3D Object Detection Evaluation 2017


The 3D object detection benchmark consists of 7481 training images and 7518 test images as well as the corresponding point clouds, comprising a total of 80.256 labeled objects. For evaluation, we compute precision-recall curves. To rank the methods we compute average precision. We require that all methods use the same parameter set for all test pairs. Our development kit provides details about the data format as well as MATLAB / C++ utility functions for reading and writing the label files.

We evaluate 3D object detection performance using the PASCAL criteria also used for 2D object detection. Far objects are thus filtered based on their bounding box height in the image plane. As only objects also appearing on the image plane are labeled, objects in don't car areas do not count as false positives. We note that the evaluation does not take care of ignoring detections that are not visible on the image plane — these detections might give rise to false positives. For cars we require an 3D bounding box overlap of 70%, while for pedestrians and cyclists we require a 3D bounding box overlap of 50%. Difficulties are defined as follows:

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

All methods are ranked based on the moderately difficult results.

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

Car


Method Setting Code Moderate Easy Hard Runtime Environment
1 TED 85.28 % 91.61 % 80.68 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
2 LIVOX_Det
This method makes use of Velodyne laser scans.
84.94 % 91.72 % 80.10 % n/a s 1 core @ 2.5 Ghz (Python + C/C++)
3 SFD 84.76 % 91.73 % 77.92 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
X. Wu, L. Peng, H. Yang, L. Xie, C. Huang, C. Deng, H. Liu and D. Cai: Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completion. CVPR 2022.
4 Anonymous 84.76 % 91.99 % 79.81 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
5 CasA++ 84.04 % 90.68 % 79.69 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
6 Anonymous 83.96 % 90.83 % 77.47 % n/a s 1 core @ 2.5 Ghz (Python + C/C++)
7 DGDNH 83.88 % 90.69 % 79.50 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
8 Anonymous 83.51 % 89.08 % 78.94 % n/a s 1 core @ 2.5 Ghz (C/C++)
9 GraR-VoI 83.27 % 91.89 % 77.78 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
10 GLENet-VR 83.23 % 91.67 % 78.43 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
11 VPFNet 83.21 % 91.02 % 78.20 % 0.06 s 2 cores @ 2.5 Ghz (Python)
H. Zhu, J. Deng, Y. Zhang, J. Ji, Q. Mao, H. Li and Y. Zhang: VPFNet: Improving 3D Object Detection with Virtual Point based LiDAR and Stereo Data Fusion. 2021.
12 GraR-Po 83.18 % 91.79 % 77.98 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
13 CasA 83.06 % 91.58 % 80.08 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
14 BtcDet
This method makes use of Velodyne laser scans.
code 82.86 % 90.64 % 78.09 % 0.09 s GPU @ 2.5 Ghz (Python + C/C++)
Q. Xu, Y. Zhong and U. Neumann: Behind the Curtain: Learning Occluded Shapes for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2022.
15 Anonymous 82.79 % 91.30 % 78.07 % n/a s 1 core @ 2.5 Ghz (C/C++)
16 GraR-Vo 82.77 % 91.29 % 77.20 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
17 3SNet 82.70 % 89.41 % 78.03 % 0.07 s GPU @ 2.5 Ghz (Python)
18 PE-RCVN 82.69 % 91.51 % 77.75 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
19 CAD 82.68 % 88.96 % 77.91 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
20 SPG_mini
This method makes use of Velodyne laser scans.
code 82.66 % 90.64 % 77.91 % 0.09 s GPU @ 2.5 Ghz (Python)
Q. Xu, Y. Zhou, W. Wang, C. Qi and D. Anguelov: SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation. Proceedings of the IEEE conference on computer vision and pattern recognition (ICCV) 2021.
21 HCPVF 82.63 % 89.34 % 77.72 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
22 DSASNet 82.63 % 89.48 % 77.94 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
23 SA3DNet
This method uses stereo information.
This method makes use of Velodyne laser scans.
82.57 % 90.49 % 77.88 % 0.05 s GPU @ 2.5 Ghz (Python)
24 SE-SSD
This method makes use of Velodyne laser scans.
code 82.54 % 91.49 % 77.15 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, L. Jiang and C. Fu: SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud. CVPR 2021.
25 TF3D
This method makes use of Velodyne laser scans.
82.46 % 89.10 % 77.78 % 0.1 s 2 cores @ 3.0 Ghz (Python)
26 DVF-V 82.45 % 89.40 % 77.56 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
A. Mahmoud, J. Hu and S. Waslander: Dense Voxel Fusion for 3D Object Detection. 2022.
27 GraR-Pi 82.42 % 90.94 % 77.00 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
28 DVF-PV 82.40 % 90.99 % 77.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
A. Mahmoud, J. Hu and S. Waslander: Dense Voxel Fusion for 3D Object Detection. 2022.
29 Anonymous 82.30 % 90.88 % 76.89 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
30 PVT-SSD 82.29 % 90.65 % 76.85 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
31 Focals Conv code 82.28 % 90.55 % 77.59 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
Y. Chen, Y. Li, X. Zhang, J. Sun and J. Jia: Focal Sparse Convolutional Networks for 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.
32 CLOCs code 82.28 % 89.16 % 77.23 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection . 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
33 TBD
This method makes use of Velodyne laser scans.
82.23 % 88.76 % 77.48 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
34 CityBrainLab 82.22 % 90.54 % 77.19 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
35 SASA
This method makes use of Velodyne laser scans.
code 82.16 % 88.76 % 77.16 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
C. Chen, Z. Chen, J. Zhang and D. Tao: SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object Detection. arXiv preprint arXiv:2201.01976 2022.
36 ImpDet 82.14 % 88.39 % 76.98 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
37 SPG
This method makes use of Velodyne laser scans.
code 82.13 % 90.50 % 78.90 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Xu, Y. Zhou, W. Wang, C. Qi and D. Anguelov: SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation. Proceedings of the IEEE conference on computer vision and pattern recognition (ICCV) 2021.
38 VoTr-TSD code 82.09 % 89.90 % 79.14 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
J. Mao, Y. Xue, M. Niu, H. Bai, J. Feng, X. Liang, H. Xu and C. Xu: Voxel Transformer for 3D Object Detection. ICCV 2021.
39 TBD 82.09 % 89.50 % 79.29 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
40 Pyramid R-CNN 82.08 % 88.39 % 77.49 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
J. Mao, M. Niu, H. Bai, X. Liang, H. Xu and C. Xu: Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection. ICCV 2021.
41 FS-Net
This method makes use of Velodyne laser scans.
82.07 % 88.68 % 77.42 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
42 VoxSeT code 82.06 % 88.53 % 77.46 % 33 ms 1 core @ 2.5 Ghz (C/C++)
C. He, R. Li, S. Li and L. Zhang: Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds. CVPR 2022.
43 SRIF-RCNN 82.04 % 88.45 % 77.54 % 0.0947 s 1 core @ 2.5 Ghz (C/C++)
X. Li and D. Kong: SRIF-RCNN: Sparsely Represented Inputs Fusion of Different Sensors for 3D Object Detection. Applied Intelligence 2022.
44 LGNet 82.02 % 90.65 % 77.34 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
45 EQ-PVRCNN code 82.01 % 90.13 % 77.53 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, L. Jiang, Y. Sun, B. Schiele and J. Jia: A Unified Query-based Paradigm for Point Cloud Understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.
46 anonymous 81.99 % 88.82 % 77.26 % 0.09 s GPU @ 2.5 Ghz (Python)
47 Anonymous 81.96 % 89.90 % 77.20 % 0.1s 1 core @ 2.5 Ghz (C/C++)
48 EPNet++ 81.96 % 91.37 % 76.71 % 0.1 s GPU @ 2.5 Ghz (Python)
Z. Liu, H. tengteng, B. Li, X. Chen, X. Wang and X. Bai: EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object Detection. arXiv preprint arXiv:2112.11088 2021.
49 HMFI code 81.93 % 88.90 % 77.30 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
50 PV-RCNN++ code 81.88 % 90.14 % 77.15 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
51 GLENet 81.86 % 89.87 % 77.32 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
52 PDV code 81.86 % 90.43 % 77.36 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Hu, T. Kuai and S. Waslander: Point Density-Aware Voxels for LiDAR 3D Object Detection. CVPR 2022.
53 SGNet 81.85 % 88.83 % 77.47 % 0.09 s GPU @ 2.5 Ghz (Python)
54 Anonymous 81.85 % 89.96 % 76.51 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
55 ST-RCNN
This method makes use of Velodyne laser scans.
81.84 % 90.50 % 77.22 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
56 ST-RCNN (SNLW-RCNN)
This method makes use of Velodyne laser scans.
code 81.84 % 90.50 % 77.22 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
57 ISE-RCNN 81.83 % 89.12 % 77.29 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
58 FV2P v2 81.81 % 88.17 % 77.43 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
59 Anonymous 81.80 % 89.86 % 77.26 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
60 SqueezeRCNN 81.80 % 88.72 % 77.10 % 0.08 s 1 core @ 2.5 Ghz (Python)
61 CityBrainLab-CT3D code 81.77 % 87.83 % 77.16 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Sheng, S. Cai, Y. Liu, B. Deng, J. Huang, X. Hua and M. Zhao: Improving 3D Object Detection with Channel- wise Transformer. ICCV 2021.
62 USVLab BSAODet 81.74 % 88.89 % 77.14 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
63 JPVNet 81.73 % 88.66 % 76.94 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
64 TBD 81.73 % 89.48 % 79.05 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
65 M3DeTR code 81.73 % 90.28 % 76.96 % n/a s GPU @ 1.0 Ghz (Python)
T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis and D. Manocha: M3DeTR: Multi-representation, Multi- scale, Mutual-relation 3D Object Detection with Transformers. 2021.
66 SIENet code 81.71 % 88.22 % 77.22 % 0.08 s 1 core @ 2.5 Ghz (Python)
Z. Li, Y. Yao, Z. Quan, W. Yang and J. Xie: SIENet: Spatial Information Enhancement Network for 3D Object Detection from Point Cloud. 2021.
67 TBD 81.71 % 88.46 % 76.63 % 0.1 s 1 core @ 2.5 Ghz (Python)
68 DCCA 81.70 % 88.42 % 77.18 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
69 VCRCNN 81.68 % 90.52 % 77.26 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
70 TBD 81.68 % 87.93 % 76.92 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
71 Voxel R-CNN code 81.62 % 90.90 % 77.06 % 0.04 s GPU @ 3.0 Ghz (C/C++)
J. Deng, S. Shi, P. Li, W. Zhou, Y. Zhang and H. Li: Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection . AAAI 2021.
72 BADet code 81.61 % 89.28 % 76.58 % 0.14 s 1 core @ 2.5 Ghz (C/C++)
R. Qian, X. Lai and X. Li: BADet: Boundary-Aware 3D Object Detection from Point Clouds. Pattern Recognition 2022.
73 SARFE 81.59 % 88.88 % 76.74 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
74 FromVoxelToPoint code 81.58 % 88.53 % 77.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to- Point Decoder. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
75 H^23D R-CNN code 81.55 % 90.43 % 77.22 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
J. Deng, W. Zhou, Y. Zhang and H. Li: From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2021.
76 Anonymous 81.55 % 87.90 % 77.03 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
77 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
code 81.46 % 88.25 % 76.96 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
78 P2V-RCNN 81.45 % 88.34 % 77.20 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.
79 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 81.43 % 90.25 % 76.82 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
80 TPCG 81.41 % 89.16 % 76.90 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
81 DDet 81.38 % 89.63 % 78.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
82 XView 81.35 % 89.21 % 76.87 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
83 ISE-RCNN-PV 81.34 % 88.05 % 76.99 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
84 RangeRCNN
This method makes use of Velodyne laser scans.
81.33 % 88.47 % 77.09 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, M. Zhang, Z. Zhang, X. Zhao and S. Pu: RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representation. arXiv preprint arXiv:2009.00206 2020.
85 CAT-Det 81.32 % 89.87 % 76.68 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Zhang, J. Chen and D. Huang: CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection. CVPR 2022.
86 IKT3D
This method makes use of Velodyne laser scans.
80.97 % 87.84 % 76.43 % 0.05 s 1 core @ 2.5 Ghz (Python)
87 VPFNet code 80.97 % 88.51 % 76.74 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
C. Wang, H. Chen and L. Fu: VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection. 2021.
88 GVNet-V2 80.96 % 87.57 % 76.32 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
89 VueronNet code 80.96 % 90.06 % 73.72 % 0.06 s 1 core @ 2.0 Ghz (Python)
90 DKDet 80.94 % 87.66 % 76.23 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
91 FusionDetv2-v4 80.93 % 87.75 % 76.12 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
92 sa-voxel-centernet code 80.77 % 87.39 % 76.45 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
93 Sem-Aug 80.77 % 89.41 % 75.90 % 0.08 s GPU @ 2.5 Ghz (Python)
94 CM3DV 80.77 % 87.28 % 76.51 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
95 Associate-3Ddet_v2 80.77 % 91.53 % 75.23 % 0.04 s 1 core @ 2.5 Ghz (Python)
96 CSVoxel-RCNN 80.73 % 87.44 % 76.18 % 0.03 s GPU @ 1.0 Ghz (Python)
97 FusionDetv2-v3 80.70 % 88.05 % 76.10 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
98 StructuralIF 80.69 % 87.15 % 76.26 % 0.02 s 8 cores @ 2.5 Ghz (Python)
J. Pei An: Deep structural information fusion for 3D object detection on LiDAR-camera system. Accepted in CVIU 2021.
99 SPVB-SSD 80.68 % 86.99 % 76.23 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
100 CLOCs_PVCas code 80.67 % 88.94 % 77.15 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection . 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
101 GVNet code 80.52 % 87.63 % 75.99 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
102 SVGA-Net 80.47 % 87.33 % 75.91 % 0.03s 1 core @ 2.5 Ghz (Python + C/C++)
Q. He, Z. Wang, H. Zeng, Y. Zeng and Y. Liu: SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds. AAAI 2022.
103 SC-Voxel-RCNN 80.46 % 86.94 % 75.85 % 0.12 s GPU @ 1.0 Ghz (Python)
104 TBD 80.44 % 88.83 % 73.18 % 0.1 s 1 core @ 2.5 Ghz (Python)
105 Sem-Aug v1 code 80.40 % 88.92 % 77.37 % 0.04 s GPU @ 3.5 Ghz (Python)
106 SRDL 80.38 % 87.73 % 76.27 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
107 Fast-CLOCs 80.35 % 89.10 % 76.99 % 0.1 s GPU @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: Fast-CLOCs: Fast Camera-LiDAR Object Candidates Fusion for 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022.
108 FPV-SSD 80.34 % 87.72 % 75.40 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
109 SPANet 80.34 % 91.05 % 74.89 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
Y. Ye: SPANet: Spatial and Part-Aware Aggregation Network for 3D Object Detection. Pacific Rim International Conference on Artificial Intelligence 2021.
110 IA-SSD (single) code 80.32 % 88.87 % 75.10 % 0.013 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
111 TBD 80.29 % 87.37 % 73.05 % 0.1 s 1 core @ 2.5 Ghz (Python)
112 CIA-SSD
This method makes use of Velodyne laser scans.
code 80.28 % 89.59 % 72.87 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, S. Chen, L. Jiang and C. Fu: CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud. AAAI 2021.
113 FusionDetv1 80.28 % 87.45 % 76.21 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
114 DVF 80.21 % 88.97 % 75.22 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
115 VCT 80.19 % 89.12 % 77.19 % 0.2 s 1 core @ 2.5 Ghz (Python)
116 PVTr 80.16 % 86.90 % 75.98 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
117 IA-SSD (multi) code 80.13 % 88.34 % 75.04 % 0.014 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
118 TBD 80.12 % 88.30 % 75.29 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
119 EBM3DOD code 80.12 % 91.05 % 72.78 % 0.12 s 1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.
120 TBD 80.12 % 86.50 % 75.72 % 0.06 s GPU @ 2.5 Ghz (Python)
121 ATT_SSD 80.11 % 88.94 % 74.91 % 0.01 s 1 core @ 2.5 Ghz (Python)
122 TBD code 80.06 % 88.75 % 74.84 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
123 3D-CVF at SPA
This method makes use of Velodyne laser scans.
80.05 % 89.20 % 73.11 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
J. Yoo, Y. Kim, J. Kim and J. Choi: 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection. ECCV 2020.
124 TBD 80.02 % 88.45 % 74.85 % TBD GPU @ 2.5 Ghz (Python + C/C++)
125 MVOD 80.01 % 88.53 % 77.24 % 0.16 s 1 core @ 2.5 Ghz (C/C++)
126 SIF 79.88 % 86.84 % 75.89 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
127 RangeIoUDet
This method makes use of Velodyne laser scans.
79.80 % 88.60 % 76.76 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, Z. Zhang, M. Zhang, X. Zhao and S. Pu: RangeIoUDet: Range Image Based Real-Time 3D Object Detector Optimized by Intersection Over Union. CVPR 2021.
128 SA-SSD code 79.79 % 88.75 % 74.16 % 0.04 s 1 core @ 2.5 Ghz (Python)
C. He, H. Zeng, J. Huang, X. Hua and L. Zhang: Structure Aware Single-stage 3D Object Detection from Point Cloud. CVPR 2020.
129 DGT-Det3D 79.78 % 86.76 % 75.73 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
130 KpNet 79.75 % 88.92 % 72.17 % 0.42 s 1 core @ 2.5 Ghz (C/C++)
131 KpNet 79.74 % 88.88 % 72.13 % 42 s 1 core @ 2.5 Ghz (C/C++)
132 STD code 79.71 % 87.95 % 75.09 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
133 MGAF-3DSSD code 79.68 % 88.16 % 72.39 % 0.1 s 1 core @ 2.5 Ghz (Python)
J. Li, H. Dai, L. Shao and Y. Ding: Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
134 TBD 79.67 % 88.33 % 74.30 % 0.03 s GPU @ 2.5 Ghz (Python)
135 mbdf-netv1 code 79.66 % 90.19 % 74.76 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
136 PTA-RCNN 79.61 % 87.84 % 74.43 % 0.08 s 1 core @ 2.5 Ghz (Python)
137 Struc info fusion II 79.59 % 88.97 % 72.51 % 0.05 s GPU @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.
138 3DSSD code 79.57 % 88.36 % 74.55 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
139 EBM3DOD baseline code 79.52 % 88.80 % 72.30 % 0.05 s 1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.
140 Struc info fusion I 79.49 % 88.70 % 74.25 % 0.05 s 1 core @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.
141 Point-GNN
This method makes use of Velodyne laser scans.
code 79.47 % 88.33 % 72.29 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
142 SECOND 79.46 % 87.44 % 73.97 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
143 AGS-SSD[la] 79.39 % 88.13 % 74.11 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
144 SSL-PointGNN code 79.36 % 87.78 % 74.15 % 0.56 s GPU @ 1.5 Ghz (Python)
E. Erçelik, E. Yurtsever, M. Liu, Z. Yang, H. Zhang, P. Topçam, M. Listl, Y. Çaylı and A. Knoll: 3D Object Detection with a Self-supervised Lidar Scene Flow Backbone. arXiv preprint arXiv:2205.00705 2022.
145 NV-RCNN 79.32 % 87.58 % 74.74 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
146 USVLab BSAODet (S) 79.30 % 88.02 % 76.05 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
147 EPNet code 79.28 % 89.81 % 74.59 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
T. Huang, Z. Liu, X. Chen and X. Bai: EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection. ECCV 2020.
148 DVFENet 79.18 % 86.20 % 74.58 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection. Neurocomputing 2021.
149 ITCA-SSD code 79.11 % 88.66 % 72.12 % 0.05 s 1 core @ 2.5 Ghz (Python)
150 Faraway-Frustum
This method makes use of Velodyne laser scans.
code 79.05 % 87.45 % 76.14 % 0.1 s GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-frustum: Dealing with lidar sparsity for 3D object detection using fusion. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) 2021.
151 3D IoU-Net 79.03 % 87.96 % 72.78 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, S. Krylov, Y. Ding and L. Shao: 3D IoU-Net: IoU Guided 3D Object Detector for Point Clouds. arXiv preprint arXiv:2004.04962 2020.
152 SERCNN
This method makes use of Velodyne laser scans.
78.96 % 87.74 % 74.30 % 0.1 s 1 core @ 2.5 Ghz (Python)
D. Zhou, J. Fang, X. Song, L. Liu, J. Yin, Y. Dai, H. Li and R. Yang: Joint 3D Instance Segmentation and Object Detection for Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020.
153 NV2P-RCNN 78.92 % 87.36 % 74.16 % 0.1 s GPU @ 2.5 Ghz (Python)
154 MSADet 78.81 % 88.31 % 73.82 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
155 CSNet8306 code 78.74 % 89.57 % 72.09 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
156 MVAF-Net code 78.71 % 87.87 % 75.48 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
G. Wang, B. Tian, Y. Zhang, L. Chen, D. Cao and J. Wu: Multi-View Adaptive Fusion Network for 3D Object Detection. arXiv preprint arXiv:2011.00652 2020.
157 CenterFuse 78.70 % 86.92 % 73.87 % 0.059 sec/frame 2 x V100
158 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 78.49 % 87.81 % 73.51 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
159 CLOCs_SecCas 78.45 % 86.38 % 72.45 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
160 CSNet 78.42 % 87.39 % 71.75 % 0.1 s 1 core @ 2.5 Ghz (Python)
161 FusionDetv2-v2 78.42 % 86.59 % 73.87 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
162 Patches - EMP
This method makes use of Velodyne laser scans.
78.41 % 89.84 % 73.15 % 0.5 s GPU @ 2.5 Ghz (Python)
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
163 HotSpotNet 78.31 % 87.60 % 73.34 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
164 FusionDetv2-v5 78.30 % 86.94 % 73.44 % 0.05 s 1 core @ 2.5 Ghz (Java + C/C++)
165 Sem-Aug-PointRCNN++ 78.06 % 86.69 % 73.85 % 0.1 s 8 cores @ 3.0 Ghz (Python)
166 CF-cd-io-tv 78.05 % 86.38 % 73.29 % 1 s 1 core @ 2.5 Ghz (C/C++)
167 3D-VDNet 78.05 % 87.13 % 72.90 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
168 VPN 77.93 % 85.02 % 72.97 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
169 CenterNet3D 77.90 % 86.20 % 73.03 % 0.04 s GPU @ 1.5 Ghz (Python)
G. Wang, B. Tian, Y. Ai, T. Xu, L. Chen and D. Cao: CenterNet3D:An Anchor free Object Detector for Autonomous Driving. 2020.
170 TBD 77.85 % 86.46 % 72.39 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
171 CF-ctdep-tv-ta 77.75 % 85.27 % 74.83 % 1 s 1 core @ 2.5 Ghz (C/C++)
172 IoU-2B 77.74 % 85.65 % 71.30 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
173 Reprod-Two-Branch 77.73 % 85.60 % 74.24 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
174 CVFNet 77.70 % 88.75 % 71.95 % 28.1ms 1 core @ 2.5 Ghz (Python)
175 AutoAlign 77.58 % 86.84 % 73.23 % 0.1 s 1 core @ 2.5 Ghz (Python)
176 TBD 77.56 % 85.38 % 72.32 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
177 CFF-tv 77.53 % 85.01 % 74.01 % 1 s 1 core @ 2.5 Ghz (C/C++)
178 TCDVF 77.49 % 85.55 % 72.29 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
179 CFF-ep25 77.48 % 84.84 % 72.96 % 1 s 1 core @ 2.5 Ghz (C/C++)
180 UberATG-MMF
This method makes use of Velodyne laser scans.
77.43 % 88.40 % 70.22 % 0.08 s GPU @ 2.5 Ghz (Python)
M. Liang*, B. Yang*, Y. Chen, R. Hu and R. Urtasun: Multi-Task Multi-Sensor Fusion for 3D Object Detection. CVPR 2019.
181 CFF-tv-v2 77.41 % 85.18 % 72.81 % 1 s 1 core @ 2.5 Ghz (C/C++)
182 Associate-3Ddet code 77.40 % 85.99 % 70.53 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
L. Du, X. Ye, X. Tan, J. Feng, Z. Xu, E. Ding and S. Wen: Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection. The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
183 Fast Point R-CNN
This method makes use of Velodyne laser scans.
77.40 % 85.29 % 70.24 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, S. Liu, X. Shen and J. Jia: Fast Point R-CNN. Proceedings of the IEEE international conference on computer vision (ICCV) 2019.
184 cff-tv-v2-ep25 77.38 % 84.44 % 72.82 % 1 s 1 core @ 2.5 Ghz (C/C++)
185 3D_att
This method makes use of Velodyne laser scans.
77.27 % 88.46 % 70.11 % 0.17 s GPU @ 2.5 Ghz (Python)
186 cp-tv-kp-io-sc 77.25 % 85.41 % 72.42 % 1 s 1 core @ 2.5 Ghz (C/C++)
187 Patches
This method makes use of Velodyne laser scans.
77.20 % 88.67 % 71.82 % 0.15 s GPU @ 2.0 Ghz
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
188 CF-ctdep-tv 77.12 % 84.71 % 74.02 % 1 s 1 core @ 2.5 Ghz (C/C++)
189 CF-base-tv 77.09 % 83.72 % 73.71 % 1 s 1 core @ 2.5 Ghz (C/C++)
190 Sem-Aug-PointRCNN code 77.04 % 82.75 % 73.21 % 0.1 s GPU @ 3.5 Ghz (C/C++)
191 KeyFuse2B 76.95 % 84.86 % 72.53 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
192 KeyPoint-IoUHead 76.81 % 84.61 % 72.16 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
193 HRI-VoxelFPN 76.70 % 85.64 % 69.44 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
H. Kuang, B. Wang, J. An, M. Zhang and Z. Zhang: Voxel-FPN:multi-scale voxel feature aggregation in 3D object detection from point clouds. sensors 2020.
194 DKAnet 76.70 % 84.57 % 71.54 % 0.05 s 1 core @ 2.0 Ghz (Python)
195 cff-tv-t 76.68 % 85.58 % 70.69 % 1 s 1 core @ 2.5 Ghz (C/C++)
196 SARPNET 76.64 % 85.63 % 71.31 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. Ye, H. Chen, C. Zhang, X. Hao and Z. Zhang: SARPNET: Shape Attention Regional Proposal Network for LiDAR-based 3D Object Detection. Neurocomputing 2019.
197 Anonymous 76.60 % 85.29 % 71.77 % 1 1 core @ 2.5 Ghz (Python)
198 DTFI 76.59 % 85.29 % 71.78 % 0.03 s 1 core @ 2.5 Ghz (Python)
199 CSNet8299 code 76.55 % 86.49 % 71.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
200 3D IoU Loss
This method makes use of Velodyne laser scans.
76.50 % 86.16 % 71.39 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
D. Zhou, J. Fang, X. Song, C. Guan, J. Yin, Y. Dai and R. Yang: IoU Loss for 2D/3D Object Detection. International Conference on 3D Vision (3DV) 2019.
201 HNet-3DSSD
This method makes use of Velodyne laser scans.
code 76.48 % 86.06 % 69.71 % 0.05 s GPU @ 2.5 Ghz (Python)
202 F-ConvNet
This method makes use of Velodyne laser scans.
code 76.39 % 87.36 % 66.69 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
203 variance_point 76.27 % 87.44 % 72.03 % 0.05 s 1 core @ 2.5 Ghz (Python)
204 SegVoxelNet 76.13 % 86.04 % 70.76 % 0.04 s 1 core @ 2.5 Ghz (Python)
H. Yi, S. Shi, M. Ding, J. Sun, K. Xu, H. Zhou, Z. Wang, S. Li and G. Wang: SegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloud. ICRA 2020.
205 S-AT GCN 76.04 % 83.20 % 71.17 % 0.02 s GPU @ 2.0 Ghz (Python)
L. Wang, C. Wang, X. Zhang, T. Lan and J. Li: S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection. CoRR 2021.
206 KPP3D code 76.00 % 86.66 % 71.07 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
207 TANet code 75.94 % 84.39 % 68.82 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
208 CF-base-train 75.93 % 83.47 % 71.22 % 1 s 1 core @ 2.5 Ghz (C/C++)
209 cp-tv-kp 75.85 % 83.50 % 72.54 % 1 s 1 core @ 2.5 Ghz (C/C++)
210 PointRGCN 75.73 % 85.97 % 70.60 % 0.26 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
211 cp-tv 75.67 % 83.31 % 72.24 % 1 s 1 core @ 2.5 Ghz (C/C++)
212 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 75.64 % 86.96 % 70.70 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
213 Self-Calib Conv 75.59 % 83.54 % 71.96 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
214 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 75.43 % 86.10 % 68.88 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
215 CF-ctdep-train 75.43 % 83.03 % 71.31 % 1 s 1 core @ 2.5 Ghz (C/C++)
216 R-GCN 75.26 % 83.42 % 68.73 % 0.16 s GPU @ 2.5 Ghz (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
217 epBRM
This method makes use of Velodyne laser scans.
code 75.15 % 85.00 % 69.84 % 0.1 s GPU @ >3.5 Ghz (Python + C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
218 MAFF-Net(DAF-Pillar) 75.04 % 85.52 % 67.61 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Z. Zhang, Z. Liang, M. Zhang, X. Zhao, Y. Ming, T. Wenming and S. Pu: MAFF-Net: Filter False Positive for 3D Vehicle Detection with Multi-modal Adaptive Feature Fusion. arXiv preprint arXiv:2009.10945 2020.
219 sscl-20p 74.82 % 86.06 % 69.87 % 0.02 s 1 core @ 2.5 Ghz (Python)
220 PI-RCNN 74.82 % 84.37 % 70.03 % 0.1 s 1 core @ 2.5 Ghz (Python)
L. Xie, C. Xiang, Z. Yu, G. Xu, Z. Yang, D. Cai and X. He: PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module. AAAI 2020 : The Thirty-Fourth AAAI Conference on Artificial Intelligence 2020.
221 MF 74.70 % 83.42 % 66.51 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
222 LazyTorch-CP-Infer-O 74.57 % 81.82 % 70.24 % 1 s 1 core @ 2.5 Ghz (C/C++)
223 LazyTorch-CP-Small-P 74.44 % 81.73 % 70.14 % 1 s 1 core @ 2.5 Ghz (C/C++)
224 City-CF-fixed 74.37 % 83.23 % 69.65 % 1 s 1 core @ 2.5 Ghz (C/C++)
225 PointPillars
This method makes use of Velodyne laser scans.
code 74.31 % 82.58 % 68.99 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
226 ARPNET 74.04 % 84.69 % 68.64 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
227 CenterPoint (pcdet) 73.96 % 81.17 % 69.48 % 0.051 sec/frame 2 x V100
228 PC-CNN-V2
This method makes use of Velodyne laser scans.
73.79 % 85.57 % 65.65 % 0.5 s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Du, M. Ang, S. Karaman and D. Rus: A General Pipeline for 3D Detection of Vehicles. 2018 IEEE International Conference on Robotics and Automation (ICRA) 2018.
229 C-GCN 73.62 % 83.49 % 67.01 % 0.147 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
230 3DBN
This method makes use of Velodyne laser scans.
73.53 % 83.77 % 66.23 % 0.13s 1080Ti (Python+C/C++)
X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.
231 Dune-DCF-e11 73.51 % 80.89 % 68.89 % 1 s 1 core @ 2.5 Ghz (C/C++)
232 CrazyTensor-CP 73.50 % 81.04 % 69.87 % 1 s 1 core @ 2.5 Ghz (Python)
233 PointRGBNet 73.49 % 83.99 % 68.56 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
P. Xie Desheng: Real-time Detection of 3D Objects Based on Multi-Sensor Information Fusion. Automotive Engineering 2022.
234 City-CF 73.48 % 80.85 % 69.07 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
235 RangeDet code 73.44 % 80.53 % 67.28 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
236 PSM_stereo 73.43 % 81.28 % 66.98 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
237 Dune-DCF-e15 73.29 % 80.34 % 68.54 % 1 s 1 core @ 2.5 Ghz (C/C++)
238 SCNet
This method makes use of Velodyne laser scans.
73.17 % 83.34 % 67.93 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
239 Dune-DCF-e09 73.15 % 80.40 % 68.57 % 1 s 1 core @ 2.5 Ghz (C/C++)
240 AFTD 73.12 % 82.71 % 68.09 % 1 s 1 core @ 2.5 Ghz (Python + C/C++)
241 PP-PCdet code 73.07 % 83.32 % 68.18 % 0.01 s 1 core @ 2.5 Ghz (Python)
242 PFF3D
This method makes use of Velodyne laser scans.
code 72.93 % 81.11 % 67.24 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
243 CrazyTensor-CF 72.92 % 79.87 % 68.41 % 1 s 1 core @ 2.5 Ghz (C/C++)
244 DASS 72.31 % 81.85 % 65.99 % 0.09 s 1 core @ 2.0 Ghz (Python)
O. Unal, L. Van Gool and D. Dai: Improving Point Cloud Semantic Segmentation by Learning 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2021.
245 HS3D code 72.25 % 83.57 % 67.49 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
246 TBD_BD code 72.16 % 83.36 % 66.87 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
247 TBD 71.94 % 83.20 % 66.83 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
248 AVOD-FPN
This method makes use of Velodyne laser scans.
code 71.76 % 83.07 % 65.73 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
249 PointPainting
This method makes use of Velodyne laser scans.
71.70 % 82.11 % 67.08 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
250 Contrastive PP code 71.64 % 84.80 % 66.49 % 0.01 s 1 core @ 2.5 Ghz (Python)
251 new_stereo 70.79 % 80.05 % 66.04 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
252 WS3D
This method makes use of Velodyne laser scans.
70.59 % 80.99 % 64.23 % 0.1 s GPU @ 2.5 Ghz (Python)
Q. Meng, W. Wang, T. Zhou, J. Shen, L. Van Gool and D. Dai: Weakly Supervised 3D Object Detection from Lidar Point Cloud. 2020.
253 F-PointNet
This method makes use of Velodyne laser scans.
code 69.79 % 82.19 % 60.59 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
254 FusionDetv2-baseline 68.87 % 79.05 % 63.68 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
255 UberATG-ContFuse
This method makes use of Velodyne laser scans.
68.78 % 83.68 % 61.67 % 0.06 s GPU @ 2.5 Ghz (Python)
M. Liang, B. Yang, S. Wang and R. Urtasun: Deep Continuous Fusion for Multi-Sensor 3D Object Detection. ECCV 2018.
256 MLOD
This method makes use of Velodyne laser scans.
code 67.76 % 77.24 % 62.05 % 0.12 s GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
257 DSGN++
This method uses stereo information.
code 67.37 % 83.21 % 59.91 % 0.2 s GeForce RTX 2080Ti
Y. Chen, S. Huang, S. Liu, B. Yu and J. Jia: DSGN++: Exploiting Visual-Spatial Relation for Stereo-based 3D Detectors. arXiv preprint arXiv:2204.03039 2022.
258 DisposalNet
This method uses stereo information.
67.33 % 77.55 % 62.44 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
259 Anonymous 66.97 % 83.77 % 58.41 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
260 AVOD
This method makes use of Velodyne laser scans.
code 66.47 % 76.39 % 60.23 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
261 StereoDistill 66.39 % 81.66 % 57.39 % 0.4 s 1 core @ 2.5 Ghz (Python)
262 FusionDetv2-v1 65.65 % 75.21 % 60.65 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
263 MMLAB LIGA-Stereo
This method uses stereo information.
code 64.66 % 81.39 % 57.22 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
264 BirdNet+
This method makes use of Velodyne laser scans.
code 64.04 % 76.15 % 59.79 % 0.11 s Titan Xp (PyTorch)
A. Barrera, J. Beltrán, C. Guindel, J. Iglesias and F. García: BirdNet+: Two-Stage 3D Object Detection in LiDAR through a Sparsity-Invariant Bird’s Eye View. IEEE Access 2021.
265 CZY 63.68 % 77.56 % 57.82 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
266 MV3D
This method makes use of Velodyne laser scans.
63.63 % 74.97 % 54.00 % 0.36 s GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
267 SD3DOD 62.00 % 76.09 % 55.46 % 0.04 s GPU @ 2.5 Ghz (Python)
268 AEC3D 61.99 % 72.16 % 57.11 % 18 ms GPU @ 2.5 Ghz (Python)
269 VN3D 61.41 % 72.37 % 56.86 % 0.02 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
270 SNVC
This method uses stereo information.
code 61.34 % 78.54 % 54.23 % 1 s GPU @ 1.0 Ghz (Python)
S. Li, Z. Liu, Z. Shen and K. Cheng: Stereo Neural Vernier Caliper. Proceedings of the AAAI Conference on Artificial Intelligence 2022.
271 RCD 60.56 % 70.54 % 55.58 % 0.1 s GPU @ 2.5 Ghz (Python)
A. Bewley, P. Sun, T. Mensink, D. Anguelov and C. Sminchisescu: Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection. Conference on Robot Learning (CoRL) 2020.
272 Anonymous 58.57 % 77.81 % 52.13 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
273 A3DODWTDA
This method makes use of Velodyne laser scans.
code 56.82 % 62.84 % 48.12 % 0.08 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
274 FD 56.40 % 73.05 % 52.25 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
275 PS++ code 55.28 % 74.80 % 46.70 % PS++ s 1 core @ 2.5 Ghz (C/C++)
276 PL++ (SDN+GDC)
This method uses stereo information.
This method makes use of Velodyne laser scans.
code 54.88 % 68.38 % 49.16 % 0.6 s GPU @ 2.5 Ghz (C/C++)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.
277 MV3D (LIDAR)
This method makes use of Velodyne laser scans.
54.54 % 68.35 % 49.16 % 0.24 s GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
278 CDN
This method uses stereo information.
code 54.22 % 74.52 % 46.36 % 0.6 s GPU @ 2.5 Ghz (Python)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems (NeurIPS) 2020.
279 CG-Stereo
This method uses stereo information.
53.58 % 74.39 % 46.50 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
280 PS code 52.88 % 74.41 % 44.38 % PS s 1 core @ 2.5 Ghz (C/C++)
281 UPF_3D
This method uses stereo information.
52.83 % 78.24 % 46.12 % 0.29 s 1 core @ 2.5 Ghz (Python)
282 DSGN
This method uses stereo information.
code 52.18 % 73.50 % 45.14 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
283 BirdNet+ (legacy)
This method makes use of Velodyne laser scans.
code 51.85 % 70.14 % 50.03 % 0.1 s Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird’s Eye View. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
284 ppt 50.41 % 54.19 % 45.14 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
285 Complexer-YOLO
This method makes use of Velodyne laser scans.
47.34 % 55.93 % 42.60 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
286 ESGN
This method uses stereo information.
46.39 % 65.80 % 38.42 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
287 Disp R-CNN (velo)
This method uses stereo information.
code 45.78 % 68.21 % 37.73 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
288 CDN-PL++
This method uses stereo information.
44.86 % 64.31 % 38.11 % 0.4 s GPU @ 2.5 Ghz (C/C++)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems 2020.
289 Disp R-CNN
This method uses stereo information.
code 43.27 % 67.02 % 36.43 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
290 Pseudo-LiDAR++
This method uses stereo information.
code 42.43 % 61.11 % 36.99 % 0.4 s GPU @ 2.5 Ghz (Python)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.
291 ART 42.42 % 63.38 % 36.44 % 20ms s 1 core @ 2.5 Ghz (C/C++)
292 YOLOStereo3D
This method uses stereo information.
code 41.25 % 65.68 % 30.42 % 0.1 s GPU 1080Ti
Y. Liu, L. Wang and M. Liu: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
293 BEVC 40.72 % 50.05 % 36.42 % 35ms GPU @ 1.5 Ghz (Python)
294 RT3D-GMP
This method uses stereo information.
38.76 % 45.79 % 30.00 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
295 ZoomNet
This method uses stereo information.
code 38.64 % 55.98 % 30.97 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
L. Z. Xu: ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2020.
296 OC Stereo
This method uses stereo information.
code 37.60 % 55.15 % 30.25 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
297 Pseudo-Lidar
This method uses stereo information.
code 34.05 % 54.53 % 28.25 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Wang, W. Chao, D. Garg, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
298 Stereo CenterNet
This method uses stereo information.
31.30 % 49.94 % 25.62 % 0.04 s GPU @ 2.5 Ghz (Python)
Y. Shi, Y. Guo, Z. Mi and X. Li: Stereo CenterNet-based 3D object detection for autonomous driving. Neurocomputing 2022.
299 Stereo R-CNN
This method uses stereo information.
code 30.23 % 47.58 % 23.72 % 0.3 s GPU @ 2.5 Ghz (Python)
P. Li, X. Chen and S. Shen: Stereo R-CNN based 3D Object Detection for Autonomous Driving. CVPR 2019.
300 BirdNet
This method makes use of Velodyne laser scans.
27.26 % 40.99 % 25.32 % 0.11 s Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
301 SparseLiDAR_fusion 26.23 % 36.85 % 21.45 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
302 GCDR 23.92 % 34.89 % 19.59 % 0.28 s 1 core @ 2.5 Ghz (Python)
303 VMDet_boost 23.79 % 33.89 % 20.33 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
304 RT3DStereo
This method uses stereo information.
23.28 % 29.90 % 18.96 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
305 Digging_M3D 21.24 % 29.15 % 19.18 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
306 VMDet 20.95 % 30.51 % 17.85 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
307 Anonymous 20.23 % 28.29 % 17.55 % 40 s 1 core @ 2.5 Ghz (C/C++)
308 SARM3D 19.70 % 25.20 % 17.35 % 0.03 s GPU @ 2.5 Ghz (Python)
309 RT3D
This method makes use of Velodyne laser scans.
19.14 % 23.74 % 18.86 % 0.09 s GPU @ 1.8Ghz
Y. Zeng, Y. Hu, S. Liu, J. Ye, Y. Han, X. Li and N. Sun: RT3D: Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving. IEEE Robotics and Automation Letters 2018.
310 MonoInsight 19.04 % 27.71 % 16.03 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
311 CMKD* 18.69 % 28.55 % 16.77 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
312 Mix-Teaching 18.54 % 26.89 % 15.79 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
313 StereoFENet
This method uses stereo information.
18.41 % 29.14 % 14.20 % 0.15 s 1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
314 anonymity 18.00 % 28.10 % 15.60 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
315 SCSTSV-MonoFlex 17.91 % 27.38 % 15.58 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
316 MoGDE 17.88 % 27.07 % 15.66 % 0.03 s GPU @ 2.5 Ghz (Python)
317 LPCG-Monoflex 17.80 % 25.56 % 15.38 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
318 PS-fld code 17.74 % 23.74 % 15.14 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
Y. Chen, H. Dai and Y. Ding: Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
319 MonoDDE 17.14 % 24.93 % 15.10 % 0.04 s 1 core @ 2.5 Ghz (Python)
Z. Li, Z. Qu, Y. Zhou, J. Liu, H. Wang and L. Jiang: Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection. CVPR 2022.
320 Anonymous 17.08 % 24.43 % 15.25 % 40 s 1 core @ 2.5 Ghz (C/C++)
321 OPA-3D code 17.05 % 24.60 % 14.25 % 0.04 s 1 core @ 3.5 Ghz (Python)
322 Mobile Stereo R-CNN
This method uses stereo information.
17.04 % 26.97 % 13.26 % 1.8 s NVIDIA Jetson TX2
M. Hussein, M. Khalil and B. Abdullah: 3D Object Detection using Mobile Stereo R- CNN on Nvidia Jetson TX2. International Conference on Advanced Engineering, Technology and Applications (ICAETA) 2021.
323 anonymity 16.99 % 27.20 % 15.08 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
324 DD3D code 16.87 % 23.19 % 14.36 % n/a s 1 core @ 2.5 Ghz (C/C++)
D. Park, R. Ambrus, V. Guizilini, J. Li and A. Gaidon: Is Pseudo-Lidar needed for Monocular 3D Object detection?. IEEE/CVF International Conference on Computer Vision (ICCV) .
325 Shape-Aware 16.52 % 23.84 % 13.88 % 0.05 s 1 core @ 2.5 Ghz (Python)
326 MonoCon code 16.46 % 22.50 % 13.95 % 0.02 s GPU @ 2.5 Ghz (Python)
T. Xianpeng Liu: Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection. AAAI 2022.
327 DID-M3D 16.29 % 24.40 % 13.75 % 0.04 s 1 core @ 2.5 Ghz (Python)
328 MonoDETR code 16.26 % 24.52 % 13.93 % 0.04 s 1 core @ 2.5 Ghz (Python)
R. Zhang, H. Qiu, T. Wang, X. Xu, Z. Guo, Y. Qiao, P. Gao and H. Li: MonoDETR: Depth-aware Transformer for Monocular 3D Object Detection. arXiv preprint arXiv:2203.13310 2022.
329 Lite-FPN-GUPNet 16.20 % 23.58 % 13.56 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
330 gupnet_se 16.10 % 23.62 % 13.41 % 0.03s 1 core @ 2.5 Ghz (C/C++)
331 zongmuDistill 16.08 % 25.11 % 13.69 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
332 MonoDistill 16.03 % 22.97 % 13.60 % 0.04 s 1 core @ 2.5 Ghz (Python)
333 MDNet 16.01 % 24.59 % 13.49 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
334 DDS code 15.90 % 23.81 % 13.21 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
335 mono3d code 15.73 % 23.96 % 13.35 % TBD TBD
336 monopd code 15.72 % 23.51 % 13.07 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
337 OBMO_GUPNet 15.70 % 22.71 % 13.23 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
338 GPENet code 15.44 % 22.41 % 12.84 % 0.02 s GPU @ 2.5 Ghz (Python)
339 MonoDTR 15.39 % 21.99 % 12.73 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
K. Huang, T. Wu, H. Su and W. Hsu: MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer. CVPR 2022.
340 mono3d 15.26 % 23.41 % 12.80 % 0.03 s GPU @ 2.5 Ghz (Python)
341 HBD 15.17 % 21.71 % 13.06 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
342 EW code 15.13 % 21.16 % 12.81 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
343 ZongmuMono3d code 15.08 % 23.79 % 13.25 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
344 GUPNet code 15.02 % 22.26 % 13.12 % NA s 1 core @ 2.5 Ghz (Python + C/C++)
Y. Lu, X. Ma, L. Yang, T. Zhang, Y. Liu, Q. Chu, J. Yan and W. Ouyang: Geometry Uncertainty Projection Network for Monocular 3D Object Detection. arXiv preprint arXiv:2107.13774 2021.
345 HomoLoss(monoflex) code 14.94 % 21.75 % 13.07 % 0.04 s 1 core @ 2.5 Ghz (Python)
J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homography Loss for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
346 Anonymous 14.87 % 23.93 % 12.45 % 40 s 1 core @ 2.5 Ghz (C/C++)
347 Anonymous 14.84 % 22.73 % 13.08 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
348 M3DSSD++ code 14.75 % 23.61 % 11.80 % 0.16s 1 core @ 2.5 Ghz (C/C++)
349 MonoFlex 14.73 % 22.29 % 12.77 % 0.03 s 1 core @ 2.5 Ghz (Python)
350 SGM3D 14.65 % 22.46 % 12.97 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
Z. Zhou, L. Du, X. Ye, Z. Zou, X. Tan, E. Ding, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection. 2021.
351 Anonymous code 14.56 % 20.65 % 11.92 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
352 SAIC_ADC_Mono3D code 14.54 % 18.98 % 12.86 % 50 s GPU @ 2.5 Ghz (Python)
353 CA3D 14.49 % 20.89 % 12.19 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
354 MonoEdge 14.47 % 21.08 % 12.73 % 0.05 s GPU @ 2.5 Ghz (Python)
355 MDSNet 14.46 % 24.30 % 11.12 % 0.07 s 1 core @ 2.5 Ghz (Python)
356 MonoGround 14.36 % 21.37 % 12.62 % 0.03 s 1 core @ 2.5 Ghz (Python)
357 MonoEdge-RCNN 14.35 % 19.74 % 11.94 % 0.05 s 1 core @ 2.5 Ghz (Python)
358 ANM 14.33 % 20.84 % 11.61 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
359 DLE code 14.33 % 24.23 % 10.30 % 0.06 s NVIDIA Tesla V100
C. Liu, S. Gu, L. Gool and R. Timofte: Deep Line Encoding for Monocular 3D Object Detection and Depth Prediction. Proceedings of the British Machine Vision Conference (BMVC) 2021.
360 SwinMono3D 14.24 % 22.61 % 10.11 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
361 AutoShape code 14.17 % 22.47 % 11.36 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
Z. Liu, D. Zhou, F. Lu, J. Fang and L. Zhang: AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.
362 MonoEdge-Rotate 14.13 % 21.60 % 12.27 % 0.05 s GPU @ 2.5 Ghz (Python)
363 EM code 14.00 % 22.93 % 11.26 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
364 MAOLoss code 14.00 % 20.05 % 11.81 % 0.05 s 1 core @ 2.5 Ghz (Python)
365 E2E-DA 13.97 % 19.73 % 11.82 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
366 MonoFlex 13.89 % 19.94 % 12.07 % 0.03 s GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.
367 MonoEF 13.87 % 21.29 % 11.71 % 0.03 s 1 core @ 2.5 Ghz (Python)
Y. Zhou, Y. He, H. Zhu, C. Wang, H. Li and Q. Jiang: Monocular 3D Object Detection: An Extrinsic Parameter Free Approach. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
368 MonoAug 13.85 % 20.06 % 11.43 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
369 DEPT 13.83 % 20.43 % 11.66 % 0.03 s 1 core @ 2.5 Ghz (Python)
370 K3D 13.80 % 20.04 % 11.67 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
371 none 13.79 % 18.84 % 11.52 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
372 DFR-Net 13.63 % 19.40 % 10.35 % 0.18 s 1080 Ti (Pytorch)
Z. Zou, X. Ye, L. Du, X. Cheng, X. Tan, L. Zhang, J. Feng, X. Xue and E. Ding: The devil is in the task: Exploiting reciprocal appearance-localization features for monocular 3d object detection . ICCV 2021.
373 MonoFar 13.52 % 18.08 % 11.58 % 0.04 s 1 core @ 2.5 Ghz (Python)
374 CaDDN code 13.41 % 19.17 % 11.46 % 0.63 s GPU @ 2.5 Ghz (Python)
C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.
375 PCT code 13.37 % 21.00 % 11.31 % 0.045 s 1 core @ 2.5 Ghz (C/C++)
L. Wang, L. Zhang, Y. Zhu, Z. Zhang, T. He, M. Li and X. Xue: Progressive Coordinate Transforms for Monocular 3D Object Detection. NeurIPS 2021.
376 Ground-Aware code 13.25 % 21.65 % 9.91 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
Y. Liu, Y. Yuan and M. Liu: Ground-aware Monocular 3D Object Detection for Autonomous Driving. IEEE Robotics and Automation Letters 2021.
377 MK3D 13.19 % 20.48 % 11.10 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
378 Aug3D-RPN 12.99 % 17.82 % 9.78 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth. 2021.
379 HomoLoss(imvoxelnet) code 12.99 % 20.10 % 10.50 % 0.20 s 1 core @ 2.5 Ghz (Python)
J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homogrpahy Loss for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
380 DDMP-3D 12.78 % 19.71 % 9.80 % 0.18 s 1 core @ 2.5 Ghz (Python)
L. Wang, L. Du, X. Ye, Y. Fu, G. Guo, X. Xue, J. Feng and L. Zhang: Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection. CVPR 2020.
381 RetinaMono 12.73 % 19.41 % 10.45 % 0.02 s 1 core @ 2.5 Ghz (Python)
382 Kinematic3D code 12.72 % 19.07 % 9.17 % 0.12 s 1 core @ 1.5 Ghz (C/C++)
G. Brazil, G. Pons-Moll, X. Liu and B. Schiele: Kinematic 3D Object Detection in Monocular Video. ECCV 2020 .
383 M3DGAF 12.66 % 19.48 % 10.99 % 0.07 s 1 core @ 2.5 Ghz (Python)
384 MonoRCNN code 12.65 % 18.36 % 10.03 % 0.07 s GPU @ 2.5 Ghz (Python)
X. Shi, Q. Ye, X. Chen, C. Chen, Z. Chen and T. Kim: Geometry-based Distance Decomposition for Monocular 3D Object Detection. ICCV 2021.
385 MP-Mono 12.37 % 17.89 % 9.58 % 0.16 s GPU @ 2.5 Ghz (Python)
386 GrooMeD-NMS code 12.32 % 18.10 % 9.65 % 0.12 s 1 core @ 2.5 Ghz (Python)
A. Kumar, G. Brazil and X. Liu: GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection. CVPR 2021.
387 MonoRUn code 12.30 % 19.65 % 10.58 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
388 monodle code 12.26 % 17.23 % 10.29 % 0.04 s GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for Monocular 3D Object Detection. CVPR 2021 .
389 LT-M3OD 12.26 % 18.15 % 10.05 % 0.03 s 1 core @ 2.5 Ghz (Python)
390 PPTrans 12.06 % 19.79 % 10.48 % 0.2 s GPU @ 2.5 Ghz (Python)
391 YoloMono3D code 12.06 % 18.28 % 8.42 % 0.05 s GPU @ 2.5 Ghz (Python)
Y. Liu, L. Wang and L. Ming: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
392 IAFA 12.01 % 17.81 % 10.61 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
D. Zhou, X. Song, Y. Dai, J. Yin, F. Lu, M. Liao, J. Fang and L. Zhang: IAFA: Instance-Aware Feature Aggregation for 3D Object Detection from a Single Image. Proceedings of the Asian Conference on Computer Vision 2020.
393 GAC3D 12.00 % 17.75 % 9.15 % 0.25 s 1 core @ 2.5 Ghz (Python)
M. Bui, D. Ngo, H. Pham and D. Nguyen: GAC3D: improving monocular 3D object detection with ground-guide model and adaptive convolution. 2021.
394 CMAN 11.87 % 17.77 % 9.16 % 0.15 s 1 core @ 2.5 Ghz (Python)
395 PGD-FCOS3D code 11.76 % 19.05 % 9.39 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
T. Wang, X. Zhu, J. Pang and D. Lin: Probabilistic and Geometric Depth: Detecting Objects in Perspective. Conference on Robot Learning (CoRL) 2021.
396 D4LCN code 11.72 % 16.65 % 9.51 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
397 MonoAug 11.47 % 16.40 % 9.26 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
398 KM3D code 11.45 % 16.73 % 9.92 % 0.03 s 1 core @ 2.5 Ghz (Python)
P. Li: Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training. 2020.
399 RefinedMPL 11.14 % 18.09 % 8.94 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
400 PatchNet code 11.12 % 15.68 % 10.17 % 0.4 s 1 core @ 2.5 Ghz (C/C++)
X. Ma, S. Liu, Z. Xia, H. Zhang, X. Zeng and W. Ouyang: Rethinking Pseudo-LiDAR Representation. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
401 ImVoxelNet code 10.97 % 17.15 % 9.15 % 0.2 s GPU @ 2.5 Ghz (Python)
D. Rukhovich, A. Vorontsova and A. Konushin: ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection. arXiv preprint arXiv:2106.01178 2021.
402 COF3D 10.91 % 17.86 % 8.20 % 200 s 1 core @ 2.5 Ghz (Python + C/C++)
403 MM 10.74 % 15.80 % 8.64 % 1 s 1 core @ 2.5 Ghz (C/C++)
404 AM3D 10.74 % 16.50 % 9.52 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
X. Ma, Z. Wang, H. Li, P. Zhang, W. Ouyang and X. Fan: Accurate Monocular Object Detection via Color- Embedded 3D Reconstruction for Autonomous Driving. Proceedings of the IEEE international Conference on Computer Vision (ICCV) 2019.
405 Lite-FPN 10.64 % 15.32 % 8.59 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
406 Keypoint-3D 10.42 % 15.97 % 7.91 % 14 s 1 core @ 2.5 Ghz (C/C++)
407 RTM3D code 10.34 % 14.41 % 8.77 % 0.05 s GPU @ 1.0 Ghz (Python)
P. Li, H. Zhao, P. Liu and F. Cao: RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving. 2020.
408 E2E-DA-Lite (Res18) 10.32 % 15.56 % 8.89 % 0.01 s GPU @ 2.5 Ghz (Python)
409 MonoPair 9.99 % 13.04 % 8.65 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
410 Neighbor-Vote 9.90 % 15.57 % 8.89 % 0.1 s GPU @ 2.5 Ghz (Python)
X. Chu, J. Deng, Y. Li, Z. Yuan, Y. Zhang, J. Ji and Y. Zhang: Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance Voting. ACM MM 2021.
411 SMOKE code 9.76 % 14.03 % 7.84 % 0.03 s GPU @ 2.5 Ghz (Python)
Z. Liu, Z. Wu and R. Tóth: SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation. 2020.
412 M3D-RPN code 9.71 % 14.76 % 7.42 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
413 QD-3DT
This is an online method (no batch processing).
code 9.33 % 12.81 % 7.86 % 0.03 s GPU @ 2.5 Ghz (Python)
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.
414 TopNet-HighRes
This method makes use of Velodyne laser scans.
9.28 % 12.67 % 7.95 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
415 MonoCInIS 7.94 % 15.82 % 6.68 % 0,13 s GPU @ 2.5 Ghz (C/C++)
J. Heylen, M. De Wolf, B. Dawagne, M. Proesmans, L. Van Gool, W. Abbeloos, H. Abdelkawy and D. Reino: MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.
416 Geo3D 7.70 % 11.52 % 6.80 % 0.04 s GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
417 SS3D 7.68 % 10.78 % 6.51 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
418 MonoCInIS 7.66 % 15.21 % 6.24 % 0,14 s GPU @ 2.5 Ghz (Python)
J. Heylen, M. De Wolf, B. Dawagne, M. Proesmans, L. Van Gool, W. Abbeloos, H. Abdelkawy and D. Reino: MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.
419 Mono3D_PLiDAR code 7.50 % 10.76 % 6.10 % 0.1 s NVIDIA GeForce 1080 (pytorch)
X. Weng and K. Kitani: Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud. arXiv:1903.09847 2019.
420 MonoPSR code 7.25 % 10.76 % 5.85 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
421 Decoupled-3D 7.02 % 11.08 % 5.63 % 0.08 s GPU @ 2.5 Ghz (C/C++)
Y. Cai, B. Li, Z. Jiao, H. Li, X. Zeng and X. Wang: Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation. AAAI 2020.
422 VoxelJones code 6.35 % 7.39 % 5.80 % .18 s 1 core @ 2.5 Ghz (Python + C/C++)
M. Motro and J. Ghosh: Vehicular Multi-object Tracking with Persistent Detector Failures. arXiv preprint arXiv:1907.11306 2019.
423 MonoGRNet code 5.74 % 9.61 % 4.25 % 0.04s NVIDIA P40
Z. Qin, J. Wang and Y. Lu: MonoGRNet: A Geometric Reasoning Network for 3D Object Localization. The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) 2019.
424 A3DODWTDA (image) code 5.27 % 6.88 % 4.45 % 0.8 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
425 MonoFENet 5.14 % 8.35 % 4.10 % 0.15 s 1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
426 TLNet (Stereo)
This method uses stereo information.
code 4.37 % 7.64 % 3.74 % 0.1 s 1 core @ 2.5 Ghz (Python)
Z. Qin, J. Wang and Y. Lu: Triangulation Learning Network: from Monocular to Stereo 3D Object Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
427 CSoR
This method makes use of Velodyne laser scans.
4.06 % 5.61 % 3.17 % 3.5 s 4 cores @ >3.5 Ghz (Python + C/C++)
L. Plotkin: PyDriver: Entwicklung eines Frameworks für räumliche Detektion und Klassifikation von Objekten in Fahrzeugumgebung. 2015.
428 Shift R-CNN (mono) code 3.87 % 6.88 % 2.83 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
429 MVRA + I-FRCNN+ 3.27 % 5.19 % 2.49 % 0.18 s GPU @ 2.5 Ghz (Python)
H. Choi, H. Kang and Y. Hyun: Multi-View Reprojection Architecture for Orientation Estimation. The IEEE International Conference on Computer Vision (ICCV) Workshops 2019.
430 SparVox3D 3.20 % 5.27 % 2.56 % 0.05 s GPU @ 2.0 Ghz (Python)
E. Balatkan and F. Kıraç: Improving Regression Performance on Monocular 3D Object Detection Using Bin-Mixing and Sparse Voxel Data. 2021 6th International Conference on Computer Science and Engineering (UBMK) 2021.
431 TopNet-UncEst
This method makes use of Velodyne laser scans.
3.02 % 3.24 % 2.26 % 0.09 s NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
432 GS3D 2.90 % 4.47 % 2.47 % 2 s 1 core @ 2.5 Ghz (C/C++)
B. Li, W. Ouyang, L. Sheng, X. Zeng and X. Wang: GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
433 3D-GCK 2.52 % 3.27 % 2.11 % 24 ms Tesla V100
N. Gählert, J. Wan, N. Jourdan, J. Finkbeiner, U. Franke and J. Denzler: Single-Shot 3D Detection of Vehicles from Monocular RGB Images via Geometrically Constrained Keypoints in Real-Time. 2020 IEEE Intelligent Vehicles Symposium (IV) 2020.
434 WeakM3D code 2.26 % 5.03 % 1.63 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
L. Peng, S. Yan, B. Wu, Z. Yang, X. He and D. Cai: WeakM3D: Towards Weakly Supervised Monocular 3D Object Detection. ICLR 2022.
435 ROI-10D 2.02 % 4.32 % 1.46 % 0.2 s GPU @ 3.5 Ghz (Python)
F. Manhardt, W. Kehl and A. Gaidon: ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape. Computer Vision and Pattern Recognition (CVPR) 2019.
436 CDTrack3D code 1.92 % 3.20 % 1.63 % 0.0106 s NVIDIA RTX 3090 GPU, i9 10850k CPU
437 FQNet 1.51 % 2.77 % 1.01 % 0.5 s 1 core @ 2.5 Ghz (Python)
L. Liu, J. Lu, C. Xu, Q. Tian and J. Zhou: Deep Fitting Degree Scoring Network for Monocular 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
438 3D-SSMFCNN code 1.41 % 1.88 % 1.11 % 0.1 s GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.
439 test code 0.03 % 0.01 % 0.03 % 50 s 1 core @ 2.5 Ghz (Python)
440 MonoDET code 0.01 % 0.03 % 0.01 % 0.04 s 1 core @ 2.5 Ghz (Python)
441 mBoW
This method makes use of Velodyne laser scans.
0.00 % 0.00 % 0.00 % 10 s 1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
Table as LaTeX | Only published Methods

Pedestrian


Method Setting Code Moderate Easy Hard Runtime Environment
1 CasA++ 49.29 % 56.33 % 46.70 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
2 TED 49.21 % 55.85 % 46.52 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
3 VPFNet code 48.36 % 54.65 % 44.98 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
C. Wang, H. Chen and L. Fu: VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection. 2021.
4 CAD 47.91 % 55.98 % 44.63 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
5 PV-RCNN++ code 47.19 % 54.29 % 43.49 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
6 CasA 47.09 % 54.04 % 44.56 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
7 EQ-PVRCNN code 47.02 % 55.84 % 42.94 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, L. Jiang, Y. Sun, B. Schiele and J. Jia: A Unified Query-based Paradigm for Point Cloud Understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.
8 PiFeNet 46.71 % 56.39 % 42.71 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
D. Le, H. Shi, H. Rezatofighi and J. Cai: PiFeNet: Pillar-Feature Network for Real-Time 3D Pedestrian Detection from Point Cloud. 2021.
9 ISE-RCNN 45.66 % 51.44 % 42.43 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
10 CAT-Det 45.44 % 54.26 % 41.94 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Zhang, J. Chen and D. Huang: CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection. CVPR 2022.
11 HotSpotNet 45.37 % 53.10 % 41.47 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
12 PE-RCVN 45.01 % 50.29 % 41.85 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
13 variance_point 44.89 % 53.72 % 41.21 % 0.05 s 1 core @ 2.5 Ghz (Python)
14 VPN 44.56 % 54.13 % 41.73 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
15 EPNet++ 44.38 % 52.79 % 41.29 % 0.1 s GPU @ 2.5 Ghz (Python)
Z. Liu, H. tengteng, B. Li, X. Chen, X. Wang and X. Bai: EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object Detection. arXiv preprint arXiv:2112.11088 2021.
16 TANet code 44.34 % 53.72 % 40.49 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
17 CFF-tv 44.33 % 52.72 % 41.11 % 1 s 1 core @ 2.5 Ghz (C/C++)
18 TBD 44.32 % 49.37 % 41.24 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
19 3DSSD code 44.27 % 54.64 % 40.23 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
20 AutoAlign 44.08 % 53.99 % 40.82 % 0.1 s 1 core @ 2.5 Ghz (Python)
21 ISE-RCNN-PV 43.78 % 50.03 % 40.50 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
22 Point-GNN
This method makes use of Velodyne laser scans.
code 43.77 % 51.92 % 40.14 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
23 VCT 43.65 % 50.27 % 41.43 % 0.2 s 1 core @ 2.5 Ghz (Python)
24 USVLab BSAODet 43.63 % 51.71 % 41.09 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
25 CFF-ep25 43.47 % 51.85 % 40.69 % 1 s 1 core @ 2.5 Ghz (C/C++)
26 FV2P v2 43.47 % 50.64 % 40.09 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
27 F-ConvNet
This method makes use of Velodyne laser scans.
code 43.38 % 52.16 % 38.80 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
28 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 43.35 % 53.10 % 40.06 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
29 FS-Net
This method makes use of Velodyne laser scans.
43.31 % 49.82 % 40.89 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
30 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 43.29 % 52.17 % 40.29 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
31 FromVoxelToPoint code 43.28 % 51.80 % 40.71 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to- Point Decoder. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
32 VMVS
This method makes use of Velodyne laser scans.
43.27 % 53.44 % 39.51 % 0.25 s GPU @ 2.5 Ghz (Python)
J. Ku, A. Pon, S. Walsh and S. Waslander: Improving 3D object detection for pedestrians with virtual multi-view synthesis orientation estimation. IROS 2019.
33 cff-tv-v2-ep25 43.25 % 51.40 % 40.23 % 1 s 1 core @ 2.5 Ghz (C/C++)
34 P2V-RCNN 43.19 % 50.91 % 40.81 % 0.1 s 2 cores @ 2.5 Ghz (Python)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.
35 KeyFuse2B 43.18 % 51.49 % 40.70 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
36 CF-ctdep-tv-ta 43.11 % 50.40 % 40.51 % 1 s 1 core @ 2.5 Ghz (C/C++)
37 MGAF-3DSSD code 43.09 % 50.65 % 39.65 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
38 Reprod-Two-Branch 43.07 % 52.07 % 40.40 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
39 SGNet 43.00 % 49.68 % 40.45 % 0.09 s GPU @ 2.5 Ghz (Python)
40 Frustum-PointPillars code 42.89 % 51.22 % 39.28 % 0.06 s 4 cores @ 3.0 Ghz (Python)
A. Paigwar, D. Sierra-Gonzalez, \. Erkent and C. Laugier: Frustum-PointPillars: A Multi-Stage Approach for 3D Object Detection using RGB Camera and LiDAR. International Conference on Computer Vision, ICCV, Workshop on Autonomous Vehicle Vision 2021.
41 CFF-tv-v2 42.77 % 51.08 % 40.15 % 1 s 1 core @ 2.5 Ghz (C/C++)
42 Fast-CLOCs 42.72 % 52.10 % 39.08 % 0.1 s GPU @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: Fast-CLOCs: Fast Camera-LiDAR Object Candidates Fusion for 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022.
43 CF-base-tv 42.66 % 50.01 % 39.76 % 1 s 1 core @ 2.5 Ghz (C/C++)
44 HMFI code 42.65 % 50.88 % 39.78 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
45 TBD 42.65 % 50.88 % 39.78 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
46 USVLab BSAODet (S) 42.62 % 49.52 % 39.12 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
47 STD code 42.47 % 53.29 % 38.35 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
48 Anonymous 42.32 % 47.96 % 39.69 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
49 AVOD-FPN
This method makes use of Velodyne laser scans.
code 42.27 % 50.46 % 39.04 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
50 SemanticVoxels 42.19 % 50.90 % 39.52 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
J. Fei, W. Chen, P. Heidenreich, S. Wirges and C. Stiller: SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentation. MFI 2020.
51 TBD 42.19 % 49.89 % 39.34 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
52 TBD 42.19 % 49.89 % 39.34 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
53 F-PointNet
This method makes use of Velodyne laser scans.
code 42.15 % 50.53 % 38.08 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
54 TBD
This method makes use of Velodyne laser scans.
42.05 % 48.66 % 38.94 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
55 3SNet 42.02 % 48.91 % 39.39 % 0.07 s GPU @ 2.5 Ghz (Python)
56 CF-ctdep-tv 41.98 % 49.14 % 39.03 % 1 s 1 core @ 2.5 Ghz (C/C++)
57 Self-Calib Conv 41.95 % 48.88 % 39.52 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
58 PointPillars
This method makes use of Velodyne laser scans.
code 41.92 % 51.45 % 38.89 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
59 CenterFuse 41.80 % 49.77 % 38.49 % 0.059 sec/frame 2 x V100
60 cp-tv-kp 41.70 % 48.77 % 39.41 % 1 s 1 core @ 2.5 Ghz (C/C++)
61 epBRM
This method makes use of Velodyne laser scans.
code 41.52 % 49.17 % 39.08 % 0.10 s 1 core @ 2.5 Ghz (C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
62 TCDVF 41.47 % 49.44 % 38.41 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
63 DGT-Det3D 41.40 % 49.06 % 38.43 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
64 tbd 41.10 % 50.56 % 37.49 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
65 IA-SSD (single) code 41.03 % 47.90 % 37.98 % 0.013 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
66 PointPainting
This method makes use of Velodyne laser scans.
40.97 % 50.32 % 37.87 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
67 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
code 40.89 % 46.97 % 38.80 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
68 SARFE 40.79 % 47.29 % 38.25 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
69 IoU-2B 40.62 % 50.33 % 36.94 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
70 MSADet 40.58 % 49.54 % 38.19 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
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71 TBD 40.57 % 47.65 % 38.33 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
72 PDV code 40.56 % 47.80 % 38.46 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Hu, T. Kuai and S. Waslander: Point Density-Aware Voxels for LiDAR 3D Object Detection. CVPR 2022.
73 cp-tv 40.55 % 47.71 % 38.23 % 1 s 1 core @ 2.5 Ghz (C/C++)
74 CM3DV 40.43 % 46.10 % 38.32 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
75 cff-tv-t 40.41 % 49.46 % 37.02 % 1 s 1 core @ 2.5 Ghz (C/C++)
76 SVGA-Net 40.39 % 48.48 % 37.92 % 0.03s 1 core @ 2.5 Ghz (Python + C/C++)
Q. He, Z. Wang, H. Zeng, Y. Zeng and Y. Liu: SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds. AAAI 2022.
77 FusionDetv2-v3 40.38 % 46.86 % 37.41 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
78 P2V_PCV1 40.27 % 45.43 % 38.24 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
79 sa-voxel-centernet code 40.24 % 46.08 % 38.07 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
80 KeyPoint-IoUHead 40.15 % 47.86 % 37.01 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
81 TBD 40.07 % 46.11 % 37.87 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
82 TPCG 39.97 % 46.35 % 37.66 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
83 M3DeTR code 39.94 % 45.70 % 37.66 % n/a s GPU @ 1.0 Ghz (Python)
T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis and D. Manocha: M3DeTR: Multi-representation, Multi- scale, Mutual-relation 3D Object Detection with Transformers. 2021.
84 cp-tv-kp-io-sc 39.92 % 48.06 % 37.68 % 1 s 1 core @ 2.5 Ghz (C/C++)
85 FusionDetv2-v5 39.91 % 47.50 % 37.39 % 0.05 s 1 core @ 2.5 Ghz (Java + C/C++)
86 DDet 39.87 % 45.82 % 38.00 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
87 CF-cd-io-tv 39.82 % 48.67 % 36.45 % 1 s 1 core @ 2.5 Ghz (C/C++)
88 MVOD 39.82 % 46.22 % 37.56 % 0.16 s 1 core @ 2.5 Ghz (C/C++)
89 FusionDetv2-v4 39.68 % 46.93 % 37.31 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
90 DSASNet 39.65 % 47.14 % 37.05 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
91 VCRCNN 39.64 % 45.19 % 37.55 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
92 AFTD 39.45 % 48.28 % 36.07 % 1 s 1 core @ 2.5 Ghz (Python + C/C++)
93 TBD code 39.45 % 47.08 % 37.12 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
94 Dune-DCF-e09 39.43 % 47.29 % 36.74 % 1 s 1 core @ 2.5 Ghz (C/C++)
95 LazyTorch-CP-Infer-O 39.43 % 47.38 % 36.96 % 1 s 1 core @ 2.5 Ghz (C/C++)
96 SRDL 39.43 % 47.30 % 36.99 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
97 FusionDetv1 39.42 % 47.30 % 36.97 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
98 AGS-SSD[la] 39.41 % 46.04 % 36.28 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
99 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 39.37 % 47.98 % 36.01 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
100 ST-RCNN
This method makes use of Velodyne laser scans.
39.36 % 44.96 % 37.09 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
101 LazyTorch-CP-Small-P 39.33 % 47.27 % 36.89 % 1 s 1 core @ 2.5 Ghz (C/C++)
102 FusionDetv2-v2 39.31 % 44.98 % 37.22 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
103 ARPNET 39.31 % 48.32 % 35.93 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
104 CenterPoint (pcdet) 39.28 % 47.25 % 36.78 % 0.051 sec/frame 2 x V100
105 Dune-DCF-e11 39.26 % 47.32 % 36.63 % 1 s 1 core @ 2.5 Ghz (C/C++)
106 ATT_SSD 39.24 % 45.92 % 36.35 % 0.01 s 1 core @ 2.5 Ghz (Python)
107 City-CF-fixed 39.22 % 47.68 % 36.55 % 1 s 1 core @ 2.5 Ghz (C/C++)
108 IA-SSD (multi) code 39.03 % 46.51 % 35.61 % 0.014 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
109 NV-RCNN 38.75 % 47.05 % 36.52 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
110 SIF 38.74 % 46.23 % 36.06 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
111 CrazyTensor-CP 38.67 % 46.58 % 36.15 % 1 s 1 core @ 2.5 Ghz (Python)
112 SCNet
This method makes use of Velodyne laser scans.
38.66 % 47.83 % 35.70 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
113 Dune-DCF-e15 38.61 % 46.41 % 36.02 % 1 s 1 core @ 2.5 Ghz (C/C++)
114 Faraway-Frustum
This method makes use of Velodyne laser scans.
code 38.58 % 46.33 % 35.71 % 0.1 s GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-frustum: Dealing with lidar sparsity for 3D object detection using fusion. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) 2021.
115 IKT3D
This method makes use of Velodyne laser scans.
38.58 % 44.18 % 36.56 % 0.05 s 1 core @ 2.5 Ghz (Python)
116 FPV-SSD 38.45 % 45.83 % 36.03 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
117 CF-base-train 38.44 % 45.89 % 35.55 % 1 s 1 core @ 2.5 Ghz (C/C++)
118 TBD 38.27 % 46.35 % 36.39 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
119 City-CF 38.04 % 45.42 % 35.40 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
120 CF-ctdep-train 38.03 % 44.75 % 35.49 % 1 s 1 core @ 2.5 Ghz (C/C++)
121 PVTr 37.58 % 43.99 % 35.48 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
122 DVFENet 37.50 % 43.55 % 35.33 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection. Neurocomputing 2021.
123 MLOD
This method makes use of Velodyne laser scans.
code 37.47 % 47.58 % 35.07 % 0.12 s GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
124 S-AT GCN 37.37 % 44.63 % 34.92 % 0.02 s GPU @ 2.0 Ghz (Python)
L. Wang, C. Wang, X. Zhang, T. Lan and J. Li: S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection. CoRR 2021.
125 HS3D code 36.86 % 45.62 % 33.67 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
126 XView 36.79 % 42.44 % 34.96 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
127 FusionDetv2-baseline 36.66 % 41.34 % 34.60 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
128 TBD 36.53 % 44.11 % 34.30 % TBD GPU @ 2.5 Ghz (Python + C/C++)
129 PFF3D
This method makes use of Velodyne laser scans.
code 36.07 % 43.93 % 32.86 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
130 NV2P-RCNN 35.98 % 43.18 % 33.88 % 0.1 s GPU @ 2.5 Ghz (Python)
131 CrazyTensor-CF 35.83 % 43.50 % 33.23 % 1 s 1 core @ 2.5 Ghz (C/C++)
132 BirdNet+
This method makes use of Velodyne laser scans.
code 35.06 % 41.55 % 32.93 % 0.11 s Titan Xp (PyTorch)
A. Barrera, J. Beltrán, C. Guindel, J. Iglesias and F. García: BirdNet+: Two-Stage 3D Object Detection in LiDAR through a Sparsity-Invariant Bird’s Eye View. IEEE Access 2021.
133 TBD_BD code 34.86 % 42.56 % 32.49 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
134 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 34.59 % 42.27 % 31.37 % 0.0047s 1 core @ 2.5 Ghz (python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
135 KPP3D code 32.91 % 41.34 % 30.45 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
136 DSGN++
This method uses stereo information.
code 32.74 % 43.05 % 29.54 % 0.2 s GeForce RTX 2080Ti
Y. Chen, S. Huang, S. Liu, B. Yu and J. Jia: DSGN++: Exploiting Visual-Spatial Relation for Stereo-based 3D Detectors. arXiv preprint arXiv:2204.03039 2022.
137 StereoDistill 32.23 % 44.12 % 28.95 % 0.4 s 1 core @ 2.5 Ghz (Python)
138 PP-PCdet code 32.04 % 39.23 % 29.79 % 0.01 s 1 core @ 2.5 Ghz (Python)
139 Contrastive PP code 31.64 % 38.47 % 29.30 % 0.01 s 1 core @ 2.5 Ghz (Python)
140 BirdNet+ (legacy)
This method makes use of Velodyne laser scans.
code 31.46 % 37.99 % 29.46 % 0.1 s Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird’s Eye View. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
141 SparsePool code 30.38 % 37.84 % 26.94 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
142 MMLAB LIGA-Stereo
This method uses stereo information.
code 30.00 % 40.46 % 27.07 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
143 DisposalNet
This method uses stereo information.
29.77 % 37.21 % 27.62 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
144 SparsePool code 27.92 % 35.52 % 25.87 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
145 AVOD
This method makes use of Velodyne laser scans.
code 27.86 % 36.10 % 25.76 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
146 PS++ code 27.45 % 36.89 % 24.01 % PS++ s 1 core @ 2.5 Ghz (C/C++)
147 CSW3D
This method makes use of Velodyne laser scans.
26.64 % 33.75 % 23.34 % 0.03 s 4 cores @ 2.5 Ghz (C/C++)
J. Hu, T. Wu, H. Fu, Z. Wang and K. Ding: Cascaded Sliding Window Based Real-Time 3D Region Proposal for Pedestrian Detection. ROBIO 2019.
148 PointRGBNet 26.40 % 34.77 % 24.03 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
P. Xie Desheng: Real-time Detection of 3D Objects Based on Multi-Sensor Information Fusion. Automotive Engineering 2022.
149 PS code 26.01 % 35.52 % 23.24 % PS s 1 core @ 2.5 Ghz (C/C++)
150 Disp R-CNN (velo)
This method uses stereo information.
code 25.80 % 37.12 % 22.04 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
151 CZY 25.47 % 32.33 % 23.60 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
152 Disp R-CNN
This method uses stereo information.
code 25.40 % 35.75 % 21.79 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
153 FusionDetv2-v1 24.55 % 30.58 % 23.64 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
154 CG-Stereo
This method uses stereo information.
24.31 % 33.22 % 20.95 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
155 YOLOStereo3D
This method uses stereo information.
code 19.75 % 28.49 % 16.48 % 0.1 s GPU 1080Ti
Y. Liu, L. Wang and M. Liu: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
156 AEC3D 19.00 % 24.39 % 17.43 % 18 ms GPU @ 2.5 Ghz (Python)
157 BEVC 17.65 % 23.49 % 15.92 % 35ms GPU @ 1.5 Ghz (Python)
158 OC Stereo
This method uses stereo information.
code 17.58 % 24.48 % 15.60 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
159 BirdNet
This method makes use of Velodyne laser scans.
17.08 % 22.04 % 15.82 % 0.11 s Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
160 VN3D 15.69 % 19.56 % 13.17 % 0.02 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
161 DSGN
This method uses stereo information.
code 15.55 % 20.53 % 14.15 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
162 Complexer-YOLO
This method makes use of Velodyne laser scans.
13.96 % 17.60 % 12.70 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
163 Anonymous 11.69 % 17.79 % 10.09 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
164 RT3D-GMP
This method uses stereo information.
11.41 % 16.23 % 10.12 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
165 DD3D code 11.04 % 16.64 % 9.38 % n/a s 1 core @ 2.5 Ghz (C/C++)
D. Park, R. Ambrus, V. Guizilini, J. Li and A. Gaidon: Is Pseudo-Lidar needed for Monocular 3D Object detection?. IEEE/CVF International Conference on Computer Vision (ICCV) .
166 PS-fld code 10.82 % 16.95 % 9.26 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
Y. Chen, H. Dai and Y. Ding: Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
167 DEPT 10.81 % 16.28 % 9.15 % 0.03 s 1 core @ 2.5 Ghz (Python)
168 OPA-3D code 10.49 % 15.65 % 8.80 % 0.04 s 1 core @ 3.5 Ghz (Python)
169 anonymity 10.39 % 16.89 % 9.29 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
170 ESGN
This method uses stereo information.
10.27 % 14.05 % 9.02 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
171 MonoDTR 10.18 % 15.33 % 8.61 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
K. Huang, T. Wu, H. Su and W. Hsu: MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer. CVPR 2022.
172 LT-M3OD 9.99 % 14.85 % 8.52 % 0.03 s 1 core @ 2.5 Ghz (Python)
173 GUPNet code 9.76 % 14.95 % 8.41 % NA s 1 core @ 2.5 Ghz (Python + C/C++)
Y. Lu, X. Ma, L. Yang, T. Zhang, Y. Liu, Q. Chu, J. Yan and W. Ouyang: Geometry Uncertainty Projection Network for Monocular 3D Object Detection. arXiv preprint arXiv:2107.13774 2021.
174 MonoInsight 9.42 % 14.41 % 7.96 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
175 GPENet code 9.36 % 14.61 % 7.91 % 0.02 s GPU @ 2.5 Ghz (Python)
176 Lite-FPN-GUPNet 9.32 % 14.13 % 7.93 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
177 mono3d code 9.20 % 14.53 % 7.82 % TBD TBD
178 ZongmuMono3d code 9.18 % 14.23 % 7.82 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
179 SGM3D 8.81 % 13.99 % 7.26 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
Z. Zhou, L. Du, X. Ye, Z. Zou, X. Tan, E. Ding, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection. 2021.
180 CMKD* 8.79 % 13.94 % 7.42 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
181 SCSTSV-MonoFlex 8.75 % 13.10 % 7.38 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
182 gupnet_se 8.65 % 13.40 % 7.78 % 0.03s 1 core @ 2.5 Ghz (C/C++)
183 SwinMono3D 8.54 % 12.96 % 7.19 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
184 SARM3D 8.48 % 12.95 % 7.25 % 0.03 s GPU @ 2.5 Ghz (Python)
185 MonoCon code 8.41 % 13.10 % 6.94 % 0.02 s GPU @ 2.5 Ghz (Python)
T. Xianpeng Liu: Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection. AAAI 2022.
186 HBD 8.33 % 13.47 % 6.99 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
187 MonoEdge 8.33 % 12.11 % 7.03 % 0.05 s GPU @ 2.5 Ghz (Python)
188 GCDR 8.27 % 11.50 % 7.37 % 0.28 s 1 core @ 2.5 Ghz (Python)
189 MonoFlex 8.16 % 11.89 % 6.81 % 0.03 s 1 core @ 2.5 Ghz (Python)
190 CaDDN code 8.14 % 12.87 % 6.76 % 0.63 s GPU @ 2.5 Ghz (Python)
C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.
191 MonoGround 7.89 % 12.37 % 7.13 % 0.03 s 1 core @ 2.5 Ghz (Python)
192 HomoLoss(monoflex) code 7.66 % 11.87 % 6.82 % 0.04 s 1 core @ 2.5 Ghz (Python)
J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homography Loss for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
193 MonoFar 7.62 % 11.21 % 6.47 % 0.04 s 1 core @ 2.5 Ghz (Python)
194 K3D 7.60 % 12.58 % 6.73 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
195 ANM 7.54 % 11.92 % 6.37 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
196 SAIC_ADC_Mono3D code 7.54 % 12.06 % 6.41 % 50 s GPU @ 2.5 Ghz (Python)
197 Mix-Teaching 7.47 % 11.67 % 6.61 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
198 M3DGAF 7.42 % 11.68 % 6.65 % 0.07 s 1 core @ 2.5 Ghz (Python)
199 LPCG-Monoflex 7.33 % 10.82 % 6.18 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
200 MonoDDE 7.32 % 11.13 % 6.67 % 0.04 s 1 core @ 2.5 Ghz (Python)
Z. Li, Z. Qu, Y. Zhou, J. Liu, H. Wang and L. Jiang: Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection. CVPR 2022.
201 MonoAug 7.31 % 11.31 % 6.12 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
202 RefinedMPL 7.18 % 11.14 % 5.84 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
203 Shape-Aware 7.16 % 10.40 % 5.93 % 0.05 s 1 core @ 2.5 Ghz (Python)
204 MDSNet 7.09 % 10.68 % 6.06 % 0.07 s 1 core @ 2.5 Ghz (Python)
205 MonoEdge-Rotate 7.02 % 10.47 % 5.84 % 0.05 s GPU @ 2.5 Ghz (Python)
206 TopNet-HighRes
This method makes use of Velodyne laser scans.
6.92 % 10.40 % 6.63 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
207 MonoRUn code 6.78 % 10.88 % 5.83 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
208 MonoPair 6.68 % 10.02 % 5.53 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
209 mono3d 6.62 % 10.10 % 5.46 % 0.03 s GPU @ 2.5 Ghz (Python)
210 monodle code 6.55 % 9.64 % 5.44 % 0.04 s GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for Monocular 3D Object Detection. CVPR 2021 .
211 MonoAug 6.36 % 9.59 % 5.24 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
212 MonoFlex 6.31 % 9.43 % 5.26 % 0.03 s GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.
213 E2E-DA 5.95 % 8.79 % 5.72 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
214 MDNet 5.66 % 8.24 % 4.74 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
215 M3DSSD++ code 5.65 % 8.10 % 4.72 % 0.16s 1 core @ 2.5 Ghz (C/C++)
216 MK3D 5.00 % 7.29 % 4.54 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
217 Aug3D-RPN 4.71 % 6.01 % 3.87 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth. 2021.
218 MM 4.70 % 7.81 % 4.01 % 1 s 1 core @ 2.5 Ghz (C/C++)
219 Shift R-CNN (mono) code 4.66 % 7.95 % 4.16 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
220 Lite-FPN 4.38 % 6.57 % 3.56 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
221 COF3D 4.37 % 6.02 % 3.55 % 200 s 1 core @ 2.5 Ghz (Python + C/C++)
222 MAOLoss code 4.18 % 5.81 % 3.67 % 0.05 s 1 core @ 2.5 Ghz (Python)
223 MonoPSR code 4.00 % 6.12 % 3.30 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
224 MP-Mono 3.75 % 5.09 % 3.50 % 0.16 s GPU @ 2.5 Ghz (Python)
225 Geo3D 3.65 % 5.74 % 3.01 % 0.04 s GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
226 DFR-Net 3.62 % 6.09 % 3.39 % 0.18 s 1080 Ti (Pytorch)
Z. Zou, X. Ye, L. Du, X. Cheng, X. Tan, L. Zhang, J. Feng, X. Xue and E. Ding: The devil is in the task: Exploiting reciprocal appearance-localization features for monocular 3d object detection . ICCV 2021.
227 DDMP-3D 3.55 % 4.93 % 3.01 % 0.18 s 1 core @ 2.5 Ghz (Python)
L. Wang, L. Du, X. Ye, Y. Fu, G. Guo, X. Xue, J. Feng and L. Zhang: Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection. CVPR 2020.
228 E2E-DA-Lite (Res18) 3.51 % 5.82 % 3.42 % 0.01 s GPU @ 2.5 Ghz (Python)
229 M3D-RPN code 3.48 % 4.92 % 2.94 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
230 D4LCN code 3.42 % 4.55 % 2.83 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
231 MoGDE 3.42 % 5.57 % 2.73 % 0.03 s GPU @ 2.5 Ghz (Python)
232 CMAN 3.41 % 4.62 % 2.87 % 0.15 s 1 core @ 2.5 Ghz (Python)
233 QD-3DT
This is an online method (no batch processing).
code 3.37 % 5.53 % 3.02 % 0.03 s GPU @ 2.5 Ghz (Python)
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.
234 MonoEF 2.79 % 4.27 % 2.21 % 0.03 s 1 core @ 2.5 Ghz (Python)
Y. Zhou, Y. He, H. Zhu, C. Wang, H. Li and Q. Jiang: Monocular 3D Object Detection: An Extrinsic Parameter Free Approach. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
235 RT3DStereo
This method uses stereo information.
2.45 % 3.28 % 2.35 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
236 TopNet-UncEst
This method makes use of Velodyne laser scans.
1.87 % 3.42 % 1.73 % 0.09 s NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
237 PPTrans 1.85 % 2.68 % 1.44 % 0.2 s GPU @ 2.5 Ghz (Python)
238 SS3D 1.78 % 2.31 % 1.48 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
239 PGD-FCOS3D code 1.49 % 2.28 % 1.38 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
T. Wang, X. Zhu, J. Pang and D. Lin: Probabilistic and Geometric Depth: Detecting Objects in Perspective. Conference on Robot Learning (CoRL) 2021.
240 SparVox3D 1.35 % 1.93 % 1.04 % 0.05 s GPU @ 2.0 Ghz (Python)
E. Balatkan and F. Kıraç: Improving Regression Performance on Monocular 3D Object Detection Using Bin-Mixing and Sparse Voxel Data. 2021 6th International Conference on Computer Science and Engineering (UBMK) 2021.
241 EM code 1.18 % 1.09 % 0.80 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
242 CDTrack3D code 1.01 % 1.48 % 0.69 % 0.0106 s NVIDIA RTX 3090 GPU, i9 10850k CPU
243 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 TED 74.12 % 88.82 % 66.84 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
2 CasA++ 73.79 % 87.76 % 66.84 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
3 CasA 73.47 % 87.91 % 66.17 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
4 ISE-RCNN-PV 71.94 % 84.94 % 64.09 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
5 SGNet 70.40 % 86.75 % 62.73 % 0.09 s GPU @ 2.5 Ghz (Python)
6 HMFI code 70.37 % 84.02 % 62.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
7 CAD 69.94 % 84.68 % 62.21 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
8 SARFE 69.67 % 84.88 % 62.26 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
9 ISE-RCNN 69.18 % 82.62 % 62.77 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
10 EQ-PVRCNN code 69.10 % 85.41 % 62.30 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, L. Jiang, Y. Sun, B. Schiele and J. Jia: A Unified Query-based Paradigm for Point Cloud Understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.
11 sa-voxel-centernet code 69.03 % 81.88 % 61.66 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
12 Anonymous 69.00 % 82.74 % 62.46 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
13 CAT-Det 68.81 % 83.68 % 61.45 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Zhang, J. Chen and D. Huang: CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection. CVPR 2022.
14 BtcDet
This method makes use of Velodyne laser scans.
code 68.68 % 82.81 % 61.81 % 0.09 s GPU @ 2.5 Ghz (Python + C/C++)
Q. Xu, Y. Zhong and U. Neumann: Behind the Curtain: Learning Occluded Shapes for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2022.
15 CM3DV 68.67 % 81.47 % 61.40 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
16 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
code 68.54 % 82.19 % 61.33 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
17 FS-Net
This method makes use of Velodyne laser scans.
68.35 % 81.81 % 60.96 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
18 TPCG 68.15 % 82.13 % 61.56 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
19 PE-RCVN 68.13 % 84.96 % 60.34 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
20 PDV code 67.81 % 83.04 % 60.46 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Hu, T. Kuai and S. Waslander: Point Density-Aware Voxels for LiDAR 3D Object Detection. CVPR 2022.
21 USVLab BSAODet 67.79 % 82.65 % 60.26 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
22 RangeIoUDet
This method makes use of Velodyne laser scans.
67.77 % 83.12 % 60.26 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, Z. Zhang, M. Zhang, X. Zhao and S. Pu: RangeIoUDet: Range Image Based Real-Time 3D Object Detector Optimized by Intersection Over Union. CVPR 2021.
23 DDet 67.55 % 82.03 % 60.23 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
24 3SNet 67.52 % 81.09 % 60.34 % 0.07 s GPU @ 2.5 Ghz (Python)
25 TBD
This method makes use of Velodyne laser scans.
67.37 % 80.50 % 61.18 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
26 PV-RCNN++ code 67.33 % 82.22 % 60.04 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
27 USVLab BSAODet (S) 67.25 % 81.94 % 59.19 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
28 CF-ctdep-tv-ta 67.08 % 85.49 % 59.86 % 1 s 1 core @ 2.5 Ghz (C/C++)
29 SPG_mini
This method makes use of Velodyne laser scans.
code 66.96 % 80.21 % 60.50 % 0.09 s GPU @ 2.5 Ghz (Python)
Q. Xu, Y. Zhou, W. Wang, C. Qi and D. Anguelov: SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation. Proceedings of the IEEE conference on computer vision and pattern recognition (ICCV) 2021.
30 IKT3D
This method makes use of Velodyne laser scans.
66.87 % 80.35 % 60.14 % 0.05 s 1 core @ 2.5 Ghz (Python)
31 VCRCNN 66.78 % 81.29 % 59.32 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
32 M3DeTR code 66.74 % 83.83 % 59.03 % n/a s GPU @ 1.0 Ghz (Python)
T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis and D. Manocha: M3DeTR: Multi-representation, Multi- scale, Mutual-relation 3D Object Detection with Transformers. 2021.
33 DSASNet 66.71 % 81.82 % 59.37 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
34 TBD 66.63 % 85.08 % 60.36 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
35 MSADet 66.49 % 84.21 % 59.73 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
36 FV2P v2 66.38 % 83.53 % 59.92 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
37 VCT 66.38 % 82.37 % 60.01 % 0.2 s 1 core @ 2.5 Ghz (Python)
38 Reprod-Two-Branch 66.28 % 82.71 % 58.88 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
39 CFF-tv 66.26 % 82.09 % 58.54 % 1 s 1 core @ 2.5 Ghz (C/C++)
40 IA-SSD (single) code 66.25 % 82.36 % 59.70 % 0.013 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
41 cff-tv-v2-ep25 66.14 % 82.40 % 58.96 % 1 s 1 core @ 2.5 Ghz (C/C++)
42 CenterFuse 66.10 % 85.19 % 58.95 % 0.059 sec/frame 2 x V100
43 CFF-ep25 65.99 % 81.91 % 58.22 % 1 s 1 core @ 2.5 Ghz (C/C++)
44 HotSpotNet 65.95 % 82.59 % 59.00 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
45 ST-RCNN
This method makes use of Velodyne laser scans.
65.61 % 78.82 % 58.44 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
46 TBD 65.48 % 79.90 % 57.95 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
47 CFF-tv-v2 65.47 % 81.67 % 58.22 % 1 s 1 core @ 2.5 Ghz (C/C++)
48 CF-ctdep-tv 65.43 % 82.29 % 57.86 % 1 s 1 core @ 2.5 Ghz (C/C++)
49 JPVNet 65.41 % 80.66 % 59.26 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
50 Fast-CLOCs 65.31 % 82.83 % 57.43 % 0.1 s GPU @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: Fast-CLOCs: Fast Camera-LiDAR Object Candidates Fusion for 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022.
51 TCDVF 65.19 % 79.41 % 58.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
52 TBD 65.13 % 83.80 % 58.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
53 F-ConvNet
This method makes use of Velodyne laser scans.
code 65.07 % 81.98 % 56.54 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
54 MVOD 64.95 % 79.52 % 57.53 % 0.16 s 1 core @ 2.5 Ghz (C/C++)
55 TBD 64.92 % 76.57 % 58.43 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
56 PVTr 64.51 % 81.09 % 57.50 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
57 FPV-SSD 64.40 % 78.36 % 56.72 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
58 DGT-Det3D 64.38 % 78.27 % 57.79 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
59 AutoAlign 64.36 % 80.41 % 56.88 % 0.1 s 1 core @ 2.5 Ghz (Python)
60 FusionDetv2-v5 64.28 % 78.57 % 57.02 % 0.05 s 1 core @ 2.5 Ghz (Java + C/C++)
61 cff-tv-t 64.16 % 83.46 % 57.21 % 1 s 1 core @ 2.5 Ghz (C/C++)
62 TBD code 64.12 % 77.56 % 57.95 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
63 TBD 64.12 % 79.27 % 57.48 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
64 KeyFuse2B 64.10 % 82.28 % 57.19 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
65 3DSSD code 64.10 % 82.48 % 56.90 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
66 VPFNet code 64.10 % 77.64 % 58.00 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
C. Wang, H. Chen and L. Fu: VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection. 2021.
67 CF-base-tv 63.97 % 79.52 % 56.61 % 1 s 1 core @ 2.5 Ghz (C/C++)
68 PointPainting
This method makes use of Velodyne laser scans.
63.78 % 77.63 % 55.89 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
69 IoU-2B 63.75 % 82.21 % 56.43 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
70 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 63.71 % 78.60 % 57.65 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
71 CF-cd-io-tv 63.69 % 82.60 % 56.66 % 1 s 1 core @ 2.5 Ghz (C/C++)
72 KeyPoint-IoUHead 63.65 % 81.44 % 56.94 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
73 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 63.52 % 79.17 % 56.93 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
74 Point-GNN
This method makes use of Velodyne laser scans.
code 63.48 % 78.60 % 57.08 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
75 MGAF-3DSSD code 63.43 % 80.64 % 55.15 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
76 FromVoxelToPoint code 63.41 % 81.49 % 56.40 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to- Point Decoder. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
77 FusionDetv2-v4 63.38 % 79.65 % 56.61 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
78 P2V-RCNN 63.13 % 78.62 % 56.81 % 0.1 s 2 cores @ 2.5 Ghz (Python)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.
79 cp-tv-kp-io-sc 63.03 % 79.30 % 56.66 % 1 s 1 core @ 2.5 Ghz (C/C++)
80 H^23D R-CNN code 62.74 % 78.67 % 55.78 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
J. Deng, W. Zhou, Y. Zhang and H. Li: From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2021.
81 SVGA-Net 62.28 % 78.58 % 54.88 % 0.03s 1 core @ 2.5 Ghz (Python + C/C++)
Q. He, Z. Wang, H. Zeng, Y. Zeng and Y. Liu: SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds. AAAI 2022.
82 ATT_SSD 62.07 % 76.56 % 55.87 % 0.01 s 1 core @ 2.5 Ghz (Python)
83 SRDL 62.02 % 77.35 % 55.52 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
84 FusionDetv1 62.02 % 77.33 % 55.52 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
85 cp-tv 62.01 % 77.26 % 55.09 % 1 s 1 core @ 2.5 Ghz (C/C++)
86 Faraway-Frustum
This method makes use of Velodyne laser scans.
code 62.00 % 77.36 % 55.40 % 0.1 s GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-frustum: Dealing with lidar sparsity for 3D object detection using fusion. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) 2021.
87 DVFENet 62.00 % 78.73 % 55.18 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection. Neurocomputing 2021.
88 FusionDetv2-v3 61.96 % 79.43 % 55.28 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
89 NV2P-RCNN 61.95 % 73.58 % 55.62 % 0.1 s GPU @ 2.5 Ghz (Python)
90 IA-SSD (multi) code 61.94 % 78.35 % 55.70 % 0.014 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
91 KPP3D code 61.85 % 76.43 % 55.78 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
92 Self-Calib Conv 61.84 % 77.26 % 55.37 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
93 VPN 61.82 % 77.81 % 55.33 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
94 FusionDetv2-v2 61.78 % 76.70 % 54.75 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
95 S-AT GCN 61.70 % 75.24 % 55.32 % 0.02 s GPU @ 2.0 Ghz (Python)
L. Wang, C. Wang, X. Zhang, T. Lan and J. Li: S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection. CoRR 2021.
96 SIF 61.61 % 77.13 % 55.11 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
97 STD code 61.59 % 78.69 % 55.30 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
98 Dune-DCF-e11 61.03 % 80.38 % 54.07 % 1 s 1 core @ 2.5 Ghz (C/C++)
99 P2V_PCV1 60.84 % 75.25 % 54.70 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
100 City-CF 60.84 % 79.32 % 53.83 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
101 NV-RCNN 60.66 % 78.34 % 54.39 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
102 Dune-DCF-e15 60.53 % 78.68 % 53.73 % 1 s 1 core @ 2.5 Ghz (C/C++)
103 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 60.30 % 75.42 % 53.81 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
104 EPNet++ 59.71 % 76.15 % 53.67 % 0.1 s GPU @ 2.5 Ghz (Python)
Z. Liu, H. tengteng, B. Li, X. Chen, X. Wang and X. Bai: EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object Detection. arXiv preprint arXiv:2112.11088 2021.
105 cp-tv-kp 59.68 % 75.54 % 53.34 % 1 s 1 core @ 2.5 Ghz (C/C++)
106 TBD 59.61 % 74.98 % 53.52 % TBD GPU @ 2.5 Ghz (Python + C/C++)
107 City-CF-fixed 59.56 % 77.39 % 53.32 % 1 s 1 core @ 2.5 Ghz (C/C++)
108 XView 59.55 % 77.24 % 53.47 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
109 TANet code 59.44 % 75.70 % 52.53 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
110 TBD_BD code 59.42 % 77.20 % 53.38 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
111 CF-ctdep-train 59.35 % 77.73 % 52.26 % 1 s 1 core @ 2.5 Ghz (C/C++)
112 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 58.82 % 74.96 % 52.53 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
113 CF-base-train 58.80 % 76.64 % 51.15 % 1 s 1 core @ 2.5 Ghz (C/C++)
114 CrazyTensor-CF 58.72 % 78.24 % 51.39 % 1 s 1 core @ 2.5 Ghz (C/C++)
115 HS3D code 58.65 % 74.75 % 52.98 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
116 PointPillars
This method makes use of Velodyne laser scans.
code 58.65 % 77.10 % 51.92 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
117 variance_point 58.45 % 75.33 % 51.51 % 0.05 s 1 core @ 2.5 Ghz (Python)
118 AGS-SSD[la] 58.35 % 72.99 % 52.76 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
119 ARPNET 58.20 % 74.21 % 52.13 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
120 Dune-DCF-e09 57.82 % 74.49 % 51.53 % 1 s 1 core @ 2.5 Ghz (C/C++)
121 AFTD 57.44 % 75.50 % 51.12 % 1 s 1 core @ 2.5 Ghz (Python + C/C++)
122 LazyTorch-CP-Small-P 56.82 % 73.06 % 50.70 % 1 s 1 core @ 2.5 Ghz (C/C++)
123 LazyTorch-CP-Infer-O 56.77 % 73.03 % 50.73 % 1 s 1 core @ 2.5 Ghz (C/C++)
124 CenterPoint (pcdet) 56.67 % 73.04 % 50.60 % 0.051 sec/frame 2 x V100
125 FusionDetv2-baseline 56.34 % 71.16 % 50.70 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
126 Contrastive PP code 56.24 % 71.38 % 49.15 % 0.01 s 1 core @ 2.5 Ghz (Python)
127 epBRM
This method makes use of Velodyne laser scans.
code 56.13 % 72.08 % 49.91 % 0.10 s 1 core @ 2.5 Ghz (C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
128 F-PointNet
This method makes use of Velodyne laser scans.
code 56.12 % 72.27 % 49.01 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
129 CrazyTensor-CP 55.31 % 72.10 % 49.40 % 1 s 1 core @ 2.5 Ghz (Python)
130 PP-PCdet code 54.25 % 68.87 % 48.06 % 0.01 s 1 core @ 2.5 Ghz (Python)
131 TBD 53.95 % 70.44 % 47.22 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
132 TBD 53.95 % 70.44 % 47.22 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
133 BirdNet+
This method makes use of Velodyne laser scans.
code 53.84 % 65.67 % 49.06 % 0.11 s Titan Xp (PyTorch)
A. Barrera, J. Beltrán, C. Guindel, J. Iglesias and F. García: BirdNet+: Two-Stage 3D Object Detection in LiDAR through a Sparsity-Invariant Bird’s Eye View. IEEE Access 2021.
134 tbd 53.00 % 68.71 % 46.32 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
135 PointRGBNet 52.15 % 67.05 % 46.78 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
P. Xie Desheng: Real-time Detection of 3D Objects Based on Multi-Sensor Information Fusion. Automotive Engineering 2022.
136 DisposalNet
This method uses stereo information.
51.33 % 65.51 % 45.05 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
137 PiFeNet 51.10 % 67.50 % 44.66 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
D. Le, H. Shi, H. Rezatofighi and J. Cai: PiFeNet: Pillar-Feature Network for Real-Time 3D Pedestrian Detection from Point Cloud. 2021.
138 SCNet
This method makes use of Velodyne laser scans.
50.79 % 67.98 % 45.15 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
139 AVOD-FPN
This method makes use of Velodyne laser scans.
code 50.55 % 63.76 % 44.93 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
140 MLOD
This method makes use of Velodyne laser scans.
code 49.43 % 68.81 % 42.84 % 0.12 s GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
141 BirdNet+ (legacy)
This method makes use of Velodyne laser scans.
code 47.72 % 67.38 % 42.89 % 0.1 s Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird’s Eye View. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
142 PFF3D
This method makes use of Velodyne laser scans.
code 46.78 % 63.27 % 41.37 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
143 CZY 45.32 % 59.97 % 40.44 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
144 StereoDistill 44.02 % 63.96 % 39.19 % 0.4 s 1 core @ 2.5 Ghz (Python)
145 DSGN++
This method uses stereo information.
code 43.90 % 62.82 % 39.21 % 0.2 s GeForce RTX 2080Ti
Y. Chen, S. Huang, S. Liu, B. Yu and J. Jia: DSGN++: Exploiting Visual-Spatial Relation for Stereo-based 3D Detectors. arXiv preprint arXiv:2204.03039 2022.
146 AVOD
This method makes use of Velodyne laser scans.
code 42.08 % 57.19 % 38.29 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
147 SparsePool code 37.33 % 52.61 % 33.39 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
148 MMLAB LIGA-Stereo
This method uses stereo information.
code 36.86 % 54.44 % 32.06 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
149 FusionDetv2-v1 36.58 % 51.38 % 32.88 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
150 SparsePool code 32.61 % 40.87 % 29.05 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
151 CG-Stereo
This method uses stereo information.
30.89 % 47.40 % 27.23 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
152 PS++ code 30.75 % 47.77 % 26.67 % PS++ s 1 core @ 2.5 Ghz (C/C++)
153 BirdNet
This method makes use of Velodyne laser scans.
30.25 % 43.98 % 27.21 % 0.11 s Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
154 PS code 26.77 % 41.22 % 23.76 % PS s 1 core @ 2.5 Ghz (C/C++)
155 Disp R-CNN (velo)
This method uses stereo information.
code 24.40 % 40.05 % 21.12 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
156 Disp R-CNN
This method uses stereo information.
code 24.40 % 40.04 % 21.12 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
157 AEC3D 22.41 % 31.40 % 21.56 % 18 ms GPU @ 2.5 Ghz (Python)
158 VN3D 21.53 % 30.76 % 21.03 % 0.02 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
159 Complexer-YOLO
This method makes use of Velodyne laser scans.
18.53 % 24.27 % 17.31 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
160 DSGN
This method uses stereo information.
code 18.17 % 27.76 % 16.21 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
161 OC Stereo
This method uses stereo information.
code 16.63 % 29.40 % 14.72 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
162 BEVC 14.08 % 22.30 % 13.44 % 35ms GPU @ 1.5 Ghz (Python)
163 RT3D-GMP
This method uses stereo information.
12.99 % 18.31 % 10.63 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
164 ESGN
This method uses stereo information.
7.69 % 13.84 % 6.75 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
165 CMKD* 6.67 % 12.52 % 6.34 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
166 PS-fld code 6.18 % 11.22 % 5.21 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
Y. Chen, H. Dai and Y. Ding: Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
167 anonymity 5.68 % 9.27 % 4.99 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
168 mono3d code 5.27 % 10.08 % 4.12 % TBD TBD
169 Anonymous 5.24 % 9.60 % 4.50 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
170 anonymity 5.22 % 9.08 % 4.67 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
171 Mix-Teaching 4.91 % 8.04 % 4.15 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
172 DD3D code 4.79 % 7.52 % 4.22 % n/a s 1 core @ 2.5 Ghz (C/C++)
D. Park, R. Ambrus, V. Guizilini, J. Li and A. Gaidon: Is Pseudo-Lidar needed for Monocular 3D Object detection?. IEEE/CVF International Conference on Computer Vision (ICCV) .
173 MonoPSR code 4.74 % 8.37 % 3.68 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
174 TopNet-UncEst
This method makes use of Velodyne laser scans.
4.54 % 7.13 % 3.81 % 0.09 s NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
175 LT-M3OD 4.52 % 7.87 % 4.38 % 0.03 s 1 core @ 2.5 Ghz (Python)
176 E2E-DA 4.42 % 7.36 % 3.34 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
177 LPCG-Monoflex 4.38 % 6.98 % 3.56 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
178 DEPT 4.29 % 7.67 % 3.33 % 0.03 s 1 core @ 2.5 Ghz (Python)
179 Lite-FPN-GUPNet 4.19 % 6.22 % 3.59 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
180 MAOLoss code 4.06 % 6.71 % 3.16 % 0.05 s 1 core @ 2.5 Ghz (Python)
181 E2E-DA-Lite (Res18) 3.99 % 6.87 % 3.04 % 0.01 s GPU @ 2.5 Ghz (Python)
182 MonoInsight 3.92 % 6.23 % 3.27 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
183 MDNet 3.88 % 6.93 % 3.10 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
184 SCSTSV-MonoFlex 3.82 % 6.65 % 3.06 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
185 SAIC_ADC_Mono3D code 3.81 % 6.73 % 3.03 % 50 s GPU @ 2.5 Ghz (Python)
186 Shape-Aware 3.78 % 6.26 % 3.35 % 0.05 s 1 core @ 2.5 Ghz (Python)
187 MonoDDE 3.78 % 5.94 % 3.33 % 0.04 s 1 core @ 2.5 Ghz (Python)
Z. Li, Z. Qu, Y. Zhou, J. Liu, H. Wang and L. Jiang: Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection. CVPR 2022.
188 ZongmuMono3d code 3.77 % 7.21 % 3.15 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
189 DFR-Net 3.58 % 5.69 % 3.10 % 0.18 s 1080 Ti (Pytorch)
Z. Zou, X. Ye, L. Du, X. Cheng, X. Tan, L. Zhang, J. Feng, X. Xue and E. Ding: The devil is in the task: Exploiting reciprocal appearance-localization features for monocular 3d object detection . ICCV 2021.
190 HomoLoss(monoflex) code 3.50 % 5.48 % 2.99 % 0.04 s 1 core @ 2.5 Ghz (Python)
J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homography Loss for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
191 OPA-3D code 3.45 % 5.16 % 2.86 % 0.04 s 1 core @ 3.5 Ghz (Python)
192 CaDDN code 3.41 % 7.00 % 3.30 % 0.63 s GPU @ 2.5 Ghz (Python)
C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.
193 SARM3D 3.37 % 4.93 % 2.95 % 0.03 s GPU @ 2.5 Ghz (Python)
194 RT3DStereo
This method uses stereo information.
3.37 % 5.29 % 2.57 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
195 MonoDTR 3.27 % 5.05 % 3.19 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
K. Huang, T. Wu, H. Su and W. Hsu: MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer. CVPR 2022.
196 GUPNet code 3.21 % 5.58 % 2.66 % NA s 1 core @ 2.5 Ghz (Python + C/C++)
Y. Lu, X. Ma, L. Yang, T. Zhang, Y. Liu, Q. Chu, J. Yan and W. Ouyang: Geometry Uncertainty Projection Network for Monocular 3D Object Detection. arXiv preprint arXiv:2107.13774 2021.
197 MonoAug 3.15 % 5.22 % 2.67 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
198 GPENet code 3.05 % 5.45 % 2.56 % 0.02 s GPU @ 2.5 Ghz (Python)
199 MoGDE 2.94 % 5.08 % 2.74 % 0.03 s GPU @ 2.5 Ghz (Python)
200 M3DSSD++ code 2.94 % 5.18 % 2.43 % 0.16s 1 core @ 2.5 Ghz (C/C++)
201 SGM3D 2.92 % 5.49 % 2.64 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
Z. Zhou, L. Du, X. Ye, Z. Zou, X. Tan, E. Ding, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection. 2021.
202 K3D 2.81 % 5.17 % 2.57 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
203 MonoFar 2.75 % 4.46 % 2.64 % 0.04 s 1 core @ 2.5 Ghz (Python)
204 ANM 2.69 % 4.69 % 2.68 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
205 MonoGround 2.68 % 4.62 % 2.53 % 0.03 s 1 core @ 2.5 Ghz (Python)
206 MDSNet 2.68 % 5.37 % 2.22 % 0.07 s 1 core @ 2.5 Ghz (Python)
207 monodle code 2.66 % 4.59 % 2.45 % 0.04 s GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for Monocular 3D Object Detection. CVPR 2021 .
208 SwinMono3D 2.54 % 3.76 % 2.31 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
209 mono3d 2.53 % 4.71 % 2.22 % 0.03 s GPU @ 2.5 Ghz (Python)
210 MonoEdge-Rotate 2.51 % 4.28 % 2.13 % 0.05 s GPU @ 2.5 Ghz (Python)
211 DDMP-3D 2.50 % 4.18 % 2.32 % 0.18 s 1 core @ 2.5 Ghz (Python)
L. Wang, L. Du, X. Ye, Y. Fu, G. Guo, X. Xue, J. Feng and L. Zhang: Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection. CVPR 2020.
212 Aug3D-RPN 2.43 % 4.36 % 2.55 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth. 2021.
213 QD-3DT
This is an online method (no batch processing).
code 2.39 % 4.16 % 1.85 % 0.03 s GPU @ 2.5 Ghz (Python)
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.
214 MonoFlex 2.35 % 4.17 % 2.04 % 0.03 s GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.
215 M3DGAF 2.30 % 4.28 % 2.12 % 0.07 s 1 core @ 2.5 Ghz (Python)
216 MonoAug 2.23 % 3.68 % 1.69 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
217 MonoEdge 2.19 % 3.15 % 1.77 % 0.05 s GPU @ 2.5 Ghz (Python)
218 gupnet_se 2.13 % 3.84 % 2.13 % 0.03s 1 core @ 2.5 Ghz (C/C++)
219 MonoPair 2.12 % 3.79 % 1.83 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
220 MonoFlex 2.10 % 3.39 % 1.67 % 0.03 s 1 core @ 2.5 Ghz (Python)
221 MK3D 2.02 % 3.75 % 1.96 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
222 Geo3D 2.00 % 3.47 % 1.52 % 0.04 s GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
223 MonoCon code 1.92 % 2.80 % 1.55 % 0.02 s GPU @ 2.5 Ghz (Python)
T. Xianpeng Liu: Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection. AAAI 2022.
224 RefinedMPL 1.82 % 3.23 % 1.77 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
225 TopNet-HighRes
This method makes use of Velodyne laser scans.
1.67 % 2.49 % 1.88 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
226 D4LCN code 1.67 % 2.45 % 1.36 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
227 MP-Mono 1.58 % 2.36 % 1.69 % 0.16 s GPU @ 2.5 Ghz (Python)
228 COF3D 1.46 % 2.34 % 1.28 % 200 s 1 core @ 2.5 Ghz (Python + C/C++)
229 SS3D 1.45 % 2.80 % 1.35 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
230 PGD-FCOS3D code 1.38 % 2.81 % 1.20 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
T. Wang, X. Zhu, J. Pang and D. Lin: Probabilistic and Geometric Depth: Detecting Objects in Perspective. Conference on Robot Learning (CoRL) 2021.
231 PPTrans 1.38 % 2.31 % 1.20 % 0.2 s GPU @ 2.5 Ghz (Python)
232 HBD 1.24 % 2.45 % 1.22 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
233 GCDR 1.17 % 1.96 % 1.02 % 0.28 s 1 core @ 2.5 Ghz (Python)
234 CMAN 1.05 % 1.59 % 1.11 % 0.15 s 1 core @ 2.5 Ghz (Python)
235 MonoEF 0.92 % 1.80 % 0.71 % 0.03 s 1 core @ 2.5 Ghz (Python)
Y. Zhou, Y. He, H. Zhu, C. Wang, H. Li and Q. Jiang: Monocular 3D Object Detection: An Extrinsic Parameter Free Approach. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
236 M3D-RPN code 0.65 % 0.94 % 0.47 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
237 MonoRUn code 0.61 % 1.01 % 0.48 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
238 Lite-FPN 0.41 % 0.50 % 0.24 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
239 Shift R-CNN (mono) code 0.29 % 0.48 % 0.31 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
240 MM 0.27 % 0.48 % 0.24 % 1 s 1 core @ 2.5 Ghz (C/C++)
241 CDTrack3D code 0.06 % 0.06 % 0.07 % 0.0106 s NVIDIA RTX 3090 GPU, i9 10850k CPU
242 mBoW
This method makes use of Velodyne laser scans.
0.00 % 0.00 % 0.00 % 10 s 1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
Table as LaTeX | Only published Methods

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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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