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 VPFNet 96.15 % 96.64 % 91.14 % 0.06 s 2 cores @ 2.5 Ghz (Python)
3 ZEEWAIN-AI 96.14 % 95.22 % 88.94 % 0.3 s GPU @ 2.5 Ghz (Python)
4 Anonymous 96.12 % 98.74 % 91.12 % 0.1 s GPU @ 2.5 Ghz (Python)
5 SFD 96.12 % 99.07 % 91.12 % 0.1 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 sfd 95.86 % 98.93 % 90.98 % 0.1 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 DFNet-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 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.
18 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.
19 JPVNet 95.52 % 96.41 % 90.72 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
20 Anonymous 95.50 % 98.36 % 90.63 % 0.1s 1 core @ 2.5 Ghz (C/C++)
21 DFNet-PV 95.49 % 96.42 % 92.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
22 SPANet 95.46 % 96.54 % 90.47 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
23 ISE-RCNN-PV 95.46 % 96.20 % 92.88 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
24 ISE-RCNN 95.43 % 96.38 % 92.82 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
25 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++)
26 SPG_mini
This method makes use of Velodyne laser scans.
95.32 % 96.23 % 92.68 % 0.09 s GPU @ 2.5 Ghz (Python)
27 TBD 95.29 % 96.17 % 90.56 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
28 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.
29 DGDNH 95.18 % 98.28 % 92.61 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
30 VPFNet code 95.17 % 96.06 % 92.66 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
31 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.
32 EPNet++ 95.17 % 96.73 % 92.10 % 0.1 s GPU @ 2.5 Ghz (Python)
33 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.
34 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.
35 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.
36 TBD 95.10 % 96.48 % 92.62 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
37 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.
38 3DIoU++ 95.06 % 96.37 % 90.52 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
39 PV-RCNN-v2 95.05 % 96.08 % 92.42 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
40 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.
41 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.
42 VueronNet code 94.97 % 97.85 % 89.68 % 0.06 s 1 core @ 2.0 Ghz (Python)
43 VoTr-TSD 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.
44 CSVoxel-RCNN 94.91 % 96.33 % 92.11 % 0.03 s GPU @ 1.0 Ghz (Python)
45 FrustumRCNN 94.90 % 95.98 % 92.39 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
46 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.
47 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.
48 E^2-PV-RCNN 94.80 % 95.95 % 92.26 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
49 SRIF-RCNN 94.79 % 95.63 % 92.35 % 0.0947 s 1 core @ 2.5 Ghz (C/C++)
50 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++)
51 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++)
52 TPCG 94.78 % 95.96 % 92.25 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
53 VCRCNN 94.77 % 96.06 % 92.28 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
54 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.
55 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++)
56 MSG-PGNN 94.75 % 95.86 % 92.16 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
57 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.
58 FusionDetv2-v4 94.73 % 95.94 % 92.00 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
59 Generalized-SIENet 94.72 % 95.76 % 92.19 % 0.08 s 1 core @ 2.5 Ghz (Python)
60 SqueezeRCNN 94.72 % 96.02 % 92.12 % 0.08 s 1 core @ 2.5 Ghz (Python)
61 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.
62 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.
63 3DIoU_v2 94.70 % 96.15 % 92.37 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
64 HyBrid Feature Det 94.69 % 95.89 % 92.11 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
65 PC-RGNN 94.68 % 95.80 % 92.20 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
66 DDet 94.66 % 95.82 % 92.17 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
67 LZY_RCNN 94.65 % 95.81 % 92.08 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
68 FusionDetv2-v3 94.64 % 96.16 % 91.94 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
69 TransCyclistNet 94.64 % 96.08 % 92.10 % 0.08 s 1 core @ 2.5 Ghz (Python)
70 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.
71 FSA-PVRCNN
This method makes use of Velodyne laser scans.
94.63 % 95.81 % 92.06 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
72 Fast VP-RCNN code 94.62 % 98.00 % 91.91 % 0.05 s 1 core @ 3.5 Ghz (C/C++)
73 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) .
74 TransDet3D 94.61 % 95.83 % 92.07 % 0.08 s 1 core @ 2.5 Ghz (Python)
75 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.
76 Point Image Fusion 94.61 % 95.70 % 92.11 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
77 MSL3D 94.60 % 95.76 % 92.16 % 0.03 s GPU @ 2.5 Ghz (Python)
78 Multi-Sensor3D 94.60 % 95.76 % 92.16 % 0.03 s GPU @ 2.5 Ghz (Python)
79 SA-voxel-centernet code 94.59 % 95.80 % 92.05 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
80 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.
81 WHUT-iou_ssd code 94.54 % 95.77 % 91.91 % 0.045s 1 core @ 2.5 Ghz (C/C++)
82 sa-voxel-centernet code 94.53 % 95.88 % 92.00 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
83 anonymous code 94.53 % 97.51 % 91.80 % 0.05s 1 core @ >3.5 Ghz (python)
84 FPC3D
This method makes use of the epipolar geometry.
94.52 % 96.06 % 91.72 % 33 s 1 core @ 2.5 Ghz (C/C++)
85 FPC-RCNN 94.51 % 96.15 % 91.80 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
86 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.
87 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.
88 GNN-RCNN 94.44 % 95.85 % 91.96 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
89 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.
90 SAA-SECOND 94.39 % 95.67 % 91.63 % 38m s 1 core @ 2.5 Ghz (C/C++)
91 SVGA-Net
This method makes use of Velodyne laser scans.
94.28 % 95.69 % 91.73 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
92 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.
93 SRDL
This method uses stereo information.
This method makes use of Velodyne laser scans.
94.24 % 95.86 % 91.80 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
94 FusionDetv1 94.23 % 95.84 % 91.80 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
95 FusionDetv2-v2 94.21 % 95.75 % 89.89 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
96 TBD 94.21 % 95.51 % 91.69 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
97 SARFE 94.18 % 95.74 % 91.57 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
98 FPCR-CNN 94.13 % 95.95 % 91.20 % 0.05 s 1 core @ 2.5 Ghz (Python)
99 SAA-PV-RCNN 94.11 % 95.01 % 92.50 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
100 NV2P-RCNN 94.07 % 97.82 % 91.20 % 0.1 s GPU @ 2.5 Ghz (Python)
101 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.
102 VueronNet 94.03 % 96.70 % 87.58 % 0.08 s GPU @ 2.5 Ghz (Python)
103 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.
104 SIF 93.95 % 95.51 % 91.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
105 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.
106 LPCG-Monoflex 93.86 % 96.90 % 83.94 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
107 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.
108 LIGA-Stereo-old
This method uses stereo information.
93.77 % 96.66 % 83.97 % 0.375 s Titan Xp
109 Associate-3Ddet_v2 93.77 % 96.83 % 88.57 % 0.04 s 1 core @ 2.5 Ghz (Python)
110 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.
111 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.
112 XView-PartA^2 93.71 % 95.42 % 91.26 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
113 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.
114 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.
115 TBD 93.64 % 95.31 % 91.21 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
116 FusionDetv2-v5 93.61 % 95.33 % 89.22 % 0.05 s 1 core @ 2.5 Ghz (Java + C/C++)
117 KpNet 93.60 % 96.76 % 85.98 % 0.42 s 1 core @ 2.5 Ghz (C/C++)
118 KpNet 93.60 % 96.74 % 85.97 % 42 s 1 core @ 2.5 Ghz (C/C++)
119 AM-SSD 93.58 % 96.78 % 90.61 % 0.04 s 1 core @ 2.5 Ghz (Python)
120 VPV 93.57 % 96.46 % 88.58 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
121 IA-SSD (multi) 93.56 % 96.10 % 90.68 % 0.014 s 1 core @ 2.5 Ghz (C/C++)
122 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.
123 IA-SSD (single) 93.54 % 96.26 % 88.49 % 0.013 s 1 core @ 2.5 Ghz (C/C++)
124 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.
125 CM3DV 93.53 % 96.79 % 88.35 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
126 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.
127 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.
128 Seg-RCNN code 93.49 % 96.74 % 88.10 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
129 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++)
130 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.
131 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.
132 PPAF
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++)
133 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.
134 DGIST MT-CNN 93.39 % 95.16 % 85.50 % 0.09 s GPU @ 1.0 Ghz (Python)
135 Sem-Aug v1 code 93.39 % 96.39 % 90.70 % 0.04 s GPU @ 3.5 Ghz (Python)
136 TBD 93.38 % 94.17 % 88.39 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
137 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.
138 TBD 93.33 % 96.02 % 90.50 % TBD GPU @ 2.5 Ghz (Python + C/C++)
139 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.
140 VPN 93.30 % 96.19 % 88.30 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
141 RoIFusion code 93.30 % 96.30 % 88.22 % 0.22 s 1 core @ 3.0 Ghz (Python)
142 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.
143 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.
144 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.
145 demo 93.21 % 96.16 % 90.23 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
146 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.
147 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.
148 ASCNet 93.17 % 96.09 % 90.43 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
149 VCT 93.17 % 96.31 % 90.52 % 0.2 s 1 core @ 2.5 Ghz (Python)
150 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.
151 Sem-Aug-PointRCNN code 93.17 % 95.78 % 88.35 % 0.1 s GPU @ 3.5 Ghz (C/C++)
152 MVOD 93.16 % 96.17 % 92.56 % 0.16 s 1 core @ 2.5 Ghz (C/C++)
153 MBDF-Net 93.15 % 96.26 % 90.25 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
154 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.
155 KAIST-VDCLab 93.08 % 96.27 % 85.66 % 0.04 s 1 core @ 2.5 Ghz (Python)
156 SGNet 93.08 % 96.43 % 90.53 % 0.09 s GPU @ 2.5 Ghz (Python)
157 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.
158 HVPR 93.04 % 95.91 % 87.88 % 0.02 s GPU @ 2.5 Ghz (Python)
159 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.
160 MBDF-Net-1 92.85 % 95.98 % 89.91 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
161 YF 92.85 % 96.04 % 89.96 % 0.04 s GPU @ 2.5 Ghz (C/C++)
162 FPGNN 92.83 % 96.26 % 87.69 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
163 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.
164 DPointNet 92.77 % 95.55 % 89.63 % 0.07s 1 core @ 2.5 Ghz (C/C++)
165 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.
166 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.
167 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.
168 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.
169 TBD 92.66 % 95.60 % 90.55 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
170 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.
171 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.
172 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.
173 NV-RCNN 92.51 % 95.86 % 89.95 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
174 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.
175 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.
176 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.
177 3D-VDNet 92.35 % 95.42 % 89.37 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
178 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.
179 CVFNet 92.27 % 95.61 % 88.75 % 28.1ms 1 core @ 2.5 Ghz (Python)
180 CCFNET 92.25 % 95.85 % 89.36 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
181 LSNet 92.23 % 96.06 % 87.35 % 0.09 s GPU @ 2.5 Ghz (Python + C/C++)
182 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.
183 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.
184 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.
185 RangeDet code 92.03 % 95.20 % 87.14 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
186 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.
187 VGCN 91.97 % 94.91 % 89.34 % 0.09 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
188 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.
189 MKFFNet 91.88 % 95.29 % 89.21 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
190 AutoAlign 91.87 % 95.15 % 89.21 % 0.1 s 1 core @ 2.5 Ghz (Python)
191 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.
192 weakm3d 91.81 % 94.51 % 85.35 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
193 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.
194 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.
195 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.
196 sscl-20p 91.71 % 97.14 % 88.72 % 0.02 s 1 core @ 2.5 Ghz (Python)
197 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.
198 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)
199 HS3D code 91.62 % 95.51 % 86.94 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
200 VOXEL_3D 91.61 % 94.50 % 86.37 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
201 mono3d 91.60 % 94.60 % 84.86 % 0.03 s GPU @ 2.5 Ghz (Python)
202 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.
203 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)
204 MKFFNet 91.54 % 95.32 % 89.02 % 0.01s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
205 MKFFNet 91.51 % 95.19 % 89.01 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
206 CA3D 91.48 % 95.19 % 82.01 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
207 PointRGBNet 91.48 % 95.40 % 86.50 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
208 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.
209 AIMC-RUC 91.45 % 96.94 % 86.28 % 0.11 s 1 core @ 2.5 Ghz (Python)
210 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.
211 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++)
212 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.
213 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)
214 SC(DLA34)
This method uses stereo information.
91.27 % 96.61 % 83.50 % 0.04 s GPU @ 2.5 Ghz (Python)
215 GAA 91.20 % 94.50 % 82.97 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
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 Geo3D 91.04 % 94.28 % 78.91 % 0.04 s GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
221 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.
222 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.
223 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.
224 SCSTSV-MonoFlex 90.99 % 96.44 % 81.16 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
225 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.
226 GA-Aug 90.92 % 93.93 % 83.03 % 0.04 s GPU @ 2.5 Ghz (Python)
227 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.
228 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.
229 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.
230 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 .
231 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.
232 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.
233 APL-Second 90.70 % 95.82 % 85.51 % 0.05 s 1 core @ 2.5 Ghz (Python)
234 vadin-TBD 90.69 % 95.92 % 80.91 % 0.04 s 1 core @ 2.5 Ghz (Python)
235 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.
236 NF2 90.62 % 94.14 % 81.30 % 0.1 s GPU @ 2.5 Ghz (Python)
237 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.
238 MonoFlex 90.52 % 95.59 % 83.00 % 0.03 s 1 core @ 2.5 Ghz (Python)
239 FADNet code 90.49 % 96.15 % 80.71 % 0.04 s GPU @ >3.5 Ghz (Python)
240 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.
241 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.
242 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.
243 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.
244 MonoGeo 90.14 % 95.11 % 80.19 % 0.05 s 1 core @ 2.5 Ghz (Python)
245 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.
246 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.
247 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.
248 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.
249 LCA 89.94 % 93.40 % 82.76 % 0.05 s 1 core @ 2.5 Ghz (Python)
250 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.
251 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.
252 Digging_M3D 89.77 % 93.73 % 79.68 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
253 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.
254 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.
255 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.
256 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.
257 MonoDDE 89.19 % 96.76 % 81.60 % 0.04 s 1 core @ 2.5 Ghz (Python)
258 PPTrans 89.17 % 95.17 % 81.77 % 0.2 s GPU @ 2.5 Ghz (Python)
259 FusionDetv2-v1 89.10 % 94.82 % 84.28 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
260 M3DSSD++ 89.06 % 94.94 % 77.17 % 0.16s 1 core @ 2.5 Ghz (C/C++)
261 GAC3D++ 88.93 % 94.23 % 79.09 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
262 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.
263 Keypoint-3D 88.87 % 93.31 % 76.10 % 14 s 1 core @ 2.5 Ghz (C/C++)
264 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.
265 FusionDetv2-baseline 88.76 % 94.28 % 85.65 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
266 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.
267 EACV 88.70 % 94.51 % 81.15 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
268 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.
269 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)
270 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.
271 BFF 88.49 % 90.84 % 78.84 % 8.4 s 4 cores @ 1.5 Ghz (Python)
272 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.
273 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.
274 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.
275 CMKD 88.41 % 95.28 % 81.68 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
276 deleted 88.38 % 96.52 % 81.01 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
277 MonoLCD 88.33 % 93.74 % 78.59 % 0.04 s 1 core @ 2.5 Ghz (Python)
278 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.
279 MonoCon 88.22 % 93.59 % 76.18 % 0.02 s GPU @ 2.5 Ghz (Python)
280 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.
281 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.
282 Object Transformer 87.67 % 93.33 % 79.98 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
283 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.
284 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.
285 EG_DETR 87.10 % 93.04 % 79.05 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
286 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.
287 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.
288 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.
289 MP-Mono 86.54 % 90.66 % 65.72 % 0.16 s GPU @ 2.5 Ghz (Python)
290 ANM 86.45 % 94.32 % 76.54 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
291 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.
292 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.
293 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.
294 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.
295 PLDet3d 85.51 % 88.65 % 77.30 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
296 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.
297 AEC3D 85.22 % 90.74 % 80.82 % 18 ms GPU @ 2.5 Ghz (Python)
298 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.
299 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 .
300 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.
301 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.
302 LPCG-M3D 84.95 % 87.35 % 77.05 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
303 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.
304 VN3D 84.80 % 90.73 % 78.41 % 0.02 s 1 core @ 2.5 Ghz (Python)
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305 ZongmuMono3d code 84.64 % 93.06 % 75.29 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
306 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.
307 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.
308 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.
309 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.
310 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.
311 OSE+ 83.92 % 95.20 % 76.69 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
312 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.
313 Deprecated 83.39 % 89.00 % 64.29 % Deprecated Deprecated
314 DA-Mono3D 83.36 % 88.94 % 64.23 % 0.09s 1 core @ 2.5 Ghz (C/C++)
315 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.
316 MM 82.99 % 93.44 % 73.29 % 1 s 1 core @ 2.5 Ghz (C/C++)
317 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.
318 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.
319 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.
320 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.
321 vadin-TBD2 code 82.54 % 92.81 % 72.80 % 0.20 s 1 core @ 2.5 Ghz (Python)
322 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.
323 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.
324 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.
325 SwinMono3D 81.71 % 91.99 % 61.78 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
326 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.
327 ITS-MDPL 81.56 % 92.61 % 74.23 % 0.16 s GPU @ 2.5 Ghz (Python)
328 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.
329 SOD 81.18 % 94.24 % 66.44 % 0.1 s 1 core @ 2.5 Ghz (Python)
330 none 81.07 % 91.14 % 68.85 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
331 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.
332 K3D 80.86 % 93.58 % 71.18 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
333 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.
334 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 2021.
335 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.
336 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.
337 COF3D 80.16 % 87.85 % 61.97 % 200 s 1 core @ 2.5 Ghz (Python + C/C++)
338 KMC code 79.99 % 89.71 % 73.33 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
339 RelationNet3D_dla34 code 79.78 % 83.69 % 69.35 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
340 Lite-FPN 79.65 % 87.04 % 65.56 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
341 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.
342 E2E-DA 79.40 % 92.12 % 69.52 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
343 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.
344 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.
345 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.
346 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.
347 MonoHMOO 78.21 % 92.33 % 61.58 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
348 E2E-DA-Lite (Res18) 78.21 % 90.79 % 66.16 % 0.01 s GPU @ 2.5 Ghz (Python)
349 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.
350 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.
351 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.
352 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.
353 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.
354 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.
355 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.
356 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.
357 RelationNet3D_res18 code 76.96 % 87.14 % 67.49 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
358 AutoShape 76.82 % 83.75 % 63.71 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
359 Mobile Stereo R-CNN
This method uses stereo information.
76.73 % 90.08 % 62.23 % 1.8 s NVIDIA Jetson TX2
360 RelationNet3D 76.62 % 81.36 % 68.48 % 0.04 s GPU @ 2.5 Ghz (Python)
361 ICCV 76.45 % 85.48 % 65.52 % 0.04 s GPU @ 2.5 Ghz (Python)
362 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.
363 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.
364 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.
365 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.
366 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.
367 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.
368 MAOLoss code 73.79 % 89.31 % 63.59 % 0.05 s 1 core @ 2.5 Ghz (Python)
369 NCL code 71.91 % 64.71 % 71.78 % NA s 1 core @ 2.5 Ghz (Python)
370 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 .
371 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.
372 BEVC 70.93 % 79.97 % 64.46 % 35ms GPU @ 1.5 Ghz (Python)
373 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.
374 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.
375 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.
376 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.
377 RetinaMono 69.83 % 74.54 % 60.95 % 0.02 s 1 core @ 2.5 Ghz (Python)
378 RetinaMono code 69.01 % 75.18 % 58.98 % 0.02 s 1 core @ 2.5 Ghz (Python)
379 TBD 68.30 % 88.62 % 59.00 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
380 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.
381 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.
382 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.
383 MDSNet 67.79 % 90.97 % 53.39 % 0.07 s 1 core @ 2.5 Ghz (Python)
384 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.
385 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.
386 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.
387 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.
388 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.
389 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.
390 Y4 code 63.60 % 81.79 % 56.20 % 0.03 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
391 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.
392 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.
393 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.
394 Graph-NMS 61.93 % 78.55 % 53.72 % 36 ms GPU @ 2.5 Ghz (Python)
395 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.
396 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.
397 FPIOD
This method makes use of Velodyne laser scans.
code 60.04 % 78.81 % 50.13 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
398 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.
399 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.
400 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.
401 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.
402 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.
403 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). .
404 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.
405 Graph-NMS-baseline 52.92 % 76.21 % 43.38 % 47 ms GPU @ 2.5 Ghz (Python)
406 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.
407 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 .
408 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.
409 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.
410 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.
411 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.
412 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.
413 R-AGNO-Net 36.55 % 49.87 % 35.20 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
414 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.
415 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.
416 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.
417 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.
418 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.
419 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.
420 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.
421 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.
422 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.
423 TBD 0.00 % 0.00 % 0.00 % 0.1 s 1 core @ 2.5 Ghz (Python)
424 TBD 0.00 % 0.00 % 0.00 % 0.1 s 1 core @ 2.5 Ghz (Python)
425 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 VueronNet 80.75 % 89.91 % 76.56 % 0.08 s GPU @ 2.5 Ghz (Python)
2 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.
3 NF2 79.59 % 88.28 % 75.47 % 0.1 s GPU @ 2.5 Ghz (Python)
4 DGIST MT-CNN 79.38 % 88.58 % 74.83 % 0.09 s GPU @ 1.0 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 ZEEWAIN-AI 78.20 % 88.46 % 73.35 % 0.3 s GPU @ 2.5 Ghz (Python)
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++)
34 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)
35 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.
36 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.
37 ADLAB 63.25 % 70.86 % 60.52 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
38 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.
39 HIKVISION-AFree 62.78 % 73.95 % 60.46 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
40 PiFeNet 62.68 % 71.97 % 59.77 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
41 Fast-CLOCs 62.57 % 76.22 % 60.13 % 0.1 s GPU @ 2.5 Ghz (Python)
42 KAIST-VDCLab 62.35 % 79.37 % 57.42 % 0.04 s 1 core @ 2.5 Ghz (Python)
43 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.
44 EACV 62.29 % 79.38 % 57.16 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
45 VCT 62.00 % 71.19 % 58.52 % 0.2 s 1 core @ 2.5 Ghz (Python)
46 GAA 61.92 % 77.67 % 56.56 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
47 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.
48 TBD 61.74 % 70.58 % 59.45 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
49 PE-RCVN 61.64 % 69.49 % 59.55 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
50 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.
51 H^23D R-CNN 61.50 % 72.21 % 57.50 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
52 SAA-PV-RCNN 61.41 % 70.35 % 58.18 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
53 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 .
54 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.
55 EA-M-RCNN(BorderAtt) 61.06 % 73.07 % 56.86 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
56 ISE-RCNN-PV 61.06 % 70.59 % 57.26 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
57 Regionlets 60.83 % 73.79 % 54.72 % 1 s >8 cores @ 2.5 Ghz (C/C++)
X. Wang, M. Yang, S. Zhu and Y. Lin: Regionlets for Generic Object Detection. T-PAMI 2015.
W. Zou, X. Wang, M. Sun and Y. Lin: Generic Object Detection with Dense Neural Patterns and Regionlets. British Machine Vision Conference 2014.
C. Long, X. Wang, G. Hua, M. Yang and Y. Lin: Accurate Object Detection with Location Relaxation and Regionlets Relocalization. Asian Conference on Computer Vision 2014.
58 GA-Aug 60.78 % 76.78 % 55.00 % 0.04 s GPU @ 2.5 Ghz (Python)
59 ISE-RCNN 60.70 % 69.41 % 58.49 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
60 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.
61 TBD 60.30 % 70.50 % 57.06 % 0.3 s 1 core @ 2.5 Ghz (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 BFF 58.72 % 76.95 % 53.70 % 8.4 s 4 cores @ 1.5 Ghz (Python)
72 SRDL
This method uses stereo information.
This method makes use of Velodyne laser scans.
58.70 % 68.45 % 56.23 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
73 FusionDetv1 58.68 % 68.44 % 56.22 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
74 P2V_PCV1 58.59 % 68.62 % 56.34 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
75 EG_DETR 58.58 % 74.53 % 53.76 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
76 MSL3D 58.57 % 69.07 % 55.86 % 0.03 s GPU @ 2.5 Ghz (Python)
77 SA-voxel-centernet code 58.50 % 66.89 % 56.25 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
78 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.
79 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++)
80 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.
81 TPCG 58.17 % 67.39 % 55.55 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
82 XView-PartA^2 58.17 % 67.12 % 55.81 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
83 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.
84 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.
85 EPNet++ 58.10 % 68.58 % 55.58 % 0.1 s GPU @ 2.5 Ghz (Python)
86 WHUT-iou_ssd code 58.03 % 66.60 % 55.82 % 0.045s 1 core @ 2.5 Ghz (C/C++)
87 E^2-PV-RCNN 58.01 % 67.39 % 55.77 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
88 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.
89 Point Image Fusion 57.91 % 66.47 % 55.48 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
90 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.
91 PPAF
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++)
92 sa-voxel-centernet code 57.79 % 66.03 % 55.61 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
93 FSA-PVRCNN
This method makes use of Velodyne laser scans.
57.67 % 65.80 % 55.34 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
94 SVGA-Net
This method makes use of Velodyne laser scans.
57.57 % 67.48 % 55.11 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
95 TBD 57.56 % 66.43 % 55.21 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
96 FPC-RCNN 57.46 % 66.88 % 55.09 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
97 SGNet 57.36 % 65.93 % 53.82 % 0.09 s GPU @ 2.5 Ghz (Python)
98 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.
99 MVOD 57.33 % 66.72 % 54.31 % 0.16 s 1 core @ 2.5 Ghz (C/C++)
100 SIF 57.32 % 67.78 % 54.86 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
101 GNN-RCNN 57.32 % 66.78 % 55.77 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
102 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.
103 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.
104 FPCR-CNN 57.14 % 66.59 % 54.43 % 0.05 s 1 core @ 2.5 Ghz (Python)
105 FusionDetv2-v3 56.93 % 65.96 % 54.73 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
106 IA-SSD (single) 56.87 % 66.69 % 54.68 % 0.013 s 1 core @ 2.5 Ghz (C/C++)
107 DDet 56.81 % 65.02 % 54.65 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
108 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.
109 FusionDetv2-v4 56.60 % 65.80 % 54.55 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
110 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.
111 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.
112 TBD 56.42 % 65.60 % 53.61 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
113 TBD 56.42 % 65.60 % 53.61 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
114 FusionDetv2-v5 56.40 % 65.17 % 53.89 % 0.05 s 1 core @ 2.5 Ghz (Java + C/C++)
115 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.
116 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.
117 TBD_IOU1 56.02 % 66.44 % 53.62 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
118 SAA-SECOND 55.95 % 65.50 % 52.88 % 38m s 1 core @ 2.5 Ghz (C/C++)
119 TBD_IOU 55.90 % 64.94 % 53.60 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
120 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.
121 VCRCNN 55.66 % 64.59 % 53.53 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
122 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.
123 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.
124 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++)
125 TBD 55.43 % 65.62 % 51.97 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
126 FusionDetv2-v2 55.32 % 62.74 % 52.43 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
127 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.
128 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++)
129 ANM 55.08 % 74.09 % 48.00 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
130 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.
131 NV-RCNN 54.98 % 64.78 % 51.64 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
132 GAC3D++ 54.96 % 74.08 % 50.06 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
133 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.
134 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.
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 MKFFNet 53.55 % 62.18 % 50.80 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
143 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.
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 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.
148 FusionDetv2-baseline 52.96 % 58.96 % 50.94 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
149 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.
150 demo 52.90 % 63.79 % 49.54 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
151 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.
152 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.
153 VGCN 52.80 % 61.86 % 50.66 % 0.09 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
154 tbd 52.78 % 63.45 % 50.79 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
155 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.
156 NV2P-RCNN 52.52 % 61.80 % 50.24 % 0.1 s GPU @ 2.5 Ghz (Python)
157 HS3D code 52.50 % 63.73 % 49.78 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
158 IA-SSD (multi) 52.45 % 65.07 % 50.20 % 0.014 s 1 core @ 2.5 Ghz (C/C++)
159 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.
160 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.
161 SCSTSV-MonoFlex 52.18 % 67.51 % 45.90 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
162 MKFFNet 51.96 % 60.31 % 49.70 % 0.01s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
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 AF_MCLS 49.95 % 62.85 % 45.78 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
166 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.
167 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.
168 Y4 code 49.24 % 68.07 % 44.42 % 0.03 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
169 vadin-TBD 48.97 % 63.77 % 44.60 % 0.04 s 1 core @ 2.5 Ghz (Python)
170 MonoFlex 48.64 % 61.96 % 44.17 % 0.03 s 1 core @ 2.5 Ghz (Python)
171 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.
172 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.
173 RelationNet3D_dla34 code 47.93 % 63.34 % 43.42 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
174 LIGA-Stereo-old
This method uses stereo information.
47.75 % 58.61 % 44.39 % 0.375 s Titan Xp
175 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.
176 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.
177 RoIFusion code 46.81 % 56.26 % 44.58 % 0.22 s 1 core @ 3.0 Ghz (Python)
178 mono3d 46.73 % 58.34 % 41.92 % 0.03 s GPU @ 2.5 Ghz (Python)
179 deleted 46.14 % 58.97 % 42.48 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
180 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.
181 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.
182 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.
183 M3DSSD++ 44.89 % 58.54 % 40.66 % 0.16s 1 core @ 2.5 Ghz (C/C++)
184 CBi-GNN-persons 44.88 % 58.17 % 40.50 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
185 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.
186 Deprecated 44.65 % 60.33 % 38.51 % Deprecated Deprecated
187 MonoGeo 44.63 % 58.49 % 40.41 % 0.05 s 1 core @ 2.5 Ghz (Python)
188 Geo3D 44.42 % 58.74 % 40.13 % 0.04 s GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
189 LPCG-Monoflex 44.13 % 62.44 % 39.46 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
190 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.
191 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.
192 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.
193 MonoLCD 43.71 % 57.69 % 39.60 % 0.04 s 1 core @ 2.5 Ghz (Python)
194 DA-Mono3D 43.57 % 59.80 % 39.23 % 0.09s 1 core @ 2.5 Ghz (C/C++)
195 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.
196 MonoDDE 43.36 % 57.80 % 39.00 % 0.04 s 1 core @ 2.5 Ghz (Python)
197 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.
198 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.
199 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.
200 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.
201 MonoCon 42.44 % 56.57 % 36.34 % 0.02 s GPU @ 2.5 Ghz (Python)
202 RelationNet3D_res18 code 42.25 % 57.61 % 37.93 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
203 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.
204 ICCV 41.88 % 55.14 % 37.55 % 0.04 s GPU @ 2.5 Ghz (Python)
205 PLDet3d 41.86 % 55.94 % 37.64 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
206 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.
207 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.
208 M3D-RPN(S-R) 41.46 % 56.64 % 37.31 % 0.16 s GPU @ 1.5 Ghz (Python)
209 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 .
210 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.
211 MP-Mono 40.87 % 55.79 % 36.81 % 0.16 s GPU @ 2.5 Ghz (Python)
212 SwinMono3D 40.54 % 56.73 % 36.18 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
213 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.
214 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.
215 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.
216 FusionDetv2-v1 39.60 % 46.77 % 38.75 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
217 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.
218 Graph-NMS-baseline 39.46 % 52.32 % 35.60 % 47 ms GPU @ 2.5 Ghz (Python)
219 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.
220 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.
221 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). .
222 Graph-NMS 38.40 % 51.24 % 35.24 % 36 ms GPU @ 2.5 Ghz (Python)
223 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.
224 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.
225 Lite-FPN 36.93 % 50.63 % 32.88 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
226 NCL code 36.89 % 42.81 % 34.76 % NA s 1 core @ 2.5 Ghz (Python)
227 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.
228 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.
229 COF3D 35.34 % 50.45 % 31.28 % 200 s 1 core @ 2.5 Ghz (Python + C/C++)
230 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.
231 MonoHMOO 34.74 % 49.26 % 30.37 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
232 PPTrans 34.45 % 45.02 % 30.94 % 0.2 s GPU @ 2.5 Ghz (Python)
233 CMKD 34.41 % 47.09 % 30.59 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
234 PointRGBNet 33.92 % 44.35 % 30.43 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
235 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 2021.
236 ZongmuMono3d code 33.47 % 45.86 % 29.84 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
237 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.
238 FADNet code 32.64 % 42.43 % 29.13 % 0.04 s GPU @ >3.5 Ghz (Python)
239 MM 32.48 % 45.03 % 28.58 % 1 s 1 core @ 2.5 Ghz (C/C++)
240 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.
241 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.
242 K3D 31.71 % 44.40 % 27.90 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
243 FPIOD
This method makes use of Velodyne laser scans.
code 30.96 % 45.07 % 28.48 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
244 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.
245 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.
246 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.
247 SOD 29.47 % 46.61 % 26.97 % 0.1 s 1 core @ 2.5 Ghz (Python)
248 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.
249 AEC3D 28.32 % 37.26 % 24.98 % 18 ms GPU @ 2.5 Ghz (Python)
250 BEVC 26.52 % 35.31 % 24.72 % 35ms GPU @ 1.5 Ghz (Python)
251 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.
252 VN3D 23.61 % 30.51 % 21.97 % 0.02 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
253 MAOLoss code 23.60 % 32.07 % 21.55 % 0.05 s 1 core @ 2.5 Ghz (Python)
254 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.
255 E2E-DA 19.96 % 29.93 % 17.46 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
256 E2E-DA-Lite (Res18) 19.87 % 32.25 % 17.37 % 0.01 s GPU @ 2.5 Ghz (Python)
257 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.
258 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.
259 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.
260 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.
261 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.
262 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 HIKVISION-AFree 82.54 % 91.47 % 75.88 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
3 SARFE 82.00 % 90.01 % 75.30 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
4 SGNet 82.00 % 91.55 % 75.30 % 0.09 s GPU @ 2.5 Ghz (Python)
5 ISE-RCNN 81.95 % 87.68 % 75.17 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
6 NF2 81.75 % 89.67 % 71.43 % 0.1 s GPU @ 2.5 Ghz (Python)
7 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.
8 anonymous code 81.52 % 89.31 % 74.71 % 0.05s 1 core @ >3.5 Ghz (python)
9 Fast VP-RCNN code 81.41 % 89.29 % 74.88 % 0.05 s 1 core @ 3.5 Ghz (C/C++)
10 Point Image Fusion 80.73 % 87.99 % 74.32 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
11 TPCG 80.71 % 87.73 % 74.17 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
12 SAA-PV-RCNN 80.71 % 88.94 % 73.79 % 0.08 s 1 core @ 2.5 Ghz (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 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++)
16 VCRCNN 80.46 % 87.34 % 73.68 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
17 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.
18 GNN-RCNN 80.35 % 89.53 % 73.85 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
19 XView-PartA^2 80.16 % 88.15 % 73.92 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
20 Generalized-SIENet 80.07 % 89.23 % 73.76 % 0.08 s 1 core @ 2.5 Ghz (Python)
21 WHUT-iou_ssd code 79.98 % 87.31 % 74.30 % 0.045s 1 core @ 2.5 Ghz (C/C++)
22 sa-voxel-centernet code 79.98 % 88.08 % 73.61 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
23 E^2-PV-RCNN 79.94 % 87.22 % 73.58 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
24 SA-voxel-centernet code 79.92 % 86.43 % 73.47 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
25 FPC-RCNN 79.89 % 88.62 % 73.18 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
26 DDet 79.47 % 88.65 % 72.64 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
27 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.
28 PV-RCNN-v2 79.22 % 85.76 % 72.35 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
29 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.
30 JPVNet 78.73 % 87.42 % 72.45 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
31 TBD 78.73 % 88.55 % 71.87 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
32 IA-SSD (single) 78.71 % 88.99 % 72.03 % 0.013 s 1 core @ 2.5 Ghz (C/C++)
33 PE-RCVN 78.71 % 90.77 % 71.83 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
34 PPAF
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++)
35 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.
36 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.
37 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.
38 AutoAlign 77.96 % 89.63 % 71.27 % 0.1 s 1 core @ 2.5 Ghz (Python)
39 MVOD 77.92 % 85.92 % 71.24 % 0.16 s 1 core @ 2.5 Ghz (C/C++)
40 CBi-GNN-persons 77.89 % 88.05 % 69.54 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
41 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++)
42 SAA-SECOND 77.47 % 88.50 % 70.23 % 38m s 1 core @ 2.5 Ghz (C/C++)
43 TBD 77.31 % 87.22 % 70.53 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
44 TBD 77.06 % 90.28 % 70.46 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
45 MSL3D 76.96 % 85.93 % 70.41 % 0.03 s GPU @ 2.5 Ghz (Python)
46 FSA-PVRCNN
This method makes use of Velodyne laser scans.
76.95 % 84.41 % 71.00 % 0.06 s 1 core @ 2.5 Ghz (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 SVGA-Net
This method makes use of Velodyne laser scans.
76.83 % 86.14 % 70.98 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
50 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.
51 FusionDetv2-v5 76.60 % 85.95 % 69.75 % 0.05 s 1 core @ 2.5 Ghz (Java + C/C++)
52 FusionDetv2-v3 76.48 % 86.15 % 69.64 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
53 MKFFNet 76.34 % 86.90 % 69.83 % 0.01s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
54 NV2P-RCNN 76.29 % 83.53 % 69.57 % 0.1 s GPU @ 2.5 Ghz (Python)
55 VCT 76.08 % 86.69 % 69.91 % 0.2 s 1 core @ 2.5 Ghz (Python)
56 FusionDetv2-v4 75.92 % 87.66 % 70.06 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
57 FPCR-CNN 75.83 % 87.74 % 68.98 % 0.05 s 1 core @ 2.5 Ghz (Python)
58 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.
59 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.
60 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.
61 Fast-CLOCs 75.07 % 89.73 % 67.93 % 0.1 s GPU @ 2.5 Ghz (Python)
62 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++)
63 EA-M-RCNN(BorderAtt) 74.85 % 88.69 % 68.01 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
64 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.
65 MKFFNet 74.69 % 84.92 % 68.29 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
66 P2V_PCV1 74.57 % 85.38 % 68.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
67 VPFNet code 74.52 % 82.60 % 66.04 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
68 MKFFNet 74.39 % 85.89 % 68.02 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
69 FusionDetv2-v2 74.38 % 86.62 % 67.96 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
70 RoIFusion code 74.27 % 85.15 % 68.29 % 0.22 s 1 core @ 3.0 Ghz (Python)
71 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.
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 SRDL
This method uses stereo information.
This method makes use of Velodyne laser scans.
73.68 % 85.44 % 66.94 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
77 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.
78 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.
79 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.
80 TBD_IOU1 73.46 % 87.05 % 66.14 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
81 tbd 73.43 % 84.88 % 65.72 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
82 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.
83 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++)
84 SIF 73.19 % 85.18 % 65.41 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
85 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.
86 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.
87 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.
88 NV-RCNN 73.07 % 85.64 % 66.60 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
89 HS3D code 73.02 % 84.59 % 67.13 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
90 demo 72.90 % 87.20 % 66.56 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
91 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.
92 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.
93 FusionDetv2-baseline 72.68 % 82.09 % 66.42 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
94 DGIST MT-CNN 72.57 % 86.82 % 63.47 % 0.09 s GPU @ 1.0 Ghz (Python)
95 VGCN 72.28 % 86.81 % 65.68 % 0.09 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
96 ZEEWAIN-AI 72.25 % 83.84 % 63.80 % 0.3 s GPU @ 2.5 Ghz (Python)
97 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.
98 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.
99 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.
100 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.
101 IA-SSD (multi) 70.46 % 84.98 % 65.55 % 0.014 s 1 core @ 2.5 Ghz (C/C++)
102 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.
103 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.
104 TBD 70.12 % 81.15 % 63.79 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
105 YF 70.10 % 80.50 % 64.21 % 0.04 s GPU @ 2.5 Ghz (C/C++)
106 TBD 69.99 % 85.54 % 65.02 % TBD GPU @ 2.5 Ghz (Python + C/C++)
107 CCFNET 69.17 % 83.76 % 62.87 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
108 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.
109 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.
110 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.
111 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.
112 EPNet++ 68.30 % 80.27 % 63.00 % 0.1 s GPU @ 2.5 Ghz (Python)
113 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.
114 AF_MCLS 67.97 % 85.45 % 60.95 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
115 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.
116 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.
117 TBD 67.15 % 82.44 % 60.87 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
118 TBD 67.15 % 82.44 % 60.87 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
119 VueronNet 66.92 % 82.71 % 59.01 % 0.08 s GPU @ 2.5 Ghz (Python)
120 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.
121 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.
122 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.
123 PointRGBNet 65.98 % 79.87 % 59.75 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
124 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.
125 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.
126 PiFeNet 64.39 % 80.02 % 57.46 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
127 tbd 64.31 % 79.83 % 58.14 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
128 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.
129 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.
130 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) .
131 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.
132 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.
133 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.
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 EACV 55.01 % 73.41 % 48.75 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
144 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)
145 mono3d 54.82 % 70.77 % 47.55 % 0.03 s GPU @ 2.5 Ghz (Python)
146 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.
147 M3DSSD++ 54.62 % 69.35 % 46.25 % 0.16s 1 core @ 2.5 Ghz (C/C++)
148 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.
149 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.
150 SCSTSV-MonoFlex 54.42 % 75.37 % 46.25 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
151 GAA 54.24 % 71.23 % 47.41 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
152 vadin-TBD 54.12 % 70.14 % 46.16 % 0.04 s 1 core @ 2.5 Ghz (Python)
153 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 .
154 LPCG-Monoflex 53.04 % 72.36 % 46.11 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
155 GA-Aug 52.71 % 67.62 % 46.37 % 0.04 s GPU @ 2.5 Ghz (Python)
156 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.
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 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.
163 MP-Mono 48.54 % 69.48 % 41.58 % 0.16 s GPU @ 2.5 Ghz (Python)
164 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.
165 EG_DETR 48.42 % 67.71 % 42.99 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
166 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.
167 CMKD 47.21 % 66.52 % 41.56 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
168 KAIST-VDCLab 46.65 % 68.60 % 41.79 % 0.04 s 1 core @ 2.5 Ghz (Python)
169 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.
170 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.
171 SwinMono3D 45.72 % 67.95 % 38.55 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
172 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.
173 MonoLCD 44.57 % 65.17 % 39.96 % 0.04 s 1 core @ 2.5 Ghz (Python)
174 Deprecated 44.26 % 63.15 % 37.38 % Deprecated Deprecated
175 DA-Mono3D 43.98 % 63.35 % 39.14 % 0.09s 1 core @ 2.5 Ghz (C/C++)
176 FADNet code 43.40 % 59.77 % 37.28 % 0.04 s GPU @ >3.5 Ghz (Python)
177 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.
178 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.
179 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.
180 MonoCon 42.49 % 59.39 % 35.94 % 0.02 s GPU @ 2.5 Ghz (Python)
181 GAC3D++ 41.87 % 61.03 % 35.78 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
182 PLDet3d 41.84 % 60.16 % 37.65 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
183 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 .
184 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.
185 SOD 40.95 % 60.07 % 34.02 % 0.1 s 1 core @ 2.5 Ghz (Python)
186 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.
187 PPTrans 39.55 % 48.93 % 33.74 % 0.2 s GPU @ 2.5 Ghz (Python)
188 RelationNet3D_dla34 code 39.52 % 59.56 % 34.51 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
189 ANM 39.39 % 57.77 % 34.59 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
190 OSE+ 39.26 % 58.13 % 34.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
191 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.
192 RelationNet3D_res18 code 37.41 % 56.22 % 32.56 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
193 AEC3D 37.15 % 48.48 % 34.86 % 18 ms GPU @ 2.5 Ghz (Python)
194 ICCV 36.70 % 53.31 % 31.94 % 0.04 s GPU @ 2.5 Ghz (Python)
195 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.
196 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.
197 Y4 code 35.92 % 53.50 % 31.89 % 0.03 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
198 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.
199 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.
200 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.
201 COF3D 32.97 % 51.77 % 28.26 % 200 s 1 core @ 2.5 Ghz (Python + C/C++)
202 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.
203 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.
204 VN3D 31.81 % 42.58 % 29.09 % 0.02 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
205 ZongmuMono3d code 31.56 % 44.68 % 27.48 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
206 BEVC 30.39 % 43.61 % 27.46 % 35ms GPU @ 1.5 Ghz (Python)
207 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.
208 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.
209 MM 27.89 % 42.15 % 24.73 % 1 s 1 core @ 2.5 Ghz (C/C++)
210 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.
211 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.
212 K3D 27.29 % 38.82 % 23.86 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
213 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.
214 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.
215 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 2021.
216 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.
217 E2E-DA-Lite (Res18) 25.82 % 42.48 % 21.16 % 0.01 s GPU @ 2.5 Ghz (Python)
218 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.
219 E2E-DA 24.46 % 39.34 % 19.59 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
220 MonoHMOO 23.59 % 37.41 % 21.20 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
221 FPIOD
This method makes use of Velodyne laser scans.
code 23.10 % 37.02 % 19.66 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
222 Graph-NMS-baseline 23.07 % 35.72 % 20.54 % 47 ms GPU @ 2.5 Ghz (Python)
223 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.
224 MAOLoss code 22.58 % 34.37 % 20.49 % 0.05 s 1 core @ 2.5 Ghz (Python)
225 Graph-NMS 22.43 % 34.13 % 19.65 % 36 ms GPU @ 2.5 Ghz (Python)
226 Lite-FPN 19.17 % 24.40 % 15.68 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
227 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.
228 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.
229 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.
230 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.
231 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.
232 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 VPFNet 96.04 % 96.63 % 90.99 % 0.06 s 2 cores @ 2.5 Ghz (Python)
3 SFD 96.01 % 99.06 % 90.97 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
4 Anonymous 96.00 % 98.72 % 90.97 % 0.1 s GPU @ 2.5 Ghz (Python)
5 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.
6 PE-RCVN 95.85 % 96.89 % 90.87 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
7 CityBrainLab 95.83 % 98.56 % 90.79 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
8 Anonymous 95.81 % 96.53 % 92.69 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
9 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.
10 sfd 95.75 % 98.92 % 90.83 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
11 SECOND 95.67 % 96.42 % 90.37 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
12 DFNet-V 95.63 % 96.59 % 90.71 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
13 NFAF3D-light 95.62 % 96.66 % 92.48 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
14 Fast-CLOCs 95.57 % 96.66 % 90.70 % 0.1 s GPU @ 2.5 Ghz (Python)
15 EA-M-RCNN(BorderAtt) 95.44 % 96.37 % 90.31 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
16 JPVNet 95.38 % 96.40 % 90.52 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
17 DFNet-PV 95.35 % 96.40 % 92.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
18 ISE-RCNN-PV 95.34 % 96.18 % 92.70 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
19 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.
20 Anonymous 95.32 % 98.34 % 90.36 % 0.1s 1 core @ 2.5 Ghz (C/C++)
21 ISE-RCNN 95.30 % 96.36 % 92.63 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
22 TBD 95.20 % 96.16 % 90.42 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
23 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.
24 DGDNH 95.09 % 98.27 % 92.48 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
25 SPANet 95.03 % 96.31 % 89.99 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
26 TBD 95.03 % 96.47 % 92.50 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
27 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.
28 VPFNet code 95.01 % 96.03 % 92.41 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
29 EPNet++ 95.00 % 96.70 % 91.82 % 0.1 s GPU @ 2.5 Ghz (Python)
30 3DIoU++ 94.97 % 96.36 % 90.40 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
31 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.
32 PV-RCNN-v2 94.90 % 96.07 % 92.22 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
33 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.
34 VoTr-TSD 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.
35 FrustumRCNN 94.79 % 95.97 % 92.23 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
36 CSVoxel-RCNN 94.74 % 96.31 % 91.84 % 0.03 s GPU @ 1.0 Ghz (Python)
37 SRIF-RCNN 94.70 % 95.62 % 92.21 % 0.0947 s 1 core @ 2.5 Ghz (C/C++)
38 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.
39 E^2-PV-RCNN 94.69 % 95.94 % 92.09 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
40 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++)
41 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++)
42 TPCG 94.66 % 95.95 % 92.08 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
43 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.
44 MSG-PGNN 94.65 % 95.85 % 92.01 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
45 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.
46 VCRCNN 94.64 % 96.05 % 92.09 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
47 SqueezeRCNN 94.61 % 96.02 % 91.97 % 0.08 s 1 core @ 2.5 Ghz (Python)
48 FusionDetv2-v4 94.60 % 95.92 % 91.79 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
49 Generalized-SIENet 94.59 % 95.74 % 91.99 % 0.08 s 1 core @ 2.5 Ghz (Python)
50 HyBrid Feature Det 94.59 % 95.87 % 91.97 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
51 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.
52 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.
53 3DIoU_v2 94.57 % 96.14 % 92.18 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
54 LZY_RCNN 94.56 % 95.80 % 91.94 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
55 PC-RGNN 94.55 % 95.79 % 92.03 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
56 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++)
57 DDet 94.54 % 95.80 % 91.99 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
58 TransCyclistNet 94.52 % 96.07 % 91.94 % 0.08 s 1 core @ 2.5 Ghz (Python)
59 Fast VP-RCNN code 94.52 % 97.99 % 91.74 % 0.05 s 1 core @ 3.5 Ghz (C/C++)
60 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.
61 FusionDetv2-v3 94.51 % 96.14 % 91.73 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
62 TransDet3D 94.50 % 95.82 % 91.89 % 0.08 s 1 core @ 2.5 Ghz (Python)
63 Point Image Fusion 94.49 % 95.69 % 91.92 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
64 MSL3D 94.46 % 95.74 % 91.94 % 0.03 s GPU @ 2.5 Ghz (Python)
65 Multi-Sensor3D 94.46 % 95.74 % 91.94 % 0.03 s GPU @ 2.5 Ghz (Python)
66 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.
67 SA-voxel-centernet code 94.45 % 95.78 % 91.84 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
68 WHUT-iou_ssd code 94.45 % 95.76 % 91.75 % 0.045s 1 core @ 2.5 Ghz (C/C++)
69 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.
70 anonymous code 94.43 % 97.50 % 91.66 % 0.05s 1 core @ >3.5 Ghz (python)
71 FSA-PVRCNN
This method makes use of Velodyne laser scans.
94.43 % 95.76 % 91.77 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
72 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.
73 FPC-RCNN 94.40 % 96.13 % 91.63 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
74 sa-voxel-centernet code 94.39 % 95.86 % 91.80 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
75 FPC3D
This method makes use of the epipolar geometry.
94.39 % 96.04 % 91.51 % 33 s 1 core @ 2.5 Ghz (C/C++)
76 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) .
77 GNN-RCNN 94.32 % 95.84 % 91.79 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
78 SAA-SECOND 94.24 % 95.64 % 91.40 % 38m s 1 core @ 2.5 Ghz (C/C++)
79 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.
80 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.
81 SVGA-Net
This method makes use of Velodyne laser scans.
94.13 % 95.68 % 91.48 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
82 SRDL
This method uses stereo information.
This method makes use of Velodyne laser scans.
94.08 % 95.83 % 91.55 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
83 TBD 94.07 % 95.49 % 91.44 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
84 FusionDetv1 94.07 % 95.82 % 91.54 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
85 SARFE 94.07 % 95.73 % 91.40 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
86 FusionDetv2-v2 94.05 % 95.73 % 89.66 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
87 SAA-PV-RCNN 94.02 % 95.00 % 92.34 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
88 FPCR-CNN 93.99 % 95.94 % 90.99 % 0.05 s 1 core @ 2.5 Ghz (Python)
89 NV2P-RCNN 93.91 % 97.80 % 90.96 % 0.1 s GPU @ 2.5 Ghz (Python)
90 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.
91 SIF 93.79 % 95.48 % 91.30 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
92 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.
93 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.
94 XView-PartA^2 93.59 % 95.41 % 91.09 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
95 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.
96 LIGA-Stereo-old
This method uses stereo information.
93.54 % 96.63 % 83.68 % 0.375 s Titan Xp
97 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.
98 TBD 93.53 % 95.30 % 91.03 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
99 IA-SSD (multi) 93.47 % 96.07 % 90.51 % 0.014 s 1 core @ 2.5 Ghz (C/C++)
100 Associate-3Ddet_v2 93.46 % 96.66 % 88.20 % 0.04 s 1 core @ 2.5 Ghz (Python)
101 FusionDetv2-v5 93.44 % 95.31 % 88.96 % 0.05 s 1 core @ 2.5 Ghz (Java + C/C++)
102 IA-SSD (single) 93.41 % 96.23 % 88.34 % 0.013 s 1 core @ 2.5 Ghz (C/C++)
103 VPV 93.39 % 96.44 % 88.32 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
104 PPAF
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++)
105 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.
106 TBD 93.31 % 94.17 % 88.30 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
107 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.
108 Sem-Aug v1 code 93.26 % 96.36 % 90.51 % 0.04 s GPU @ 3.5 Ghz (Python)
109 LPCG-Monoflex 93.26 % 96.68 % 83.34 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
110 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.
111 TBD 93.20 % 95.96 % 90.30 % TBD GPU @ 2.5 Ghz (Python + C/C++)
112 AM-SSD 93.18 % 96.56 % 90.13 % 0.04 s 1 core @ 2.5 Ghz (Python)
113 demo 93.15 % 96.15 % 90.08 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
114 VCT 93.09 % 96.30 % 90.38 % 0.2 s 1 core @ 2.5 Ghz (Python)
115 VPN 93.08 % 96.16 % 88.01 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
116 MVOD 93.07 % 96.15 % 92.39 % 0.16 s 1 core @ 2.5 Ghz (C/C++)
117 KpNet 93.06 % 96.63 % 85.39 % 0.42 s 1 core @ 2.5 Ghz (C/C++)
118 KpNet 93.05 % 96.63 % 85.38 % 42 s 1 core @ 2.5 Ghz (C/C++)
119 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.
120 ASCNet 93.01 % 96.05 % 90.21 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
121 Seg-RCNN code 92.99 % 96.50 % 87.54 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
122 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.
123 CM3DV 92.98 % 96.47 % 87.68 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
124 SGNet 92.95 % 96.41 % 90.35 % 0.09 s GPU @ 2.5 Ghz (Python)
125 Sem-Aug-PointRCNN code 92.92 % 95.66 % 88.07 % 0.1 s GPU @ 3.5 Ghz (C/C++)
126 HVPR 92.89 % 95.89 % 87.65 % 0.02 s GPU @ 2.5 Ghz (Python)
127 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.
128 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.
129 MBDF-Net 92.77 % 96.19 % 89.70 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
130 YF 92.74 % 96.02 % 89.76 % 0.04 s GPU @ 2.5 Ghz (C/C++)
131 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.
132 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.
133 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.
134 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.
135 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.
136 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.
137 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.
138 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.
139 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.
140 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.
141 MBDF-Net-1 92.37 % 95.87 % 89.26 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
142 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.
143 NV-RCNN 92.34 % 95.83 % 89.70 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
144 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.
145 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.
146 3D-VDNet 92.19 % 95.39 % 89.10 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
147 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.
148 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.
149 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.
150 RangeDet code 91.92 % 95.16 % 86.98 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
151 CCFNET 91.90 % 95.79 % 88.82 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
152 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.
153 VGCN 91.80 % 94.88 % 89.06 % 0.09 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
154 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.
155 KAIST-VDCLab 91.74 % 95.40 % 84.25 % 0.04 s 1 core @ 2.5 Ghz (Python)
156 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.
157 MKFFNet 91.72 % 95.26 % 88.92 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
158 AutoAlign 91.60 % 95.07 % 88.86 % 0.1 s 1 core @ 2.5 Ghz (Python)
159 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.
160 CVFNet 91.55 % 95.20 % 87.80 % 28.1ms 1 core @ 2.5 Ghz (Python)
161 VOXEL_3D 91.52 % 94.49 % 86.23 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
162 MKFFNet 91.38 % 95.30 % 88.77 % 0.01s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
163 PointRGBNet 91.33 % 95.39 % 86.29 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
164 MKFFNet 91.29 % 95.17 % 88.69 % 0.1 s 1 core @ 2.5 Ghz (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 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.
169 SC(DLA34)
This method uses stereo information.
91.02 % 96.54 % 83.15 % 0.04 s GPU @ 2.5 Ghz (Python)
170 sscl-20p 90.94 % 96.89 % 87.60 % 0.02 s 1 core @ 2.5 Ghz (Python)
171 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.
172 SCSTSV-MonoFlex 90.81 % 96.36 % 80.90 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
173 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.
174 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.
175 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.
176 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.
177 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.
178 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.
179 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++)
180 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.
181 vadin-TBD 90.49 % 95.86 % 80.66 % 0.04 s 1 core @ 2.5 Ghz (Python)
182 DPointNet 90.38 % 93.61 % 87.34 % 0.07s 1 core @ 2.5 Ghz (C/C++)
183 MonoFlex 90.29 % 95.51 % 82.68 % 0.03 s 1 core @ 2.5 Ghz (Python)
184 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 .
185 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.
186 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.
187 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.
188 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.
189 mono3d 90.02 % 93.54 % 83.19 % 0.03 s GPU @ 2.5 Ghz (Python)
190 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.
191 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.
192 FADNet code 89.84 % 95.89 % 79.98 % 0.04 s GPU @ >3.5 Ghz (Python)
193 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.
194 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.
195 MonoGeo 89.44 % 94.67 % 79.27 % 0.05 s 1 core @ 2.5 Ghz (Python)
196 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.
197 Geo3D 89.28 % 93.60 % 77.21 % 0.04 s GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
198 GA-Aug 89.24 % 92.66 % 81.31 % 0.04 s GPU @ 2.5 Ghz (Python)
199 Digging_M3D 89.23 % 93.54 % 79.15 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
200 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.
201 GAA 89.21 % 93.59 % 80.80 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
202 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.
203 MonoDDE 89.07 % 96.72 % 81.42 % 0.04 s 1 core @ 2.5 Ghz (Python)
204 FusionDetv2-v1 89.00 % 94.78 % 84.10 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
205 PPTrans 88.70 % 95.04 % 81.15 % 0.2 s GPU @ 2.5 Ghz (Python)
206 GAC3D++ 88.69 % 94.16 % 78.74 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
207 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.
208 FusionDetv2-baseline 88.58 % 94.20 % 85.36 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
209 deleted 88.13 % 96.48 % 80.66 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
210 M3DSSD++ 88.04 % 94.75 % 76.03 % 0.16s 1 core @ 2.5 Ghz (C/C++)
211 MonoCon 87.92 % 93.52 % 75.83 % 0.02 s GPU @ 2.5 Ghz (Python)
212 MonoLCD 87.86 % 93.62 % 78.09 % 0.04 s 1 core @ 2.5 Ghz (Python)
213 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.
214 CMKD 87.79 % 95.17 % 80.92 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
215 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.
216 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.
217 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.
218 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.
219 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.
220 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.
221 Object Transformer 87.23 % 93.00 % 79.42 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
222 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.
223 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.
224 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.
225 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.
226 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.
227 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.
228 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.
229 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.
230 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.
231 ANM 85.83 % 94.15 % 75.77 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
232 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.
233 MP-Mono 84.72 % 90.17 % 64.19 % 0.16 s GPU @ 2.5 Ghz (Python)
234 AEC3D 84.59 % 90.38 % 80.13 % 18 ms GPU @ 2.5 Ghz (Python)
235 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.
236 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.
237 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.
238 ZongmuMono3d code 84.21 % 92.95 % 74.77 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
239 VN3D 84.12 % 90.42 % 77.70 % 0.02 s 1 core @ 2.5 Ghz (Python)
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240 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.
241 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.
242 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.
243 PLDet3d 83.76 % 88.25 % 75.11 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
244 LPCG-M3D 83.39 % 86.97 % 75.09 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
245 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.
246 Deprecated 83.08 % 88.95 % 64.00 % Deprecated Deprecated
247 DA-Mono3D 83.00 % 88.87 % 63.87 % 0.09s 1 core @ 2.5 Ghz (C/C++)
248 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 .
249 OSE+ 82.44 % 94.33 % 75.05 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
250 MM 82.35 % 93.17 % 72.50 % 1 s 1 core @ 2.5 Ghz (C/C++)
251 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.
252 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.
253 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.
254 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.
255 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.
256 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.
257 vadin-TBD2 code 80.67 % 91.94 % 70.64 % 0.20 s 1 core @ 2.5 Ghz (Python)
258 SOD 80.62 % 94.15 % 65.94 % 0.1 s 1 core @ 2.5 Ghz (Python)
259 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.
260 ITS-MDPL 80.56 % 92.45 % 73.05 % 0.16 s GPU @ 2.5 Ghz (Python)
261 SwinMono3D 80.47 % 91.08 % 60.77 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
262 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.
263 none 80.17 % 90.64 % 67.96 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
264 K3D 80.16 % 93.38 % 70.41 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
265 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.
266 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.
267 RelationNet3D_dla34 code 79.59 % 83.64 % 69.13 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
268 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 2021.
269 E2E-DA 79.33 % 92.09 % 69.45 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
270 Lite-FPN 79.13 % 86.85 % 65.03 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
271 KMC code 79.09 % 89.31 % 72.31 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
272 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.
273 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.
274 COF3D 77.98 % 87.17 % 60.10 % 200 s 1 core @ 2.5 Ghz (Python + C/C++)
275 E2E-DA-Lite (Res18) 77.94 % 90.63 % 65.85 % 0.01 s GPU @ 2.5 Ghz (Python)
276 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.
277 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.
278 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.
279 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.
280 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.
281 AutoShape 76.61 % 83.71 % 63.47 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
282 RelationNet3D_res18 code 76.45 % 86.98 % 66.83 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
283 RelationNet3D 76.44 % 81.31 % 68.25 % 0.04 s GPU @ 2.5 Ghz (Python)
284 MonoHMOO 75.95 % 91.51 % 59.55 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
285 ICCV 75.90 % 85.36 % 64.93 % 0.04 s GPU @ 2.5 Ghz (Python)
286 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.
287 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.
288 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.
289 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.
290 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.
291 Mobile Stereo R-CNN
This method uses stereo information.
74.13 % 88.80 % 59.84 % 1.8 s NVIDIA Jetson TX2
292 MAOLoss code 73.51 % 89.22 % 63.24 % 0.05 s 1 core @ 2.5 Ghz (Python)
293 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.
294 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.
295 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.
296 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.
297 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.
298 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.
299 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.
300 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.
301 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.
302 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.
303 TBD 63.75 % 85.52 % 54.42 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
304 DPM-VOC+VP 63.58 % 79.09 % 46.59 % 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.
305 AOG-View 62.62 % 77.62 % 48.27 % 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.
306 LSVM-MDPM-sv 57.48 % 70.23 % 42.54 % 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.
307 SAMME48LDCF code 57.26 % 76.28 % 43.55 % 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.
308 BirdNet
This method makes use of Velodyne laser scans.
56.94 % 79.20 % 54.88 % 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.
309 VeloFCN
This method makes use of Velodyne laser scans.
51.05 % 70.03 % 44.82 % 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 .
310 Mono3D_PLiDAR code 49.39 % 76.90 % 41.13 % 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.
311 DPM-C8B1
This method uses stereo information.
48.00 % 57.76 % 35.52 % 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.
312 LTN 46.54 % 48.96 % 41.58 % 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.
313 sensekitti code 46.12 % 49.16 % 42.79 % 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.
314 Kinematic3D code 45.50 % 58.33 % 34.81 % 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 .
315 weakm3d 41.50 % 41.21 % 38.11 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
316 MonoCInIS 40.75 % 45.00 % 34.48 % 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.
317 3D-CVF at SPA
This method makes use of Velodyne laser scans.
39.79 % 40.44 % 36.10 % 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.
318 SPG_mini
This method makes use of Velodyne laser scans.
38.75 % 39.26 % 38.57 % 0.09 s GPU @ 2.5 Ghz (Python)
319 SPG
This method makes use of Velodyne laser scans.
code 38.73 % 40.02 % 38.52 % 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.
320 DGIST MT-CNN 38.47 % 39.69 % 35.22 % 0.09 s GPU @ 1.0 Ghz (Python)
321 SA-SSD code 38.30 % 39.40 % 37.07 % 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.
322 BtcDet
This method makes use of Velodyne laser scans.
38.00 % 39.26 % 36.82 % 0.09 s GPU @ 2.5 Ghz (Python + C/C++)
323 VueronNet code 37.99 % 39.17 % 36.47 % 0.06 s 1 core @ 2.0 Ghz (Python)
324 NF2 37.91 % 38.81 % 34.27 % 0.1 s GPU @ 2.5 Ghz (Python)
325 VueronNet 37.86 % 39.24 % 35.49 % 0.08 s GPU @ 2.5 Ghz (Python)
326 HS3D code 37.35 % 39.43 % 33.26 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
327 Point-GNN
This method makes use of Velodyne laser scans.
code 37.20 % 38.66 % 36.29 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
328 FPGNN 36.87 % 38.36 % 36.08 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
329 RT3D-GMP
This method uses stereo information.
36.31 % 44.06 % 27.32 % 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.
330 Graph-NMS 32.53 % 41.35 % 28.13 % 36 ms GPU @ 2.5 Ghz (Python)
331 AOG code 29.81 % 33.28 % 23.91 % 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.
332 NCL code 29.49 % 26.49 % 29.89 % NA s 1 core @ 2.5 Ghz (Python)
333 RetinaMono 28.87 % 30.96 % 25.60 % 0.02 s 1 core @ 2.5 Ghz (Python)
334 RetinaMono code 28.68 % 31.39 % 24.70 % 0.02 s 1 core @ 2.5 Ghz (Python)
335 Graph-NMS-baseline 26.84 % 38.30 % 22.04 % 47 ms GPU @ 2.5 Ghz (Python)
336 SubCat48LDCF code 26.68 % 34.33 % 19.44 % 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.
337 Y4 code 25.53 % 32.98 % 22.95 % 0.03 s 1 core @ 2.5 Ghz (Python)
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