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 NFAF3D 96.20 % 96.78 % 93.26 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
2 SFD 96.17 % 98.97 % 91.13 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
3 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.
4 Anonymous 96.12 % 98.74 % 91.12 % 0.1 s GPU @ 2.5 Ghz (Python)
5 Anonymous 96.12 % 99.07 % 91.12 % n/a s 1 core @ 2.5 Ghz (Python + C/C++)
6 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.
7 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.
8 PE-RCVN 95.94 % 96.90 % 90.98 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
9 CityBrainLab 95.94 % 98.58 % 90.92 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
10 Anonymous 95.91 % 96.55 % 92.86 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
11 EA-M-RCNN(BorderAtt) 95.88 % 96.68 % 90.89 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
12 Anonymous 95.86 % 98.93 % 90.98 % n/a s 1 core @ 2.5 Ghz (C/C++)
13 NFAF3D-light 95.79 % 96.69 % 92.75 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
14 SECOND 95.79 % 96.44 % 90.55 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
15 DVF-V 95.77 % 96.60 % 90.89 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
16 Fast-CLOCs 95.75 % 96.69 % 90.95 % 0.1 s GPU @ 2.5 Ghz (Python)
17 DSGN++
This method uses stereo information.
95.70 % 98.08 % 88.27 % 0.4 s 1 core @ 2.5 Ghz (C/C++)
18 TBD 95.69 % 96.33 % 93.07 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
19 BANet code 95.61 % 98.75 % 90.64 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
R. Qian, X. Lai and X. Li: Boundary-Aware 3D Object Detection from Point Clouds. 2021.
20 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.
21 JPVNet 95.52 % 96.41 % 90.72 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
22 Anonymous 95.50 % 98.36 % 90.63 % 0.1s 1 core @ 2.5 Ghz (C/C++)
23 DVF-PV 95.49 % 96.42 % 92.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
24 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.
25 ISE-RCNN-PV 95.46 % 96.20 % 92.88 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
26 ISE-RCNN 95.43 % 96.38 % 92.82 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
27 PLNL-3DSSD
This method makes use of Velodyne laser scans.
95.38 % 96.37 % 90.31 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
28 SPG_mini
This method makes use of Velodyne laser scans.
95.32 % 96.23 % 92.68 % 0.09 s GPU @ 2.5 Ghz (Python)
29 EQ-PVRCNN 95.32 % 98.23 % 92.65 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
30 anonymous 95.31 % 96.36 % 92.57 % 0.09 s GPU @ 2.5 Ghz (Python)
31 TBD 95.29 % 96.17 % 90.56 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
32 Anonymous code 95.28 % 96.30 % 92.69 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
33 DGDNH 95.24 % 98.36 % 92.69 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
34 VoxSeT 95.23 % 96.16 % 90.49 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
35 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.
36 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.
37 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.
38 EPNet++ 95.17 % 96.73 % 92.10 % 0.1 s GPU @ 2.5 Ghz (Python)
39 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.
40 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.
41 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.
42 TBD 95.10 % 96.48 % 92.62 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
43 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.
44 3DIoU++ 95.06 % 96.37 % 90.52 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
45 PV-RCNN++ 95.05 % 96.08 % 92.42 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
46 PDV 95.00 % 96.07 % 92.44 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
47 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.
48 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.
49 VueronNet code 94.97 % 97.85 % 89.68 % 0.06 s 1 core @ 2.0 Ghz (Python)
50 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.
51 CSVoxel-RCNN 94.91 % 96.33 % 92.11 % 0.03 s GPU @ 1.0 Ghz (Python)
52 TBD 94.85 % 97.04 % 92.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
53 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.
54 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.
55 E^2-PV-RCNN 94.80 % 95.95 % 92.26 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
56 SRIF-RCNN 94.79 % 95.63 % 92.35 % 0.0947 s 1 core @ 2.5 Ghz (C/C++)
57 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++)
58 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++)
59 TPCG 94.78 % 95.96 % 92.25 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
60 VCRCNN 94.77 % 96.06 % 92.28 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
61 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.
62 SCIR-Net
This method makes use of Velodyne laser scans.
94.76 % 96.15 % 91.99 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
Q. He, H. Zeng, Y. Zeng and Y. Liu: SCIR-Net: Structured Color Image Representation Based 3D Object Detection Network from Point Clouds. AAAI 2022.
63 MSG-PGNN 94.75 % 95.86 % 92.16 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
64 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.
65 FusionDetv2-v4 94.73 % 95.94 % 92.00 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
66 Generalized-SIENet 94.72 % 95.76 % 92.19 % 0.08 s 1 core @ 2.5 Ghz (Python)
67 SqueezeRCNN 94.72 % 96.02 % 92.12 % 0.08 s 1 core @ 2.5 Ghz (Python)
68 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.
69 CAT-Det 94.71 % 95.97 % 92.07 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
70 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.
71 3DIoU_v2 94.70 % 96.15 % 92.37 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
72 HyBrid Feature Det 94.69 % 95.89 % 92.11 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
73 PC-RGNN 94.68 % 95.80 % 92.20 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
74 DDet 94.66 % 95.82 % 92.17 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
75 FPV-SSD 94.66 % 96.92 % 91.78 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
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76 LZY_RCNN 94.65 % 95.81 % 92.08 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
77 FusionDetv2-v3 94.64 % 96.16 % 91.94 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
78 TransCyclistNet 94.64 % 96.08 % 92.10 % 0.08 s 1 core @ 2.5 Ghz (Python)
79 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.
80 Fast VP-RCNN code 94.62 % 98.00 % 91.91 % 0.05 s 1 core @ 3.5 Ghz (C/C++)
81 DD3D code 94.62 % 95.32 % 89.89 % 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) .
82 TransDet3D 94.61 % 95.83 % 92.07 % 0.08 s 1 core @ 2.5 Ghz (Python)
83 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.
84 Point Image Fusion 94.61 % 95.70 % 92.11 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
85 SA-voxel-centernet code 94.59 % 95.80 % 92.05 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
86 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.
87 WHUT-iou_ssd code 94.54 % 95.77 % 91.91 % 0.045s 1 core @ 2.5 Ghz (C/C++)
88 sa-voxel-centernet code 94.53 % 95.88 % 92.00 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
89 anonymous code 94.53 % 97.51 % 91.80 % 0.05s 1 core @ >3.5 Ghz (python)
90 FPC3D
This method makes use of the epipolar geometry.
94.52 % 96.06 % 91.72 % 33 s 1 core @ 2.5 Ghz (C/C++)
91 FPC-RCNN 94.51 % 96.15 % 91.80 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
92 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.
93 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.
94 GNN-RCNN 94.44 % 95.85 % 91.96 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
95 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.
96 SAA-SECOND 94.39 % 95.67 % 91.63 % 38m s 1 core @ 2.5 Ghz (C/C++)
97 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.
98 FusionDetv1 94.23 % 95.84 % 91.80 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
99 FusionDetv2-v2 94.21 % 95.75 % 89.89 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
100 TBD 94.21 % 95.51 % 91.69 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
101 SARFE 94.18 % 95.74 % 91.57 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
102 FPCR-CNN 94.13 % 95.95 % 91.20 % 0.05 s 1 core @ 2.5 Ghz (Python)
103 SAA-PV-RCNN 94.11 % 95.01 % 92.50 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
104 NV2P-RCNN 94.07 % 97.82 % 91.20 % 0.1 s GPU @ 2.5 Ghz (Python)
105 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.
106 VueronNet 94.03 % 96.70 % 87.58 % 0.08 s GPU @ 2.5 Ghz (Python)
107 DGIST MT-CNN 94.00 % 95.25 % 86.76 % 0.09 s GPU @ 1.0 Ghz (Python)
108 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. arXiv preprint arXiv:2011.01404 2020.
109 SIF 93.95 % 95.51 % 91.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
110 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.
111 LPCG-Monoflex 93.86 % 96.90 % 83.94 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
112 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.
113 LIGA-Stereo-old
This method uses stereo information.
93.77 % 96.66 % 83.97 % 0.375 s Titan Xp
114 Associate-3Ddet_v2 93.77 % 96.83 % 88.57 % 0.04 s 1 core @ 2.5 Ghz (Python)
115 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.
116 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.
117 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.
118 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.
119 FusionDetv2-v5 93.61 % 95.33 % 89.22 % 0.05 s 1 core @ 2.5 Ghz (Java + C/C++)
120 KpNet 93.60 % 96.76 % 85.98 % 0.42 s 1 core @ 2.5 Ghz (C/C++)
121 KpNet 93.60 % 96.74 % 85.97 % 42 s 1 core @ 2.5 Ghz (C/C++)
122 AM-SSD 93.58 % 96.78 % 90.61 % 0.04 s 1 core @ 2.5 Ghz (Python)
123 TF3D
This method makes use of Velodyne laser scans.
93.57 % 96.59 % 88.64 % 0.1 s 2 cores @ 3.0 Ghz (Python)
124 DVF 93.57 % 96.46 % 88.58 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
125 IA-SSD (multi) 93.56 % 96.10 % 90.68 % 0.014 s 1 core @ 2.5 Ghz (C/C++)
126 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.
127 IA-SSD (single) 93.54 % 96.26 % 88.49 % 0.013 s 1 core @ 2.5 Ghz (C/C++)
128 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.
129 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.
130 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.
131 BtcDet
This method makes use of Velodyne laser scans.
93.47 % 96.23 % 88.55 % 0.09 s GPU @ 2.5 Ghz (Python + C/C++)
132 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.
133 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.
134 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++)
135 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.
136 Sem-Aug v1 code 93.39 % 96.39 % 90.70 % 0.04 s GPU @ 3.5 Ghz (Python)
137 TBD 93.38 % 94.17 % 88.39 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
138 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.
139 DSASNet 93.33 % 96.55 % 88.47 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
140 TBD 93.33 % 96.02 % 90.50 % TBD GPU @ 2.5 Ghz (Python + C/C++)
141 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++)
142 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.
143 VPN 93.30 % 96.19 % 88.30 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
144 RoIFusion code 93.30 % 96.30 % 88.22 % 0.22 s 1 core @ 3.0 Ghz (Python)
145 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.
146 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.
147 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.
148 demo 93.21 % 96.16 % 90.23 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
149 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.
150 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.
151 ASCNet 93.17 % 96.09 % 90.43 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
152 VCT 93.17 % 96.31 % 90.52 % 0.2 s 1 core @ 2.5 Ghz (Python)
153 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.
154 Sem-Aug-PointRCNN code 93.17 % 95.78 % 88.35 % 0.1 s GPU @ 3.5 Ghz (C/C++)
155 MVOD 93.16 % 96.17 % 92.56 % 0.16 s 1 core @ 2.5 Ghz (C/C++)
156 MBDF-Net 93.15 % 96.26 % 90.25 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
157 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.
158 KAIST-VDCLab 93.08 % 96.27 % 85.66 % 0.04 s 1 core @ 2.5 Ghz (Python)
159 SGNet 93.08 % 96.43 % 90.53 % 0.09 s GPU @ 2.5 Ghz (Python)
160 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.
161 MSADet 92.95 % 96.18 % 89.95 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
162 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.
163 MBDF-Net-1 92.85 % 95.98 % 89.91 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
164 YF 92.85 % 96.04 % 89.96 % 0.04 s GPU @ 2.5 Ghz (C/C++)
165 FPGNN 92.83 % 96.26 % 87.69 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
166 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.
167 DPointNet 92.77 % 95.55 % 89.63 % 0.07s 1 core @ 2.5 Ghz (C/C++)
168 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.
169 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.
170 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.
171 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.
172 TBD 92.66 % 95.60 % 90.55 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
173 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.
174 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.
175 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.
176 NV-RCNN 92.51 % 95.86 % 89.95 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
177 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.
178 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.
179 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.
180 3D-VDNet 92.35 % 95.42 % 89.37 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
181 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.
182 CVFNet 92.27 % 95.61 % 88.75 % 28.1ms 1 core @ 2.5 Ghz (Python)
183 LSNet 92.23 % 96.06 % 87.35 % 0.09 s GPU @ 2.5 Ghz (Python + C/C++)
184 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.
185 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.
186 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.
187 RangeDet code 92.03 % 95.20 % 87.14 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
188 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.
189 VGCN 91.97 % 94.91 % 89.34 % 0.09 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
190 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.
191 MKFFNet 91.88 % 95.29 % 89.21 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
192 AutoAlign 91.87 % 95.15 % 89.21 % 0.1 s 1 core @ 2.5 Ghz (Python)
193 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.
194 WeakM3D 91.81 % 94.51 % 85.35 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
195 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.
196 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.
197 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.
198 sscl-20p 91.71 % 97.14 % 88.72 % 0.02 s 1 core @ 2.5 Ghz (Python)
199 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.
200 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)
201 HS3D code 91.62 % 95.51 % 86.94 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
202 mono3d 91.60 % 94.60 % 84.86 % 0.03 s GPU @ 2.5 Ghz (Python)
203 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.
204 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)
205 CA3D 91.48 % 95.19 % 82.01 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
206 PointRGBNet 91.48 % 95.40 % 86.50 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
207 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.
208 AIMC-RUC 91.45 % 96.94 % 86.28 % 0.11 s 1 core @ 2.5 Ghz (Python)
209 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.
210 FPC3D_all
This method makes use of Velodyne laser scans.
91.42 % 95.52 % 86.76 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
211 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.
212 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)
213 PP-PCdet code 91.32 % 94.82 % 88.18 % 0.01 s 1 core @ 2.5 Ghz (Python)
214 Contrastive PP code 91.29 % 96.93 % 88.11 % 0.01 s 1 core @ 2.5 Ghz (Python)
215 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.
216 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.
217 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.
218 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.
219 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.
220 3D_att
This method makes use of Velodyne laser scans.
91.05 % 96.70 % 85.88 % 0.17 s GPU @ 2.5 Ghz (Python)
221 Geo3D 91.04 % 94.28 % 78.91 % 0.04 s GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
222 FII-CenterNet 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.
223 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.
224 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.
225 Mix-Teaching-M3D 91.02 % 96.35 % 83.41 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
226 SCSTSV-MonoFlex 90.99 % 96.44 % 81.16 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
227 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.
228 GA-Aug 90.92 % 93.93 % 83.03 % 0.04 s GPU @ 2.5 Ghz (Python)
229 MonoEF code 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.
230 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.
231 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.
232 MonoDistill 90.81 % 95.92 % 81.08 % 0.04 s 1 core @ 2.5 Ghz (Python)
233 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 .
234 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.
235 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.
236 APL-Second 90.70 % 95.82 % 85.51 % 0.05 s 1 core @ 2.5 Ghz (Python)
237 vadin-TBD 90.69 % 95.92 % 80.91 % 0.04 s 1 core @ 2.5 Ghz (Python)
238 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.
239 MonoGround 90.63 % 93.94 % 80.80 % 0.03 s 1 core @ 2.5 Ghz (Python)
240 NF2 90.62 % 94.14 % 81.30 % 0.1 s GPU @ 2.5 Ghz (Python)
241 MonoEdge 90.62 % 93.52 % 80.91 % 0.05 s GPU @ 2.5 Ghz (Python)
242 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.
243 MonoFlex 90.52 % 95.59 % 83.00 % 0.03 s 1 core @ 2.5 Ghz (Python)
244 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.
245 MonoEdge-Rotate 90.30 % 93.53 % 80.60 % 0.05 s GPU @ 2.5 Ghz (Python)
246 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.
247 PS-fld 90.27 % 95.75 % 82.32 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
248 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.
249 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.
250 MonoGeo 90.14 % 95.11 % 80.19 % 0.05 s 1 core @ 2.5 Ghz (Python)
251 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.
252 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.
253 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.
254 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.
255 LCA 89.94 % 93.40 % 82.76 % 0.05 s 1 core @ 2.5 Ghz (Python)
256 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.
257 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.
258 Digging_M3D 89.77 % 93.73 % 79.68 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
259 TBD_BD code 89.73 % 94.18 % 86.84 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
260 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.
261 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.
262 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.
263 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.
264 MonoDDE 89.19 % 96.76 % 81.60 % 0.04 s 1 core @ 2.5 Ghz (Python)
265 PPTrans 89.17 % 95.17 % 81.77 % 0.2 s GPU @ 2.5 Ghz (Python)
266 FusionDetv2-v1 89.10 % 94.82 % 84.28 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
267 M3DSSD++ code 89.06 % 94.94 % 77.17 % 0.16s 1 core @ 2.5 Ghz (C/C++)
268 GAC3D++ 88.93 % 94.23 % 79.09 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
269 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.
270 Keypoint-3D 88.87 % 93.31 % 76.10 % 14 s 1 core @ 2.5 Ghz (C/C++)
271 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.
272 FusionDetv2-baseline 88.76 % 94.28 % 85.65 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
273 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.
274 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.
275 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)
276 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.
277 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.
278 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.
279 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.
280 MonoDTR 88.41 % 93.90 % 76.20 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
281 CMKD 88.41 % 95.28 % 81.68 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
282 CMKD 88.40 % 95.03 % 81.67 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
283 deleted 88.38 % 96.52 % 81.01 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
284 MonoLCD 88.33 % 93.74 % 78.59 % 0.04 s 1 core @ 2.5 Ghz (Python)
285 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.
286 MonoCon 88.22 % 93.59 % 76.18 % 0.02 s GPU @ 2.5 Ghz (Python)
287 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.
288 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.
289 SAIC_ADC_Mono3D code 87.78 % 95.21 % 80.14 % 50 s GPU @ 2.5 Ghz (Python)
290 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.
291 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.
292 EG_DETR 87.10 % 93.04 % 79.05 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
293 ZongmuMono3dV2 code 87.07 % 93.28 % 77.72 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
294 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.
295 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.
296 Anonymous code 86.77 % 94.34 % 71.93 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
297 BirdNet+
This method makes use of Velodyne laser scans.
code 86.73 % 92.61 % 81.80 % 0.11 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.
298 MP-Mono 86.54 % 90.66 % 65.72 % 0.16 s GPU @ 2.5 Ghz (Python)
299 ANM 86.45 % 94.32 % 76.54 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
300 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.
301 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.
302 gupnet_se 86.33 % 94.27 % 74.08 % 0.03s 1 core @ 2.5 Ghz (C/C++)
303 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.
304 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.
305 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.
306 AEC3D 85.22 % 90.74 % 80.82 % 18 ms GPU @ 2.5 Ghz (Python)
307 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.
308 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 .
309 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.
310 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.
311 LPCG-M3D 84.95 % 87.35 % 77.05 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
312 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.
313 VN3D 84.80 % 90.73 % 78.41 % 0.02 s 1 core @ 2.5 Ghz (Python)
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314 ZongmuMono3d code 84.64 % 93.06 % 75.29 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
315 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.
316 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.
317 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.
318 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.
319 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.
320 OSE+ 83.92 % 95.20 % 76.69 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
321 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.
322 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.
323 ppt 83.13 % 85.27 % 77.48 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
324 SGM3D 83.05 % 93.66 % 73.35 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
325 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.
326 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.
327 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.
328 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.
329 vadin-TBD2 code 82.54 % 92.81 % 72.80 % 0.20 s 1 core @ 2.5 Ghz (Python)
330 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.
331 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.
332 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.
333 SwinMono3D 81.71 % 91.99 % 61.78 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
334 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.
335 SD3DOD 81.64 % 92.60 % 75.97 % 0.04 s GPU @ 2.5 Ghz (Python)
336 ITS-MDPL 81.56 % 92.61 % 74.23 % 0.16 s GPU @ 2.5 Ghz (Python)
337 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.
338 SOD 81.18 % 94.24 % 66.44 % 0.1 s 1 core @ 2.5 Ghz (Python)
339 none 81.07 % 91.14 % 68.85 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
340 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.
341 K3D 80.86 % 93.58 % 71.18 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
342 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.
343 EGFN
This method uses stereo information.
80.58 % 93.07 % 70.68 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
344 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.
345 EW code 80.54 % 87.53 % 67.16 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
346 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.
347 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.
348 COF3D 80.16 % 87.85 % 61.97 % 200 s 1 core @ 2.5 Ghz (Python + C/C++)
349 EM code 80.05 % 87.00 % 66.77 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
350 KMC code 79.99 % 89.71 % 73.33 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
351 RelationNet3D_dla34 code 79.78 % 83.69 % 69.35 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
352 Lite-FPN 79.65 % 87.04 % 65.56 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
353 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.
354 E2E-DA 79.40 % 92.12 % 69.52 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
355 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.
356 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.
357 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.
358 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.
359 MonoHMOO 78.21 % 92.33 % 61.58 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
360 E2E-DA-Lite (Res18) 78.21 % 90.79 % 66.16 % 0.01 s GPU @ 2.5 Ghz (Python)
361 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.
362 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.
363 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.
364 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.
365 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.
366 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.
367 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.
368 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.
369 RelationNet3D_res18 code 76.96 % 87.14 % 67.49 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
370 AutoShape 76.82 % 83.75 % 63.71 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
371 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.
372 RelationNet3D 76.62 % 81.36 % 68.48 % 0.04 s GPU @ 2.5 Ghz (Python)
373 ICCV 76.45 % 85.48 % 65.52 % 0.04 s GPU @ 2.5 Ghz (Python)
374 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.
375 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.
376 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.
377 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.
378 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.
379 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.
380 MAOLoss code 73.79 % 89.31 % 63.59 % 0.05 s 1 core @ 2.5 Ghz (Python)
381 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 .
382 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.
383 BEVC 70.93 % 79.97 % 64.46 % 35ms GPU @ 1.5 Ghz (Python)
384 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.
385 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.
386 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.
387 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.
388 RetinaMono 69.83 % 74.54 % 60.95 % 0.02 s 1 core @ 2.5 Ghz (Python)
389 RetinaMono code 69.01 % 75.18 % 58.98 % 0.02 s 1 core @ 2.5 Ghz (Python)
390 TBD 68.30 % 88.62 % 59.00 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
391 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.
392 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.
393 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.
394 MDSNet 67.79 % 90.97 % 53.39 % 0.07 s 1 core @ 2.5 Ghz (Python)
395 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.
396 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.
397 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.
398 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.
399 MM 65.65 % 88.80 % 56.53 % 1 s 1 core @ 2.5 Ghz (C/C++)
400 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.
401 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.
402 Y4 code 63.60 % 81.79 % 56.20 % 0.03 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
403 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.
404 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.
405 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.
406 Graph-NMS 61.93 % 78.55 % 53.72 % 36 ms GPU @ 2.5 Ghz (Python)
407 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.
408 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.
409 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.
410 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.
411 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.
412 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.
413 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.
414 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). .
415 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.
416 Graph-NMS-baseline 52.92 % 76.21 % 43.38 % 47 ms GPU @ 2.5 Ghz (Python)
417 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.
418 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 .
419 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.
420 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.
421 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.
422 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.
423 CDTrack3D code 42.11 % 46.54 % 34.20 % 0.0106 s NVIDIA RTX 3090 GPU, i9 10850k CPU
424 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.
425 R-AGNO-Net 36.55 % 49.87 % 35.20 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
426 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.
427 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.
428 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.
429 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.
430 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.
431 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.
432 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.
433 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.
434 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.
435 TBD 0.00 % 0.00 % 0.00 % 0.1 s 1 core @ 2.5 Ghz (Python)
436 TBD 0.00 % 0.00 % 0.00 % 0.1 s 1 core @ 2.5 Ghz (Python)
437 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 % 0.08 s GPU @ 2.5 Ghz (Python)
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 NF2 79.59 % 88.28 % 75.47 % 0.1 s GPU @ 2.5 Ghz (Python)
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 HHA-ACF
This method makes use of Velodyne laser scans.
77.12 % 85.32 % 72.69 % 0.14 s 1 core @ 2.5 Ghz (C/C++)
7 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.
8 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.
9 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.
10 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.
11 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.
12 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.
13 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.
14 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.
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 DD3D code 70.79 % 84.61 % 65.69 % 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) .
21 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.
22 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.
23 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.
24 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.
25 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.
26 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.
27 Frustum-PointPillars 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.
28 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)
29 FII-CenterNet 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.
30 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.
31 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)
32 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.
33 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.
34 EQ-PVRCNN 65.01 % 77.19 % 61.95 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
35 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)
36 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.
37 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.
38 PV-RCNN++ 63.47 % 72.82 % 60.96 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
39 ADLAB 63.25 % 70.86 % 60.52 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
40 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.
41 PiFeNet 62.68 % 71.97 % 59.77 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
42 Fast-CLOCs 62.57 % 76.22 % 60.13 % 0.1 s GPU @ 2.5 Ghz (Python)
43 KAIST-VDCLab 62.35 % 79.37 % 57.42 % 0.04 s 1 core @ 2.5 Ghz (Python)
44 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.
45 VCT 62.00 % 71.19 % 58.52 % 0.2 s 1 core @ 2.5 Ghz (Python)
46 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.
47 TBD 61.74 % 70.58 % 59.45 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
48 PE-RCVN 61.64 % 69.49 % 59.55 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
49 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.
50 H^23D R-CNN 61.50 % 72.21 % 57.50 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
51 SAA-PV-RCNN 61.41 % 70.35 % 58.18 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
52 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 .
53 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.
54 EA-M-RCNN(BorderAtt) 61.06 % 73.07 % 56.86 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
55 ISE-RCNN-PV 61.06 % 70.59 % 57.26 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
56 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.
57 GA-Aug 60.78 % 76.78 % 55.00 % 0.04 s GPU @ 2.5 Ghz (Python)
58 ISE-RCNN 60.70 % 69.41 % 58.49 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
59 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.
60 TBD 60.30 % 70.50 % 57.06 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
61 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++)
62 Generalized-SIENet 59.54 % 69.16 % 57.33 % 0.08 s 1 core @ 2.5 Ghz (Python)
63 VPN 59.48 % 70.97 % 55.29 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
64 AutoAlign 59.48 % 70.17 % 55.37 % 0.1 s 1 core @ 2.5 Ghz (Python)
65 Fast VP-RCNN code 59.32 % 69.51 % 56.66 % 0.05 s 1 core @ 3.5 Ghz (C/C++)
66 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.
67 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.
68 SARFE 59.07 % 68.25 % 56.74 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
69 anonymous code 59.04 % 69.62 % 56.45 % 0.05s 1 core @ >3.5 Ghz (python)
70 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.
71 FusionDetv1 58.68 % 68.44 % 56.22 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
72 P2V_PCV1 58.59 % 68.62 % 56.34 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
73 EG_DETR 58.58 % 74.53 % 53.76 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
74 SA-voxel-centernet code 58.50 % 66.89 % 56.25 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
75 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.
76 SCIR-Net
This method makes use of Velodyne laser scans.
58.37 % 68.18 % 55.78 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
Q. He, H. Zeng, Y. Zeng and Y. Liu: SCIR-Net: Structured Color Image Representation Based 3D Object Detection Network from Point Clouds. AAAI 2022.
77 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.
78 TPCG 58.17 % 67.39 % 55.55 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
79 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.
80 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.
81 EPNet++ 58.10 % 68.58 % 55.58 % 0.1 s GPU @ 2.5 Ghz (Python)
82 DSGN++
This method uses stereo information.
58.09 % 69.70 % 54.45 % 0.4 s 1 core @ 2.5 Ghz (C/C++)
83 WHUT-iou_ssd code 58.03 % 66.60 % 55.82 % 0.045s 1 core @ 2.5 Ghz (C/C++)
84 E^2-PV-RCNN 58.01 % 67.39 % 55.77 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
85 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.
86 Point Image Fusion 57.91 % 66.47 % 55.48 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
87 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.
88 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++)
89 sa-voxel-centernet code 57.79 % 66.03 % 55.61 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
90 FPC-RCNN 57.46 % 66.88 % 55.09 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
91 SGNet 57.36 % 65.93 % 53.82 % 0.09 s GPU @ 2.5 Ghz (Python)
92 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. arXiv preprint arXiv:2011.01404 2020.
93 PDV 57.34 % 65.94 % 54.21 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
94 MVOD 57.33 % 66.72 % 54.31 % 0.16 s 1 core @ 2.5 Ghz (C/C++)
95 SIF 57.32 % 67.78 % 54.86 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
96 GNN-RCNN 57.32 % 66.78 % 55.77 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
97 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.
98 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.
99 FPCR-CNN 57.14 % 66.59 % 54.43 % 0.05 s 1 core @ 2.5 Ghz (Python)
100 FusionDetv2-v3 56.93 % 65.96 % 54.73 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
101 IA-SSD (single) 56.87 % 66.69 % 54.68 % 0.013 s 1 core @ 2.5 Ghz (C/C++)
102 DDet 56.81 % 65.02 % 54.65 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
103 CAT-Det 56.75 % 67.15 % 53.44 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
104 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.
105 FusionDetv2-v4 56.60 % 65.80 % 54.55 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
106 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.
107 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.
108 TBD 56.42 % 65.60 % 53.61 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
109 TBD 56.42 % 65.60 % 53.61 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
110 FusionDetv2-v5 56.40 % 65.17 % 53.89 % 0.05 s 1 core @ 2.5 Ghz (Java + C/C++)
111 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.
112 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.
113 TBD_IOU1 56.02 % 66.44 % 53.62 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
114 SAA-SECOND 55.95 % 65.50 % 52.88 % 38m s 1 core @ 2.5 Ghz (C/C++)
115 TBD_IOU 55.90 % 64.94 % 53.60 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
116 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.
117 VCRCNN 55.66 % 64.59 % 53.53 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
118 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.
119 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.
120 gupnet_se 55.50 % 74.80 % 50.57 % 0.03s 1 core @ 2.5 Ghz (C/C++)
121 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++)
122 TBD 55.43 % 65.62 % 51.97 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
123 FusionDetv2-v2 55.32 % 62.74 % 52.43 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
124 MSADet 55.31 % 66.73 % 52.97 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
125 TBD 55.14 % 63.44 % 53.89 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
126 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.
127 FPC3D_all
This method makes use of Velodyne laser scans.
55.08 % 64.74 % 52.59 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
128 ANM 55.08 % 74.09 % 48.00 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
129 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.
130 NV-RCNN 54.98 % 64.78 % 51.64 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
131 GAC3D++ 54.96 % 74.08 % 50.06 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
132 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.
133 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.
134 DSASNet 54.67 % 64.59 % 51.05 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
135 tbd 54.44 % 64.96 % 50.57 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
136 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.
137 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.
138 OSE+ 54.12 % 68.48 % 49.93 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
139 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.
140 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.
141 MKFFNet 53.64 % 63.25 % 51.07 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
142 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.
143 Mix-Teaching-M3D 53.52 % 67.34 % 47.45 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
144 TBD 53.34 % 64.17 % 51.00 % TBD GPU @ 2.5 Ghz (Python + C/C++)
145 ASCNet 53.28 % 62.40 % 50.88 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
146 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.
147 FPV-SSD 53.10 % 62.00 % 50.67 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
148 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.
149 FusionDetv2-baseline 52.96 % 58.96 % 50.94 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
150 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.
151 demo 52.90 % 63.79 % 49.54 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
152 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.
153 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.
154 VGCN 52.80 % 61.86 % 50.66 % 0.09 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
155 tbd 52.78 % 63.45 % 50.79 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
156 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.
157 NV2P-RCNN 52.52 % 61.80 % 50.24 % 0.1 s GPU @ 2.5 Ghz (Python)
158 HS3D code 52.50 % 63.73 % 49.78 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
159 IA-SSD (multi) 52.45 % 65.07 % 50.20 % 0.014 s 1 core @ 2.5 Ghz (C/C++)
160 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.
161 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.
162 SCSTSV-MonoFlex 52.18 % 67.51 % 45.90 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
163 YF 51.82 % 61.37 % 48.59 % 0.04 s GPU @ 2.5 Ghz (C/C++)
164 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.
165 TBD_BD code 50.60 % 60.91 % 48.47 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
166 MonoGround 50.35 % 63.96 % 45.75 % 0.03 s 1 core @ 2.5 Ghz (Python)
167 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.
168 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.
169 Y4 code 49.24 % 68.07 % 44.42 % 0.03 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
170 vadin-TBD 48.97 % 63.77 % 44.60 % 0.04 s 1 core @ 2.5 Ghz (Python)
171 MonoFlex 48.64 % 61.96 % 44.17 % 0.03 s 1 core @ 2.5 Ghz (Python)
172 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.
173 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.
174 MonoEdge 48.26 % 61.11 % 42.37 % 0.05 s GPU @ 2.5 Ghz (Python)
175 RelationNet3D_dla34 code 47.93 % 63.34 % 43.42 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
176 LIGA-Stereo-old
This method uses stereo information.
47.75 % 58.61 % 44.39 % 0.375 s Titan Xp
177 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.
178 MonoEdge-Rotate 47.56 % 62.79 % 43.02 % 0.05 s GPU @ 2.5 Ghz (Python)
179 BirdNet+
This method makes use of Velodyne laser scans.
code 47.50 % 54.78 % 45.53 % 0.11 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.
180 Contrastive PP code 47.05 % 56.47 % 45.04 % 0.01 s 1 core @ 2.5 Ghz (Python)
181 RoIFusion code 46.81 % 56.26 % 44.58 % 0.22 s 1 core @ 3.0 Ghz (Python)
182 mono3d 46.73 % 58.34 % 41.92 % 0.03 s GPU @ 2.5 Ghz (Python)
183 deleted 46.14 % 58.97 % 42.48 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
184 PP-PCdet code 45.82 % 54.17 % 43.68 % 0.01 s 1 core @ 2.5 Ghz (Python)
185 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.
186 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.
187 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.
188 M3DSSD++ code 44.89 % 58.54 % 40.66 % 0.16s 1 core @ 2.5 Ghz (C/C++)
189 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.
190 MonoGeo 44.63 % 58.49 % 40.41 % 0.05 s 1 core @ 2.5 Ghz (Python)
191 Geo3D 44.42 % 58.74 % 40.13 % 0.04 s GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
192 LPCG-Monoflex 44.13 % 62.44 % 39.46 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
193 SAIC_ADC_Mono3D code 43.96 % 59.27 % 39.83 % 50 s GPU @ 2.5 Ghz (Python)
194 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.
195 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.
196 MonoEF code 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.
197 MonoLCD 43.71 % 57.69 % 39.60 % 0.04 s 1 core @ 2.5 Ghz (Python)
198 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.
199 MonoDDE 43.36 % 57.80 % 39.00 % 0.04 s 1 core @ 2.5 Ghz (Python)
200 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.
201 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.
202 MonoDTR 42.86 % 59.44 % 38.57 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
203 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.
204 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.
205 MonoCon 42.44 % 56.57 % 36.34 % 0.02 s GPU @ 2.5 Ghz (Python)
206 RelationNet3D_res18 code 42.25 % 57.61 % 37.93 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
207 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.
208 ICCV 41.88 % 55.14 % 37.55 % 0.04 s GPU @ 2.5 Ghz (Python)
209 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.
210 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.
211 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 .
212 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.
213 MP-Mono 40.87 % 55.79 % 36.81 % 0.16 s GPU @ 2.5 Ghz (Python)
214 SwinMono3D 40.54 % 56.73 % 36.18 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
215 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.
216 PS-fld 40.47 % 55.47 % 36.65 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
217 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.
218 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.
219 FusionDetv2-v1 39.60 % 46.77 % 38.75 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
220 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.
221 Graph-NMS-baseline 39.46 % 52.32 % 35.60 % 47 ms GPU @ 2.5 Ghz (Python)
222 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.
223 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.
224 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). .
225 Graph-NMS 38.40 % 51.24 % 35.24 % 36 ms GPU @ 2.5 Ghz (Python)
226 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.
227 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.
228 Lite-FPN 36.93 % 50.63 % 32.88 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
229 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.
230 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.
231 ZongmuMono3dV2 code 35.86 % 48.61 % 32.10 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
232 COF3D 35.34 % 50.45 % 31.28 % 200 s 1 core @ 2.5 Ghz (Python + C/C++)
233 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.
234 MonoHMOO 34.74 % 49.26 % 30.37 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
235 PPTrans 34.45 % 45.02 % 30.94 % 0.2 s GPU @ 2.5 Ghz (Python)
236 CMKD 34.41 % 47.09 % 30.59 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
237 PointRGBNet 33.92 % 44.35 % 30.43 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
238 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.
239 ZongmuMono3d code 33.47 % 45.86 % 29.84 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
240 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.
241 EGFN
This method uses stereo information.
32.60 % 44.09 % 29.10 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
242 SGM3D 32.48 % 45.03 % 28.58 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
243 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.
244 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.
245 K3D 31.71 % 44.40 % 27.90 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
246 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.
247 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.
248 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.
249 CDTrack3D code 29.98 % 42.95 % 27.59 % 0.0106 s NVIDIA RTX 3090 GPU, i9 10850k CPU
250 SOD 29.47 % 46.61 % 26.97 % 0.1 s 1 core @ 2.5 Ghz (Python)
251 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.
252 AEC3D 28.32 % 37.26 % 24.98 % 18 ms GPU @ 2.5 Ghz (Python)
253 BEVC 26.52 % 35.31 % 24.72 % 35ms GPU @ 1.5 Ghz (Python)
254 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.
255 MM 25.20 % 35.72 % 21.90 % 1 s 1 core @ 2.5 Ghz (C/C++)
256 VN3D 23.61 % 30.51 % 21.97 % 0.02 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
257 MAOLoss code 23.60 % 32.07 % 21.55 % 0.05 s 1 core @ 2.5 Ghz (Python)
258 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.
259 E2E-DA 19.96 % 29.93 % 17.46 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
260 E2E-DA-Lite (Res18) 19.87 % 32.25 % 17.37 % 0.01 s GPU @ 2.5 Ghz (Python)
261 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.
262 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.
263 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.
264 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.
265 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.
266 EW code 1.71 % 1.74 % 1.69 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
267 EM code 1.70 % 1.67 % 1.61 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
268 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 ISE-RCNN-PV 82.78 % 88.08 % 75.96 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
2 SARFE 82.00 % 90.01 % 75.30 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
3 SGNet 82.00 % 91.55 % 75.30 % 0.09 s GPU @ 2.5 Ghz (Python)
4 ISE-RCNN 81.95 % 87.68 % 75.17 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
5 NF2 81.75 % 89.67 % 71.43 % 0.1 s GPU @ 2.5 Ghz (Python)
6 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.
7 anonymous code 81.52 % 89.31 % 74.71 % 0.05s 1 core @ >3.5 Ghz (python)
8 Fast VP-RCNN code 81.41 % 89.29 % 74.88 % 0.05 s 1 core @ 3.5 Ghz (C/C++)
9 Point Image Fusion 80.73 % 87.99 % 74.32 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
10 TPCG 80.71 % 87.73 % 74.17 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
11 SAA-PV-RCNN 80.71 % 88.94 % 73.79 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
12 CAT-Det 80.70 % 87.94 % 73.86 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
13 SPG_mini
This method makes use of Velodyne laser scans.
80.58 % 87.77 % 74.86 % 0.09 s GPU @ 2.5 Ghz (Python)
14 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.
15 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++)
16 BtcDet
This method makes use of Velodyne laser scans.
80.46 % 88.41 % 74.59 % 0.09 s GPU @ 2.5 Ghz (Python + C/C++)
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 80.37 % 89.07 % 74.20 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
20 GNN-RCNN 80.35 % 89.53 % 73.85 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
21 Generalized-SIENet 80.07 % 89.23 % 73.76 % 0.08 s 1 core @ 2.5 Ghz (Python)
22 WHUT-iou_ssd code 79.98 % 87.31 % 74.30 % 0.045s 1 core @ 2.5 Ghz (C/C++)
23 sa-voxel-centernet code 79.98 % 88.08 % 73.61 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
24 E^2-PV-RCNN 79.94 % 87.22 % 73.58 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
25 SA-voxel-centernet code 79.92 % 86.43 % 73.47 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
26 FPC-RCNN 79.89 % 88.62 % 73.18 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
27 PDV 79.84 % 88.76 % 73.04 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
28 DSASNet 79.56 % 87.41 % 73.08 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
29 DDet 79.47 % 88.65 % 72.64 % 0.1 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++ 79.22 % 85.76 % 72.35 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
32 TBD 78.83 % 84.96 % 72.18 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
33 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.
34 JPVNet 78.73 % 87.42 % 72.45 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
35 IA-SSD (single) 78.71 % 88.99 % 72.03 % 0.013 s 1 core @ 2.5 Ghz (C/C++)
36 PE-RCVN 78.71 % 90.77 % 71.83 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
37 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++)
38 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.
39 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.
40 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.
41 AutoAlign 77.96 % 89.63 % 71.27 % 0.1 s 1 core @ 2.5 Ghz (Python)
42 MVOD 77.92 % 85.92 % 71.24 % 0.16 s 1 core @ 2.5 Ghz (C/C++)
43 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++)
44 SAA-SECOND 77.47 % 88.50 % 70.23 % 38m s 1 core @ 2.5 Ghz (C/C++)
45 TBD 77.31 % 87.22 % 70.53 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
46 TBD 77.06 % 90.28 % 70.46 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
47 ASCNet 76.95 % 83.81 % 70.84 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
48 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.
49 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.
50 FusionDetv2-v5 76.60 % 85.95 % 69.75 % 0.05 s 1 core @ 2.5 Ghz (Java + C/C++)
51 FusionDetv2-v3 76.48 % 86.15 % 69.64 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
52 NV2P-RCNN 76.29 % 83.53 % 69.57 % 0.1 s GPU @ 2.5 Ghz (Python)
53 VCT 76.08 % 86.69 % 69.91 % 0.2 s 1 core @ 2.5 Ghz (Python)
54 FusionDetv2-v4 75.92 % 87.66 % 70.06 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
55 FPCR-CNN 75.83 % 87.74 % 68.98 % 0.05 s 1 core @ 2.5 Ghz (Python)
56 FPV-SSD 75.33 % 83.30 % 68.10 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
57 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.
58 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.
59 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.
60 Fast-CLOCs 75.07 % 89.73 % 67.93 % 0.1 s GPU @ 2.5 Ghz (Python)
61 SCIR-Net
This method makes use of Velodyne laser scans.
74.88 % 84.92 % 68.13 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
Q. He, H. Zeng, Y. Zeng and Y. Liu: SCIR-Net: Structured Color Image Representation Based 3D Object Detection Network from Point Clouds. AAAI 2022.
62 EA-M-RCNN(BorderAtt) 74.85 % 88.69 % 68.01 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
63 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.
64 DGIST MT-CNN 74.77 % 86.95 % 66.05 % 0.09 s GPU @ 1.0 Ghz (Python)
65 P2V_PCV1 74.57 % 85.38 % 68.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
66 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.
67 MKFFNet 74.39 % 85.89 % 68.02 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
68 FusionDetv2-v2 74.38 % 86.62 % 67.96 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
69 RoIFusion code 74.27 % 85.15 % 68.29 % 0.22 s 1 core @ 3.0 Ghz (Python)
70 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.
71 MSADet 74.06 % 87.54 % 68.55 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
72 VPN 73.99 % 89.56 % 66.86 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
73 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.
74 TBD_IOU 73.84 % 87.97 % 66.36 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
75 FusionDetv1 73.69 % 85.39 % 66.94 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
76 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.
77 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. arXiv preprint arXiv:2011.01404 2020.
78 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.
79 TBD_IOU1 73.46 % 87.05 % 66.14 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
80 tbd 73.43 % 84.88 % 65.72 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
81 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.
82 FPC3D_all
This method makes use of Velodyne laser scans.
73.25 % 84.12 % 66.56 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
83 SIF 73.19 % 85.18 % 65.41 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
84 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.
85 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.
86 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.
87 NV-RCNN 73.07 % 85.64 % 66.60 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
88 HS3D code 73.02 % 84.59 % 67.13 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
89 demo 72.90 % 87.20 % 66.56 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
90 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.
91 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.
92 FusionDetv2-baseline 72.68 % 82.09 % 66.42 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
93 VGCN 72.28 % 86.81 % 65.68 % 0.09 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
94 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.
95 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.
96 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.
97 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.
98 IA-SSD (multi) 70.46 % 84.98 % 65.55 % 0.014 s 1 core @ 2.5 Ghz (C/C++)
99 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.
100 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.
101 TBD 70.12 % 81.15 % 63.79 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
102 YF 70.10 % 80.50 % 64.21 % 0.04 s GPU @ 2.5 Ghz (C/C++)
103 TBD 69.99 % 85.54 % 65.02 % TBD GPU @ 2.5 Ghz (Python + C/C++)
104 TBD_BD code 69.56 % 83.36 % 63.39 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
105 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.
106 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.
107 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.
108 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.
109 EPNet++ 68.30 % 80.27 % 63.00 % 0.1 s GPU @ 2.5 Ghz (Python)
110 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.
111 Contrastive PP code 67.62 % 79.63 % 60.81 % 0.01 s 1 core @ 2.5 Ghz (Python)
112 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.
113 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.
114 TBD 67.15 % 82.44 % 60.87 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
115 TBD 67.15 % 82.44 % 60.87 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
116 VueronNet 66.92 % 82.71 % 59.01 % 0.08 s GPU @ 2.5 Ghz (Python)
117 PP-PCdet code 66.58 % 78.44 % 60.22 % 0.01 s 1 core @ 2.5 Ghz (Python)
118 FII-CenterNet 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.
119 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.
120 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.
121 PointRGBNet 65.98 % 79.87 % 59.75 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
122 BirdNet+
This method makes use of Velodyne laser scans.
code 65.40 % 72.96 % 60.23 % 0.11 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.
123 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.
124 PiFeNet 64.39 % 80.02 % 57.46 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
125 tbd 64.31 % 79.83 % 58.14 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
126 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.
127 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.
128 DD3D code 62.21 % 79.98 % 54.91 % 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) .
129 DSGN++
This method uses stereo information.
62.10 % 77.71 % 55.78 % 0.4 s 1 core @ 2.5 Ghz (C/C++)
130 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.
131 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.
132 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.
133 Mix-Teaching-M3D 58.65 % 75.15 % 50.54 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
134 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.
135 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)
136 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.
137 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)
138 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.
139 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.
140 FusionDetv2-v1 56.30 % 66.22 % 52.29 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
141 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.
142 LIGA-Stereo-old
This method uses stereo information.
55.77 % 74.25 % 49.68 % 0.375 s Titan Xp
143 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)
144 mono3d 54.82 % 70.77 % 47.55 % 0.03 s GPU @ 2.5 Ghz (Python)
145 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.
146 M3DSSD++ code 54.62 % 69.35 % 46.25 % 0.16s 1 core @ 2.5 Ghz (C/C++)
147 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.
148 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.
149 SCSTSV-MonoFlex 54.42 % 75.37 % 46.25 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
150 vadin-TBD 54.12 % 70.14 % 46.16 % 0.04 s 1 core @ 2.5 Ghz (Python)
151 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 .
152 LPCG-Monoflex 53.04 % 72.36 % 46.11 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
153 GA-Aug 52.71 % 67.62 % 46.37 % 0.04 s GPU @ 2.5 Ghz (Python)
154 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.
155 MonoEdge-Rotate 51.89 % 68.91 % 45.33 % 0.05 s GPU @ 2.5 Ghz (Python)
156 MonoGround 51.67 % 67.78 % 45.11 % 0.03 s 1 core @ 2.5 Ghz (Python)
157 Geo3D 51.26 % 71.75 % 44.44 % 0.04 s GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
158 MonoFlex 51.23 % 66.73 % 44.57 % 0.03 s 1 core @ 2.5 Ghz (Python)
159 MonoDDE 51.10 % 70.85 % 44.02 % 0.04 s 1 core @ 2.5 Ghz (Python)
160 MonoGeo 50.48 % 65.42 % 42.48 % 0.05 s 1 core @ 2.5 Ghz (Python)
161 deleted 50.22 % 68.25 % 44.84 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
162 MonoDTR 49.48 % 64.93 % 42.76 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
163 MonoEdge 49.33 % 65.93 % 42.44 % 0.05 s GPU @ 2.5 Ghz (Python)
164 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.
165 MP-Mono 48.54 % 69.48 % 41.58 % 0.16 s GPU @ 2.5 Ghz (Python)
166 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.
167 EG_DETR 48.42 % 67.71 % 42.99 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
168 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.
169 CMKD 47.21 % 66.52 % 41.56 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
170 KAIST-VDCLab 46.65 % 68.60 % 41.79 % 0.04 s 1 core @ 2.5 Ghz (Python)
171 gupnet_se 46.47 % 68.66 % 37.89 % 0.03s 1 core @ 2.5 Ghz (C/C++)
172 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.
173 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.
174 SwinMono3D 45.72 % 67.95 % 38.55 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
175 CMKD 45.69 % 65.71 % 40.11 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
176 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.
177 MonoLCD 44.57 % 65.17 % 39.96 % 0.04 s 1 core @ 2.5 Ghz (Python)
178 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.
179 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.
180 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.
181 MonoCon 42.49 % 59.39 % 35.94 % 0.02 s GPU @ 2.5 Ghz (Python)
182 SAIC_ADC_Mono3D code 42.37 % 60.72 % 36.57 % 50 s GPU @ 2.5 Ghz (Python)
183 GAC3D++ 41.87 % 61.03 % 35.78 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
184 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 .
185 MonoEF code 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.
186 PS-fld 41.13 % 58.13 % 35.90 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
187 SOD 40.95 % 60.07 % 34.02 % 0.1 s 1 core @ 2.5 Ghz (Python)
188 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.
189 PPTrans 39.55 % 48.93 % 33.74 % 0.2 s GPU @ 2.5 Ghz (Python)
190 RelationNet3D_dla34 code 39.52 % 59.56 % 34.51 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
191 ANM 39.39 % 57.77 % 34.59 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
192 OSE+ 39.26 % 58.13 % 34.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
193 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.
194 RelationNet3D_res18 code 37.41 % 56.22 % 32.56 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
195 ZongmuMono3dV2 code 37.18 % 49.10 % 32.04 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
196 AEC3D 37.15 % 48.48 % 34.86 % 18 ms GPU @ 2.5 Ghz (Python)
197 ICCV 36.70 % 53.31 % 31.94 % 0.04 s GPU @ 2.5 Ghz (Python)
198 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.
199 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.
200 Y4 code 35.92 % 53.50 % 31.89 % 0.03 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
201 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.
202 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.
203 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.
204 COF3D 32.97 % 51.77 % 28.26 % 200 s 1 core @ 2.5 Ghz (Python + C/C++)
205 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.
206 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.
207 VN3D 31.81 % 42.58 % 29.09 % 0.02 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
208 ZongmuMono3d code 31.56 % 44.68 % 27.48 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
209 BEVC 30.39 % 43.61 % 27.46 % 35ms GPU @ 1.5 Ghz (Python)
210 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.
211 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.
212 SGM3D 27.89 % 42.21 % 24.73 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
213 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.
214 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.
215 K3D 27.29 % 38.82 % 23.86 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
216 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.
217 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.
218 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.
219 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.
220 E2E-DA-Lite (Res18) 25.82 % 42.48 % 21.16 % 0.01 s GPU @ 2.5 Ghz (Python)
221 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.
222 E2E-DA 24.46 % 39.34 % 19.59 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
223 MonoHMOO 23.59 % 37.41 % 21.20 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
224 Graph-NMS-baseline 23.07 % 35.72 % 20.54 % 47 ms GPU @ 2.5 Ghz (Python)
225 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.
226 MAOLoss code 22.58 % 34.37 % 20.49 % 0.05 s 1 core @ 2.5 Ghz (Python)
227 Graph-NMS 22.43 % 34.13 % 19.65 % 36 ms GPU @ 2.5 Ghz (Python)
228 Lite-FPN 19.17 % 24.40 % 15.68 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
229 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.
230 CDTrack3D code 16.04 % 23.84 % 13.15 % 0.0106 s NVIDIA RTX 3090 GPU, i9 10850k CPU
231 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.
232 EGFN
This method uses stereo information.
13.45 % 21.13 % 11.72 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
233 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.
234 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.
235 MM 6.79 % 11.33 % 6.28 % 1 s 1 core @ 2.5 Ghz (C/C++)
236 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.
237 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 NFAF3D 96.12 % 96.76 % 93.12 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
2 SFD 96.05 % 98.95 % 90.96 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
3 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.
4 Anonymous 96.01 % 99.06 % 90.97 % n/a s 1 core @ 2.5 Ghz (Python + C/C++)
5 Anonymous 96.00 % 98.72 % 90.97 % 0.1 s GPU @ 2.5 Ghz (Python)
6 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.
7 PE-RCVN 95.85 % 96.89 % 90.87 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
8 CityBrainLab 95.83 % 98.56 % 90.79 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
9 Anonymous 95.81 % 96.53 % 92.69 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
10 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.
11 Anonymous 95.75 % 98.92 % 90.83 % n/a s 1 core @ 2.5 Ghz (C/C++)
12 SECOND 95.67 % 96.42 % 90.37 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
13 DVF-V 95.63 % 96.59 % 90.71 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
14 NFAF3D-light 95.62 % 96.66 % 92.48 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
15 TBD 95.62 % 96.32 % 92.95 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
16 DSGN++
This method uses stereo information.
95.58 % 98.04 % 88.09 % 0.4 s 1 core @ 2.5 Ghz (C/C++)
17 Fast-CLOCs 95.57 % 96.66 % 90.70 % 0.1 s GPU @ 2.5 Ghz (Python)
18 EA-M-RCNN(BorderAtt) 95.44 % 96.37 % 90.31 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
19 JPVNet 95.38 % 96.40 % 90.52 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
20 DVF-PV 95.35 % 96.40 % 92.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
21 ISE-RCNN-PV 95.34 % 96.18 % 92.70 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
22 BANet code 95.34 % 98.65 % 90.28 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
R. Qian, X. Lai and X. Li: Boundary-Aware 3D Object Detection from Point Clouds. 2021.
23 Anonymous 95.32 % 98.34 % 90.36 % 0.1s 1 core @ 2.5 Ghz (C/C++)
24 ISE-RCNN 95.30 % 96.36 % 92.63 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
25 Anonymous code 95.23 % 96.29 % 92.60 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
26 TBD 95.20 % 96.16 % 90.42 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
27 EQ-PVRCNN 95.20 % 98.22 % 92.47 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
28 anonymous 95.19 % 96.35 % 92.38 % 0.09 s GPU @ 2.5 Ghz (Python)
29 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.
30 DGDNH 95.15 % 98.35 % 92.55 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
31 VoxSeT 95.13 % 96.15 % 90.38 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
32 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.
33 TBD 95.03 % 96.47 % 92.50 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
34 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.
35 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.
36 EPNet++ 95.00 % 96.70 % 91.82 % 0.1 s GPU @ 2.5 Ghz (Python)
37 3DIoU++ 94.97 % 96.36 % 90.40 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
38 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.
39 PDV 94.91 % 96.06 % 92.30 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
40 PV-RCNN++ 94.90 % 96.07 % 92.22 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
41 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.
42 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.
43 CSVoxel-RCNN 94.74 % 96.31 % 91.84 % 0.03 s GPU @ 1.0 Ghz (Python)
44 TBD 94.73 % 97.01 % 92.18 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
45 SRIF-RCNN 94.70 % 95.62 % 92.21 % 0.0947 s 1 core @ 2.5 Ghz (C/C++)
46 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.
47 E^2-PV-RCNN 94.69 % 95.94 % 92.09 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
48 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++)
49 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++)
50 TPCG 94.66 % 95.95 % 92.08 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
51 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.
52 MSG-PGNN 94.65 % 95.85 % 92.01 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
53 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.
54 VCRCNN 94.64 % 96.05 % 92.09 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
55 SqueezeRCNN 94.61 % 96.02 % 91.97 % 0.08 s 1 core @ 2.5 Ghz (Python)
56 FusionDetv2-v4 94.60 % 95.92 % 91.79 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
57 Generalized-SIENet 94.59 % 95.74 % 91.99 % 0.08 s 1 core @ 2.5 Ghz (Python)
58 HyBrid Feature Det 94.59 % 95.87 % 91.97 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
59 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.
60 CAT-Det 94.57 % 95.95 % 91.88 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
61 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.
62 3DIoU_v2 94.57 % 96.14 % 92.18 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
63 LZY_RCNN 94.56 % 95.80 % 91.94 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
64 PC-RGNN 94.55 % 95.79 % 92.03 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
65 SCIR-Net
This method makes use of Velodyne laser scans.
94.55 % 96.11 % 91.68 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
Q. He, H. Zeng, Y. Zeng and Y. Liu: SCIR-Net: Structured Color Image Representation Based 3D Object Detection Network from Point Clouds. AAAI 2022.
66 DDet 94.54 % 95.80 % 91.99 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
67 TransCyclistNet 94.52 % 96.07 % 91.94 % 0.08 s 1 core @ 2.5 Ghz (Python)
68 Fast VP-RCNN code 94.52 % 97.99 % 91.74 % 0.05 s 1 core @ 3.5 Ghz (C/C++)
69 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.
70 FusionDetv2-v3 94.51 % 96.14 % 91.73 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
71 TransDet3D 94.50 % 95.82 % 91.89 % 0.08 s 1 core @ 2.5 Ghz (Python)
72 Point Image Fusion 94.49 % 95.69 % 91.92 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
73 FPV-SSD 94.48 % 96.89 % 91.52 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
74 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.
75 SA-voxel-centernet code 94.45 % 95.78 % 91.84 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
76 WHUT-iou_ssd code 94.45 % 95.76 % 91.75 % 0.045s 1 core @ 2.5 Ghz (C/C++)
77 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.
78 anonymous code 94.43 % 97.50 % 91.66 % 0.05s 1 core @ >3.5 Ghz (python)
79 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.
80 FPC-RCNN 94.40 % 96.13 % 91.63 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
81 sa-voxel-centernet code 94.39 % 95.86 % 91.80 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
82 FPC3D
This method makes use of the epipolar geometry.
94.39 % 96.04 % 91.51 % 33 s 1 core @ 2.5 Ghz (C/C++)
83 DD3D code 94.39 % 95.25 % 89.47 % 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) .
84 GNN-RCNN 94.32 % 95.84 % 91.79 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
85 SAA-SECOND 94.24 % 95.64 % 91.40 % 38m s 1 core @ 2.5 Ghz (C/C++)
86 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.
87 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.
88 TBD 94.07 % 95.49 % 91.44 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
89 FusionDetv1 94.07 % 95.82 % 91.54 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
90 SARFE 94.07 % 95.73 % 91.40 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
91 FusionDetv2-v2 94.05 % 95.73 % 89.66 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
92 SAA-PV-RCNN 94.02 % 95.00 % 92.34 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
93 FPCR-CNN 93.99 % 95.94 % 90.99 % 0.05 s 1 core @ 2.5 Ghz (Python)
94 NV2P-RCNN 93.91 % 97.80 % 90.96 % 0.1 s GPU @ 2.5 Ghz (Python)
95 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.
96 SIF 93.79 % 95.48 % 91.30 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
97 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.
98 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.
99 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.
100 LIGA-Stereo-old
This method uses stereo information.
93.54 % 96.63 % 83.68 % 0.375 s Titan Xp
101 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.
102 TF3D
This method makes use of Velodyne laser scans.
93.49 % 96.57 % 88.54 % 0.1 s 2 cores @ 3.0 Ghz (Python)
103 IA-SSD (multi) 93.47 % 96.07 % 90.51 % 0.014 s 1 core @ 2.5 Ghz (C/C++)
104 Associate-3Ddet_v2 93.46 % 96.66 % 88.20 % 0.04 s 1 core @ 2.5 Ghz (Python)
105 FusionDetv2-v5 93.44 % 95.31 % 88.96 % 0.05 s 1 core @ 2.5 Ghz (Java + C/C++)
106 IA-SSD (single) 93.41 % 96.23 % 88.34 % 0.013 s 1 core @ 2.5 Ghz (C/C++)
107 DVF 93.39 % 96.44 % 88.32 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
108 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++)
109 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.
110 TBD 93.31 % 94.17 % 88.30 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
111 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.
112 Sem-Aug v1 code 93.26 % 96.36 % 90.51 % 0.04 s GPU @ 3.5 Ghz (Python)
113 LPCG-Monoflex 93.26 % 96.68 % 83.34 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
114 DSASNet 93.25 % 96.54 % 88.34 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
115 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++)
116 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.
117 TBD 93.20 % 95.96 % 90.30 % TBD GPU @ 2.5 Ghz (Python + C/C++)
118 AM-SSD 93.18 % 96.56 % 90.13 % 0.04 s 1 core @ 2.5 Ghz (Python)
119 demo 93.15 % 96.15 % 90.08 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
120 VCT 93.09 % 96.30 % 90.38 % 0.2 s 1 core @ 2.5 Ghz (Python)
121 VPN 93.08 % 96.16 % 88.01 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
122 MVOD 93.07 % 96.15 % 92.39 % 0.16 s 1 core @ 2.5 Ghz (C/C++)
123 KpNet 93.06 % 96.63 % 85.39 % 0.42 s 1 core @ 2.5 Ghz (C/C++)
124 KpNet 93.05 % 96.63 % 85.38 % 42 s 1 core @ 2.5 Ghz (C/C++)
125 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.
126 ASCNet 93.01 % 96.05 % 90.21 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
127 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.
128 SGNet 92.95 % 96.41 % 90.35 % 0.09 s GPU @ 2.5 Ghz (Python)
129 Sem-Aug-PointRCNN code 92.92 % 95.66 % 88.07 % 0.1 s GPU @ 3.5 Ghz (C/C++)
130 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.
131 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.
132 MBDF-Net 92.77 % 96.19 % 89.70 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
133 YF 92.74 % 96.02 % 89.76 % 0.04 s GPU @ 2.5 Ghz (C/C++)
134 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.
135 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.
136 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.
137 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.
138 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.
139 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.
140 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.
141 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.
142 MSADet 92.45 % 96.08 % 89.27 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
143 PointPainting
This method makes use of Velodyne laser scans.
92.43 % 98.36 % 89.49 % 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.
144 3D IoU-Net 92.42 % 96.31 % 87.60 % 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.
145 MBDF-Net-1 92.37 % 95.87 % 89.26 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
146 CLOCs_SecCas 92.37 % 95.16 % 88.43 % 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.
147 NV-RCNN 92.34 % 95.83 % 89.70 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
148 DASS 92.25 % 96.20 % 87.26 % 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.
149 S-AT GCN 92.24 % 95.02 % 90.46 % 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.
150 3D-VDNet 92.19 % 95.39 % 89.10 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
151 SegVoxelNet 92.16 % 95.86 % 86.90 % 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.
152 PointRGCN 92.15 % 97.48 % 86.83 % 0.26 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
153 F-ConvNet
This method makes use of Velodyne laser scans.
code 91.98 % 95.81 % 79.83 % 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.
154 RangeDet code 91.92 % 95.16 % 86.98 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
155 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 91.87 % 95.86 % 86.78 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
156 VGCN 91.80 % 94.88 % 89.06 % 0.09 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
157 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 91.77 % 95.90 % 86.92 % 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.
158 KAIST-VDCLab 91.74 % 95.40 % 84.25 % 0.04 s 1 core @ 2.5 Ghz (Python)
159 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 91.73 % 95.00 % 88.86 % 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.
160 MKFFNet 91.72 % 95.26 % 88.92 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
161 AutoAlign 91.60 % 95.07 % 88.86 % 0.1 s 1 core @ 2.5 Ghz (Python)
162 C-GCN 91.57 % 95.63 % 86.13 % 0.147 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
163 CVFNet 91.55 % 95.20 % 87.80 % 28.1ms 1 core @ 2.5 Ghz (Python)
164 PointRGBNet 91.33 % 95.39 % 86.29 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
165 EgoNet code 91.23 % 96.11 % 80.96 % 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.
166 AIMC-RUC 91.18 % 96.84 % 85.94 % 0.11 s 1 core @ 2.5 Ghz (Python)
167 CA3D 91.08 % 95.05 % 81.53 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
168 Contrastive PP code 91.06 % 96.86 % 87.69 % 0.01 s 1 core @ 2.5 Ghz (Python)
169 PFF3D
This method makes use of Velodyne laser scans.
code 91.06 % 94.86 % 86.28 % 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.
170 PP-PCdet code 91.05 % 94.77 % 87.72 % 0.01 s 1 core @ 2.5 Ghz (Python)
171 Stereo CenterNet
This method uses stereo information.
91.02 % 96.54 % 83.15 % 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.
172 sscl-20p 90.94 % 96.89 % 87.60 % 0.02 s 1 core @ 2.5 Ghz (Python)
173 Mix-Teaching-M3D 90.84 % 96.31 % 83.11 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
174 MonoFlex 90.82 % 95.95 % 83.11 % 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.
175 SCSTSV-MonoFlex 90.81 % 96.36 % 80.90 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
176 MAFF-Net(DAF-Pillar) 90.78 % 94.17 % 83.17 % 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.
177 HRI-VoxelFPN 90.76 % 96.35 % 85.37 % 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.
178 KM3D code 90.70 % 96.34 % 80.72 % 0.03 s 1 core @ 2.5 Ghz (Python)
P. Li: Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training. 2020.
179 PointPillars
This method makes use of Velodyne laser scans.
code 90.70 % 93.84 % 87.47 % 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.
180 WS3D
This method makes use of Velodyne laser scans.
90.69 % 94.85 % 85.94 % 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.
181 MonoEF code 90.65 % 96.19 % 82.95 % 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.
182 FPC3D_all
This method makes use of Velodyne laser scans.
90.60 % 95.35 % 85.79 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
183 QD-3DT
This is an online method (no batch processing).
code 90.49 % 92.61 % 80.32 % 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.
184 vadin-TBD 90.49 % 95.86 % 80.66 % 0.04 s 1 core @ 2.5 Ghz (Python)
185 MonoGround 90.42 % 93.87 % 80.54 % 0.03 s 1 core @ 2.5 Ghz (Python)
186 DPointNet 90.38 % 93.61 % 87.34 % 0.07s 1 core @ 2.5 Ghz (C/C++)
187 MonoDistill 90.33 % 95.82 % 80.38 % 0.04 s 1 core @ 2.5 Ghz (Python)
188 MonoEdge 90.32 % 93.43 % 80.57 % 0.05 s GPU @ 2.5 Ghz (Python)
189 MonoFlex 90.29 % 95.51 % 82.68 % 0.03 s 1 core @ 2.5 Ghz (Python)
190 3D_att
This method makes use of Velodyne laser scans.
90.26 % 96.42 % 84.81 % 0.17 s GPU @ 2.5 Ghz (Python)
191 monodle code 90.23 % 93.46 % 80.11 % 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 .
192 3D IoU Loss
This method makes use of Velodyne laser scans.
90.21 % 95.60 % 84.96 % 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.
193 MonoCInIS 90.20 % 95.80 % 82.00 % 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.
194 ARPNET 90.11 % 93.42 % 82.56 % 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.
195 TANet code 90.11 % 93.52 % 84.61 % 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.
196 MonoEdge-Rotate 90.07 % 93.46 % 80.29 % 0.05 s GPU @ 2.5 Ghz (Python)
197 mono3d 90.02 % 93.54 % 83.19 % 0.03 s GPU @ 2.5 Ghz (Python)
198 CG-Stereo
This method uses stereo information.
89.98 % 96.28 % 82.21 % 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.
199 Deep3DBox 89.88 % 94.62 % 76.40 % 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.
200 PS-fld 89.78 % 95.60 % 81.68 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
201 GPP code 89.68 % 93.94 % 80.60 % 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.
202 SubCNN 89.53 % 94.11 % 79.14 % 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.
203 MonoGeo 89.44 % 94.67 % 79.27 % 0.05 s 1 core @ 2.5 Ghz (Python)
204 SCNet
This method makes use of Velodyne laser scans.
89.36 % 95.23 % 84.03 % 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.
205 Geo3D 89.28 % 93.60 % 77.21 % 0.04 s GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
206 GA-Aug 89.24 % 92.66 % 81.31 % 0.04 s GPU @ 2.5 Ghz (Python)
207 TBD_BD code 89.23 % 94.01 % 86.17 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
208 Digging_M3D 89.23 % 93.54 % 79.15 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
209 AVOD
This method makes use of Velodyne laser scans.
code 89.22 % 94.98 % 82.14 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
210 IAFA 89.14 % 92.96 % 79.40 % 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.
211 MonoDDE 89.07 % 96.72 % 81.42 % 0.04 s 1 core @ 2.5 Ghz (Python)
212 FusionDetv2-v1 89.00 % 94.78 % 84.10 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
213 PPTrans 88.70 % 95.04 % 81.15 % 0.2 s GPU @ 2.5 Ghz (Python)
214 GAC3D++ 88.69 % 94.16 % 78.74 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
215 AVOD-FPN
This method makes use of Velodyne laser scans.
code 88.61 % 94.65 % 83.71 % 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.
216 FusionDetv2-baseline 88.58 % 94.20 % 85.36 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
217 deleted 88.13 % 96.48 % 80.66 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
218 M3DSSD++ code 88.04 % 94.75 % 76.03 % 0.16s 1 core @ 2.5 Ghz (C/C++)
219 MonoCon 87.92 % 93.52 % 75.83 % 0.02 s GPU @ 2.5 Ghz (Python)
220 MonoLCD 87.86 % 93.62 % 78.09 % 0.04 s 1 core @ 2.5 Ghz (Python)
221 CMKD 87.83 % 94.87 % 80.95 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
222 DeepStereoOP 87.81 % 93.68 % 77.60 % 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.
223 CMKD 87.79 % 95.17 % 80.92 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
224 MonoRUn code 87.64 % 95.44 % 77.75 % 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.
225 3DBN
This method makes use of Velodyne laser scans.
87.59 % 93.34 % 79.91 % 0.13s 1080Ti (Python+C/C++)
X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.
226 FQNet 87.49 % 93.66 % 73.61 % 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.
227 Shift R-CNN (mono) code 87.47 % 93.75 % 77.19 % 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.
228 MonoPSR code 87.45 % 93.29 % 72.26 % 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.
229 Mono3D code 87.28 % 93.13 % 77.00 % 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.
230 SAIC_ADC_Mono3D code 87.27 % 95.04 % 79.50 % 50 s GPU @ 2.5 Ghz (Python)
231 SMOKE code 87.02 % 92.94 % 77.12 % 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.
232 3DOP
This method uses stereo information.
code 86.93 % 91.31 % 76.72 % 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.
233 CDN
This method uses stereo information.
code 86.90 % 95.79 % 79.05 % 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.
234 RTM3D code 86.73 % 91.75 % 77.18 % 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.
235 MonoDTR 86.70 % 93.12 % 74.53 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
236 ZongmuMono3dV2 code 86.56 % 93.12 % 77.11 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
237 MonoRCNN code 86.48 % 91.90 % 66.71 % 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.
238 Anonymous code 86.45 % 94.21 % 71.62 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
239 BirdNet+
This method makes use of Velodyne laser scans.
code 86.13 % 92.39 % 81.11 % 0.11 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.
240 MonoPair 86.11 % 91.65 % 76.45 % 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.
241 DSGN
This method uses stereo information.
code 86.03 % 95.42 % 78.27 % 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.
242 GUPNet code 85.90 % 93.92 % 73.55 % 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.
243 ANM 85.83 % 94.15 % 75.77 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
244 gupnet_se 85.55 % 93.91 % 73.23 % 0.03s 1 core @ 2.5 Ghz (C/C++)
245 StereoFENet
This method uses stereo information.
85.14 % 91.28 % 76.80 % 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.
246 MP-Mono 84.72 % 90.17 % 64.19 % 0.16 s GPU @ 2.5 Ghz (Python)
247 AEC3D 84.59 % 90.38 % 80.13 % 18 ms GPU @ 2.5 Ghz (Python)
248 PL++ (SDN+GDC)
This method uses stereo information.
This method makes use of Velodyne laser scans.
code 84.42 % 94.83 % 76.95 % 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.
249 SS3D 84.38 % 92.57 % 69.82 % 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.
250 CDN-PL++
This method uses stereo information.
84.21 % 94.45 % 76.69 % 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.
251 ZongmuMono3d code 84.21 % 92.95 % 74.77 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
252 VN3D 84.12 % 90.42 % 77.70 % 0.02 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
253 MonoFENet 84.09 % 91.42 % 75.93 % 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.
254 Complexer-YOLO
This method makes use of Velodyne laser scans.
83.89 % 91.77 % 79.24 % 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.
255 ZoomNet
This method uses stereo information.
code 83.79 % 94.14 % 68.78 % 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.
256 LPCG-M3D 83.39 % 86.97 % 75.09 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
257 DLE code 83.19 % 94.06 % 61.13 % 0.06 s NVIDIA Tesla V100
C. Liu, S. Gu, L. Gool and R. Timofte: Deep Line Encoding for Monocular 3D Object Detection and Depth Prediction. Proceedings of the British Machine Vision Conference (BMVC) 2021.
258 M3D-RPN code 82.81 % 88.38 % 67.08 % 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 .
259 SGM3D 82.51 % 93.46 % 72.67 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
260 OSE+ 82.44 % 94.33 % 75.05 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
261 Disp R-CNN (velo)
This method uses stereo information.
code 82.09 % 93.31 % 69.78 % 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.
262 D4LCN code 82.08 % 90.01 % 63.98 % 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.
263 Disp R-CNN
This method uses stereo information.
code 81.96 % 93.49 % 67.35 % 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.
264 Pseudo-LiDAR++
This method uses stereo information.
code 81.87 % 94.14 % 74.29 % 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.
265 BS3D 81.22 % 94.66 % 68.39 % 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.
266 YOLOStereo3D
This method uses stereo information.
code 80.88 % 93.65 % 61.17 % 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.
267 vadin-TBD2 code 80.67 % 91.94 % 70.64 % 0.20 s 1 core @ 2.5 Ghz (Python)
268 SD3DOD 80.64 % 92.32 % 74.74 % 0.04 s GPU @ 2.5 Ghz (Python)
269 SOD 80.62 % 94.15 % 65.94 % 0.1 s 1 core @ 2.5 Ghz (Python)
270 FRCNN+Or code 80.57 % 91.50 % 67.49 % 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.
271 ITS-MDPL 80.56 % 92.45 % 73.05 % 0.16 s GPU @ 2.5 Ghz (Python)
272 SwinMono3D 80.47 % 91.08 % 60.77 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
273 DDMP-3D 80.20 % 90.73 % 61.82 % 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.
274 none 80.17 % 90.64 % 67.96 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
275 K3D 80.16 % 93.38 % 70.41 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
276 Ground-Aware code 80.05 % 90.98 % 60.51 % 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.
277 GrooMeD-NMS code 79.93 % 90.05 % 63.43 % 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.
278 EGFN
This method uses stereo information.
79.84 % 92.74 % 69.76 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
279 EW code 79.73 % 87.07 % 66.46 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
280 RelationNet3D_dla34 code 79.59 % 83.64 % 69.13 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
281 PGD-FCOS3D code 79.46 % 91.51 % 68.48 % 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.
282 EM code 79.33 % 86.76 % 66.14 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
283 E2E-DA 79.33 % 92.09 % 69.45 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
284 Lite-FPN 79.13 % 86.85 % 65.03 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
285 KMC code 79.09 % 89.31 % 72.31 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
286 YoloMono3D code 78.50 % 91.43 % 58.80 % 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.
287 3D-GCK 78.44 % 88.59 % 66.28 % 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.
288 COF3D 77.98 % 87.17 % 60.10 % 200 s 1 core @ 2.5 Ghz (Python + C/C++)
289 E2E-DA-Lite (Res18) 77.94 % 90.63 % 65.85 % 0.01 s GPU @ 2.5 Ghz (Python)
290 3D-SSMFCNN code 77.82 % 77.84 % 68.67 % 0.1 s GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.
291 DFR-Net 77.41 % 89.79 % 59.20 % 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.
292 AutoShape code 77.31 % 86.41 % 64.06 % 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.
293 ImVoxelNet code 77.18 % 89.07 % 67.35 % 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.
294 Aug3D-RPN 76.89 % 84.89 % 60.21 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth. 2021.
295 AutoShape 76.61 % 83.71 % 63.47 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
296 RelationNet3D_res18 code 76.45 % 86.98 % 66.83 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
297 RelationNet3D 76.44 % 81.31 % 68.25 % 0.04 s GPU @ 2.5 Ghz (Python)
298 MonoHMOO 75.95 % 91.51 % 59.55 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
299 ICCV 75.90 % 85.36 % 64.93 % 0.04 s GPU @ 2.5 Ghz (Python)
300 3DVP code 75.71 % 84.44 % 64.41 % 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.
301 GS3D 75.63 % 85.79 % 61.85 % 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.
302 Pose-RCNN 75.41 % 89.49 % 63.57 % 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.
303 SubCat code 75.26 % 83.31 % 59.55 % 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.
304 3D FCN
This method makes use of Velodyne laser scans.
74.54 % 86.65 % 67.73 % >5 s 1 core @ 2.5 Ghz (C/C++)
B. Li: 3D Fully Convolutional Network for Vehicle Detection in Point Cloud. IROS 2017.
305 Mobile Stereo R-CNN
This method uses stereo information.
74.13 % 88.80 % 59.84 % 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.
306 MAOLoss code 73.51 % 89.22 % 63.24 % 0.05 s 1 core @ 2.5 Ghz (Python)
307 OC Stereo
This method uses stereo information.
code 73.34 % 86.86 % 61.37 % 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.
308 GAC3D 70.49 % 83.27 % 52.04 % 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.
309 ROI-10D 68.14 % 75.32 % 58.98 % 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.
310 BirdNet+ (legacy)
This method makes use of Velodyne laser scans.
code 67.65 % 91.82 % 65.11 % 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.
311 multi-task CNN 67.51 % 79.00 % 58.80 % 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.
312 CaDDN code 67.31 % 78.28 % 59.52 % 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.
313 Decoupled-3D 67.23 % 87.34 % 53.84 % 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.
314 BdCost48LDCF code 65.50 % 80.44 % 51.24 % 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.
315 OC-DPM 65.32 % 77.35 % 51.00 % 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.
316 RefinedMPL 64.02 % 87.95 % 52.06 % 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.
317 TBD 63.75 % 85.52 % 54.42 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
318 DPM-VOC+VP 63.58 % 79.09 % 46.59 % 8 s 1 core @ 2.5 Ghz (C/C++)
B. Pepik, M.