Object Detection Evaluation 2012


The object detection and object orientation estimation benchmark consists of 7481 training images and 7518 test images, comprising a total of 80.256 labeled objects. All images are color and saved as png. For evaluation, we compute precision-recall curves for object detection and orientation-similarity-recall curves for joint object detection and orientation estimation. In the latter case not only the object 2D bounding box has to be located correctly, but also the orientation estimate in bird's eye view is evaluated. To rank the methods we compute average precision and average orientation similiarity. 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 object detection performance using the PASCAL criteria and object detection and orientation estimation performance using the measure discussed in our CVPR 2012 publication. For cars we require an overlap of 70%, while for pedestrians and cyclists we require an overlap of 50% for a detection. Detections in don't care areas or detections which are smaller than the minimum size do not count as false positive. 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 that for the hard evaluation ~2 % of the provided bounding boxes have not been recognized by humans, thereby upper bounding recall at 98 %. Hence, the hard evaluation is only given for reference.
Note 1: On 25.04.2017, we have fixed a bug in the object detection evaluation script. As of now, the submitted detections are filtered based on the min. bounding box height for the respective category which we have been done before only for the ground truth detections, thus leading to false positives for the category "Easy" when bounding boxes of height 25-39 Px were submitted (and to false positives for all categories if bounding boxes smaller than 25 Px were submitted). We like to thank Amy Wu, Matt Wilder, Pekka Jänis and Philippe Vandermersch for their feedback. The last leaderboards right before the changes can be found here!

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 Anonymous 97.19 % 98.27 % 94.59 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
2 GraR-VoI 96.38 % 96.81 % 91.20 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
3 GraR-Po 96.18 % 96.84 % 91.11 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
4 SFD 96.17 % 98.97 % 91.13 % 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.
5 VPFNet 96.15 % 96.64 % 91.14 % 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.
6 Anonymous 96.12 % 99.07 % 91.12 % n/a s 1 core @ 2.5 Ghz (Python + C/C++)
7 CLOCs code 96.07 % 96.77 % 91.11 % 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.
8 GraR-Vo 96.05 % 96.67 % 93.01 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
9 TED 96.03 % 96.64 % 93.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
10 ImpDet 96.00 % 96.73 % 90.96 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
11 CLOCs_PVCas code 95.96 % 96.76 % 91.08 % 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.
12 CityBrainLab 95.96 % 96.59 % 90.94 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
13 PE-RCVN 95.94 % 96.90 % 90.98 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
14 Anonymous 95.91 % 96.55 % 92.86 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
15 PVT-SSD 95.90 % 96.75 % 90.69 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
16 GraR-Pi 95.89 % 98.59 % 92.85 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
17 Anonymous 95.86 % 98.93 % 90.98 % n/a s 1 core @ 2.5 Ghz (C/C++)
18 GLENet-VR 95.81 % 96.85 % 90.91 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
19 HCPVF 95.80 % 96.62 % 93.03 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
20 Anonymous 95.79 % 96.69 % 92.75 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
21 SECOND 95.79 % 96.44 % 90.55 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
22 DVF-V 95.77 % 96.60 % 90.89 % 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.
23 LIVOX_Det
This method makes use of Velodyne laser scans.
95.75 % 98.62 % 93.05 % n/a s 1 core @ 2.5 Ghz (Python + C/C++)
24 Fast-CLOCs 95.75 % 96.69 % 90.95 % 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.
25 DSGN++
This method uses stereo information.
code 95.70 % 98.08 % 88.27 % 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.
26 TBD 95.69 % 96.33 % 93.07 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
27 CasA 95.62 % 96.52 % 92.86 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
28 BADet code 95.61 % 98.75 % 90.64 % 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.
29 SE-SSD
This method makes use of Velodyne laser scans.
code 95.60 % 96.69 % 90.53 % 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.
30 JPVNet 95.52 % 96.41 % 90.72 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
31 Anonymous 95.50 % 98.36 % 90.63 % 0.1s 1 core @ 2.5 Ghz (C/C++)
32 DVF-PV 95.49 % 96.42 % 92.57 % 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.
33 Anonymous 95.47 % 98.35 % 90.55 % n/a s 1 core @ 2.5 Ghz (C/C++)
34 SPANet 95.46 % 96.54 % 90.47 % 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.
35 ISE-RCNN-PV 95.46 % 96.20 % 92.88 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
36 LGNet 95.43 % 96.52 % 92.73 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
37 TBD 95.43 % 96.06 % 90.38 % 0.1 s 1 core @ 2.5 Ghz (Python)
38 ISE-RCNN 95.43 % 96.38 % 92.82 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
39 Anonymous 95.41 % 96.49 % 90.68 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
40 GLENet 95.40 % 96.61 % 90.57 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
41 Anonymous 95.40 % 96.62 % 90.56 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
42 PTA-RCNN 95.39 % 96.40 % 92.40 % 0.08 s 1 core @ 2.5 Ghz (Python)
43 SASA
This method makes use of Velodyne laser scans.
code 95.35 % 96.01 % 92.53 % 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.
44 SPG_mini
This method makes use of Velodyne laser scans.
code 95.32 % 96.23 % 92.68 % 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.
45 EQ-PVRCNN code 95.32 % 98.23 % 92.65 % 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 95.31 % 96.36 % 92.57 % 0.09 s GPU @ 2.5 Ghz (Python)
47 TBD 95.29 % 96.17 % 90.56 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
48 Focals Conv code 95.28 % 96.30 % 92.69 % 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.
49 CasA++ 95.28 % 95.83 % 94.28 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
50 DGDNH 95.24 % 98.36 % 92.69 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
51 VoxSeT code 95.23 % 96.16 % 90.49 % 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.
52 PC-CNN-V2
This method makes use of Velodyne laser scans.
95.20 % 96.06 % 89.37 % 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.
53 VPFNet code 95.17 % 96.06 % 92.66 % 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.
54 F-PointNet
This method makes use of Velodyne laser scans.
code 95.17 % 95.85 % 85.42 % 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.
55 EPNet++ 95.17 % 96.73 % 92.10 % 0.1 s GPU @ 2.5 Ghz (Python)
Z. Liu, H. tengteng, B. Li, X. Chen, X. Wang and X. Bai: EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object Detection. arXiv preprint arXiv:2112.11088 2021.
56 SA-SSD code 95.16 % 97.92 % 90.15 % 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.
57 HMFI code 95.16 % 96.29 % 92.45 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
58 Pyramid R-CNN 95.13 % 95.88 % 92.62 % 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.
59 Voxel R-CNN code 95.11 % 96.49 % 92.45 % 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.
60 3DSSD code 95.10 % 97.69 % 92.18 % 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.
61 PV-RCNN++ code 95.05 % 96.08 % 92.42 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
62 PDV code 95.00 % 96.07 % 92.44 % 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.
63 ATT_SSD 94.99 % 96.02 % 92.18 % 0.01 s 1 core @ 2.5 Ghz (Python)
64 USVLab BSAODet 94.99 % 96.22 % 92.30 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
65 MVRA + I-FRCNN+ 94.98 % 95.87 % 82.52 % 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.
66 SIENet code 94.97 % 96.02 % 92.40 % 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 VueronNet code 94.97 % 97.85 % 89.68 % 0.06 s 1 core @ 2.0 Ghz (Python)
68 VoTr-TSD code 94.94 % 95.97 % 92.44 % 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.
69 GVNet-V2 94.92 % 96.29 % 92.21 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
70 AGS-SSD[la] 94.90 % 96.18 % 92.07 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
71 TBD code 94.90 % 95.98 % 92.11 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
72 GVNet code 94.86 % 96.30 % 92.22 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
73 TBD 94.85 % 97.04 % 92.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
74 M3DeTR code 94.83 % 97.39 % 92.10 % 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.
75 StructuralIF 94.81 % 96.14 % 92.12 % 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.
76 CSVoxel-RCNN 94.81 % 96.23 % 92.09 % 0.03 s GPU @ 1.0 Ghz (Python)
77 SRIF-RCNN 94.79 % 95.63 % 92.35 % 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.
78 ST-RCNN
This method makes use of Velodyne laser scans.
94.79 % 98.06 % 92.12 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
79 ST-RCNN (SNLW-RCNN)
This method makes use of Velodyne laser scans.
code 94.79 % 98.06 % 92.12 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
80 TPCG 94.78 % 95.96 % 92.25 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
81 DCCA 94.77 % 95.75 % 92.27 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
82 VCRCNN 94.77 % 96.06 % 92.28 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
83 XView 94.77 % 95.89 % 92.23 % 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.
84 TBD 94.74 % 95.88 % 91.96 % 0.06 s GPU @ 2.5 Ghz (Python)
85 P2V-RCNN 94.73 % 96.03 % 92.34 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.
86 FusionDetv2-v4 94.73 % 95.94 % 92.00 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
87 SqueezeRCNN 94.72 % 96.02 % 92.12 % 0.08 s 1 core @ 2.5 Ghz (Python)
88 SC-Voxel-RCNN 94.71 % 96.13 % 91.94 % 0.12 s GPU @ 1.0 Ghz (Python)
89 SPG
This method makes use of Velodyne laser scans.
code 94.71 % 97.80 % 92.19 % 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.
90 CAT-Det 94.71 % 95.97 % 92.07 % 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.
91 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 94.70 % 98.17 % 92.04 % 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.
92 DKDet 94.68 % 96.03 % 91.92 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
93 SVGA-Net 94.67 % 96.05 % 91.86 % 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.
94 DDet 94.66 % 95.82 % 92.17 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
95 FPV-SSD 94.66 % 96.92 % 91.78 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
96 FusionDetv2-v3 94.64 % 96.16 % 91.94 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
97 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
code 94.64 % 95.86 % 92.10 % 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.
98 RangeIoUDet
This method makes use of Velodyne laser scans.
94.61 % 95.74 % 91.98 % 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.
99 USVLab BSAODet (S) 94.60 % 96.10 % 90.03 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
100 CM3DV 94.59 % 95.80 % 92.05 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
101 DVFENet 94.57 % 95.35 % 91.77 % 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.
102 IKT3D
This method makes use of Velodyne laser scans.
94.57 % 95.77 % 91.97 % 0.05 s 1 core @ 2.5 Ghz (Python)
103 sa-voxel-centernet code 94.53 % 95.88 % 92.00 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
104 SPVB-SSD 94.49 % 95.80 % 91.90 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
105 TuSimple code 94.47 % 95.12 % 86.45 % 1.6 s GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016.
106 EPNet code 94.44 % 96.15 % 89.99 % 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.
107 SERCNN
This method makes use of Velodyne laser scans.
94.42 % 96.33 % 89.96 % 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.
108 TCDVF 94.31 % 95.02 % 91.33 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
109 UberATG-MMF
This method makes use of Velodyne laser scans.
94.25 % 97.41 % 89.87 % 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.
110 SRDL 94.24 % 95.86 % 91.80 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
111 FusionDetv1 94.23 % 95.84 % 91.80 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
112 TBD 94.22 % 95.00 % 91.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
113 FusionDetv2-v2 94.21 % 95.75 % 89.89 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
114 SARFE 94.18 % 95.74 % 91.57 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
115 NV2P-RCNN 94.07 % 97.82 % 91.20 % 0.1 s GPU @ 2.5 Ghz (Python)
116 DGT-Det3D 94.03 % 95.55 % 91.58 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
117 RangeRCNN
This method makes use of Velodyne laser scans.
94.03 % 95.48 % 91.74 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, M. Zhang, Z. Zhang, X. Zhao and S. Pu: RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representation. arXiv preprint arXiv:2009.00206 2020.
118 VueronNet 94.03 % 96.70 % 87.58 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
119 DGIST MT-CNN 94.00 % 95.25 % 86.76 % 0.09 s GPU @ 1.0 Ghz (Python)
120 Faraway-Frustum
This method makes use of Velodyne laser scans.
code 93.99 % 95.81 % 91.72 % 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.
121 DD3D code 93.99 % 94.69 % 89.37 % 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) .
122 SIF 93.95 % 95.51 % 91.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
123 TBD 93.89 % 94.52 % 86.44 % 0.1 s 1 core @ 2.5 Ghz (Python)
124 MGAF-3DSSD code 93.87 % 94.45 % 86.37 % 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.
125 LPCG-Monoflex 93.86 % 96.90 % 83.94 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
126 MMLAB LIGA-Stereo
This method uses stereo information.
code 93.82 % 96.43 % 86.19 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
127 DKAnet 93.79 % 95.16 % 89.27 % 0.05 s 1 core @ 2.0 Ghz (Python)
128 Sem-Aug 93.77 % 96.79 % 88.78 % 0.08 s GPU @ 2.5 Ghz (Python)
129 Associate-3Ddet_v2 93.77 % 96.83 % 88.57 % 0.04 s 1 core @ 2.5 Ghz (Python)
130 CF-ctdep-tv-ta 93.75 % 95.08 % 91.08 % 1 s 1 core @ 2.5 Ghz (C/C++)
131 Patches - EMP
This method makes use of Velodyne laser scans.
93.75 % 97.91 % 90.56 % 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.
132 CAD 93.73 % 96.84 % 88.74 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
133 CIA-SSD
This method makes use of Velodyne laser scans.
code 93.72 % 96.87 % 86.20 % 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.
134 Anonymous 93.68 % 95.19 % 91.12 % 1 1 core @ 2.5 Ghz (Python)
135 CF-base-tv 93.68 % 94.86 % 90.87 % 1 s 1 core @ 2.5 Ghz (C/C++)
136 DTFI 93.67 % 95.17 % 91.10 % 0.03 s 1 core @ 2.5 Ghz (Python)
137 QD-3DT
This is an online method (no batch processing).
code 93.66 % 94.26 % 83.63 % 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.
138 MVAF-Net code 93.66 % 95.37 % 90.90 % 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.
139 SSL-PointGNN code 93.65 % 96.61 % 88.53 % 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.
140 SA3DNet
This method uses stereo information.
This method makes use of Velodyne laser scans.
93.62 % 96.57 % 88.65 % 0.05 s GPU @ 2.5 Ghz (Python)
141 FusionDetv2-v5 93.61 % 95.33 % 89.22 % 0.05 s 1 core @ 2.5 Ghz (Java + C/C++)
142 KpNet 93.60 % 96.76 % 85.98 % 0.42 s 1 core @ 2.5 Ghz (C/C++)
143 KpNet 93.60 % 96.74 % 85.97 % 42 s 1 core @ 2.5 Ghz (C/C++)
144 TF3D
This method makes use of Velodyne laser scans.
93.57 % 96.59 % 88.64 % 0.1 s 2 cores @ 3.0 Ghz (Python)
145 DVF 93.57 % 96.46 % 88.58 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
146 IA-SSD (multi) code 93.56 % 96.10 % 90.68 % 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.
147 MonoPair 93.55 % 96.61 % 83.55 % 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.
148 IA-SSD (single) code 93.54 % 96.26 % 88.49 % 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.
149 EBM3DOD code 93.54 % 96.81 % 88.33 % 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.
150 Deep MANTA 93.50 % 98.89 % 83.21 % 0.7 s GPU @ 2.5 Ghz (Python + C/C++)
F. Chabot, M. Chaouch, J. Rabarisoa, C. Teulière and T. Chateau: Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image. CVPR 2017.
151 Point-GNN
This method makes use of Velodyne laser scans.
code 93.50 % 96.58 % 88.35 % 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.
152 FV2P v2 93.49 % 94.33 % 90.85 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
153 BtcDet
This method makes use of Velodyne laser scans.
code 93.47 % 96.23 % 88.55 % 0.09 s GPU @ 2.5 Ghz (Python + C/C++)
Q. Xu, Y. Zhong and U. Neumann: Behind the Curtain: Learning Occluded Shapes for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2022.
154 Struc info fusion II 93.45 % 96.72 % 88.31 % 0.05 s GPU @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.
155 TBD 93.45 % 96.35 % 90.35 % 0.03 s GPU @ 2.5 Ghz (Python)
156 EBM3DOD baseline code 93.45 % 96.72 % 88.25 % 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.
157 TBD
This method makes use of Velodyne laser scans.
93.43 % 96.52 % 90.71 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
158 StereoDistill 93.43 % 97.61 % 87.71 % 0.4 s 1 core @ 2.5 Ghz (Python)
159 RRC code 93.40 % 95.68 % 87.37 % 3.6 s GPU @ 2.5 Ghz (C/C++)
J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y. Tai and L. Xu: Accurate Single Stage Detector Using Recurrent Rolling Convolution. CVPR 2017.
160 Sem-Aug v1 code 93.39 % 96.39 % 90.70 % 0.04 s GPU @ 3.5 Ghz (Python)
161 TBD 93.38 % 94.17 % 88.39 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
162 3D-CVF at SPA
This method makes use of Velodyne laser scans.
93.36 % 96.78 % 86.11 % 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.
163 PVTr 93.36 % 94.54 % 90.82 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
164 Anonymous 93.34 % 96.44 % 83.76 % 40 s 1 core @ 2.5 Ghz (C/C++)
165 DSASNet 93.33 % 96.55 % 88.47 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
166 3SNet 93.33 % 96.40 % 90.65 % 0.07 s GPU @ 2.5 Ghz (Python)
167 TBD 93.33 % 96.02 % 90.50 % TBD GPU @ 2.5 Ghz (Python + C/C++)
168 SNVC
This method uses stereo information.
code 93.32 % 96.33 % 85.81 % 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.
169 FS-Net
This method makes use of Velodyne laser scans.
93.32 % 96.39 % 90.64 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
170 CF-ctdep-tv 93.31 % 94.89 % 90.87 % 1 s 1 core @ 2.5 Ghz (C/C++)
171 Struc info fusion I 93.31 % 96.59 % 88.23 % 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.
172 VPN 93.30 % 96.19 % 88.30 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
173 CityBrainLab-CT3D code 93.30 % 96.28 % 90.58 % 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.
174 MonoInsight 93.29 % 96.21 % 83.59 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
175 ITCA-SSD code 93.27 % 96.65 % 88.14 % 0.05 s 1 core @ 2.5 Ghz (Python)
176 Reprod-Two-Branch 93.26 % 94.83 % 90.61 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
177 STD code 93.22 % 96.14 % 90.53 % 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.
178 SARPNET 93.21 % 96.07 % 88.09 % 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.
179 CFF-ep25 93.20 % 94.81 % 90.76 % 1 s 1 core @ 2.5 Ghz (C/C++)
180 H^23D R-CNN code 93.20 % 96.20 % 90.55 % 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.
181 Fast Point R-CNN
This method makes use of Velodyne laser scans.
93.18 % 96.13 % 87.68 % 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.
182 VCT 93.17 % 96.31 % 90.52 % 0.2 s 1 core @ 2.5 Ghz (Python)
183 sensekitti code 93.17 % 94.79 % 84.38 % 4.5 s GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.
184 Sem-Aug-PointRCNN code 93.17 % 95.78 % 88.35 % 0.1 s GPU @ 3.5 Ghz (C/C++)
185 MVOD 93.16 % 96.17 % 92.56 % 0.16 s 1 core @ 2.5 Ghz (C/C++)
186 SJTU-HW 93.11 % 96.30 % 82.21 % 0.85s GPU @ 1.5 Ghz (Python + C/C++)
S. Zhang, X. Zhao, L. Fang, F. Haiping and S. Haitao: LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR. IEEE International Conference on Image Processing 2018.
L. Fang, X. Zhao and S. Zhang: Small-objectness sensitive detection based on shifted single shot detector. Multimedia Tools and Applications 2018.
187 SGNet 93.08 % 96.43 % 90.53 % 0.09 s GPU @ 2.5 Ghz (Python)
188 FromVoxelToPoint code 93.06 % 96.08 % 90.53 % 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.
189 CFF-tv 93.06 % 94.68 % 90.61 % 1 s 1 core @ 2.5 Ghz (C/C++)
190 cp-tv 92.99 % 94.52 % 90.36 % 1 s 1 core @ 2.5 Ghz (C/C++)
191 Anonymous 92.99 % 96.18 % 87.80 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
192 MSADet 92.95 % 96.18 % 89.95 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
193 CLOCs_SecCas 92.95 % 95.43 % 89.21 % 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.
194 cff-tv-v2-ep25 92.94 % 94.65 % 90.62 % 1 s 1 core @ 2.5 Ghz (C/C++)
195 KPP3D code 92.93 % 98.38 % 89.93 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
196 mbdf-netv1 code 92.83 % 96.00 % 89.90 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
197 CF-base-train 92.82 % 94.98 % 90.19 % 1 s 1 core @ 2.5 Ghz (C/C++)
198 MDNet 92.82 % 96.14 % 85.37 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
199 HotSpotNet 92.81 % 96.21 % 89.80 % 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.
200 SegVoxelNet 92.73 % 96.00 % 87.60 % 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.
201 Patches
This method makes use of Velodyne laser scans.
92.72 % 96.34 % 87.63 % 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.
202 CenterNet3D 92.69 % 95.76 % 89.81 % 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.
203 R-GCN 92.67 % 96.19 % 87.66 % 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.
204 PI-RCNN 92.66 % 96.17 % 87.68 % 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.
205 PointPainting
This method makes use of Velodyne laser scans.
92.58 % 98.39 % 89.71 % 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.
206 DASS 92.53 % 96.23 % 87.75 % 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.
207 NV-RCNN 92.51 % 95.86 % 89.95 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
208 Anonymous 92.51 % 95.88 % 85.35 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
209 3D IoU-Net 92.47 % 96.31 % 87.67 % 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.
210 CSNet8306 code 92.47 % 96.05 % 87.61 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
211 CSNet 92.46 % 95.99 % 89.24 % 0.1 s 1 core @ 2.5 Ghz (Python)
212 Associate-3Ddet code 92.45 % 95.61 % 87.32 % 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.
213 S-AT GCN 92.44 % 95.06 % 90.78 % 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.
214 LazyTorch-CP-Small-P 92.38 % 94.71 % 90.04 % 1 s 1 core @ 2.5 Ghz (C/C++)
215 3D-VDNet 92.35 % 95.42 % 89.37 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
216 LazyTorch-CP-Infer-O 92.35 % 94.76 % 90.09 % 1 s 1 core @ 2.5 Ghz (C/C++)
217 cp-tv-kp 92.34 % 94.32 % 90.25 % 1 s 1 core @ 2.5 Ghz (C/C++)
218 PointRGCN 92.33 % 97.51 % 87.07 % 0.26 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
219 Sem-Aug-PointRCNN++ 92.32 % 95.65 % 87.62 % 0.1 s 8 cores @ 3.0 Ghz (Python)
220 CSNet8299 code 92.31 % 96.14 % 87.54 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
221 PSM_stereo 92.27 % 95.63 % 87.24 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
222 CVFNet 92.27 % 95.61 % 88.75 % 28.1ms 1 core @ 2.5 Ghz (Python)
223 CenterFuse 92.26 % 95.04 % 89.24 % 0.059 sec/frame 2 x V100
224 F-ConvNet
This method makes use of Velodyne laser scans.
code 92.19 % 95.85 % 80.09 % 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.
225 Self-Calib Conv 92.17 % 94.45 % 90.19 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
226 PFF3D
This method makes use of Velodyne laser scans.
code 92.15 % 95.37 % 87.54 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
227 AFTD 92.14 % 95.56 % 87.45 % 1 s 1 core @ 2.5 Ghz (Python + C/C++)
228 cp-tv-kp-io-sc 92.11 % 95.02 % 88.97 % 1 s 1 core @ 2.5 Ghz (C/C++)
229 CF-cd-io-tv 92.06 % 94.80 % 88.80 % 1 s 1 core @ 2.5 Ghz (C/C++)
230 SDP+RPN 92.03 % 95.16 % 79.16 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 2015.
231 RangeDet code 92.03 % 95.20 % 87.14 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
232 KeyFuse2B 92.01 % 94.57 % 90.56 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
233 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 92.00 % 95.88 % 86.98 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
234 CrazyTensor-CP 91.95 % 93.64 % 89.23 % 1 s 1 core @ 2.5 Ghz (Python)
235 CFF-tv-v2 91.91 % 94.72 % 90.57 % 1 s 1 core @ 2.5 Ghz (C/C++)
236 Dune-DCF-e09 91.90 % 94.97 % 88.74 % 1 s 1 core @ 2.5 Ghz (C/C++)
237 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 91.90 % 95.92 % 87.11 % 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.
238 CrazyTensor-CF 91.89 % 94.98 % 88.70 % 1 s 1 core @ 2.5 Ghz (C/C++)
239 AutoAlign 91.87 % 95.15 % 89.21 % 0.1 s 1 core @ 2.5 Ghz (Python)
240 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 91.86 % 95.03 % 89.06 % 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.
241 WeakM3D code 91.81 % 94.51 % 85.35 % 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.
242 KeyPoint-IoUHead 91.79 % 94.65 % 88.61 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
243 epBRM
This method makes use of Velodyne laser scans.
code 91.77 % 94.59 % 88.45 % 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.
244 TBD 91.75 % 94.53 % 86.54 % 0.1 s 1 core @ 2.5 Ghz (Python)
245 C-GCN 91.73 % 95.64 % 86.37 % 0.147 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
246 ITVD code 91.73 % 95.85 % 79.31 % 0.3 s GPU @ 2.5 Ghz (C/C++)
Y. Wei Liu: Improving Tiny Vehicle Detection in Complex Scenes. IEEE International Conference on Multimedia and Expo (ICME) 2018.
247 sscl-20p 91.71 % 97.14 % 88.72 % 0.02 s 1 core @ 2.5 Ghz (Python)
248 Dune-DCF-e11 91.68 % 94.69 % 88.57 % 1 s 1 core @ 2.5 Ghz (C/C++)
249 City-CF-fixed 91.67 % 94.87 % 88.80 % 1 s 1 core @ 2.5 Ghz (C/C++)
250 Dune-DCF-e15 91.67 % 94.72 % 88.53 % 1 s 1 core @ 2.5 Ghz (C/C++)
251 SINet+ code 91.67 % 94.17 % 78.60 % 0.3 s TITAN X GPU
X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
252 SFD-Retina
This method uses stereo information.
This method uses optical flow information.
This method makes use of Velodyne laser scans.
91.64 % 94.85 % 82.13 % 0.04 s GPU @ 2.5 Ghz (Python)
253 CF-ctdep-train 91.64 % 94.81 % 90.16 % 1 s 1 core @ 2.5 Ghz (C/C++)
254 HS3D code 91.62 % 95.51 % 86.94 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
255 mono3d 91.60 % 94.60 % 84.86 % 0.03 s GPU @ 2.5 Ghz (Python)
256 Cascade MS-CNN code 91.60 % 94.26 % 78.84 % 0.25 s GPU @ 2.5 Ghz (C/C++)
Z. Cai and N. Vasconcelos: Cascade R-CNN: High Quality Object Detection and Instance Segmentation. arXiv preprint arXiv:1906.09756 2019.
Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A unified multi-scale deep convolutional neural network for fast object detection. European conference on computer vision 2016.
257 GFD-Retina
This method uses stereo information.
This method uses optical flow information.
This method makes use of Velodyne laser scans.
91.59 % 94.55 % 81.80 % 0.07 s GPU @ 2.5 Ghz (Python)
258 CenterPoint (pcdet) 91.58 % 93.99 % 89.15 % 0.051 sec/frame 2 x V100
259 TBD 91.56 % 94.52 % 86.29 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
260 CA3D 91.48 % 95.19 % 82.01 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
261 PointRGBNet 91.48 % 95.40 % 86.50 % 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.
262 MAFF-Net(DAF-Pillar) 91.46 % 94.38 % 83.89 % 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.
263 HRI-VoxelFPN 91.44 % 96.65 % 86.18 % 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.
264 new_stereo 91.39 % 95.01 % 86.77 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
265 EgoNet code 91.39 % 96.18 % 81.33 % 0.1 s GPU @ 1.5 Ghz (Python)
S. Li, Z. Yan, H. Li and K. Cheng: Exploring intermediate representation for monocular vehicle pose estimation. The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
266 IoU-2B 91.38 % 94.81 % 85.63 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
267 GPENet code 91.36 % 94.11 % 83.40 % 0.02 s GPU @ 2.5 Ghz (Python)
268 HNet-3DSSD
This method makes use of Velodyne laser scans.
code 91.35 % 94.91 % 87.47 % 0.05 s GPU @ 2.5 Ghz (Python)
269 City-CF 91.35 % 94.54 % 88.38 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
270 PP-PCdet code 91.32 % 94.82 % 88.18 % 0.01 s 1 core @ 2.5 Ghz (Python)
271 Contrastive PP code 91.29 % 96.93 % 88.11 % 0.01 s 1 core @ 2.5 Ghz (Python)
272 Stereo CenterNet
This method uses stereo information.
91.27 % 96.61 % 83.50 % 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.
273 PointPillars
This method makes use of Velodyne laser scans.
code 91.19 % 94.00 % 88.17 % 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.
274 LTN 91.18 % 94.68 % 81.51 % 0.4 s GPU @ >3.5 Ghz (Python)
T. Wang, X. He, Y. Cai and G. Xiao: Learning a Layout Transfer Network for Context Aware Object Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
275 WS3D
This method makes use of Velodyne laser scans.
91.15 % 95.13 % 86.52 % 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.
276 KM3D code 91.07 % 96.44 % 81.19 % 0.03 s 1 core @ 2.5 Ghz (Python)
P. Li: Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training. 2020.
277 PS++ code 91.06 % 96.23 % 83.39 % PS++ s 1 core @ 2.5 Ghz (C/C++)
278 3D_att
This method makes use of Velodyne laser scans.
91.05 % 96.70 % 85.88 % 0.17 s GPU @ 2.5 Ghz (Python)
279 Geo3D 91.04 % 94.28 % 78.91 % 0.04 s GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
280 DID-M3D 91.04 % 94.29 % 81.31 % 0.04 s 1 core @ 2.5 Ghz (Python)
281 FII-CenterNet code 91.03 % 94.48 % 83.00 % 0.09 s GPU @ 2.5 Ghz (Python)
S. Fan, F. Zhu, S. Chen, H. Zhang, B. Tian, Y. Lv and F. Wang: FII-CenterNet: An Anchor-Free Detector With Foreground Attention for Traffic Object Detection. IEEE Transactions on Vehicular Technology 2021.
282 Aston-EAS 91.02 % 93.91 % 77.93 % 0.24 s GPU @ 2.5 Ghz (Python + C/C++)
J. Wei, J. He, Y. Zhou, K. Chen, Z. Tang and Z. Xiong: Enhanced Object Detection With Deep Convolutional Neural Networks for Advanced Driving Assistance. IEEE Transactions on Intelligent Transportation Systems 2019.
283 MonoFlex 91.02 % 96.01 % 83.38 % 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.
284 Mix-Teaching 91.02 % 96.35 % 83.41 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
285 SCSTSV-MonoFlex 90.99 % 96.44 % 81.16 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
286 ARPNET 90.99 % 94.00 % 83.49 % 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.
287 cff-tv-t 90.98 % 94.68 % 84.39 % 1 s 1 core @ 2.5 Ghz (C/C++)
288 monopd code 90.93 % 96.44 % 83.36 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
289 DDS code 90.93 % 96.44 % 83.36 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
290 VMDet 90.92 % 96.39 % 81.14 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
291 MoGDE 90.91 % 96.47 % 83.66 % 0.03 s GPU @ 2.5 Ghz (Python)
292 MonoEF 90.88 % 96.32 % 83.27 % 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.
293 PatchNet code 90.87 % 93.82 % 79.62 % 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.
294 PS code 90.85 % 96.20 % 83.08 % PS s 1 core @ 2.5 Ghz (C/C++)
295 MV3D
This method makes use of Velodyne laser scans.
90.83 % 96.47 % 78.63 % 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.
296 MonoDistill 90.81 % 95.92 % 81.08 % 0.04 s 1 core @ 2.5 Ghz (Python)
297 monodle code 90.81 % 93.83 % 80.93 % 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 .
298 3D IoU Loss
This method makes use of Velodyne laser scans.
90.79 % 95.92 % 85.65 % 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.
299 SINet_VGG code 90.79 % 93.59 % 77.53 % 0.2 s TITAN X GPU
X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
300 Anonymous 90.78 % 96.42 % 83.51 % 40 s 1 core @ 2.5 Ghz (C/C++)
301 HomoLoss(monoflex) code 90.69 % 95.92 % 80.91 % 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.
302 TANet code 90.67 % 93.67 % 85.31 % 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.
303 MF 90.66 % 93.57 % 86.15 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
304 MonoGround 90.63 % 93.94 % 80.80 % 0.03 s 1 core @ 2.5 Ghz (Python)
305 MonoEdge 90.62 % 93.52 % 80.91 % 0.05 s GPU @ 2.5 Ghz (Python)
306 MonoCInIS 90.60 % 96.05 % 82.43 % 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.
307 MonoFlex 90.52 % 95.59 % 83.00 % 0.03 s 1 core @ 2.5 Ghz (Python)
308 CG-Stereo
This method uses stereo information.
90.38 % 96.31 % 82.80 % 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.
309 MonoEdge-Rotate 90.30 % 93.53 % 80.60 % 0.05 s GPU @ 2.5 Ghz (Python)
310 SCNet
This method makes use of Velodyne laser scans.
90.30 % 95.59 % 85.09 % 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.
311 CMKD* 90.28 % 95.14 % 83.91 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
312 PS-fld code 90.27 % 95.75 % 82.32 % 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.
313 MonoAug 90.24 % 95.59 % 80.47 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
314 Deep3DBox 90.19 % 94.71 % 76.82 % 1.5 s GPU @ 2.5 Ghz (C/C++)
A. Mousavian, D. Anguelov, J. Flynn and J. Kosecka: 3D Bounding Box Estimation Using Deep Learning and Geometry. CVPR 2017.
315 FQNet 90.17 % 94.72 % 76.78 % 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.
316 DeepStereoOP 90.06 % 95.15 % 79.91 % 3.4 s GPU @ 3.5 Ghz (Matlab + C/C++)
C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.
317 variance_point 90.01 % 95.79 % 87.50 % 0.05 s 1 core @ 2.5 Ghz (Python)
318 SubCNN 89.98 % 94.26 % 79.78 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.
319 MLOD
This method makes use of Velodyne laser scans.
code 89.97 % 94.88 % 84.98 % 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.
320 GPP code 89.96 % 94.02 % 81.13 % 0.23 s GPU @ 1.5 Ghz (Python + C/C++)
A. Rangesh and M. Trivedi: Ground plane polling for 6dof pose estimation of objects on the road. IEEE Transactions on Intelligent Vehicles 2020.
321 AVOD
This method makes use of Velodyne laser scans.
code 89.88 % 95.17 % 82.83 % 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.
322 SINet_PVA code 89.86 % 92.72 % 76.47 % 0.11 s TITAN X GPU
X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
323 Digging_M3D 89.77 % 93.73 % 79.68 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
324 TBD_BD code 89.73 % 94.18 % 86.84 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
325 3DOP
This method uses stereo information.
code 89.55 % 92.96 % 79.38 % 3s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
326 IAFA 89.46 % 93.08 % 79.83 % 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.
327 Mono3D code 89.37 % 94.52 % 79.15 % 4.2 s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.
328 4d-MSCNN
This method uses stereo information.
code 89.37 % 92.40 % 77.00 % 0.3 min GPU @ 3.0 Ghz (Matlab + C/C++)
P. Ferraz, B. Oliveira, F. Ferreira, C. Silva Martins and others: Three-stage RGBD architecture for vehicle and pedestrian detection using convolutional neural networks and stereo vision. IET Intelligent Transport Systems 2020.
329 MonoDDE 89.19 % 96.76 % 81.60 % 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.
330 PPTrans 89.17 % 95.17 % 81.77 % 0.2 s GPU @ 2.5 Ghz (Python)
331 FusionDetv2-v1 89.10 % 94.82 % 84.28 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
332 M3DSSD++ code 89.06 % 94.94 % 77.17 % 0.16s 1 core @ 2.5 Ghz (C/C++)
333 MonoEdge-RCNN 88.97 % 94.19 % 74.23 % 0.05 s 1 core @ 2.5 Ghz (Python)
334 AVOD-FPN
This method makes use of Velodyne laser scans.
code 88.92 % 94.70 % 84.13 % 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.
335 Keypoint-3D 88.87 % 93.31 % 76.10 % 14 s 1 core @ 2.5 Ghz (C/C++)
336 Shape-Aware 88.85 % 94.26 % 81.33 % 0.05 s 1 core @ 2.5 Ghz (Python)
337 PCT code 88.78 % 96.45 % 78.85 % 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.
338 OPA-3D code 88.77 % 96.50 % 76.55 % 0.04 s 1 core @ 3.5 Ghz (Python)
339 FusionDetv2-baseline 88.76 % 94.28 % 85.65 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
340 Lite-FPN-GUPNet 88.76 % 96.45 % 76.54 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
341 AM3D 88.71 % 92.55 % 77.78 % 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.
342 MS-CNN code 88.68 % 93.87 % 76.11 % 0.4 s GPU @ 2.5 Ghz (C/C++)
Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection. ECCV 2016.
343 MM-Retina
This method uses stereo information.
This method uses optical flow information.
This method makes use of Velodyne laser scans.
88.63 % 93.74 % 78.56 % 0.04 s GPU @ 2.5 Ghz (Python)
344 MonoPSR code 88.50 % 93.63 % 73.36 % 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.
345 Shift R-CNN (mono) code 88.48 % 94.07 % 78.34 % 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.
346 RCD 88.46 % 92.52 % 83.73 % 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.
347 MM-MRFC
This method uses optical flow information.
This method makes use of Velodyne laser scans.
88.46 % 95.54 % 78.14 % 0.05 s GPU @ 2.5 Ghz (C/C++)
A. Costea, R. Varga and S. Nedevschi: Fast Boosting based Detection using Scale Invariant Multimodal Multiresolution Filtered Features. CVPR 2017.
348 MonoDTR 88.41 % 93.90 % 76.20 % 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.
349 anonymity 88.41 % 95.28 % 81.68 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
350 anonymity 88.40 % 95.03 % 81.67 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
351 MonoFar 88.33 % 93.74 % 78.59 % 0.04 s 1 core @ 2.5 Ghz (Python)
352 3DBN
This method makes use of Velodyne laser scans.
88.29 % 93.74 % 80.74 % 0.13s 1080Ti (Python+C/C++)
X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.
353 MonoCon code 88.22 % 93.59 % 76.18 % 0.02 s GPU @ 2.5 Ghz (Python)
T. Xianpeng Liu: Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection. AAAI 2022.
354 MonoCInIS 88.16 % 96.22 % 75.72 % 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.
355 DEPT 88.00 % 96.45 % 78.40 % 0.03 s 1 core @ 2.5 Ghz (Python)
356 EW code 87.94 % 92.22 % 78.32 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
357 MonoRUn code 87.91 % 95.48 % 78.10 % 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.
358 SAIC_ADC_Mono3D code 87.78 % 95.21 % 80.14 % 50 s GPU @ 2.5 Ghz (Python)
359 MonoAug 87.76 % 93.65 % 77.91 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
360 CZY 87.55 % 94.63 % 82.97 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
361 SMOKE code 87.51 % 93.21 % 77.66 % 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.
362 zongmuDistill 87.47 % 95.44 % 79.97 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
363 CDN
This method uses stereo information.
code 87.19 % 95.85 % 79.43 % 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.
364 EG_DETR 87.10 % 93.04 % 79.05 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
365 mono3d code 87.07 % 93.28 % 77.72 % TBD TBD
366 RTM3D code 86.93 % 91.82 % 77.41 % 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.
367 MonoRCNN code 86.78 % 91.98 % 66.97 % 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.
368 Anonymous code 86.77 % 94.34 % 71.93 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
369 BirdNet+
This method makes use of Velodyne laser scans.
code 86.73 % 92.61 % 81.80 % 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.
370 UPF_3D
This method uses stereo information.
86.61 % 96.12 % 79.53 % 0.29 s 1 core @ 2.5 Ghz (Python)
371 MP-Mono 86.54 % 90.66 % 65.72 % 0.16 s GPU @ 2.5 Ghz (Python)
372 MK3D 86.48 % 94.40 % 74.21 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
373 ANM 86.45 % 94.32 % 76.54 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
374 GUPNet code 86.45 % 94.15 % 74.18 % 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.
375 GCDR 86.45 % 94.15 % 74.18 % 0.28 s 1 core @ 2.5 Ghz (Python)
376 DSGN
This method uses stereo information.
code 86.43 % 95.53 % 78.75 % 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.
377 gupnet_se 86.33 % 94.27 % 74.08 % 0.03s 1 core @ 2.5 Ghz (C/C++)
378 OBMO_GUPNet 86.27 % 96.49 % 76.47 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
379 MonoDETR code 86.17 % 93.99 % 76.19 % 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.
380 TBD 86.03 % 91.50 % 78.52 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
381 Stereo R-CNN
This method uses stereo information.
code 85.98 % 93.98 % 71.25 % 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.
382 StereoFENet
This method uses stereo information.
85.70 % 91.48 % 77.62 % 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.
383 City-ICN 85.70 % 93.91 % 73.41 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
384 Anonymous 85.65 % 92.64 % 78.83 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
385 DisposalNet
This method uses stereo information.
85.49 % 89.50 % 82.52 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
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386 LT-M3OD 85.42 % 93.99 % 75.77 % 0.03 s 1 core @ 2.5 Ghz (Python)
387 ResNet-RRC_Car 85.33 % 91.45 % 74.27 % 0.06 s GPU @ 1.5 Ghz (Python + C/C++)
H. Jeon and others: High-Speed Car Detection Using ResNet- Based Recurrent Rolling Convolution. Proceedings of the IEEE conference on systems, man, and cybernetics 2018.
388 SparseLiDAR_fusion 85.26 % 93.60 % 73.11 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
389 AEC3D 85.22 % 90.74 % 80.82 % 18 ms GPU @ 2.5 Ghz (Python)
390 PL++ (SDN+GDC)
This method uses stereo information.
This method makes use of Velodyne laser scans.
code 85.15 % 94.95 % 77.78 % 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.
391 M3D-RPN code 85.08 % 89.04 % 69.26 % 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 .
392 CDN-PL++
This method uses stereo information.
85.01 % 94.66 % 77.60 % 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.
393 M3DGAF 85.01 % 93.33 % 77.55 % 0.07 s 1 core @ 2.5 Ghz (Python)
394 SDP+CRC (ft) 85.00 % 92.06 % 71.71 % 0.6 s GPU @ 2.5 Ghz (C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
395 SS3D 84.92 % 92.72 % 70.35 % 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.
396 VN3D 84.80 % 90.73 % 78.41 % 0.02 s 1 core @ 2.5 Ghz (Python)
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397 ZongmuMono3d code 84.64 % 93.06 % 75.29 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
398 MonoFENet 84.63 % 91.68 % 76.71 % 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.
399 DLE code 84.45 % 94.66 % 62.10 % 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.
400 MV3D (LIDAR)
This method makes use of Velodyne laser scans.
84.39 % 93.08 % 79.27 % 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.
401 Complexer-YOLO
This method makes use of Velodyne laser scans.
84.16 % 91.92 % 79.62 % 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.
402 ZoomNet
This method uses stereo information.
code 83.92 % 94.22 % 69.00 % 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.
403 CMAN 83.74 % 89.74 % 65.35 % 0.15 s 1 core @ 2.5 Ghz (Python)
404 D4LCN code 83.67 % 90.34 % 65.33 % 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.
405 Faster R-CNN code 83.16 % 88.97 % 72.62 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards Real- Time Object Detection with Region Proposal Networks. NIPS 2015.
406 ppt 83.13 % 85.27 % 77.48 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
407 SGM3D 83.05 % 93.66 % 73.35 % 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.
408 Pseudo-LiDAR++
This method uses stereo information.
code 82.90 % 94.46 % 75.45 % 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.
409 Disp R-CNN
This method uses stereo information.
code 82.86 % 93.64 % 68.33 % 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.
410 BS3D 82.72 % 95.35 % 70.01 % 22 ms Titan Xp
N. Gählert, J. Wan, M. Weber, J. Zöllner, U. Franke and J. Denzler: Beyond Bounding Boxes: Using Bounding Shapes for Real-Time 3D Vehicle Detection from Monocular RGB Images. 2019 IEEE Intelligent Vehicles Symposium (IV) 2019.
411 CrazyTensor-ICN 82.70 % 90.79 % 70.61 % 1 s 1 core @ 2.5 Ghz (C/C++)
412 Disp R-CNN (velo)
This method uses stereo information.
code 82.64 % 93.45 % 70.45 % 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.
413 HBD 82.64 % 93.13 % 75.14 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
414 HomoLoss(imvoxelnet) code 82.54 % 92.81 % 72.80 % 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.
415 YOLOStereo3D
This method uses stereo information.
code 82.15 % 94.81 % 62.17 % 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.
416 Ground-Aware code 82.05 % 92.33 % 62.08 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
Y. Liu, Y. Yuan and M. Liu: Ground-aware Monocular 3D Object Detection for Autonomous Driving. IEEE Robotics and Automation Letters 2021.
417 FRCNN+Or code 82.00 % 92.91 % 68.79 % 0.09 s Titan Xp GPU
C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine 2018.
C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features. IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.
418 Anonymous 81.71 % 96.77 % 69.38 % 40 s 1 core @ 2.5 Ghz (C/C++)
419 SwinMono3D 81.71 % 91.99 % 61.78 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
420 DDMP-3D 81.70 % 91.15 % 63.12 % 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.
421 SD3DOD 81.64 % 92.60 % 75.97 % 0.04 s GPU @ 2.5 Ghz (Python)
422 A3DODWTDA (image) code 81.25 % 78.96 % 70.56 % 0.8 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
423 none 81.07 % 91.14 % 68.85 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
424 ART 81.03 % 90.97 % 74.44 % 20ms s 1 core @ 2.5 Ghz (C/C++)
425 RefineNet 81.01 % 91.91 % 65.67 % 0.20 s GPU @ 2.5 Ghz (Matlab + C++)
R. Rajaram, E. Bar and M. Trivedi: RefineNet: Refining Object Detectors for Autonomous Driving. IEEE Transactions on Intelligent Vehicles 2016.
R. Rajaram, E. Bar and M. Trivedi: RefineNet: Iterative Refinement for Accurate Object Localization. Intelligent Transportation Systems Conference 2016.
426 K3D 80.86 % 93.58 % 71.18 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
427 CaDDN code 80.73 % 93.61 % 71.09 % 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.
428 ESGN
This method uses stereo information.
80.58 % 93.07 % 70.68 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
429 PGD-FCOS3D code 80.58 % 92.04 % 69.67 % 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.
430 FD 80.38 % 92.08 % 75.65 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
431 GrooMeD-NMS code 80.28 % 90.14 % 63.78 % 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.
432 3D-GCK 80.19 % 89.55 % 68.08 % 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.
433 COF3D 80.16 % 87.85 % 61.97 % 200 s 1 core @ 2.5 Ghz (Python + C/C++)
434 EM code 80.05 % 87.00 % 66.77 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
435 Lite-FPN 79.65 % 87.04 % 65.56 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
436 YoloMono3D code 79.63 % 92.37 % 59.69 % 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.
437 E2E-DA 79.40 % 92.12 % 69.52 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
438 A3DODWTDA
This method makes use of Velodyne laser scans.
code 79.15 % 82.98 % 68.30 % 0.08 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
439 ImVoxelNet code 79.09 % 89.80 % 69.45 % 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.
440 DFR-Net 78.81 % 90.13 % 60.40 % 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.
441 spLBP 78.66 % 81.66 % 61.69 % 1.5 s 8 cores @ 2.5 Ghz (Matlab + C/C++)
Q. Hu, S. Paisitkriangkrai, C. Shen, A. Hengel and F. Porikli: Fast Detection of Multiple Objects in Traffic Scenes With a Common Detection Framework. IEEE Trans. Intelligent Transportation Systems 2016.
442 E2E-DA-Lite (Res18) 78.21 % 90.79 % 66.16 % 0.01 s GPU @ 2.5 Ghz (Python)
443 3D-SSMFCNN code 78.19 % 77.92 % 69.19 % 0.1 s GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.
444 MonoGRNet code 77.94 % 88.65 % 63.31 % 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.
445 Aug3D-RPN 77.88 % 85.57 % 61.16 % 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.
446 AutoShape code 77.66 % 86.51 % 64.40 % 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.
447 Reinspect code 77.48 % 90.27 % 66.73 % 2s 1 core @ 2.5 Ghz (C/C++)
R. Stewart, M. Andriluka and A. Ng: End-to-End People Detection in Crowded Scenes. CVPR 2016.
448 SARM3D 77.41 % 90.20 % 68.24 % 0.03 s GPU @ 2.5 Ghz (Python)
449 multi-task CNN 77.18 % 86.12 % 68.09 % 25.1 ms GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
450 Regionlets 76.99 % 88.75 % 60.49 % 1 s >8 cores @ 2.5 Ghz (C/C++)
X. Wang, M. Yang, S. Zhu and Y. Lin: Regionlets for Generic Object Detection. T-PAMI 2015.
W. Zou, X. Wang, M. Sun and Y. Lin: Generic Object Detection with Dense Neural Patterns and Regionlets. British Machine Vision Conference 2014.
C. Long, X. Wang, G. Hua, M. Yang and Y. Lin: Accurate Object Detection with Location Relaxation and Regionlets Relocalization. Asian Conference on Computer Vision 2014.
451 3DVP code 76.98 % 84.95 % 65.78 % 40 s 8 cores @ 3.5 Ghz (Matlab + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Data-Driven 3D Voxel Patterns for Object Category Recognition. IEEE Conference on Computer Vision and Pattern Recognition 2015.
452 Mobile Stereo R-CNN
This method uses stereo information.
76.73 % 90.08 % 62.23 % 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.
453 SubCat code 76.36 % 84.10 % 60.56 % 0.7 s 6 cores @ 3.5 Ghz (Matlab + C/C++)
E. Ohn-Bar and M. Trivedi: Learning to Detect Vehicles by Clustering Appearance Patterns. T-ITS 2015.
454 GS3D 76.35 % 86.23 % 62.67 % 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.
455 AOG code 76.24 % 86.08 % 61.51 % 3 s 4 cores @ 2.5 Ghz (Matlab)
T. Wu, B. Li and S. Zhu: Learning And-Or Models to Represent Context and Occlusion for Car Detection and Viewpoint Estimation. TPAMI 2016.
B. Li, T. Wu and S. Zhu: Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model. ECCV 2014.
456 Pose-RCNN 75.83 % 89.59 % 64.06 % 2 s >8 cores @ 2.5 Ghz (Python)
M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on 2016.
457 3D FCN
This method makes use of Velodyne laser scans.
74.65 % 86.74 % 67.85 % >5 s 1 core @ 2.5 Ghz (C/C++)
B. Li: 3D Fully Convolutional Network for Vehicle Detection in Point Cloud. IROS 2017.
458 OC Stereo
This method uses stereo information.
code 74.60 % 87.39 % 62.56 % 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.
459 MAOLoss code 73.79 % 89.31 % 63.59 % 0.05 s 1 core @ 2.5 Ghz (Python)
460 Kinematic3D code 71.73 % 89.67 % 54.97 % 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 .
461 AOG-View 71.26 % 85.01 % 55.73 % 3 s 1 core @ 2.5 Ghz (Matlab, C/C++)
B. Li, T. Wu and S. Zhu: Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model. ECCV 2014.
462 BEVC 70.93 % 79.97 % 64.46 % 35ms GPU @ 1.5 Ghz (Python)
463 GAC3D 70.73 % 83.30 % 52.23 % 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.
464 MV-RGBD-RF
This method makes use of Velodyne laser scans.
70.70 % 77.89 % 57.41 % 4 s 4 cores @ 2.5 Ghz (C/C++)
A. Gonzalez, D. Vazquez, A. Lopez and J. Amores: On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts.. IEEE Trans. on Cybernetics 2016.
A. Gonzalez, G. Villalonga, J. Xu, D. Vazquez, J. Amores and A. Lopez: Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection. IEEE Intelligent Vehicles Symposium (IV) 2015.
465 Vote3Deep
This method makes use of Velodyne laser scans.
70.30 % 78.95 % 63.12 % 1.5 s 4 cores @ 2.5 Ghz (C/C++)
M. Engelcke, D. Rao, D. Zeng Wang, C. Hay Tong and I. Posner: Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks. ArXiv e-prints 2016.
466 ROI-10D 70.16 % 76.56 % 61.15 % 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.
467 RetinaMono 69.83 % 74.54 % 60.95 % 0.02 s 1 core @ 2.5 Ghz (Python)
468 BirdNet+ (legacy)
This method makes use of Velodyne laser scans.
code 68.05 % 92.10 % 65.61 % 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.
469 Decoupled-3D 67.92 % 87.78 % 54.53 % 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.
470 SparVox3D 67.88 % 83.76 % 52.56 % 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.
471 Pseudo-Lidar
This method uses stereo information.
code 67.79 % 85.40 % 58.50 % 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.
472 OC-DPM 67.06 % 79.07 % 52.61 % 10 s 8 cores @ 2.5 Ghz (Matlab)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Occlusion Patterns for Object Class Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2013.
473 DPM-VOC+VP 66.72 % 82.15 % 49.01 % 8 s 1 core @ 2.5 Ghz (C/C++)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015.
474 BdCost48LDCF code 66.63 % 81.38 % 52.20 % 0.5 s 8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
475 MM 65.65 % 88.80 % 56.53 % 1 s 1 core @ 2.5 Ghz (C/C++)
476 RefinedMPL 65.24 % 88.29 % 53.20 % 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.
477 MDPM-un-BB 64.06 % 79.74 % 49.07 % 60 s 4 core @ 2.5 Ghz (MATLAB)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
478 TLNet (Stereo)
This method uses stereo information.
code 63.53 % 76.92 % 54.58 % 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.
479 PDV-Subcat 63.24 % 78.27 % 47.67 % 7 s 1 core @ 2.5 Ghz (C/C++)
J. Shen, X. Zuo, J. Li, W. Yang and H. Ling: A novel pixel neighborhood differential statistic feature for pedestrian and face detection . Pattern Recognition 2017.
480 MDSNet 62.74 % 85.94 % 50.27 % 0.07 s 1 core @ 2.5 Ghz (Python)
481 MODet
This method makes use of Velodyne laser scans.
62.54 % 66.06 % 60.04 % 0.05 s GTX1080Ti
Y. Zhang, Z. Xiang, C. Qiao and S. Chen: Accurate and Real-Time Object Detection Based on Bird's Eye View on 3D Point Clouds. 2019 International Conference on 3D Vision (3DV) 2019.
482 Graph-NMS 61.93 % 78.55 % 53.72 % 36 ms GPU @ 2.5 Ghz (Python)
483 SubCat48LDCF code 61.16 % 78.86 % 44.69 % 0.5 s 8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
484 DPM-C8B1
This method uses stereo information.
60.21 % 75.24 % 44.73 % 15 s 4 cores @ 2.5 Ghz (Matlab + C/C++)
J. Yebes, L. Bergasa and M. García-Garrido: Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes. Sensors 2015.
J. Yebes, L. Bergasa, R. Arroyo and A. Lázaro: Supervised learning and evaluation of KITTI's cars detector with DPM. IV 2014.
485 SAMME48LDCF code 58.38 % 77.47 % 44.43 % 0.5 s 8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
486 LSVM-MDPM-sv 58.36 % 71.11 % 43.22 % 10 s 4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout. NIPS 2011.
487 BirdNet
This method makes use of Velodyne laser scans.
57.12 % 79.30 % 55.16 % 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.
488 ACF-SC 56.60 % 69.90 % 43.61 % <0.3 s 1 core @ >3.5 Ghz (Matlab + C/C++)
C. Cadena, A. Dick and I. Reid: A Fast, Modular Scene Understanding System using Context-Aware Object Detection. Robotics and Automation (ICRA), 2015 IEEE International Conference on 2015.
489 LSVM-MDPM-us code 55.95 % 68.94 % 41.45 % 10 s 4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
490 VMDet_boost 54.16 % 71.33 % 47.50 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
491 ACF 54.09 % 63.05 % 41.81 % 0.2 s 1 core @ >3.5 Ghz (Matlab + C/C++)
P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014.
P. Doll\'ar: Piotr's Image and Video Matlab Toolbox (PMT). .
492 Mono3D_PLiDAR code 53.36 % 80.85 % 44.80 % 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.
493 Graph-NMS-baseline 52.92 % 76.21 % 43.38 % 47 ms GPU @ 2.5 Ghz (Python)
494 RT3D-GMP
This method uses stereo information.
51.95 % 62.41 % 39.14 % 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.
495 VeloFCN
This method makes use of Velodyne laser scans.
51.82 % 70.53 % 45.70 % 1 s GPU @ 2.5 Ghz (Python + C/C++)
B. Li, T. Zhang and T. Xia: Vehicle Detection from 3D Lidar Using Fully Convolutional Network. RSS 2016 .
496 Vote3D
This method makes use of Velodyne laser scans.
45.94 % 54.38 % 40.48 % 0.5 s 4 cores @ 2.8 Ghz (C/C++)
D. Wang and I. Posner: Voting for Voting in Online Point Cloud Object Detection. Proceedings of Robotics: Science and Systems 2015.
497 TopNet-HighRes
This method makes use of Velodyne laser scans.
45.85 % 58.04 % 41.11 % 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.
498 RT3DStereo
This method uses stereo information.
45.81 % 56.53 % 37.63 % 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.
499 Multimodal Detection
This method makes use of Velodyne laser scans.
code 45.46 % 63.91 % 37.25 % 0.06 s GPU @ 3.5 Ghz (Matlab + C/C++)
A. Asvadi, L. Garrote, C. Premebida, P. Peixoto and U. Nunes: Multimodal vehicle detection: fusing 3D- LIDAR and color camera data. Pattern Recognition Letters 2017.
500 CDTrack3D code 44.58 % 53.11 % 37.41 % 0.0106 s NVIDIA RTX 3090 GPU, i9 10850k CPU
501 RT3D
This method makes use of Velodyne laser scans.
39.69 % 50.33 % 40.04 % 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.
502 VoxelJones code 36.31 % 43.89 % 34.16 % .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.
503 CSoR
This method makes use of Velodyne laser scans.
code 21.66 % 31.52 % 17.99 % 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.
504 mBoW
This method makes use of Velodyne laser scans.
21.59 % 35.22 % 16.89 % 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.
505 DepthCN
This method makes use of Velodyne laser scans.
code 21.18 % 37.45 % 16.08 % 2.3 s GPU @ 3.5 Ghz (Matlab)
A. Asvadi, L. Garrote, C. Premebida, P. Peixoto and U. Nunes: DepthCN: vehicle detection using 3D- LIDAR and convnet. IEEE ITSC 2017.
506 YOLOv2 code 14.31 % 26.74 % 10.94 % 0.02 s GPU @ 3.5 Ghz (C/C++)
J. Redmon, S. Divvala, R. Girshick and A. Farhadi: You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
507 TopNet-UncEst
This method makes use of Velodyne laser scans.
6.24 % 7.24 % 5.42 % 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.
508 MonoDET code 5.80 % 6.41 % 5.13 % 0.04 s 1 core @ 2.5 Ghz (Python)
509 TopNet-Retina
This method makes use of Velodyne laser scans.
5.00 % 6.82 % 4.52 % 52ms GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
510 test code 4.63 % 1.85 % 4.96 % 50 s 1 core @ 2.5 Ghz (Python)
511 TopNet-DecayRate
This method makes use of Velodyne laser scans.
0.01 % 0.00 % 0.01 % 92 ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
512 LaserNet 0.00 % 0.00 % 0.00 % 12 ms GPU @ 2.5 Ghz (C/C++)
G. Meyer, A. Laddha, E. Kee, C. Vallespi-Gonzalez and C. Wellington: LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
513 Neighbor-Vote 0.00 % 0.00 % 0.00 % 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.
Table as LaTeX | Only published Methods

Pedestrian


Method Setting Code Moderate Easy Hard Runtime Environment
1 DGIST MT-CNN 80.78 % 89.66 % 76.52 % 0.09 s GPU @ 1.0 Ghz (Python)
2 VueronNet 80.75 % 89.91 % 76.56 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
3 F-PointNet
This method makes use of Velodyne laser scans.
code 80.13 % 89.83 % 75.05 % 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.
4 HHA-TFFEM
This method makes use of Velodyne laser scans.
78.53 % 87.01 % 74.70 % 0.14 s GPU @ 2.5 Ghz (Python + C/C++)
F. Tan, Z. Xia, Y. Ma and X. Feng: 3D Sensor Based Pedestrian Detection by Integrating Improved HHA Encoding and Two-Branch Feature Fusion. Remote Sensing 2022.
5 TuSimple code 78.40 % 88.87 % 73.66 % 1.6 s GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016.
6 RRC code 76.61 % 85.98 % 71.47 % 3.6 s GPU @ 2.5 Ghz (C/C++)
J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y. Tai and L. Xu: Accurate Single Stage Detector Using Recurrent Rolling Convolution. CVPR 2017.
7 WSSN
This method makes use of Velodyne laser scans.
76.42 % 84.91 % 71.86 % 0.37 s GPU @ >3.5 Ghz (Python + C/C++)
Z. Guo, W. Liao, Y. Xiao, P. Veelaert and W. Philips: Weak Segmentation Supervised Deep Neural Networks for Pedestrian Detection. Pattern Recognition 2021.
8 ECP Faster R-CNN 76.25 % 85.96 % 70.55 % 0.25 s GPU @ 2.5 Ghz (Python)
M. Braun, S. Krebs, F. Flohr and D. Gavrila: The EuroCity Persons Dataset: A Novel Benchmark for Object Detection. CoRR 2018.
9 Aston-EAS 76.07 % 86.71 % 70.02 % 0.24 s GPU @ 2.5 Ghz (Python + C/C++)
J. Wei, J. He, Y. Zhou, K. Chen, Z. Tang and Z. Xiong: Enhanced Object Detection With Deep Convolutional Neural Networks for Advanced Driving Assistance. IEEE Transactions on Intelligent Transportation Systems 2019.
10 MHN 75.99 % 87.21 % 69.50 % 0.39 s GPU @ 2.5 Ghz (Python)
J. Cao, Y. Pang, S. Zhao and X. Li: High-Level Semantic Networks for Multi- Scale Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2019.
11 FFNet code 75.81 % 87.17 % 69.86 % 1.07 s GPU @ 1.5 Ghz (Python)
C. Zhao, Y. Qian and M. Yang: Monocular Pedestrian Orientation Estimation Based on Deep 2D-3D Feedforward. Pattern Recognition 2019.
12 SJTU-HW 75.81 % 87.17 % 69.86 % 0.85s GPU @ 1.5 Ghz (Python + C/C++)
S. Zhang, X. Zhao, L. Fang, F. Haiping and S. Haitao: LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR. IEEE International Conference on Image Processing 2018.
L. Fang, X. Zhao and S. Zhang: Small-objectness sensitive detection based on shifted single shot detector. Multimedia Tools and Applications 2018.
13 MS-CNN code 74.89 % 85.71 % 68.99 % 0.4 s GPU @ 2.5 Ghz (C/C++)
Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection. ECCV 2016.
14 DD3D code 73.09 % 85.71 % 68.54 % 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) .
15 F-ConvNet
This method makes use of Velodyne laser scans.
code 72.91 % 83.63 % 67.18 % 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.
16 GN 72.29 % 82.93 % 65.56 % 1 s GPU @ 2.5 Ghz (Matlab + C/C++)
S. Jung and K. Hong: Deep network aided by guiding network for pedestrian detection. Pattern Recognition Letters 2017.
17 SubCNN 72.27 % 84.88 % 66.82 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.
18 VMVS
This method makes use of Velodyne laser scans.
71.82 % 82.80 % 66.85 % 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.
19 IVA code 71.37 % 84.61 % 64.90 % 0.4 s GPU @ 2.5 Ghz (C/C++)
Y. Zhu, J. Wang, C. Zhao, H. Guo and H. Lu: Scale-adaptive Deconvolutional Regression Network for Pedestrian Detection. ACCV 2016.
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 2015.
20 MM-MRFC
This method uses optical flow information.
This method makes use of Velodyne laser scans.
70.76 % 83.79 % 64.81 % 0.05 s GPU @ 2.5 Ghz (C/C++)
A. Costea, R. Varga and S. Nedevschi: Fast Boosting based Detection using Scale Invariant Multimodal Multiresolution Filtered Features. CVPR 2017.
21 SDP+RPN 70.42 % 82.07 % 65.09 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 2015.
22 3DOP
This method uses stereo information.
code 69.57 % 83.17 % 63.48 % 3s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
23 MonoPSR code 68.56 % 85.60 % 63.34 % 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.
24 DeepStereoOP 68.46 % 83.00 % 63.35 % 3.4 s GPU @ 3.5 Ghz (Matlab + C/C++)
C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.
25 sensekitti code 68.41 % 82.72 % 62.72 % 4.5 s GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.
26 Frustum-PointPillars code 67.51 % 76.80 % 63.81 % 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.
27 GFD-Retina
This method uses stereo information.
This method uses optical flow information.
This method makes use of Velodyne laser scans.
67.39 % 81.79 % 62.24 % 0.07 s GPU @ 2.5 Ghz (Python)
28 FII-CenterNet code 67.31 % 81.32 % 61.29 % 0.09 s GPU @ 2.5 Ghz (Python)
S. Fan, F. Zhu, S. Chen, H. Zhang, B. Tian, Y. Lv and F. Wang: FII-CenterNet: An Anchor-Free Detector With Foreground Attention for Traffic Object Detection. IEEE Transactions on Vehicular Technology 2021.
29 Mono3D code 67.29 % 80.30 % 62.23 % 4.2 s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.
30 SFD-Retina
This method uses stereo information.
This method uses optical flow information.
This method makes use of Velodyne laser scans.
67.17 % 82.48 % 62.02 % 0.04 s GPU @ 2.5 Ghz (Python)
31 CAD 66.40 % 76.10 % 63.58 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
32 GPENet code 66.37 % 80.47 % 60.72 % 0.02 s GPU @ 2.5 Ghz (Python)
33 Faster R-CNN code 66.24 % 79.97 % 61.09 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards Real- Time Object Detection with Region Proposal Networks. NIPS 2015.
34 VPFNet code 65.68 % 75.03 % 61.95 % 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.
35 EQ-PVRCNN code 65.01 % 77.19 % 61.95 % 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.
36 CasA++ 64.94 % 74.41 % 62.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
37 MM-Retina
This method uses stereo information.
This method uses optical flow information.
This method makes use of Velodyne laser scans.
64.91 % 79.88 % 59.60 % 0.04 s GPU @ 2.5 Ghz (Python)
38 TED 64.74 % 74.26 % 62.08 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
39 SDP+CRC (ft) 64.36 % 79.22 % 59.16 % 0.6 s GPU @ 2.5 Ghz (C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
40 Pose-RCNN 63.54 % 80.07 % 57.02 % 2 s >8 cores @ 2.5 Ghz (Python)
M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on 2016.
41 PV-RCNN++ code 63.47 % 72.82 % 60.96 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
42 CFM 62.84 % 74.76 % 56.06 % <2 s GPU @ 2.5 Ghz (Matlab + C/C++)
Q. Hu, P. Wang, C. Shen, A. Hengel and F. Porikli: Pushing the Limits of Deep CNNs for Pedestrian Detection. IEEE Transactions on Circuits and Systems for Video Technology 2017.
43 CasA 62.73 % 72.65 % 60.12 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
44 Fast-CLOCs 62.57 % 76.22 % 60.13 % 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.
45 PiFeNet 62.35 % 72.74 % 59.29 % 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.
46 HotSpotNet 62.31 % 71.43 % 59.24 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
47 variance_point 62.19 % 72.24 % 58.54 % 0.05 s 1 core @ 2.5 Ghz (Python)
48 Anonymous 62.11 % 69.00 % 59.15 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
49 VCT 62.00 % 71.19 % 58.52 % 0.2 s 1 core @ 2.5 Ghz (Python)
50 P2V-RCNN 61.83 % 71.76 % 59.29 % 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.
51 TBD 61.74 % 70.58 % 59.45 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
52 PE-RCVN 61.64 % 69.49 % 59.55 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
53 MonoPair 61.57 % 78.81 % 56.51 % 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.
54 monodle code 61.29 % 78.66 % 56.18 % 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 .
55 RPN+BF code 61.22 % 77.06 % 55.22 % 0.6 s GPU @ 2.5 Ghz (Matlab + C/C++)
L. Zhang, L. Lin, X. Liang and K. He: Is Faster R-CNN Doing Well for Pedestrian Detection?. ECCV 2016.
56 ISE-RCNN-PV 61.06 % 70.59 % 57.26 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
57 Regionlets 60.83 % 73.79 % 54.72 % 1 s >8 cores @ 2.5 Ghz (C/C++)
X. Wang, M. Yang, S. Zhu and Y. Lin: Regionlets for Generic Object Detection. T-PAMI 2015.
W. Zou, X. Wang, M. Sun and Y. Lin: Generic Object Detection with Dense Neural Patterns and Regionlets. British Machine Vision Conference 2014.
C. Long, X. Wang, G. Hua, M. Yang and Y. Lin: Accurate Object Detection with Location Relaxation and Regionlets Relocalization. Asian Conference on Computer Vision 2014.
58 CFF-tv 60.73 % 71.73 % 58.02 % 1 s 1 core @ 2.5 Ghz (C/C++)
59 ISE-RCNN 60.70 % 69.41 % 58.49 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
60 CFF-ep25 60.60 % 71.09 % 58.00 % 1 s 1 core @ 2.5 Ghz (C/C++)
61 3DSSD code 60.51 % 72.33 % 56.28 % 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.
62 cff-tv-v2-ep25 60.27 % 71.00 % 57.68 % 1 s 1 core @ 2.5 Ghz (C/C++)
63 Reprod-Two-Branch 60.24 % 71.39 % 57.66 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
64 KeyFuse2B 60.16 % 70.32 % 57.49 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
65 FS-Net
This method makes use of Velodyne laser scans.
60.13 % 69.08 % 57.91 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
66 CFF-tv-v2 60.10 % 70.11 % 57.57 % 1 s 1 core @ 2.5 Ghz (C/C++)
67 USVLab BSAODet (S) 60.08 % 70.93 % 57.40 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
68 USVLab BSAODet 59.83 % 69.64 % 57.31 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
69 VPN 59.48 % 70.97 % 55.29 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
70 AutoAlign 59.48 % 70.17 % 55.37 % 0.1 s 1 core @ 2.5 Ghz (Python)
71 QD-3DT
This is an online method (no batch processing).
code 59.26 % 78.41 % 54.37 % 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.
72 TANet code 59.07 % 69.90 % 56.44 % 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.
73 SARFE 59.07 % 68.25 % 56.74 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
74 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
code 58.81 % 66.93 % 56.57 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
75 TBD 58.77 % 67.92 % 55.33 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
76 SRDL 58.70 % 68.45 % 56.23 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
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77 FusionDetv1 58.68 % 68.44 % 56.22 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
78 P2V_PCV1 58.59 % 68.62 % 56.34 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
79 EG_DETR 58.58 % 74.53 % 53.76 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
80 CF-ctdep-tv-ta 58.54 % 68.36 % 56.11 % 1 s 1 core @ 2.5 Ghz (C/C++)
81 CM3DV 58.50 % 66.89 % 56.25 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
82 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 58.37 % 68.88 % 55.38 % 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.
83 Point-GNN
This method makes use of Velodyne laser scans.
code 58.20 % 71.59 % 54.06 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
84 TPCG 58.17 % 67.39 % 55.55 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
85 DeepParts 58.15 % 71.47 % 51.92 % ~1 s GPU @ 2.5 Ghz (Matlab)
Y. Tian, P. Luo, X. Wang and X. Tang: Deep Learning Strong Parts for Pedestrian Detection. ICCV 2015.
86 CompACT-Deep 58.14 % 70.93 % 52.29 % 1 s 1 core @ 2.5 Ghz (Matlab + C/C++)
Z. Cai, M. Saberian and N. Vasconcelos: Learning Complexity-Aware Cascades for Deep Pedestrian Detection. ICCV 2015.
87 EPNet++ 58.10 % 68.58 % 55.58 % 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.
88 DSGN++
This method uses stereo information.
code 58.09 % 69.70 % 54.45 % 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.
89 CF-base-tv 57.99 % 68.06 % 55.47 % 1 s 1 core @ 2.5 Ghz (C/C++)
90 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 57.96 % 68.78 % 54.01 % 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.
91 SVGA-Net 57.92 % 67.81 % 55.25 % 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.
92 AVOD-FPN
This method makes use of Velodyne laser scans.
code 57.87 % 67.95 % 55.23 % 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.
93 TBD
This method makes use of Velodyne laser scans.
57.81 % 67.54 % 54.30 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
94 sa-voxel-centernet code 57.79 % 66.03 % 55.61 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
95 Lite-FPN-GUPNet 57.56 % 76.82 % 50.42 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
96 LazyTorch-CP-Infer-O 57.47 % 67.75 % 55.08 % 1 s 1 core @ 2.5 Ghz (C/C++)
97 CenterPoint (pcdet) 57.38 % 67.60 % 54.98 % 0.051 sec/frame 2 x V100
98 SGNet 57.36 % 65.93 % 53.82 % 0.09 s GPU @ 2.5 Ghz (Python)
99 Faraway-Frustum
This method makes use of Velodyne laser scans.
code 57.35 % 67.88 % 54.42 % 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.
100 LazyTorch-CP-Small-P 57.35 % 67.67 % 55.04 % 1 s 1 core @ 2.5 Ghz (C/C++)
101 PDV code 57.34 % 65.94 % 54.21 % 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.
102 MVOD 57.33 % 66.72 % 54.31 % 0.16 s 1 core @ 2.5 Ghz (C/C++)
103 SIF 57.32 % 67.78 % 54.86 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
104 CF-ctdep-tv 57.27 % 67.17 % 54.80 % 1 s 1 core @ 2.5 Ghz (C/C++)
105 FromVoxelToPoint code 57.26 % 68.26 % 54.74 % 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.
106 Self-Calib Conv 57.25 % 66.38 % 55.44 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
107 SemanticVoxels 57.22 % 67.62 % 54.90 % 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.
108 DGT-Det3D 57.04 % 66.94 % 54.73 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
109 FusionDetv2-v3 56.93 % 65.96 % 54.73 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
110 IA-SSD (single) code 56.87 % 66.69 % 54.68 % 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 DDet 56.81 % 65.02 % 54.65 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
112 CAT-Det 56.75 % 67.15 % 53.44 % 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.
113 FV2P v2 56.74 % 66.99 % 54.48 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
114 FRCNN+Or code 56.68 % 71.64 % 51.53 % 0.09 s Titan Xp GPU
C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine 2018.
C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features. IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.
115 City-CF-fixed 56.62 % 67.73 % 53.85 % 1 s 1 core @ 2.5 Ghz (C/C++)
116 FusionDetv2-v4 56.60 % 65.80 % 54.55 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
117 AFTD 56.57 % 68.13 % 53.15 % 1 s 1 core @ 2.5 Ghz (Python + C/C++)
118 FilteredICF 56.53 % 69.79 % 50.32 % ~ 2 s >8 cores @ 2.5 Ghz (Matlab + C/C++)
S. Zhang, R. Benenson and B. Schiele: Filtered Channel Features for Pedestrian Detection. CVPR 2015.
119 ARPNET 56.42 % 69.08 % 52.69 % 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 TBD 56.42 % 65.60 % 53.61 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
121 TBD 56.42 % 65.60 % 53.61 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
122 FusionDetv2-v5 56.40 % 65.17 % 53.89 % 0.05 s 1 core @ 2.5 Ghz (Java + C/C++)
123 MonoRUn code 56.40 % 73.05 % 51.40 % 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.
124 cp-tv-kp 56.24 % 65.11 % 54.09 % 1 s 1 core @ 2.5 Ghz (C/C++)
125 3SNet 56.23 % 65.13 % 52.60 % 0.07 s GPU @ 2.5 Ghz (Python)
126 MV-RGBD-RF
This method makes use of Velodyne laser scans.
56.18 % 72.99 % 49.72 % 4 s 4 cores @ 2.5 Ghz (C/C++)
A. Gonzalez, D. Vazquez, A. Lopez and J. Amores: On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts.. IEEE Trans. on Cybernetics 2016.
A. Gonzalez, G. Villalonga, J. Xu, D. Vazquez, J. Amores and A. Lopez: Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection. IEEE Intelligent Vehicles Symposium (IV) 2015.
127 TBD 55.96 % 66.20 % 53.25 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
128 HMFI code 55.96 % 66.20 % 53.24 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
129 MGAF-3DSSD code 55.80 % 66.31 % 52.02 % 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.
130 cp-tv 55.74 % 64.83 % 53.76 % 1 s 1 core @ 2.5 Ghz (C/C++)
131 VCRCNN 55.66 % 64.59 % 53.53 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
132 GCDR 55.65 % 74.95 % 48.44 % 0.28 s 1 core @ 2.5 Ghz (Python)
133 GUPNet code 55.65 % 74.95 % 48.44 % 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.
134 MLOD
This method makes use of Velodyne laser scans.
code 55.62 % 68.42 % 51.45 % 0.12 s GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
135 Dune-DCF-e11 55.62 % 66.39 % 53.00 % 1 s 1 core @ 2.5 Ghz (C/C++)
136 IKT3D
This method makes use of Velodyne laser scans.
55.57 % 64.86 % 53.43 % 0.05 s 1 core @ 2.5 Ghz (Python)
137 gupnet_se 55.50 % 74.80 % 50.57 % 0.03s 1 core @ 2.5 Ghz (C/C++)
138 ST-RCNN
This method makes use of Velodyne laser scans.
55.46 % 64.50 % 52.89 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
139 CF-ctdep-train 55.42 % 65.48 % 52.84 % 1 s 1 core @ 2.5 Ghz (C/C++)
140 FusionDetv2-v2 55.32 % 62.74 % 52.43 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
141 MSADet 55.31 % 66.73 % 52.97 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
142 KeyPoint-IoUHead 55.20 % 66.99 % 51.68 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
143 CF-base-train 55.19 % 65.78 % 52.47 % 1 s 1 core @ 2.5 Ghz (C/C++)
144 TCDVF 55.18 % 65.12 % 52.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
145 CenterFuse 55.17 % 67.88 % 52.55 % 0.059 sec/frame 2 x V100
146 TBD 55.14 % 63.44 % 53.89 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
147 PointPillars
This method makes use of Velodyne laser scans.
code 55.10 % 65.29 % 52.39 % 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.
148 Dune-DCF-e15 55.09 % 65.54 % 52.36 % 1 s 1 core @ 2.5 Ghz (C/C++)
149 StereoDistill 55.09 % 69.00 % 50.95 % 0.4 s 1 core @ 2.5 Ghz (Python)
150 ANM 55.08 % 74.09 % 48.00 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
151 CrazyTensor-CP 55.05 % 64.92 % 52.88 % 1 s 1 core @ 2.5 Ghz (Python)
152 STD code 55.04 % 68.33 % 50.85 % 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.
153 Dune-DCF-e09 55.01 % 65.56 % 52.43 % 1 s 1 core @ 2.5 Ghz (C/C++)
154 NV-RCNN 54.98 % 64.78 % 51.64 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
155 OPA-3D code 54.92 % 73.93 % 47.87 % 0.04 s 1 core @ 3.5 Ghz (Python)
156 Vote3Deep
This method makes use of Velodyne laser scans.
54.80 % 67.99 % 51.17 % 1.5 s 4 cores @ 2.5 Ghz (C/C++)
M. Engelcke, D. Rao, D. Zeng Wang, C. Hay Tong and I. Posner: Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks. ArXiv e-prints 2016.
157 City-CF 54.79 % 65.01 % 52.24 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
158 M3DeTR code 54.78 % 63.15 % 52.49 % 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.
159 DSASNet 54.67 % 64.59 % 51.05 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
160 IoU-2B 54.61 % 69.65 % 51.43 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
161 tbd 54.44 % 64.96 % 50.57 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
162 epBRM
This method makes use of Velodyne laser scans.
code 54.13 % 62.90 % 51.95 % 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.
163 DVFENet 54.13 % 63.54 % 51.79 % 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.
164 TBD code 54.03 % 63.53 % 51.89 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
165 XView 53.83 % 62.27 % 51.61 % 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.
166 ATT_SSD 53.76 % 63.62 % 51.64 % 0.01 s 1 core @ 2.5 Ghz (Python)
167 PointPainting
This method makes use of Velodyne laser scans.
53.76 % 61.86 % 50.61 % 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.
168 MonoInsight 53.72 % 67.31 % 47.54 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
169 cff-tv-t 53.69 % 68.26 % 49.75 % 1 s 1 core @ 2.5 Ghz (C/C++)
170 PDV2 53.54 % 65.59 % 47.65 % 3.7 s 1 core @ 3.0 Ghz Matlab (C/C++)
J. Shen, X. Zuo, J. Li, W. Yang and H. Ling: A novel pixel neighborhood differential statistic feature for pedestrian and face detection . Pattern Recognition 2017.
171 Mix-Teaching 53.52 % 67.34 % 47.45 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
172 TBD 53.34 % 64.17 % 51.00 % TBD GPU @ 2.5 Ghz (Python + C/C++)
173 TAFT 53.15 % 67.62 % 47.08 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
J. Shen, X. Zuo, W. Yang, D. Prokhorov, X. Mei and H. Ling: Differential Features for Pedestrian Detection: A Taylor Series Perspective. IEEE Transactions on Intelligent Transportation Systems 2018.
174 MK3D 53.13 % 74.57 % 48.20 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
175 PVTr 53.11 % 61.97 % 51.00 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
176 FPV-SSD 53.10 % 62.00 % 50.67 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
177 Disp R-CNN
This method uses stereo information.
code 52.98 % 71.79 % 48.20 % 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.
178 FusionDetv2-baseline 52.96 % 58.96 % 50.94 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
179 Disp R-CNN (velo)
This method uses stereo information.
code 52.90 % 71.63 % 48.15 % 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.
180 pAUCEnsT 52.88 % 65.84 % 46.97 % 60 s 1 core @ 2.5 Ghz (Matlab + C/C++)
S. Paisitkriangkrai, C. Shen and A. Hengel: Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning. arXiv 2014.
181 CF-cd-io-tv 52.86 % 65.30 % 50.17 % 1 s 1 core @ 2.5 Ghz (C/C++)
182 SparVox3D 52.84 % 69.33 % 48.49 % 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.
183 cp-tv-kp-io-sc 52.77 % 64.59 % 50.47 % 1 s 1 core @ 2.5 Ghz (C/C++)
184 PFF3D
This method makes use of Velodyne laser scans.
code 52.53 % 62.12 % 50.27 % 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.
185 NV2P-RCNN 52.52 % 61.80 % 50.24 % 0.1 s GPU @ 2.5 Ghz (Python)
186 AGS-SSD[la] 52.52 % 61.65 % 50.08 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
187 HS3D code 52.50 % 63.73 % 49.78 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
188 MDNet 52.46 % 68.56 % 47.69 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
189 IA-SSD (multi) code 52.45 % 65.07 % 50.20 % 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.
190 S-AT GCN 52.30 % 62.01 % 50.10 % 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.
191 MMLAB LIGA-Stereo
This method uses stereo information.
code 52.18 % 65.59 % 49.29 % 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.
192 SCSTSV-MonoFlex 52.18 % 67.51 % 45.90 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
193 CrazyTensor-CF 52.00 % 62.19 % 49.49 % 1 s 1 core @ 2.5 Ghz (C/C++)
194 Shift R-CNN (mono) code 51.30 % 70.86 % 46.37 % 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.
195 TBD_BD code 50.60 % 60.91 % 48.47 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
196 TBD 50.45 % 60.47 % 47.02 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
197 MonoGround 50.35 % 63.96 % 45.75 % 0.03 s 1 core @ 2.5 Ghz (Python)
198 Shape-Aware 49.91 % 66.87 % 45.08 % 0.05 s 1 core @ 2.5 Ghz (Python)
199 SCNet
This method makes use of Velodyne laser scans.
49.61 % 60.95 % 46.91 % 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.
200 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 49.41 % 58.93 % 46.44 % 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.
201 MonoAug 49.10 % 65.03 % 44.42 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
202 PS++ code 49.02 % 61.65 % 44.96 % PS++ s 1 core @ 2.5 Ghz (C/C++)
203 HomoLoss(monoflex) code 48.97 % 63.77 % 44.60 % 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.
204 MonoAug 48.93 % 64.70 % 44.37 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
205 MonoFlex 48.64 % 61.96 % 44.17 % 0.03 s 1 core @ 2.5 Ghz (Python)
206 ACFD
This method makes use of Velodyne laser scans.
code 48.63 % 61.62 % 44.15 % 0.2 s 4 cores @ >3.5 Ghz (C/C++)
M. Dimitrievski, P. Veelaert and W. Philips: Semantically aware multilateral filter for depth upsampling in automotive LiDAR point clouds. IEEE Intelligent Vehicles Symposium, IV 2017, Los Angeles, CA, USA, June 11-14, 2017 2017.
207 R-CNN 48.57 % 62.88 % 43.05 % 4 s GPU @ 3.3 Ghz (C/C++)
J. Hosang, M. Omran, R. Benenson and B. Schiele: Taking a Deeper Look at Pedestrians. arXiv 2015.
208 PS code 48.39 % 60.99 % 44.42 % PS s 1 core @ 2.5 Ghz (C/C++)
209 MonoEdge 48.26 % 61.11 % 42.37 % 0.05 s GPU @ 2.5 Ghz (Python)
210 KPP3D code 47.76 % 57.06 % 45.35 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
211 MonoFlex 47.58 % 62.64 % 43.15 % 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.
212 MonoEdge-Rotate 47.56 % 62.79 % 43.02 % 0.05 s GPU @ 2.5 Ghz (Python)
213 BirdNet+
This method makes use of Velodyne laser scans.
code 47.50 % 54.78 % 45.53 % 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.
214 Contrastive PP code 47.05 % 56.47 % 45.04 % 0.01 s 1 core @ 2.5 Ghz (Python)
215 CMKD* 46.84 % 61.04 % 42.92 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
216 mono3d 46.73 % 58.34 % 41.92 % 0.03 s GPU @ 2.5 Ghz (Python)
217 M3DGAF 46.70 % 61.58 % 42.25 % 0.07 s 1 core @ 2.5 Ghz (Python)
218 PP-PCdet code 45.82 % 54.17 % 43.68 % 0.01 s 1 core @ 2.5 Ghz (Python)
219 SS3D 45.79 % 61.58 % 41.14 % 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.
220 DEPT 45.69 % 60.30 % 41.26 % 0.03 s 1 core @ 2.5 Ghz (Python)
221 ACF 45.67 % 59.81 % 40.88 % 1 s 1 core @ 3.5 Ghz (Matlab + C/C++)
P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014.
222 Fusion-DPM
This method makes use of Velodyne laser scans.
code 44.99 % 58.93 % 40.19 % ~ 30 s 1 core @ 3.5 Ghz (Matlab + C/C++)
C. Premebida, J. Carreira, J. Batista and U. Nunes: Pedestrian Detection Combining RGB and Dense LIDAR Data. IROS 2014.
223 M3DSSD++ code 44.89 % 58.54 % 40.66 % 0.16s 1 core @ 2.5 Ghz (C/C++)
224 ACF-MR 44.79 % 58.29 % 39.94 % 0.6 s 1 core @ 3.5 Ghz (C/C++)
R. Rajaram, E. Ohn-Bar and M. Trivedi: Looking at Pedestrians at Different Scales: A Multi-resolution Approach and Evaluations. T-ITS 2016.
225 Geo3D 44.42 % 58.74 % 40.13 % 0.04 s GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
226 LPCG-Monoflex 44.13 % 62.44 % 39.46 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
227 SAIC_ADC_Mono3D code 43.96 % 59.27 % 39.83 % 50 s GPU @ 2.5 Ghz (Python)
228 HA-SSVM 43.87 % 58.76 % 38.81 % 21 s 1 core @ >3.5 Ghz (Matlab + C/C++)
J. Xu, S. Ramos, D. Vázquez and A. López: Hierarchical Adaptive Structural SVM for Domain Adaptation. IJCV 2016.
229 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 43.86 % 54.55 % 40.99 % 0.0047s 1 core @ 2.5 Ghz (python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
230 MonoEF 43.73 % 58.79 % 39.45 % 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.
231 MonoFar 43.71 % 57.69 % 39.60 % 0.04 s 1 core @ 2.5 Ghz (Python)
232 Anonymous 43.68 % 58.24 % 39.56 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
233 City-ICN 43.56 % 60.56 % 38.71 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
234 D4LCN code 43.50 % 59.55 % 37.12 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
235 DisposalNet
This method uses stereo information.
43.43 % 52.99 % 41.29 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
236 CrazyTensor-ICN 43.43 % 60.32 % 38.58 % 1 s 1 core @ 2.5 Ghz (C/C++)
237 MonoDDE 43.36 % 57.80 % 39.00 % 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.
238 DPM-VOC+VP 43.26 % 59.21 % 38.12 % 8 s 1 core @ 2.5 Ghz (C/C++)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015.
239 MoGDE 43.17 % 59.10 % 39.06 % 0.03 s GPU @ 2.5 Ghz (Python)
240 ACF-SC 42.97 % 53.30 % 38.12 % <0.3 s 1 core @ >3.5 Ghz (Matlab + C/C++)
C. Cadena, A. Dick and I. Reid: A Fast, Modular Scene Understanding System using Context-Aware Object Detection. Robotics and Automation (ICRA), 2015 IEEE International Conference on 2015.
241 MonoDTR 42.86 % 59.44 % 38.57 % 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.
242 LT-M3OD 42.72 % 57.14 % 38.38 % 0.03 s 1 core @ 2.5 Ghz (Python)
243 SquaresICF code 42.61 % 57.08 % 37.85 % 1 s GPU @ >3.5 Ghz (C/C++)
R. Benenson, M. Mathias, T. Tuytelaars and L. Gool: Seeking the strongest rigid detector. CVPR 2013.
244 CG-Stereo
This method uses stereo information.
42.54 % 54.64 % 38.45 % 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.
245 MonoCon code 42.44 % 56.57 % 36.34 % 0.02 s GPU @ 2.5 Ghz (Python)
T. Xianpeng Liu: Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection. AAAI 2022.
246 BirdNet+ (legacy)
This method makes use of Velodyne laser scans.
code 41.97 % 51.38 % 40.15 % 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.
247 DDMP-3D 41.54 % 56.73 % 35.52 % 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.
248 CSW3D
This method makes use of Velodyne laser scans.
41.50 % 53.76 % 37.25 % 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.
249 M3D-RPN code 41.46 % 56.64 % 37.31 % 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 .
250 YOLOStereo3D
This method uses stereo information.
code 41.46 % 56.20 % 37.07 % 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.
251 CZY 41.01 % 50.27 % 38.93 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
252 MP-Mono 40.87 % 55.79 % 36.81 % 0.16 s GPU @ 2.5 Ghz (Python)
253 SwinMono3D 40.54 % 56.73 % 36.18 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
254 SubCat 40.50 % 53.75 % 35.66 % 1.2 s 6 cores @ 2.5 Ghz (Matlab + C/C++)
E. Ohn-Bar and M. Trivedi: Fast and Robust Object Detection Using Visual Subcategories. Computer Vision and Pattern Recognition Workshops Mobile Vision 2014.
255 PS-fld code 40.47 % 55.47 % 36.65 % 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.
256 DSGN
This method uses stereo information.
code 39.93 % 49.28 % 38.13 % 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.
257 RT3D-GMP
This method uses stereo information.
39.83 % 55.56 % 35.18 % 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.
258 FusionDetv2-v1 39.60 % 46.77 % 38.75 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
259 SparsePool code 39.59 % 50.81 % 35.91 % 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.
260 Graph-NMS-baseline 39.46 % 52.32 % 35.60 % 47 ms GPU @ 2.5 Ghz (Python)
261 SparsePool code 39.43 % 50.94 % 35.78 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
262 AVOD
This method makes use of Velodyne laser scans.
code 39.43 % 50.90 % 35.75 % 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.
263 ACF 39.12 % 48.42 % 35.03 % 0.2 s 1 core @ >3.5 Ghz (Matlab + C/C++)
P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014.
P. Doll\'ar: Piotr's Image and Video Matlab Toolbox (PMT). .
264 Graph-NMS 38.40 % 51.24 % 35.24 % 36 ms GPU @ 2.5 Ghz (Python)
265 LSVM-MDPM-sv 37.26 % 50.74 % 33.13 % 10 s 4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout. NIPS 2011.
266 multi-task CNN 37.00 % 49.38 % 33.46 % 25.1 ms GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
267 Lite-FPN 36.93 % 50.63 % 32.88 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
268 Complexer-YOLO
This method makes use of Velodyne laser scans.
36.45 % 42.16 % 32.91 % 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.
269 LSVM-MDPM-us code 35.92 % 48.73 % 31.70 % 10 s 4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
270 mono3d code 35.86 % 48.61 % 32.10 % TBD TBD
271 COF3D 35.34 % 50.45 % 31.28 % 200 s 1 core @ 2.5 Ghz (Python + C/C++)
272 CMAN 34.96 % 49.73 % 30.92 % 0.15 s 1 core @ 2.5 Ghz (Python)
273 Aug3D-RPN 34.95 % 47.22 % 30.64 % 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.
274 PPTrans 34.45 % 45.02 % 30.94 % 0.2 s GPU @ 2.5 Ghz (Python)
275 anonymity 34.41 % 47.09 % 30.59 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
276 PointRGBNet 33.92 % 44.35 % 30.43 % 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.
277 PGD-FCOS3D code 33.67 % 48.30 % 29.76 % 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.
278 ZongmuMono3d code 33.47 % 45.86 % 29.84 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
279 Vote3D
This method makes use of Velodyne laser scans.
33.04 % 42.66 % 30.59 % 0.5 s 4 cores @ 2.8 Ghz (C/C++)
D. Wang and I. Posner: Voting for Voting in Online Point Cloud Object Detection. Proceedings of Robotics: Science and Systems 2015.
280 ESGN
This method uses stereo information.
32.60 % 44.09 % 29.10 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
281 SGM3D 32.48 % 45.03 % 28.58 % 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.
282 CaDDN code 32.42 % 46.35 % 29.98 % 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.
283 SARM3D 32.33 % 42.60 % 29.23 % 0.03 s GPU @ 2.5 Ghz (Python)
284 HBD 31.99 % 44.66 % 28.21 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
285 DFR-Net 31.84 % 45.20 % 27.94 % 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.
286 K3D 31.71 % 44.40 % 27.90 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
287 OC Stereo
This method uses stereo information.
code 30.79 % 43.50 % 28.40 % 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.
288 CDTrack3D code 30.62 % 41.66 % 26.69 % 0.0106 s NVIDIA RTX 3090 GPU, i9 10850k CPU
289 mBoW
This method makes use of Velodyne laser scans.
30.26 % 41.52 % 26.34 % 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.
290 BirdNet
This method makes use of Velodyne laser scans.
30.07 % 36.82 % 28.40 % 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.
291 RT3DStereo
This method uses stereo information.
29.30 % 41.12 % 25.25 % 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.
292 MDSNet 29.25 % 41.64 % 26.01 % 0.07 s 1 core @ 2.5 Ghz (Python)
293 AEC3D 28.32 % 37.26 % 24.98 % 18 ms GPU @ 2.5 Ghz (Python)
294 BEVC 26.52 % 35.31 % 24.72 % 35ms GPU @ 1.5 Ghz (Python)
295 DPM-C8B1
This method uses stereo information.
25.34 % 36.40 % 22.00 % 15 s 4 cores @ 2.5 Ghz (Matlab + C/C++)
J. Yebes, L. Bergasa and M. García-Garrido: Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes. Sensors 2015.
J. Yebes, L. Bergasa, R. Arroyo and A. Lázaro: Supervised learning and evaluation of KITTI's cars detector with DPM. IV 2014.
296 MM 25.20 % 35.72 % 21.90 % 1 s 1 core @ 2.5 Ghz (C/C++)
297 VN3D 23.61 % 30.51 % 21.97 % 0.02 s 1 core @ 2.5 Ghz (Python)
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298 MAOLoss code 23.60 % 32.07 % 21.55 % 0.05 s 1 core @ 2.5 Ghz (Python)
299 RefinedMPL 20.81 % 30.41 % 18.72 % 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.
300 E2E-DA 19.96 % 29.93 % 17.46 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
301 E2E-DA-Lite (Res18) 19.87 % 32.25 % 17.37 % 0.01 s GPU @ 2.5 Ghz (Python)
302 TopNet-Retina
This method makes use of Velodyne laser scans.
16.45 % 22.37 % 15.43 % 52ms GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
303 TopNet-HighRes
This method makes use of Velodyne laser scans.
15.28 % 21.22 % 13.89 % 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.
304 YOLOv2 code 11.46 % 15.37 % 9.67 % 0.02 s GPU @ 3.5 Ghz (C/C++)
J. Redmon, S. Divvala, R. Girshick and A. Farhadi: You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
305 TopNet-UncEst
This method makes use of Velodyne laser scans.
8.58 % 13.00 % 7.38 % 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.
306 BIP-HETERO 7.05 % 8.51 % 6.30 % ~2 s 1 core @ 2.5 Ghz (C/C++)
A. Mekonnen, F. Lerasle, A. Herbulot and C. Briand: People Detection with Heterogeneous Features and Explicit Optimization on Computation Time. Pattern Recognition (ICPR), 2014 22nd International Conference on 2014.
307 EM code 1.70 % 1.67 % 1.61 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
308 TopNet-DecayRate
This method makes use of Velodyne laser scans.
0.01 % 0.01 % 0.01 % 92 ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
Table as LaTeX | Only published Methods

Cyclist


Method Setting Code Moderate Easy Hard Runtime Environment
1 TED 84.36 % 92.60 % 78.43 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
2 CasA++ 84.26 % 92.38 % 78.42 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
3 CasA 83.21 % 92.86 % 77.12 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
4 ISE-RCNN-PV 82.78 % 88.08 % 75.96 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
5 SARFE 82.00 % 90.01 % 75.30 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
6 SGNet 82.00 % 91.55 % 75.30 % 0.09 s GPU @ 2.5 Ghz (Python)
7 ISE-RCNN 81.95 % 87.68 % 75.17 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
8 HMFI code 81.76 % 89.35 % 74.93 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
9 RangeIoUDet
This method makes use of Velodyne laser scans.
81.67 % 90.43 % 74.90 % 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.
10 TPCG 80.71 % 87.73 % 74.17 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
11 CAT-Det 80.70 % 87.94 % 73.86 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Zhang, J. Chen and D. Huang: CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection. CVPR 2022.
12 SPG_mini
This method makes use of Velodyne laser scans.
code 80.58 % 87.77 % 74.86 % 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.
13 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
code 80.57 % 88.65 % 74.81 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
14 FS-Net
This method makes use of Velodyne laser scans.
80.54 % 87.05 % 73.81 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
15 CAD 80.53 % 91.65 % 73.56 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
16 BtcDet
This method makes use of Velodyne laser scans.
code 80.46 % 88.41 % 74.59 % 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.
17 VCRCNN 80.46 % 87.34 % 73.68 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
18 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 80.42 % 86.62 % 73.64 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
19 EQ-PVRCNN code 80.37 % 89.07 % 74.20 % 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.
20 IKT3D
This method makes use of Velodyne laser scans.
80.17 % 87.28 % 73.57 % 0.05 s 1 core @ 2.5 Ghz (Python)
21 USVLab BSAODet 80.04 % 88.51 % 73.38 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
22 Anonymous 80.00 % 87.66 % 74.48 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
23 sa-voxel-centernet code 79.98 % 88.08 % 73.61 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
24 CM3DV 79.92 % 86.43 % 73.47 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
25 PDV code 79.84 % 88.76 % 73.04 % 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.
26 CF-ctdep-tv-ta 79.76 % 90.48 % 73.04 % 1 s 1 core @ 2.5 Ghz (C/C++)
27 DSASNet 79.56 % 87.41 % 73.08 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
28 DDet 79.47 % 88.65 % 72.64 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
29 USVLab BSAODet (S) 79.34 % 89.04 % 72.50 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
30 M3DeTR code 79.29 % 87.38 % 72.46 % n/a s GPU @ 1.0 Ghz (Python)
T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis and D. Manocha: M3DeTR: Multi-representation, Multi- scale, Mutual-relation 3D Object Detection with Transformers. 2021.
31 PV-RCNN++ code 79.22 % 85.76 % 72.35 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
32 CFF-ep25 79.20 % 88.66 % 72.11 % 1 s 1 core @ 2.5 Ghz (C/C++)
33 CFF-tv 78.94 % 88.99 % 71.88 % 1 s 1 core @ 2.5 Ghz (C/C++)
34 Reprod-Two-Branch 78.92 % 88.74 % 71.93 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
35 3SNet 78.88 % 86.83 % 73.73 % 0.07 s GPU @ 2.5 Ghz (Python)
36 cff-tv-v2-ep25 78.86 % 88.39 % 71.68 % 1 s 1 core @ 2.5 Ghz (C/C++)
37 TBD 78.83 % 84.96 % 72.18 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
38 HotSpotNet 78.81 % 86.06 % 71.74 % 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.
39 JPVNet 78.73 % 87.42 % 72.45 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
40 IA-SSD (single) code 78.71 % 88.99 % 72.03 % 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 PE-RCVN 78.71 % 90.77 % 71.83 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
42 TBD
This method makes use of Velodyne laser scans.
78.59 % 86.51 % 73.55 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
43 CFF-tv-v2 78.59 % 87.90 % 71.64 % 1 s 1 core @ 2.5 Ghz (C/C++)
44 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 78.29 % 88.90 % 71.19 % 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.
45 CF-ctdep-tv 78.16 % 87.85 % 71.62 % 1 s 1 core @ 2.5 Ghz (C/C++)
46 F-ConvNet
This method makes use of Velodyne laser scans.
code 78.05 % 86.75 % 68.12 % 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.
47 PointPainting
This method makes use of Velodyne laser scans.
78.04 % 87.70 % 69.27 % 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.
48 AutoAlign 77.96 % 89.63 % 71.27 % 0.1 s 1 core @ 2.5 Ghz (Python)
49 MVOD 77.92 % 85.92 % 71.24 % 0.16 s 1 core @ 2.5 Ghz (C/C++)
50 CF-base-tv 77.87 % 86.17 % 71.35 % 1 s 1 core @ 2.5 Ghz (C/C++)
51 TBD 77.82 % 86.21 % 71.45 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
52 TCDVF 77.66 % 86.38 % 71.38 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
53 KeyFuse2B 77.58 % 89.00 % 70.66 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
54 ST-RCNN
This method makes use of Velodyne laser scans.
77.57 % 85.84 % 70.92 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
55 PVTr 77.39 % 89.56 % 70.23 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
56 TBD code 77.26 % 87.25 % 71.05 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
57 TBD 77.06 % 90.28 % 70.46 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
58 P2V-RCNN 76.93 % 88.40 % 70.35 % 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.
59 RRC code 76.81 % 86.81 % 66.59 % 3.6 s GPU @ 2.5 Ghz (C/C++)
J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y. Tai and L. Xu: Accurate Single Stage Detector Using Recurrent Rolling Convolution. CVPR 2017.
60 DGT-Det3D 76.71 % 86.58 % 70.08 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
61 FusionDetv2-v5 76.60 % 85.95 % 69.75 % 0.05 s 1 core @ 2.5 Ghz (Java + C/C++)
62 FusionDetv2-v3 76.48 % 86.15 % 69.64 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
63 NV2P-RCNN 76.29 % 83.53 % 69.57 % 0.1 s GPU @ 2.5 Ghz (Python)
64 CenterFuse 76.29 % 89.88 % 67.96 % 0.059 sec/frame 2 x V100
65 VCT 76.08 % 86.69 % 69.91 % 0.2 s 1 core @ 2.5 Ghz (Python)
66 FusionDetv2-v4 75.92 % 87.66 % 70.06 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
67 ATT_SSD 75.90 % 88.76 % 70.39 % 0.01 s 1 core @ 2.5 Ghz (Python)
68 FV2P v2 75.83 % 88.58 % 69.05 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
69 FPV-SSD 75.33 % 83.30 % 68.10 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
70 MS-CNN code 75.30 % 84.88 % 65.27 % 0.4 s GPU @ 2.5 Ghz (C/C++)
Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection. ECCV 2016.
71 TuSimple code 75.22 % 83.68 % 65.22 % 1.6 s GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016.
72 SVGA-Net 75.14 % 85.13 % 68.14 % 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.
73 Point-GNN
This method makes use of Velodyne laser scans.
code 75.08 % 85.75 % 68.69 % 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.
74 Fast-CLOCs 75.07 % 89.73 % 67.93 % 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.
75 TBD 75.02 % 88.92 % 68.08 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
76 cp-tv 74.89 % 84.13 % 68.41 % 1 s 1 core @ 2.5 Ghz (C/C++)
77 Deep3DBox 74.78 % 84.36 % 64.05 % 1.5 s GPU @ 2.5 Ghz (C/C++)
A. Mousavian, D. Anguelov, J. Flynn and J. Kosecka: 3D Bounding Box Estimation Using Deep Learning and Geometry. CVPR 2017.
78 DGIST MT-CNN 74.77 % 86.95 % 66.05 % 0.09 s GPU @ 1.0 Ghz (Python)
79 P2V_PCV1 74.57 % 85.38 % 68.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
80 VPFNet code 74.52 % 82.60 % 66.04 % 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.
81 FusionDetv2-v2 74.38 % 86.62 % 67.96 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
82 Self-Calib Conv 74.32 % 84.08 % 68.02 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
83 3DSSD code 74.12 % 87.09 % 67.67 % 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.
84 TBD 74.06 % 84.24 % 68.91 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
85 MSADet 74.06 % 87.54 % 68.55 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
86 VPN 73.99 % 89.56 % 66.86 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
87 SDP+RPN 73.85 % 82.59 % 64.87 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 2015.
88 FusionDetv1 73.69 % 85.39 % 66.94 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
89 cp-tv-kp 73.68 % 84.33 % 67.48 % 1 s 1 core @ 2.5 Ghz (C/C++)
90 SRDL 73.68 % 85.44 % 66.94 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
91 DVFENet 73.66 % 85.45 % 67.10 % 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.
92 cp-tv-kp-io-sc 73.66 % 87.24 % 66.98 % 1 s 1 core @ 2.5 Ghz (C/C++)
93 Faraway-Frustum
This method makes use of Velodyne laser scans.
code 73.63 % 85.43 % 66.64 % 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.
94 sensekitti code 73.48 % 82.90 % 64.03 % 4.5 s GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.
95 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 73.42 % 86.21 % 66.45 % 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.
96 SIF 73.19 % 85.18 % 65.41 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
97 CF-cd-io-tv 73.17 % 87.63 % 65.99 % 1 s 1 core @ 2.5 Ghz (C/C++)
98 F-PointNet
This method makes use of Velodyne laser scans.
code 73.16 % 86.86 % 65.21 % 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.
99 FromVoxelToPoint code 73.16 % 87.07 % 65.98 % 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.
100 XView 73.16 % 88.02 % 65.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
101 NV-RCNN 73.07 % 85.64 % 66.60 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
102 HS3D code 73.02 % 84.59 % 67.13 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
103 City-CF-fixed 72.86 % 86.69 % 66.70 % 1 s 1 core @ 2.5 Ghz (C/C++)
104 S-AT GCN 72.81 % 82.79 % 66.72 % 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.
105 KPP3D code 72.73 % 82.91 % 66.30 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
106 H^23D R-CNN code 72.73 % 85.50 % 65.81 % 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.
107 IoU-2B 72.71 % 89.27 % 63.76 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
108 KeyPoint-IoUHead 72.69 % 87.00 % 65.91 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
109 City-CF 72.68 % 86.44 % 66.22 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
110 FusionDetv2-baseline 72.68 % 82.09 % 66.42 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
111 AGS-SSD[la] 72.63 % 83.36 % 66.76 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
112 Dune-DCF-e15 72.38 % 86.51 % 66.02 % 1 s 1 core @ 2.5 Ghz (C/C++)
113 CF-base-train 72.24 % 86.19 % 65.04 % 1 s 1 core @ 2.5 Ghz (C/C++)
114 MonoPSR code 72.08 % 82.06 % 62.43 % 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.
115 ARPNET 71.95 % 84.96 % 65.21 % 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.
116 variance_point 71.87 % 83.16 % 63.53 % 0.05 s 1 core @ 2.5 Ghz (Python)
117 Dune-DCF-e11 71.82 % 86.21 % 65.26 % 1 s 1 core @ 2.5 Ghz (C/C++)
118 SubCNN 71.72 % 79.36 % 62.74 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.
119 STD code 71.63 % 83.99 % 64.92 % 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.
120 CF-ctdep-train 71.61 % 85.47 % 64.23 % 1 s 1 core @ 2.5 Ghz (C/C++)
121 cff-tv-t 71.22 % 88.33 % 63.92 % 1 s 1 core @ 2.5 Ghz (C/C++)
122 LazyTorch-CP-Infer-O 70.65 % 83.62 % 64.20 % 1 s 1 core @ 2.5 Ghz (C/C++)
123 CenterPoint (pcdet) 70.54 % 83.36 % 64.16 % 0.051 sec/frame 2 x V100
124 LazyTorch-CP-Small-P 70.48 % 83.46 % 64.07 % 1 s 1 core @ 2.5 Ghz (C/C++)
125 IA-SSD (multi) code 70.46 % 84.98 % 65.55 % 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.
126 MGAF-3DSSD code 70.41 % 86.42 % 63.26 % 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.
127 Dune-DCF-e09 70.28 % 83.14 % 64.12 % 1 s 1 core @ 2.5 Ghz (C/C++)
128 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 70.18 % 82.86 % 63.55 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
129 TBD 69.99 % 85.54 % 65.02 % TBD GPU @ 2.5 Ghz (Python + C/C++)
130 TBD_BD code 69.56 % 83.36 % 63.39 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
131 CrazyTensor-CP 69.31 % 83.67 % 62.97 % 1 s 1 core @ 2.5 Ghz (Python)
132 CrazyTensor-CF 69.23 % 85.28 % 62.37 % 1 s 1 core @ 2.5 Ghz (C/C++)
133 PointPillars
This method makes use of Velodyne laser scans.
code 68.98 % 83.97 % 62.17 % 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.
134 AFTD 68.83 % 88.94 % 62.04 % 1 s 1 core @ 2.5 Ghz (Python + C/C++)
135 Vote3Deep
This method makes use of Velodyne laser scans.
68.82 % 78.41 % 62.50 % 1.5 s 4 cores @ 2.5 Ghz (C/C++)
M. Engelcke, D. Rao, D. Zeng Wang, C. Hay Tong and I. Posner: Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks. ArXiv e-prints 2016.
136 3DOP
This method uses stereo information.
code 68.71 % 80.52 % 61.07 % 3s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
137 Pose-RCNN 68.40 % 81.53 % 59.43 % 2 s >8 cores @ 2.5 Ghz (Python)
M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on 2016.
138 EPNet++ 68.30 % 80.27 % 63.00 % 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.
139 TANet code 68.20 % 82.24 % 62.13 % 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.
140 Contrastive PP code 67.62 % 79.63 % 60.81 % 0.01 s 1 core @ 2.5 Ghz (Python)
141 IVA code 67.57 % 78.48 % 58.83 % 0.4 s GPU @ 2.5 Ghz (C/C++)
Y. Zhu, J. Wang, C. Zhao, H. Guo and H. Lu: Scale-adaptive Deconvolutional Regression Network for Pedestrian Detection. ACCV 2016.
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 2015.
142 DeepStereoOP 67.22 % 79.35 % 58.60 % 3.4 s GPU @ 3.5 Ghz (Matlab + C/C++)
C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.
143 TBD 67.15 % 82.44 % 60.87 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
144 TBD 67.15 % 82.44 % 60.87 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
145 VueronNet 66.92 % 82.71 % 59.01 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
146 PP-PCdet code 66.58 % 78.44 % 60.22 % 0.01 s 1 core @ 2.5 Ghz (Python)
147 FII-CenterNet code 66.54 % 79.04 % 57.76 % 0.09 s GPU @ 2.5 Ghz (Python)
S. Fan, F. Zhu, S. Chen, H. Zhang, B. Tian, Y. Lv and F. Wang: FII-CenterNet: An Anchor-Free Detector With Foreground Attention for Traffic Object Detection. IEEE Transactions on Vehicular Technology 2021.
148 epBRM
This method makes use of Velodyne laser scans.
code 66.51 % 79.65 % 60.31 % 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.
149 PFF3D
This method makes use of Velodyne laser scans.
code 66.25 % 79.44 % 60.11 % 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.
150 DD3D code 65.98 % 81.13 % 58.86 % 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) .
151 PointRGBNet 65.98 % 79.87 % 59.75 % 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.
152 BirdNet+
This method makes use of Velodyne laser scans.
code 65.40 % 72.96 % 60.23 % 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.
153 Mono3D code 65.15 % 77.19 % 57.88 % 4.2 s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.
154 tbd 64.31 % 79.83 % 58.14 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
155 DisposalNet
This method uses stereo information.
63.39 % 74.69 % 56.96 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
156 PiFeNet 63.34 % 78.05 % 56.46 % 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.
157 Faster R-CNN code 62.86 % 72.40 % 54.97 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards Real- Time Object Detection with Region Proposal Networks. NIPS 2015.
158 SCNet
This method makes use of Velodyne laser scans.
62.50 % 78.48 % 56.34 % 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.
159 CZY 62.20 % 73.99 % 56.07 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
160 DSGN++
This method uses stereo information.
code 62.10 % 77.71 % 55.78 % 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.
161 StereoDistill 61.46 % 80.92 % 54.64 % 0.4 s 1 core @ 2.5 Ghz (Python)
162 AVOD-FPN
This method makes use of Velodyne laser scans.
code 60.79 % 70.38 % 55.37 % 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.
163 SDP+CRC (ft) 60.72 % 75.63 % 53.00 % 0.6 s GPU @ 2.5 Ghz (C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
164 MonoInsight 59.86 % 76.21 % 51.17 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
165 Complexer-YOLO
This method makes use of Velodyne laser scans.
59.78 % 66.94 % 55.63 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
166 Mix-Teaching 58.65 % 75.15 % 50.54 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
167 Regionlets 58.52 % 71.12 % 50.83 % 1 s >8 cores @ 2.5 Ghz (C/C++)
X. Wang, M. Yang, S. Zhu and Y. Lin: Regionlets for Generic Object Detection. T-PAMI 2015.
W. Zou, X. Wang, M. Sun and Y. Lin: Generic Object Detection with Dense Neural Patterns and Regionlets. British Machine Vision Conference 2014.
C. Long, X. Wang, G. Hua, M. Yang and Y. Lin: Accurate Object Detection with Location Relaxation and Regionlets Relocalization. Asian Conference on Computer Vision 2014.
168 SFD-Retina
This method uses stereo information.
This method uses optical flow information.
This method makes use of Velodyne laser scans.
57.71 % 76.35 % 50.56 % 0.04 s GPU @ 2.5 Ghz (Python)
169 FRCNN+Or code 57.01 % 70.99 % 50.14 % 0.09 s Titan Xp GPU
C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine 2018.
C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features. IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.
170 GFD-Retina
This method uses stereo information.
This method uses optical flow information.
This method makes use of Velodyne laser scans.
56.93 % 74.59 % 49.91 % 0.07 s GPU @ 2.5 Ghz (Python)
171 QD-3DT
This is an online method (no batch processing).
code 56.51 % 75.55 % 49.70 % 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.
172 MonoPair 56.37 % 74.77 % 48.37 % 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.
173 FusionDetv2-v1 56.30 % 66.22 % 52.29 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
174 MLOD
This method makes use of Velodyne laser scans.
code 56.04 % 75.35 % 49.11 % 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.
175 GPENet code 55.96 % 71.89 % 48.36 % 0.02 s GPU @ 2.5 Ghz (Python)
176 MDNet 55.58 % 69.87 % 46.54 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
177 MoGDE 55.22 % 79.09 % 46.54 % 0.03 s GPU @ 2.5 Ghz (Python)
178 MM-Retina
This method uses stereo information.
This method uses optical flow information.
This method makes use of Velodyne laser scans.
54.90 % 69.95 % 48.49 % 0.04 s GPU @ 2.5 Ghz (Python)
179 mono3d 54.82 % 70.77 % 47.55 % 0.03 s GPU @ 2.5 Ghz (Python)
180 MonoFlex 54.76 % 72.41 % 46.21 % 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.
181 M3DSSD++ code 54.62 % 69.35 % 46.25 % 0.16s 1 core @ 2.5 Ghz (C/C++)
182 BirdNet+ (legacy)
This method makes use of Velodyne laser scans.
code 54.61 % 74.97 % 50.29 % 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.
183 MMLAB LIGA-Stereo
This method uses stereo information.
code 54.57 % 74.40 % 48.11 % 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.
184 SCSTSV-MonoFlex 54.42 % 75.37 % 46.25 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
185 HomoLoss(monoflex) code 54.12 % 70.14 % 46.16 % 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.
186 Shape-Aware 53.48 % 70.94 % 46.95 % 0.05 s 1 core @ 2.5 Ghz (Python)
187 monodle code 53.29 % 70.78 % 45.01 % 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 .
188 LPCG-Monoflex 53.04 % 72.36 % 46.11 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
189 PS++ code 52.87 % 72.93 % 46.56 % PS++ s 1 core @ 2.5 Ghz (C/C++)
190 AVOD
This method makes use of Velodyne laser scans.
code 52.60 % 66.45 % 46.39 % 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.
191 MonoEdge-Rotate 51.89 % 68.91 % 45.33 % 0.05 s GPU @ 2.5 Ghz (Python)
192 PS code 51.87 % 71.65 % 46.48 % PS s 1 core @ 2.5 Ghz (C/C++)
193 CMKD* 51.76 % 73.18 % 45.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
194 MonoGround 51.67 % 67.78 % 45.11 % 0.03 s 1 core @ 2.5 Ghz (Python)
195 Geo3D 51.26 % 71.75 % 44.44 % 0.04 s GPU @ 2.5 Ghz (Python)
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196 MonoFlex 51.23 % 66.73 % 44.57 % 0.03 s 1 core @ 2.5 Ghz (Python)
197 MonoDDE 51.10 % 70.85 % 44.02 % 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.
198 MonoDTR 49.48 % 64.93 % 42.76 % 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.
199 MonoEdge 49.33 % 65.93 % 42.44 % 0.05 s GPU @ 2.5 Ghz (Python)
200 M3DGAF 49.25 % 64.62 % 42.82 % 0.07 s 1 core @ 2.5 Ghz (Python)
201 MonoRUn code 49.13 % 67.47 % 43.41 % 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.
202 DEPT 49.04 % 69.01 % 42.71 % 0.03 s 1 core @ 2.5 Ghz (Python)
203 MonoAug 48.61 % 69.31 % 42.24 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
204 MP-Mono 48.54 % 69.48 % 41.58 % 0.16 s GPU @ 2.5 Ghz (Python)
205 CG-Stereo
This method uses stereo information.
48.46 % 69.98 % 42.41 % 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.
206 EG_DETR 48.42 % 67.71 % 42.99 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
207 Anonymous 48.01 % 66.15 % 41.50 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
208 BirdNet
This method makes use of Velodyne laser scans.
47.64 % 64.91 % 44.59 % 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.
209 anonymity 47.21 % 66.52 % 41.56 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
210 gupnet_se 46.47 % 68.66 % 37.89 % 0.03s 1 core @ 2.5 Ghz (C/C++)
211 Disp R-CNN (velo)
This method uses stereo information.
code 46.37 % 63.22 % 40.15 % 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.
212 Disp R-CNN
This method uses stereo information.
code 46.37 % 63.24 % 40.15 % 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.
213 LT-M3OD 45.87 % 63.54 % 39.19 % 0.03 s 1 core @ 2.5 Ghz (Python)
214 SwinMono3D 45.72 % 67.95 % 38.55 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
215 anonymity 45.69 % 65.71 % 40.11 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
216 MonoAug 45.48 % 65.80 % 39.05 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
217 Lite-FPN-GUPNet 45.16 % 64.84 % 38.38 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
218 SparsePool code 44.57 % 60.53 % 40.37 % 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.
219 MonoFar 44.57 % 65.17 % 39.96 % 0.04 s 1 core @ 2.5 Ghz (Python)
220 MK3D 43.41 % 64.38 % 36.70 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
221 Shift R-CNN (mono) code 42.96 % 63.24 % 38.22 % 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.
222 D4LCN code 42.86 % 65.29 % 36.29 % 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.
223 GCDR 42.78 % 67.11 % 37.94 % 0.28 s 1 core @ 2.5 Ghz (Python)
224 GUPNet code 42.78 % 67.11 % 37.94 % 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.
225 MonoCon code 42.49 % 59.39 % 35.94 % 0.02 s GPU @ 2.5 Ghz (Python)
T. Xianpeng Liu: Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection. AAAI 2022.
226 SAIC_ADC_Mono3D code 42.37 % 60.72 % 36.57 % 50 s GPU @ 2.5 Ghz (Python)
227 M3D-RPN code 41.54 % 61.54 % 35.23 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
228 MonoEF 41.19 % 51.06 % 35.70 % 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.
229 PS-fld code 41.13 % 58.13 % 35.90 % 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.
230 MV-RGBD-RF
This method makes use of Velodyne laser scans.
40.94 % 51.10 % 34.83 % 4 s 4 cores @ 2.5 Ghz (C/C++)
A. Gonzalez, D. Vazquez, A. Lopez and J. Amores: On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts.. IEEE Trans. on Cybernetics 2016.
A. Gonzalez, G. Villalonga, J. Xu, D. Vazquez, J. Amores and A. Lopez: Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection. IEEE Intelligent Vehicles Symposium (IV) 2015.
231 PPTrans 39.55 % 48.93 % 33.74 % 0.2 s GPU @ 2.5 Ghz (Python)
232 ANM 39.39 % 57.77 % 34.59 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
233 CrazyTensor-ICN 38.69 % 59.42 % 33.90 % 1 s 1 core @ 2.5 Ghz (C/C++)
234 DDMP-3D 38.62 % 58.70 % 34.10 % 0.18 s 1 core @ 2.5 Ghz (Python)
L. Wang, L. Du, X. Ye, Y. Fu, G. Guo, X. Xue, J. Feng and L. Zhang: Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection. CVPR 2020.
235 CMAN 38.36 % 58.12 % 31.79 % 0.15 s 1 core @ 2.5 Ghz (Python)
236 OPA-3D code 38.35 % 55.98 % 33.83 % 0.04 s 1 core @ 3.5 Ghz (Python)
237 mono3d code 37.18 % 49.10 % 32.04 % TBD TBD
238 AEC3D 37.15 % 48.48 % 34.86 % 18 ms GPU @ 2.5 Ghz (Python)
239 Aug3D-RPN 36.69 % 51.49 % 30.04 % 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.
240 SparsePool code 36.26 % 44.21 % 32.57 % 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.
241 City-ICN 35.87 % 53.37 % 29.37 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
242 SS3D 35.48 % 52.97 % 31.07 % 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.
243 DSGN
This method uses stereo information.
code 35.15 % 49.10 % 31.41 % 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.
244 pAUCEnsT 34.90 % 50.51 % 30.35 % 60 s 1 core @ 2.5 Ghz (Matlab + C/C++)
S. Paisitkriangkrai, C. Shen and A. Hengel: Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning. arXiv 2014.
245 COF3D 32.97 % 51.77 % 28.26 % 200 s 1 core @ 2.5 Ghz (Python + C/C++)
246 TopNet-Retina
This method makes use of Velodyne laser scans.
31.98 % 47.51 % 29.84 % 52ms GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
247 DFR-Net 31.93 % 48.34 % 27.95 % 0.18 s 1080 Ti (Pytorch)
Z. Zou, X. Ye, L. Du, X. Cheng, X. Tan, L. Zhang, J. Feng, X. Xue and E. Ding: The devil is in the task: Exploiting reciprocal appearance-localization features for monocular 3d object detection . ICCV 2021.
248 VN3D 31.81 % 42.58 % 29.09 % 0.02 s 1 core @ 2.5 Ghz (Python)
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249 ZongmuMono3d code 31.56 % 44.68 % 27.48 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
250 BEVC 30.39 % 43.61 % 27.46 % 35ms GPU @ 1.5 Ghz (Python)
251 OC Stereo
This method uses stereo information.
code 28.76 % 43.18 % 24.80 % 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.
252 Vote3D
This method makes use of Velodyne laser scans.
27.99 % 39.81 % 25.19 % 0.5 s 4 cores @ 2.8 Ghz (C/C++)
D. Wang and I. Posner: Voting for Voting in Online Point Cloud Object Detection. Proceedings of Robotics: Science and Systems 2015.
253 SGM3D 27.89 % 42.21 % 24.73 % 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.
254 LSVM-MDPM-us code 27.81 % 37.66 % 24.83 % 10 s 4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
255 DPM-VOC+VP 27.73 % 41.58 % 24.61 % 8 s 1 core @ 2.5 Ghz (C/C++)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015.
256 SARM3D 27.67 % 42.08 % 23.40 % 0.03 s GPU @ 2.5 Ghz (Python)
257 K3D 27.29 % 38.82 % 23.86 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
258 RefinedMPL 27.17 % 44.47 % 22.84 % 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.
259 CaDDN code 27.13 % 40.03 % 23.23 % 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.
260 PGD-FCOS3D code 26.48 % 44.28 % 23.03 % 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.
261 LSVM-MDPM-sv 26.05 % 35.70 % 23.56 % 10 s 4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout. NIPS 2011.
262 E2E-DA-Lite (Res18) 25.82 % 42.48 % 21.16 % 0.01 s GPU @ 2.5 Ghz (Python)
263 DPM-C8B1
This method uses stereo information.
25.57 % 41.47 % 21.93 % 15 s 4 cores @ 2.5 Ghz (Matlab + C/C++)
J. Yebes, L. Bergasa and M. García-Garrido: Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes. Sensors 2015.
J. Yebes, L. Bergasa, R. Arroyo and A. Lázaro: Supervised learning and evaluation of KITTI's cars detector with DPM. IV 2014.
264 HBD 24.75 % 35.65 % 22.00 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
265 E2E-DA 24.46 % 39.34 % 19.59 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
266 Graph-NMS-baseline 23.07 % 35.72 % 20.54 % 47 ms GPU @ 2.5 Ghz (Python)
267 RT3D-GMP
This method uses stereo information.
22.90 % 33.64 % 19.87 % 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.
268 MAOLoss code 22.58 % 34.37 % 20.49 % 0.05 s 1 core @ 2.5 Ghz (Python)
269 Graph-NMS 22.43 % 34.13 % 19.65 % 36 ms GPU @ 2.5 Ghz (Python)
270 Lite-FPN 19.17 % 24.40 % 15.68 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
271 mBoW
This method makes use of Velodyne laser scans.
17.63 % 26.66 % 16.02 % 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.
272 CDTrack3D code 17.28 % 27.04 % 13.92 % 0.0106 s NVIDIA RTX 3090 GPU, i9 10850k CPU
273 MDSNet 16.64 % 28.23 % 14.14 % 0.07 s 1 core @ 2.5 Ghz (Python)
274 TopNet-HighRes
This method makes use of Velodyne laser scans.
13.98 % 22.86 % 14.52 % 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.
275 ESGN
This method uses stereo information.
13.45 % 21.13 % 11.72 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
276 RT3DStereo
This method uses stereo information.
12.96 % 19.58 % 11.47 % 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.
277 TopNet-UncEst
This method makes use of Velodyne laser scans.
12.00 % 18.14 % 11.85 % 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.
278 MM 6.79 % 11.33 % 6.28 % 1 s 1 core @ 2.5 Ghz (C/C++)
279 YOLOv2 code 0.06 % 0.15 % 0.07 % 0.02 s GPU @ 3.5 Ghz (C/C++)
J. Redmon, S. Divvala, R. Girshick and A. Farhadi: You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
280 TopNet-DecayRate
This method makes use of Velodyne laser scans.
0.04 % 0.00 % 0.04 % 92 ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
Table as LaTeX | Only published Methods

Object Detection and Orientation Estimation Evaluation

Cars


Method Setting Code Moderate Easy Hard Runtime Environment
1 Anonymous 97.11 % 98.25 % 94.46 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
2 GraR-VoI 96.29 % 96.81 % 91.06 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
3 GraR-Po 96.09 % 96.83 % 90.99 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
4 SFD 96.05 % 98.95 % 90.96 % 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.
5 VPFNet 96.04 % 96.63 % 90.99 % 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.
6 Anonymous 96.01 % 99.06 % 90.97 % n/a s 1 core @ 2.5 Ghz (Python + C/C++)
7 TED 95.96 % 96.63 % 93.24 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
8 CLOCs code 95.93 % 96.77 % 90.93 % 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.
9 ImpDet 95.92 % 96.72 % 90.85 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
10 GraR-Vo 95.92 % 96.66 % 92.78 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
11 CityBrainLab 95.86 % 96.58 % 90.81 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
12 PE-RCVN 95.85 % 96.89 % 90.87 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
13 PVT-SSD 95.83 % 96.74 % 90.58 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
14 Anonymous 95.81 % 96.53 % 92.69 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
15 CLOCs_PVCas code 95.79 % 96.74 % 90.81 % 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.
16 Anonymous 95.75 % 98.92 % 90.83 % n/a s 1 core @ 2.5 Ghz (C/C++)
17 GLENet-VR 95.73 % 96.84 % 90.80 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
18 GraR-Pi 95.72 % 98.57 % 92.55 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
19 HCPVF 95.70 % 96.61 % 92.87 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
20 SECOND 95.67 % 96.42 % 90.37 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
21 LIVOX_Det
This method makes use of Velodyne laser scans.
95.64 % 98.60 % 92.90 % n/a s 1 core @ 2.5 Ghz (Python + C/C++)
22 DVF-V 95.63 % 96.59 % 90.71 % 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.
23 Anonymous 95.62 % 96.66 % 92.48 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
24 TBD 95.62 % 96.32 % 92.95 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
25 DSGN++
This method uses stereo information.
code 95.58 % 98.04 % 88.09 % 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.
26 Fast-CLOCs 95.57 % 96.66 % 90.70 % 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.
27 CasA 95.53 % 96.51 % 92.71 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
28 JPVNet 95.38 % 96.40 % 90.52 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
29 Anonymous 95.37 % 98.34 % 90.41 % n/a s 1 core @ 2.5 Ghz (C/C++)
30 DVF-PV 95.35 % 96.40 % 92.37 % 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.
31 TBD 95.34 % 96.05 % 90.25 % 0.1 s 1 core @ 2.5 Ghz (Python)
32 ISE-RCNN-PV 95.34 % 96.18 % 92.70 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
33 BADet code 95.34 % 98.65 % 90.28 % 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.
34 Anonymous 95.32 % 98.34 % 90.36 % 0.1s 1 core @ 2.5 Ghz (C/C++)
35 LGNet 95.31 % 96.51 % 92.55 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
36 GLENet 95.30 % 96.60 % 90.44 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
37 ISE-RCNN 95.30 % 96.36 % 92.63 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
38 Anonymous 95.29 % 96.61 % 90.43 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
39 SASA
This method makes use of Velodyne laser scans.
code 95.29 % 96.00 % 92.42 % 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.
40 PTA-RCNN 95.28 % 96.39 % 92.21 % 0.08 s 1 core @ 2.5 Ghz (Python)
41 Anonymous 95.23 % 96.47 % 90.45 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
42 Focals Conv code 95.23 % 96.29 % 92.60 % 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.
43 TBD 95.20 % 96.16 % 90.42 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
44 EQ-PVRCNN code 95.20 % 98.22 % 92.47 % 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.
45 anonymous 95.19 % 96.35 % 92.38 % 0.09 s GPU @ 2.5 Ghz (Python)
46 CasA++ 95.17 % 95.81 % 94.10 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
47 SE-SSD
This method makes use of Velodyne laser scans.
code 95.17 % 96.55 % 90.00 % 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.
48 DGDNH 95.15 % 98.35 % 92.55 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
49 VoxSeT code 95.13 % 96.15 % 90.38 % 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.
50 HMFI code 95.05 % 96.28 % 92.28 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
51 SPANet 95.03 % 96.31 % 89.99 % 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.
52 Pyramid R-CNN 95.03 % 95.87 % 92.46 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
J. Mao, M. Niu, H. Bai, X. Liang, H. Xu and C. Xu: Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection. ICCV 2021.
53 VPFNet code 95.01 % 96.03 % 92.41 % 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.
54 EPNet++ 95.00 % 96.70 % 91.82 % 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.
55 Voxel R-CNN code 94.96 % 96.47 % 92.24 % 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.
56 PDV code 94.91 % 96.06 % 92.30 % 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.
57 PV-RCNN++ code 94.90 % 96.07 % 92.22 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
58 ATT_SSD 94.88 % 96.01 % 92.00 % 0.01 s 1 core @ 2.5 Ghz (Python)
59 SIENet code 94.85 % 96.01 % 92.23 % 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.
60 TBD code 94.81 % 95.97 % 91.96 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
61 VoTr-TSD code 94.81 % 95.95 % 92.24 % 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.
62 USVLab BSAODet 94.79 % 96.18 % 92.05 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
63 GVNet-V2 94.77 % 96.28 % 91.98 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
64 AGS-SSD[la] 94.75 % 96.15 % 91.84 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
65 TBD 94.73 % 97.01 % 92.18 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
66 GVNet code 94.72 % 96.29 % 92.00 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
67 SRIF-RCNN 94.70 % 95.62 % 92.21 % 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.
68 M3DeTR code 94.70 % 97.37 % 91.89 % n/a s GPU @ 1.0 Ghz (Python)
T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis and D. Manocha: M3DeTR: Multi-representation, Multi- scale, Mutual-relation 3D Object Detection with Transformers. 2021.
69 ST-RCNN
This method makes use of Velodyne laser scans.
94.69 % 98.05 % 91.97 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
70 ST-RCNN (SNLW-RCNN)
This method makes use of Velodyne laser scans.
code 94.69 % 98.05 % 91.97 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
71 DCCA 94.67 % 95.74 % 92.10 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
72 TPCG 94.66 % 95.95 % 92.08 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
73 XView 94.66 % 95.88 % 92.07 % 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.
74 CSVoxel-RCNN 94.66 % 96.21 % 91.84 % 0.03 s GPU @ 1.0 Ghz (Python)
75 StructuralIF 94.64 % 96.12 % 91.85 % 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.
76 VCRCNN 94.64 % 96.05 % 92.09 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
77 SqueezeRCNN 94.61 % 96.02 % 91.97 % 0.08 s 1 core @ 2.5 Ghz (Python)
78 FusionDetv2-v4 94.60 % 95.92 % 91.79 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
79 TBD 94.60 % 95.87 % 91.71 % 0.06 s GPU @ 2.5 Ghz (Python)
80 P2V-RCNN 94.59 % 96.01 % 92.13 % 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.
81 CAT-Det 94.57 % 95.95 % 91.88 % 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.
82 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 94.57 % 98.15 % 91.85 % 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.
83 SC-Voxel-RCNN 94.54 % 96.10 % 91.67 % 0.12 s GPU @ 1.0 Ghz (Python)
84 DDet 94.54 % 95.80 % 91.99 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
85 DKDet 94.53 % 96.00 % 91.66 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
86 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
code 94.52 % 95.84 % 91.93 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
87 FusionDetv2-v3 94.51 % 96.14 % 91.73 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
88 FPV-SSD 94.48 % 96.89 % 91.52 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
89 MVRA + I-FRCNN+ 94.46 % 95.66 % 81.74 % 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.
90 SVGA-Net 94.45 % 96.02 % 91.54 % 0.03s 1 core @ 2.5 Ghz (Python + C/C++)
Q. He, Z. Wang, H. Zeng, Y. Zeng and Y. Liu: SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds. AAAI 2022.
91 CM3DV 94.45 % 95.78 % 91.84 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
92 DVFENet 94.44 % 95.33 % 91.55 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection. Neurocomputing 2021.
93 RangeIoUDet
This method makes use of Velodyne laser scans.
94.42 % 95.69 % 91.70 % 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.
94 IKT3D
This method makes use of Velodyne laser scans.
94.42 % 95.73 % 91.75 % 0.05 s 1 core @ 2.5 Ghz (Python)
95 sa-voxel-centernet code 94.39 % 95.86 % 91.80 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
96 USVLab BSAODet (S) 94.36 % 96.06 % 89.71 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
97 SPVB-SSD 94.31 % 95.78 % 91.61 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
98 SERCNN
This method makes use of Velodyne laser scans.
94.24 % 96.31 % 89.71 % 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.
99 EPNet code 94.22 % 96.13 % 89.68 % 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.
100 TCDVF 94.18 % 95.00 % 91.12 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
101 TBD 94.09 % 94.97 % 91.14 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
102 SRDL 94.08 % 95.83 % 91.55 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
103 FusionDetv1 94.07 % 95.82 % 91.54 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
104 SARFE 94.07 % 95.73 % 91.40 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
105 FusionDetv2-v2 94.05 % 95.73 % 89.66 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
106 NV2P-RCNN 93.91 % 97.80 % 90.96 % 0.1 s GPU @ 2.5 Ghz (Python)
107 RangeRCNN
This method makes use of Velodyne laser scans.
93.90 % 95.47 % 91.53 % 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.
108 DGT-Det3D 93.89 % 95.53 % 91.34 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
109 TBD 93.80 % 94.52 % 86.34 % 0.1 s 1 core @ 2.5 Ghz (Python)
110 SIF 93.79 % 95.48 % 91.30 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
111 DD3D code 93.78 % 94.67 % 88.99 % 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) .
112 MGAF-3DSSD code 93.77 % 94.45 % 86.25 % 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.
113 MMLAB LIGA-Stereo
This method uses stereo information.
code 93.71 % 96.40 % 86.00 % 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.
114 Sem-Aug 93.69 % 96.78 % 88.69 % 0.08 s GPU @ 2.5 Ghz (Python)
115 CAD 93.63 % 96.82 % 88.59 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
116 CF-ctdep-tv-ta 93.61 % 95.07 % 90.90 % 1 s 1 core @ 2.5 Ghz (C/C++)
117 DKAnet 93.59 % 95.10 % 88.98 % 0.05 s 1 core @ 2.0 Ghz (Python)
118 Patches - EMP
This method makes use of Velodyne laser scans.
93.58 % 97.88 % 90.31 % 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.
119 SA3DNet
This method uses stereo information.
This method makes use of Velodyne laser scans.
93.55 % 96.56 % 88.56 % 0.05 s GPU @ 2.5 Ghz (Python)
120 CF-base-tv 93.54 % 94.85 % 90.67 % 1 s 1 core @ 2.5 Ghz (C/C++)
121 MVAF-Net code 93.54 % 95.35 % 90.70 % 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.
122 TF3D
This method makes use of Velodyne laser scans.
93.49 % 96.57 % 88.54 % 0.1 s 2 cores @ 3.0 Ghz (Python)
123 Anonymous 93.47 % 95.15 % 90.78 % 1 1 core @ 2.5 Ghz (Python)
124 IA-SSD (multi) code 93.47 % 96.07 % 90.51 % 0.014 s 1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
125 Associate-3Ddet_v2 93.46 % 96.66 % 88.20 % 0.04 s 1 core @ 2.5 Ghz (Python)
126 DTFI 93.45 % 95.14 % 90.75 % 0.03 s 1 core @ 2.5 Ghz (Python)
127 FusionDetv2-v5 93.44 % 95.31 % 88.96 % 0.05 s 1 core @ 2.5 Ghz (Java + C/C++)
128 FV2P v2 93.43 % 94.32 % 90.75 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
129 IA-SSD (single) code 93.41 % 96.23 % 88.34 % 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.
130 DVF 93.39 % 96.44 % 88.32 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
131 TBD
This method makes use of Velodyne laser scans.
93.34 % 96.51 % 90.56 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
132 CIA-SSD
This method makes use of Velodyne laser scans.
code 93.34 % 96.65 % 85.76 % 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.
133 TBD 93.31 % 94.17 % 88.30 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
134 Deep MANTA 93.31 % 98.83 % 82.95 % 0.7 s GPU @ 2.5 Ghz (Python + C/C++)
F. Chabot, M. Chaouch, J. Rabarisoa, C. Teulière and T. Chateau: Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image. CVPR 2017.
135 StereoDistill 93.29 % 97.57 % 87.48 % 0.4 s 1 core @ 2.5 Ghz (Python)
136 Sem-Aug v1 code 93.26 % 96.36 % 90.51 % 0.04 s GPU @ 3.5 Ghz (Python)
137 LPCG-Monoflex 93.26 % 96.68 % 83.34 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
138 DSASNet 93.25 % 96.54 % 88.34 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
139 3SNet 93.24 % 96.39 % 90.51 % 0.07 s GPU @ 2.5 Ghz (Python)
140 FS-Net
This method makes use of Velodyne laser scans.
93.21 % 96.38 % 90.49 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
141 PVTr 93.20 % 94.52 % 90.59 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
142 CityBrainLab-CT3D code 93.20 % 96.26 % 90.44 % 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.
143 TBD 93.20 % 95.96 % 90.30 % TBD GPU @ 2.5 Ghz (Python + C/C++)
144 CF-ctdep-tv 93.15 % 94.88 % 90.65 % 1 s 1 core @ 2.5 Ghz (C/C++)
145 MonoInsight 93.14 % 96.17 % 83.36 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
146 Reprod-Two-Branch 93.12 % 94.81 % 90.42 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
147 VCT 93.09 % 96.30 % 90.38 % 0.2 s 1 core @ 2.5 Ghz (Python)
148 SNVC
This method uses stereo information.
code 93.09 % 96.27 % 85.51 % 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.
149 VPN 93.08 % 96.16 % 88.01 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
150 MVOD 93.07 % 96.15 % 92.39 % 0.16 s 1 core @ 2.5 Ghz (C/C++)
151 CFF-ep25 93.06 % 94.79 % 90.56 % 1 s 1 core @ 2.5 Ghz (C/C++)
152 KpNet 93.06 % 96.63 % 85.39 % 0.42 s 1 core @ 2.5 Ghz (C/C++)
153 KpNet 93.05 % 96.63 % 85.38 % 42 s 1 core @ 2.5 Ghz (C/C++)
154 H^23D R-CNN code 93.03 % 96.13 % 90.33 % 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.
155 FromVoxelToPoint code 92.98 % 96.07 % 90.40 % 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.
156 SGNet 92.95 % 96.41 % 90.35 % 0.09 s GPU @ 2.5 Ghz (Python)
157 CFF-tv 92.92 % 94.66 % 90.41 % 1 s 1 core @ 2.5 Ghz (C/C++)
158 Sem-Aug-PointRCNN code 92.92 % 95.66 % 88.07 % 0.1 s GPU @ 3.5 Ghz (C/C++)
159 EBM3DOD code 92.88 % 96.39 % 87.58 % 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.
160 Struc info fusion II 92.88 % 96.44 % 87.67 % 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.
161 Anonymous 92.82 % 96.37 % 83.10 % 40 s 1 core @ 2.5 Ghz (C/C++)
162 cp-tv 92.82 % 94.47 % 90.13 % 1 s 1 core @ 2.5 Ghz (C/C++)
163 Anonymous 92.80 % 96.17 % 87.51 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
164 cff-tv-v2-ep25 92.79 % 94.63 % 90.41 % 1 s 1 core @ 2.5 Ghz (C/C++)
165 HotSpotNet 92.74 % 96.20 % 89.68 % 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.
166 Struc info fusion I 92.71 % 96.24 % 87.55 % 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.
167 EBM3DOD baseline code 92.70 % 96.31 % 87.44 % 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.
168 CF-base-train 92.64 % 94.95 % 89.93 % 1 s 1 core @ 2.5 Ghz (C/C++)
169 MDNet 92.62 % 96.09 % 85.09 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
170 SARPNET 92.58 % 95.82 % 87.33 % 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.
171 Patches
This method makes use of Velodyne laser scans.
92.57 % 96.31 % 87.41 % 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.
172 R-GCN 92.53 % 96.16 % 87.45 % 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.
173 PI-RCNN 92.52 % 96.15 % 87.47 % 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.
174 CenterNet3D 92.48 % 95.71 % 89.54 % 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.
175 MSADet 92.45 % 96.08 % 89.27 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
176