
280 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.
281 Digging_M3D 28.84 % 39.74 % 26.08 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
282 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.
283 LPCG-Monoflex 24.81 % 35.96 % 21.86 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
284 Mix-Teaching-M3D 24.23 % 35.74 % 20.80 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
285 CMKD 23.92 % 36.92 % 21.68 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
286 PS-fld 23.76 % 32.64 % 20.64 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
287 SCSTSV-MonoFlex 23.71 % 34.59 % 20.41 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
288 CMKD 23.61 % 36.80 % 21.31 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
289 MonoDDE 23.46 % 33.58 % 20.37 % 0.04 s 1 core @ 2.5 Ghz (Python)
290 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) .
291 MonoDistill 22.59 % 31.87 % 19.72 % 0.04 s 1 core @ 2.5 Ghz (Python)
292 MonoCon code 22.10 % 31.12 % 19.00 % 0.02 s GPU @ 2.5 Ghz (Python)
T. Xianpeng Liu: Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection. AAAI 2022.
293 gupnet_se 21.98 % 32.82 % 18.70 % 0.03s 1 core @ 2.5 Ghz (C/C++)
294 ZongmuMono3d code 21.78 % 33.18 % 18.71 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
295 MDNet 21.71 % 33.31 % 18.49 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
296 Lite-FPN-GUPNet 21.53 % 31.68 % 18.38 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
297 mono3d code 21.39 % 32.17 % 18.47 % TBD TBD
298 SGM3D 21.37 % 31.49 % 18.43 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
Z. Zhou, L. Du, X. Ye, Z. Zou, X. Tan, E. Ding, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection. 2021.
299 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.
300 HBD 20.91 % 29.87 % 18.22 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
301 CA3D 20.77 % 29.57 % 17.88 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
302 mono3d 20.75 % 31.58 % 17.66 % 0.03 s GPU @ 2.5 Ghz (Python)
303 LT-M3OD 20.74 % 29.40 % 17.83 % 0.03 s 1 core @ 2.5 Ghz (Python)
304 vadin-TBD 20.68 % 29.60 % 17.81 % 0.04 s 1 core @ 2.5 Ghz (Python)
305 MonoFlex 20.67 % 30.95 % 17.72 % 0.03 s 1 core @ 2.5 Ghz (Python)
306 MonoGround 20.47 % 30.07 % 17.74 % 0.03 s 1 core @ 2.5 Ghz (Python)
307 MonoDTR 20.38 % 28.59 % 17.14 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
308 MonoEdge 20.35 % 28.80 % 17.57 % 0.05 s GPU @ 2.5 Ghz (Python)
309 SAIC_ADC_Mono3D code 20.20 % 27.09 % 18.78 % 50 s GPU @ 2.5 Ghz (Python)
310 EW code 20.19 % 31.65 % 16.67 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
311 LPCG-M3D 20.17 % 30.72 % 16.76 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
312 MonoEdge-Rotate 20.16 % 31.19 % 17.35 % 0.05 s GPU @ 2.5 Ghz (Python)
313 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.
314 M3DSSD++ code 20.03 % 32.18 % 16.47 % 0.16s 1 core @ 2.5 Ghz (C/C++)
315 MAOLoss code 19.95 % 28.29 % 16.94 % 0.05 s 1 core @ 2.5 Ghz (Python)
316 ANM 19.82 % 29.89 % 16.77 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
317 EM code 19.80 % 30.61 % 16.55 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
318 KAIST-VDCLab 19.75 % 27.98 % 17.32 % 0.04 s 1 core @ 2.5 Ghz (Python)
319 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.
320 MonoEF code 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.
321 K3D 19.60 % 28.31 % 17.62 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
322 ITS-MDPL 19.54 % 33.02 % 17.56 % 0.16 s GPU @ 2.5 Ghz (Python)
323 vadin-TBD2 code 19.25 % 29.18 % 16.21 % 0.20 s 1 core @ 2.5 Ghz (Python)
324 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.
325 SwinMono3D 19.15 % 29.65 % 14.09 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
326 GAC3D++ 19.05 % 26.94 % 16.48 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
327 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.
328 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.
329 E2E-DA 19.03 % 27.41 % 16.46 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
330 MonoGeo 18.99 % 25.86 % 16.19 % 0.05 s 1 core @ 2.5 Ghz (Python)
331 Anonymous code 18.96 % 26.54 % 16.01 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
332 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.
333 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 .
334 MonoLCD 18.68 % 25.89 % 16.30 % 0.04 s 1 core @ 2.5 Ghz (Python)
335 none 18.66 % 26.19 % 15.79 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
336 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.
337 MDSNet 18.65 % 30.92 % 14.53 % 0.07 s 1 core @ 2.5 Ghz (Python)
338 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.
339 AutoShape 18.12 % 28.25 % 14.84 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
340 PPTrans 18.12 % 28.05 % 15.41 % 0.2 s GPU @ 2.5 Ghz (Python)
341 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.
342 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.
343 MP-Mono 17.96 % 25.36 % 13.84 % 0.16 s GPU @ 2.5 Ghz (Python)
344 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.
345 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.
346 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.
347 RelationNet3D_dla34 code 17.74 % 24.27 % 15.38 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
348 TBD 17.70 % 29.97 % 15.04 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
349 RelationNet3D 17.66 % 25.56 % 15.52 % 0.04 s GPU @ 2.5 Ghz (Python)
350 MonoHMOO 17.60 % 27.39 % 13.25 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
351 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.
352 Lite-FPN 17.58 % 26.67 % 14.61 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
353 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 .
354 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.
355 RetinaMono 17.33 % 26.12 % 15.26 % 0.02 s 1 core @ 2.5 Ghz (Python)
356 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.
357 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.
358 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.
359 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.
360 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.
361 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.
362 TBD 16.22 % 24.21 % 14.11 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
363 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.
364 MM 16.09 % 24.65 % 13.99 % 1 s 1 core @ 2.5 Ghz (C/C++)
365 E2E-DA-Lite (Res18) 16.06 % 23.49 % 13.55 % 0.01 s GPU @ 2.5 Ghz (Python)
366 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.
367 MK3D 15.99 % 23.00 % 13.28 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
368 Keypoint-3D 15.54 % 23.16 % 11.83 % 14 s 1 core @ 2.5 Ghz (C/C++)
369 COF3D 15.39 % 25.36 % 11.34 % 200 s 1 core @ 2.5 Ghz (Python + C/C++)
370 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.
371 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.
372 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.
373 RelationNet3D_res18 code 14.59 % 20.54 % 12.51 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
374 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.
375 ICCV 14.30 % 19.93 % 12.37 % 0.04 s GPU @ 2.5 Ghz (Python)
376 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.
377 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.
378 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 .
379 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.
380 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.
381 Geo3D 11.86 % 16.31 % 10.26 % 0.04 s GPU @ 2.5 Ghz (Python)
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382 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.
383 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.
384 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.
385 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.
386 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.
387 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.
388 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.
389 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.
390 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.
391 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.
392 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.
393 WeakM3D 5.66 % 11.82 % 4.08 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
394 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.
395 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.
396 CDTrack3D code 4.06 % 6.58 % 3.26 % 0.0106 s NVIDIA RTX 3090 GPU, i9 10850k CPU
397 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.
398 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.
399 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 .
400 test code 0.09 % 0.04 % 0.11 % 50 s 1 core @ 2.5 Ghz (Python)
401 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.
402 GA-Aug 0.00 % 0.00 % 0.00 % 0.04 s GPU @ 2.5 Ghz (Python)
403 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 54.58 % 63.53 % 50.98 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
D. Le, H. Shi, H. Rezatofighi and J. Cai: PiFeNet: Pillar-Feature Network for Real-Time 3D Pedestrian Detection from Point Cloud. 2021.
2 EQ-PVRCNN 52.81 % 61.73 % 49.87 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
3 ADLAB 52.58 % 58.39 % 49.13 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
4 PV-RCNN++ 52.43 % 59.73 % 48.73 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
5 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.
6 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.
7 CAD 52.20 % 60.23 % 49.54 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
8 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.
9 CasA 51.37 % 57.95 % 49.08 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
10 ISE-RCNN 51.06 % 55.64 % 47.76 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
11 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.
12 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.
13 H^23D R-CNN 50.43 % 58.14 % 46.72 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
14 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.
15 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.
16 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.
17 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.
18 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.
19 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.
20 TBD 49.59 % 58.17 % 47.08 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
21 SAA-PV-RCNN 49.58 % 57.07 % 46.49 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
22 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.
23 TBD 49.56 % 58.10 % 47.05 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
24 AutoAlign 49.27 % 59.28 % 46.14 % 0.1 s 1 core @ 2.5 Ghz (Python)
25 VPN 49.19 % 57.98 % 45.26 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
26 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.
27 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.
28 CAT-Det 48.78 % 57.13 % 45.56 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
29 PE-RCVN 48.72 % 54.09 % 46.71 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
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 EA-M-RCNN(BorderAtt) 48.68 % 57.06 % 45.01 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
32 VCT 48.67 % 54.64 % 46.62 % 0.2 s 1 core @ 2.5 Ghz (Python)
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 (MM) 48.61 % 55.76 % 46.08 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
35 FS-Net
This method makes use of Velodyne laser scans.
48.50 % 54.91 % 46.29 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
36 EPNet++ 48.47 % 56.24 % 45.73 % 0.1 s GPU @ 2.5 Ghz (Python)
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 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.
39 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.
40 USVLab BSAODet (SM) 48.10 % 54.96 % 45.65 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
41 TBD 47.95 % 53.09 % 45.90 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
42 ISE-RCNN-PV 47.85 % 55.63 % 45.80 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
43 tbd 47.84 % 57.69 % 43.99 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
44 TBD 47.77 % 55.61 % 45.17 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
45 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.
46 SGNet 47.29 % 53.84 % 44.10 % 0.09 s GPU @ 2.5 Ghz (Python)
47 TCDVF 47.11 % 55.26 % 44.53 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
48 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.
49 SCIR-Net
This method makes use of Velodyne laser scans.
46.76 % 53.47 % 43.72 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
50 TBD
This method makes use of Velodyne laser scans.
46.74 % 53.44 % 43.76 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
51 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.
52 TBD_IOU1 46.59 % 53.92 % 44.29 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
53 MSADet 46.27 % 55.91 % 43.83 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
54 DGT-Det3D 46.22 % 53.98 % 43.85 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
55 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.
56 TBD_IOU 46.08 % 53.25 % 43.81 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
57 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.
58 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.
59 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.
60 SARFE 45.60 % 51.45 % 43.49 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
61 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.
62 SAA-SECOND 45.47 % 53.95 % 42.77 % 38m s 1 core @ 2.5 Ghz (C/C++)
63 PDV 45.45 % 51.95 % 43.33 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
64 FusionDetv2-v3 45.41 % 51.16 % 42.72 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
65 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.
66 Generalized-SIENet 45.39 % 51.66 % 43.51 % 0.08 s 1 core @ 2.5 Ghz (Python)
67 SA-voxel-centernet code 45.35 % 51.16 % 43.33 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
68 WHUT-iou_ssd code 45.24 % 50.30 % 43.28 % 0.045s 1 core @ 2.5 Ghz (C/C++)
69 sa-voxel-centernet code 45.20 % 51.01 % 43.25 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
70 FPCR-CNN 45.18 % 52.79 % 42.70 % 0.05 s 1 core @ 2.5 Ghz (Python)
71 TPCG 45.17 % 51.44 % 43.03 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
72 IA-SSD (single) 45.07 % 52.73 % 42.75 % 0.013 s 1 core @ 2.5 Ghz (C/C++)
73 Point Image Fusion 45.07 % 50.56 % 42.92 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
74 TBD 44.99 % 50.41 % 42.85 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
75 FPC-RCNN 44.96 % 51.54 % 42.88 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
76 NV-RCNN 44.90 % 52.65 % 41.79 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
77 FusionDetv1 44.85 % 52.42 % 42.56 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
78 Fast VP-RCNN code 44.84 % 51.19 % 42.63 % 0.05 s 1 core @ 3.5 Ghz (C/C++)
79 SRDL 44.84 % 52.42 % 42.56 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
80 FusionDetv2-v2 44.80 % 50.61 % 42.91 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
81 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.
82 FusionDetv2-v5 44.64 % 51.44 % 42.32 % 0.05 s 1 core @ 2.5 Ghz (Java + C/C++)
83 Dune-DCF-e11 44.58 % 52.44 % 41.75 % 1 s 1 core @ 2.5 Ghz (C/C++)
84 Dune-DCF-e09 44.50 % 52.64 % 41.86 % 1 s 1 core @ 2.5 Ghz (C/C++)
85 anonymous code 44.50 % 50.60 % 42.26 % 0.05s 1 core @ >3.5 Ghz (python)
86 FusionDetv2-v4 44.47 % 50.88 % 42.18 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
87 P2V_PCV1 44.33 % 49.29 % 41.71 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
88 MVOD 44.32 % 50.38 % 42.37 % 0.16 s 1 core @ 2.5 Ghz (C/C++)
89 SIF 44.28 % 52.05 % 42.03 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
90 LazyTorch-CP-Infer-O 44.27 % 51.92 % 41.99 % 1 s 1 core @ 2.5 Ghz (C/C++)
91 LazyTorch-CP-Small-P 44.25 % 51.84 % 41.97 % 1 s 1 core @ 2.5 Ghz (C/C++)
92 DDet 44.24 % 50.01 % 42.38 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
93 ST-RCNN
This method makes use of Velodyne laser scans.
44.14 % 49.78 % 41.95 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
94 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.
95 VCRCNN 44.09 % 48.82 % 42.19 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
96 LazyTorch-CP 44.08 % 51.76 % 41.80 % 1 s 1 core @ 2.5 Ghz (C/C++)
97 CrazyTensor-CP 44.06 % 51.25 % 41.50 % 1 s 1 core @ 2.5 Ghz (Python)
98 DSASNet 43.98 % 50.55 % 40.63 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
99 City-CF-fixed 43.86 % 51.92 % 41.33 % 1 s 1 core @ 2.5 Ghz (C/C++)
100 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. arXiv preprint arXiv:2011.01404 2020.
101 Dune-DCF-e15 43.63 % 51.18 % 41.11 % 1 s 1 core @ 2.5 Ghz (C/C++)
102 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.
103 FPC3D_all
This method makes use of Velodyne laser scans.
43.41 % 50.05 % 41.06 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
104 demo 43.29 % 51.78 % 40.79 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
105 FPV-SSD 43.19 % 50.37 % 40.95 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
106 City-CF 42.95 % 49.91 % 40.61 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
107 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.
108 IA-SSD (multi) 42.61 % 51.76 % 40.51 % 0.014 s 1 core @ 2.5 Ghz (C/C++)
109 HS3D code 42.60 % 51.58 % 39.27 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
110 FusionDetv2-baseline 42.53 % 47.08 % 40.71 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
111 YF 42.43 % 50.18 % 39.99 % 0.04 s GPU @ 2.5 Ghz (C/C++)
112 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.
113 TBD 41.70 % 49.81 % 39.43 % TBD GPU @ 2.5 Ghz (Python + C/C++)
114 ASCNet 41.46 % 47.25 % 38.83 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
115 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.
116 CrazyTensor-CF 40.78 % 48.79 % 38.16 % 1 s 1 core @ 2.5 Ghz (C/C++)
117 NV2P-RCNN 40.71 % 46.83 % 38.86 % 0.1 s GPU @ 2.5 Ghz (Python)
118 TBD_BD code 40.41 % 48.27 % 38.45 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
119 DSGN++
This method uses stereo information.
38.92 % 50.26 % 35.12 % 0.4 s NVIDIA Tesla V100
120 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.
121 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.
122 PP-PCdet code 38.21 % 45.14 % 36.04 % 0.01 s 1 core @ 2.5 Ghz (Python)
123 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.
124 Contrastive PP code 37.68 % 44.10 % 35.84 % 0.01 s 1 core @ 2.5 Ghz (Python)
125 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.
126 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.
127 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.
128 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.
129 FusionDetv2-v1 32.24 % 37.46 % 31.61 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
130 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.
131 PointRGBNet 29.32 % 38.07 % 26.94 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
132 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.
133 LIGA-Stereo-old
This method uses stereo information.
28.84 % 36.99 % 25.78 % 0.375 s Titan Xp
134 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.
135 OSE+ 26.02 % 36.60 % 22.89 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
136 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.
137 AEC3D 22.40 % 28.59 % 20.67 % 18 ms GPU @ 2.5 Ghz (Python)
138 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.
139 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.
140 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.
141 BEVC 20.50 % 26.84 % 18.71 % 35ms GPU @ 1.5 Ghz (Python)
142 VN3D 19.12 % 23.51 % 16.26 % 0.02 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
143 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.
144 SOD 15.49 % 23.56 % 13.38 % 0.1 s 1 core @ 2.5 Ghz (Python)
145 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.
146 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.
147 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.
148 EGFN
This method uses stereo information.
13.03 % 17.94 % 11.54 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
149 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) .
150 PS-fld 12.23 % 19.03 % 10.53 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
151 CMKD 12.00 % 18.98 % 10.21 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
152 GCDR 10.92 % 15.65 % 9.86 % 0.28 s 1 core @ 2.5 Ghz (Python)
153 LT-M3OD 10.89 % 16.63 % 9.20 % 0.03 s 1 core @ 2.5 Ghz (Python)
154 ZongmuMono3d code 10.65 % 16.19 % 9.04 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
155 MonoDTR 10.59 % 16.66 % 9.00 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
156 mono3d code 10.41 % 16.66 % 9.22 % TBD TBD
157 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.
158 Lite-FPN-GUPNet 10.08 % 15.73 % 8.52 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
159 gupnet_se 9.85 % 14.65 % 8.32 % 0.03s 1 core @ 2.5 Ghz (C/C++)
160 SwinMono3D 9.82 % 14.55 % 8.31 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
161 HBD 9.66 % 15.26 % 8.17 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
162 SCSTSV-MonoFlex 9.62 % 14.45 % 8.14 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
163 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.
164 SGM3D 9.39 % 15.39 % 8.61 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
Z. Zhou, L. Du, X. Ye, Z. Zou, X. Tan, E. Ding, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection. 2021.
165 MonoGround 9.11 % 13.67 % 7.68 % 0.03 s 1 core @ 2.5 Ghz (Python)
166 K3D 9.06 % 14.56 % 7.59 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
167 MonoFlex 8.91 % 13.26 % 7.38 % 0.03 s 1 core @ 2.5 Ghz (Python)
168 MonoLCD 8.89 % 12.73 % 7.72 % 0.04 s 1 core @ 2.5 Ghz (Python)
169 SAIC_ADC_Mono3D code 8.87 % 13.92 % 7.55 % 50 s GPU @ 2.5 Ghz (Python)
170 MonoEdge 8.87 % 13.33 % 7.50 % 0.05 s GPU @ 2.5 Ghz (Python)
171 vadin-TBD 8.81 % 13.26 % 7.41 % 0.04 s 1 core @ 2.5 Ghz (Python)
172 MonoCon code 8.73 % 13.55 % 7.83 % 0.02 s GPU @ 2.5 Ghz (Python)
T. Xianpeng Liu: Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection. AAAI 2022.
173 MonoDDE 8.41 % 12.38 % 7.16 % 0.04 s 1 core @ 2.5 Ghz (Python)
174 Mix-Teaching-M3D 8.40 % 12.34 % 7.06 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
175 ANM 8.28 % 12.83 % 7.59 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
176 mono3d 7.95 % 11.89 % 6.75 % 0.03 s GPU @ 2.5 Ghz (Python)
177 LPCG-Monoflex 7.92 % 12.11 % 6.61 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
178 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.
179 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.
180 MonoEdge-Rotate 7.53 % 11.62 % 6.79 % 0.05 s GPU @ 2.5 Ghz (Python)
181 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.
182 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.
183 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 .
184 GAC3D++ 6.92 % 10.56 % 5.70 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
185 MK3D 6.78 % 10.15 % 5.71 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
186 MonoGeo 6.77 % 9.54 % 5.83 % 0.05 s 1 core @ 2.5 Ghz (Python)
187 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.
188 RelationNet3D_dla34 code 6.54 % 10.17 % 5.51 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
189 ICCV 6.29 % 9.28 % 5.29 % 0.04 s GPU @ 2.5 Ghz (Python)
190 M3DSSD++ code 6.19 % 9.24 % 5.54 % 0.16s 1 core @ 2.5 Ghz (C/C++)
191 MDNet 6.18 % 9.48 % 5.63 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
192 E2E-DA 6.15 % 10.33 % 5.99 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
193 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.
194 MM 5.63 % 9.20 % 4.78 % 1 s 1 core @ 2.5 Ghz (C/C++)
195 MonoHMOO 5.62 % 8.69 % 5.25 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
196 RelationNet3D_res18 code 5.50 % 8.86 % 5.04 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
197 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.
198 Lite-FPN 4.79 % 7.13 % 4.26 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
199 COF3D 4.78 % 7.20 % 4.55 % 200 s 1 core @ 2.5 Ghz (Python + C/C++)
200 MAOLoss code 4.74 % 6.63 % 4.19 % 0.05 s 1 core @ 2.5 Ghz (Python)
201 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.
202 E2E-DA-Lite (Res18) 4.59 % 5.98 % 3.53 % 0.01 s GPU @ 2.5 Ghz (Python)
203 MP-Mono 4.59 % 6.04 % 3.96 % 0.16 s GPU @ 2.5 Ghz (Python)
204 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.
205 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.
206 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.
207 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 .
208 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.
209 Geo3D 3.95 % 6.18 % 3.66 % 0.04 s GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
210 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.
211 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.
212 KAIST-VDCLab 3.26 % 4.19 % 2.72 % 0.04 s 1 core @ 2.5 Ghz (Python)
213 MonoEF code 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.
214 TBD 2.58 % 3.89 % 2.25 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
215 PPTrans 2.58 % 4.01 % 2.37 % 0.2 s GPU @ 2.5 Ghz (Python)
216 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.
217 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.
218 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.
219 CDTrack3D code 1.49 % 2.49 % 1.46 % 0.0106 s NVIDIA RTX 3090 GPU, i9 10850k CPU
220 EM code 1.25 % 1.18 % 0.89 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
221 EW code 0.81 % 0.79 % 0.74 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
222 mBoW
This method makes use of Velodyne laser scans.
0.00 % 0.00 % 0.00 % 10 s 1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
Table as LaTeX | Only published Methods

Cyclist


Method Setting Code Moderate Easy Hard Runtime Environment
1 CasA 75.74 % 88.99 % 68.47 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
2 ISE-RCNN-PV 75.40 % 86.08 % 68.58 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
3 ISE-RCNN 74.49 % 85.93 % 67.96 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
4 SGNet 73.88 % 88.03 % 66.84 % 0.09 s GPU @ 2.5 Ghz (Python)
5 SARFE 73.84 % 85.63 % 66.31 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
6 Point Image Fusion 73.51 % 85.81 % 66.29 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
7 EQ-PVRCNN 73.30 % 86.25 % 65.49 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
8 CAD 72.87 % 87.09 % 65.78 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
9 FS-Net
This method makes use of Velodyne laser scans.
72.61 % 84.43 % 65.88 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
10 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.
11 anonymous code 72.55 % 85.63 % 65.33 % 0.05s 1 core @ >3.5 Ghz (python)
12 CAT-Det 72.51 % 85.35 % 65.55 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
13 TBD
This method makes use of Velodyne laser scans.
72.24 % 83.58 % 64.65 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
14 SAA-PV-RCNN 72.24 % 84.12 % 64.70 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
15 Fast VP-RCNN code 72.07 % 84.39 % 65.02 % 0.05 s 1 core @ 3.5 Ghz (C/C++)
16 VCRCNN 71.93 % 84.06 % 64.07 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
17 sa-voxel-centernet code 71.90 % 82.76 % 65.41 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
18 PV-RCNN++ 71.86 % 84.60 % 63.84 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
19 TPCG 71.81 % 84.74 % 64.48 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
20 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.
21 SA-voxel-centernet code 71.70 % 82.46 % 64.98 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
22 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.
23 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.
24 IA-SSD (single) 71.44 % 85.91 % 63.41 % 0.013 s 1 core @ 2.5 Ghz (C/C++)
25 PDV 71.31 % 85.54 % 64.40 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
26 Generalized-SIENet 71.21 % 84.64 % 64.61 % 0.08 s 1 core @ 2.5 Ghz (Python)
27 PE-RCVN 71.18 % 85.95 % 64.38 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
28 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.
29 FPC-RCNN 70.93 % 83.75 % 63.47 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
30 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.
31 USVLab BSAODet (MM) 70.85 % 85.28 % 64.09 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
32 DDet 70.76 % 84.81 % 63.98 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
33 AutoAlign 70.55 % 85.98 % 63.39 % 0.1 s 1 core @ 2.5 Ghz (Python)
34 WHUT-iou_ssd code 70.53 % 82.35 % 63.19 % 0.045s 1 core @ 2.5 Ghz (C/C++)
35 TBD 70.48 % 87.26 % 63.98 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
36 MSADet 70.38 % 86.58 % 63.63 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
37 TCDVF 70.28 % 82.85 % 63.54 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
38 USVLab BSAODet (SM) 70.24 % 84.38 % 63.24 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
39 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.
40 TBD 70.09 % 82.60 % 63.39 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
41 TBD 69.97 % 79.75 % 63.46 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
42 VCT 69.96 % 85.63 % 63.59 % 0.2 s 1 core @ 2.5 Ghz (Python)
43 ASCNet 69.48 % 81.01 % 62.42 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
44 ST-RCNN
This method makes use of Velodyne laser scans.
69.42 % 80.69 % 62.63 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
45 MVOD 69.37 % 82.85 % 61.93 % 0.16 s 1 core @ 2.5 Ghz (C/C++)
46 DSASNet 69.12 % 82.32 % 62.68 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
47 JPVNet 69.07 % 83.46 % 62.73 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
48 EA-M-RCNN(BorderAtt) 69.06 % 83.54 % 61.13 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
49 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.
50 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.
51 FusionDetv2-v5 68.84 % 80.42 % 61.90 % 0.05 s 1 core @ 2.5 Ghz (Java + C/C++)
52 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.
53 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.
54 NV2P-RCNN 68.31 % 78.63 % 61.08 % 0.1 s GPU @ 2.5 Ghz (Python)
55 FPV-SSD 68.15 % 80.32 % 60.51 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
56 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.
57 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.
58 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.
59 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.
60 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.
61 DGT-Det3D 67.44 % 80.73 % 60.71 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
62 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.
63 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.
64 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.
65 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.
66 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.
67 SCIR-Net
This method makes use of Velodyne laser scans.
67.17 % 80.95 % 60.55 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
68 FPCR-CNN 67.17 % 82.51 % 60.33 % 0.05 s 1 core @ 2.5 Ghz (Python)
69 TBD_IOU 67.09 % 82.97 % 59.90 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
70 TBD_IOU1 66.95 % 81.77 % 58.78 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
71 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.
72 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.
73 SAA-SECOND 66.71 % 81.56 % 59.60 % 38m s 1 core @ 2.5 Ghz (C/C++)
74 FusionDetv2-v3 66.60 % 80.54 % 58.90 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
75 FusionDetv2-v4 66.54 % 82.50 % 59.78 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
76 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.
77 IA-SSD (multi) 66.29 % 81.30 % 59.58 % 0.014 s 1 core @ 2.5 Ghz (C/C++)
78 FusionDetv2-v2 66.01 % 80.29 % 59.63 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
79 NV-RCNN 66.01 % 81.90 % 59.52 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
80 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.
81 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.
82 VPN 65.60 % 82.20 % 58.96 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
83 P2V_PCV1 65.34 % 78.44 % 58.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
84 TBD 64.87 % 79.46 % 58.67 % TBD GPU @ 2.5 Ghz (Python + C/C++)
85 FPC3D_all
This method makes use of Velodyne laser scans.
64.66 % 78.81 % 58.08 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
86 TBD_BD code 64.60 % 82.19 % 58.01 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
87 demo 64.55 % 78.63 % 58.57 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
88 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. arXiv preprint arXiv:2011.01404 2020.
89 FusionDetv1 64.53 % 79.62 % 57.91 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
90 Dune-DCF-e11 64.52 % 82.14 % 57.40 % 1 s 1 core @ 2.5 Ghz (C/C++)
91 SRDL 64.52 % 79.64 % 57.90 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
92 Dune-DCF-e15 64.42 % 81.10 % 57.35 % 1 s 1 core @ 2.5 Ghz (C/C++)
93 City-CF-fixed 64.39 % 81.11 % 57.74 % 1 s 1 core @ 2.5 Ghz (C/C++)
94 City-CF 64.25 % 81.33 % 57.01 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
95 SIF 64.13 % 79.32 % 57.38 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
96 FusionDetv2-baseline 63.77 % 76.64 % 57.01 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
97 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.
98 HS3D code 63.56 % 78.53 % 58.03 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
99 YF 63.54 % 75.92 % 57.59 % 0.04 s GPU @ 2.5 Ghz (C/C++)
100 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.
101 EPNet++ 62.94 % 78.57 % 56.62 % 0.1 s GPU @ 2.5 Ghz (Python)
102 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.
103 Dune-DCF-e09 62.23 % 77.53 % 55.46 % 1 s 1 core @ 2.5 Ghz (C/C++)
104 Contrastive PP code 62.10 % 75.71 % 54.84 % 0.01 s 1 core @ 2.5 Ghz (Python)
105 CrazyTensor-CF 61.95 % 80.59 % 55.16 % 1 s 1 core @ 2.5 Ghz (C/C++)
106 PP-PCdet code 61.81 % 75.56 % 55.06 % 0.01 s 1 core @ 2.5 Ghz (Python)
107 LazyTorch-CP-Infer-O 61.40 % 76.40 % 54.73 % 1 s 1 core @ 2.5 Ghz (C/C++)
108 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.
109 LazyTorch-CP 61.25 % 76.38 % 54.68 % 1 s 1 core @ 2.5 Ghz (C/C++)
110 LazyTorch-CP-Small-P 61.07 % 76.37 % 54.73 % 1 s 1 core @ 2.5 Ghz (C/C++)
111 TBD 60.58 % 76.98 % 53.49 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
112 TBD 60.58 % 76.98 % 53.49 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
113 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.
114 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.
115 CrazyTensor-CP 59.54 % 75.40 % 53.21 % 1 s 1 core @ 2.5 Ghz (Python)
116 PiFeNet 57.85 % 74.97 % 50.99 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
D. Le, H. Shi, H. Rezatofighi and J. Cai: PiFeNet: Pillar-Feature Network for Real-Time 3D Pedestrian Detection from Point Cloud. 2021.
117 PointRGBNet 57.59 % 73.09 % 51.78 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
118 tbd 57.15 % 72.89 % 50.29 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
119 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.
120 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.
121 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.
122 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.
123 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.
124 DSGN++
This method uses stereo information.
49.37 % 68.29 % 43.79 % 0.4 s NVIDIA Tesla V100
125 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.
126 FusionDetv2-v1 47.75 % 60.34 % 43.53 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
127 LIGA-Stereo-old
This method uses stereo information.
42.42 % 60.23 % 37.03 % 0.375 s Titan Xp
128 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.
129 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.
130 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.
131 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.
132 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.
133 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.
134 SOD 28.81 % 44.90 % 24.37 % 0.1 s 1 core @ 2.5 Ghz (Python)
135 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.
136 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.
137 AEC3D 26.17 % 36.57 % 25.21 % 18 ms GPU @ 2.5 Ghz (Python)
138 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.
139 VN3D 23.77 % 31.62 % 21.74 % 0.02 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
140 OSE+ 23.55 % 38.05 % 20.46 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
141 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.
142 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.
143 BEVC 16.74 % 25.98 % 16.02 % 35ms GPU @ 1.5 Ghz (Python)
144 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.
145 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.
146 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.
147 EGFN
This method uses stereo information.
9.02 % 15.78 % 7.96 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
148 CMKD 7.39 % 12.38 % 6.65 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
149 PS-fld 7.29 % 12.80 % 6.05 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
150 CMKD 6.84 % 10.90 % 5.72 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
151 mono3d code 6.52 % 11.40 % 5.19 % TBD TBD
152 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.
153 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.
154 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) .
155 LT-M3OD 5.53 % 9.17 % 4.84 % 0.03 s 1 core @ 2.5 Ghz (Python)
156 RelationNet3D_dla34 code 5.40 % 9.63 % 4.60 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
157 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.
158 Mix-Teaching-M3D 5.36 % 8.56 % 4.62 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
159 TBD 5.33 % 9.58 % 4.62 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
160 E2E-DA 4.99 % 8.31 % 4.08 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
161 LPCG-Monoflex 4.90 % 8.14 % 3.86 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
162 MDNet 4.74 % 8.10 % 4.19 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
163 Lite-FPN-GUPNet 4.70 % 7.67 % 4.61 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
164 ZongmuMono3d code 4.63 % 8.72 % 3.94 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
165 SAIC_ADC_Mono3D code 4.55 % 7.90 % 3.73 % 50 s GPU @ 2.5 Ghz (Python)
166 SCSTSV-MonoFlex 4.50 % 7.40 % 3.66 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
167 MAOLoss code 4.49 % 7.28 % 3.60 % 0.05 s 1 core @ 2.5 Ghz (Python)
168 MonoDDE 4.36 % 6.68 % 3.76 % 0.04 s 1 core @ 2.5 Ghz (Python)
169 E2E-DA-Lite (Res18) 4.31 % 8.03 % 3.20 % 0.01 s GPU @ 2.5 Ghz (Python)
170 MonoDTR 4.11 % 5.84 % 3.48 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
171 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.
172 vadin-TBD 4.09 % 6.81 % 3.78 % 0.04 s 1 core @ 2.5 Ghz (Python)
173 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.
174 MonoGeo 3.87 % 5.93 % 3.42 % 0.05 s 1 core @ 2.5 Ghz (Python)
175 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.
176 SGM3D 3.63 % 7.05 % 3.33 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
Z. Zhou, L. Du, X. Ye, Z. Zou, X. Tan, E. Ding, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection. 2021.
177 K3D 3.39 % 6.16 % 3.13 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
178 MonoLCD 3.33 % 5.27 % 2.90 % 0.04 s 1 core @ 2.5 Ghz (Python)
179 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.
180 ICCV 3.32 % 6.59 % 3.13 % 0.04 s GPU @ 2.5 Ghz (Python)
181 mono3d 3.30 % 5.84 % 2.68 % 0.03 s GPU @ 2.5 Ghz (Python)
182 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 .
183 ANM 3.26 % 4.90 % 2.87 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
184 MonoGround 3.22 % 4.81 % 2.75 % 0.03 s 1 core @ 2.5 Ghz (Python)
185 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.
186 M3DSSD++ code 3.14 % 5.73 % 3.03 % 0.16s 1 core @ 2.5 Ghz (C/C++)
187 MK3D 3.05 % 5.47 % 2.40 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
188 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.
189 MonoEdge-Rotate 3.01 % 5.36 % 2.83 % 0.05 s GPU @ 2.5 Ghz (Python)
190 RelationNet3D_res18 code 2.91 % 5.49 % 2.48 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
191 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.
192 SwinMono3D 2.78 % 4.50 % 2.53 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
193 MonoCon code 2.68 % 3.87 % 2.24 % 0.02 s GPU @ 2.5 Ghz (Python)
T. Xianpeng Liu: Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection. AAAI 2022.
194 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.
195 gupnet_se 2.61 % 4.38 % 2.34 % 0.03s 1 core @ 2.5 Ghz (C/C++)
196 MonoEdge 2.54 % 4.02 % 2.43 % 0.05 s GPU @ 2.5 Ghz (Python)
197 GAC3D++ 2.53 % 4.69 % 2.48 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
198 MonoFlex 2.51 % 4.36 % 2.38 % 0.03 s 1 core @ 2.5 Ghz (Python)
199 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.
200 KAIST-VDCLab 2.34 % 3.46 % 2.02 % 0.04 s 1 core @ 2.5 Ghz (Python)
201 Geo3D 2.21 % 4.16 % 2.18 % 0.04 s GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
202 GCDR 2.11 % 3.74 % 1.99 % 0.28 s 1 core @ 2.5 Ghz (Python)
203 PPTrans 2.07 % 3.44 % 1.77 % 0.2 s GPU @ 2.5 Ghz (Python)
204 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.
205 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.
206 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.
207 MP-Mono 1.74 % 2.78 % 1.86 % 0.16 s GPU @ 2.5 Ghz (Python)
208 MonoHMOO 1.65 % 1.91 % 1.75 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
209 HBD 1.64 % 3.15 % 1.76 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
210 COF3D 1.60 % 2.70 % 1.55 % 200 s 1 core @ 2.5 Ghz (Python + C/C++)
211 MonoEF code 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.
212 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 .
213 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.
214 Lite-FPN 0.44 % 0.52 % 0.27 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
215 MM 0.40 % 0.71 % 0.35 % 1 s 1 core @ 2.5 Ghz (C/C++)
216 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.
217 CDTrack3D code 0.07 % 0.07 % 0.05 % 0.0106 s NVIDIA RTX 3090 GPU, i9 10850k CPU
218 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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