3D Object Detection Evaluation 2017


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

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

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

All methods are ranked based on the moderately difficult results.

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

Car


Method Setting Code Moderate Easy Hard Runtime Environment
1 SFD 83.96 % 90.83 % 77.47 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
2 DGDNH 83.83 % 90.47 % 79.45 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
3 VPFNet 83.21 % 91.02 % 78.20 % 0.06 s 2 cores @ 2.5 Ghz (Python)
4 Anonymous 82.99 % 91.64 % 78.02 % 0.1 s GPU @ 2.5 Ghz (Python)
5 NFAF3D 82.97 % 91.57 % 77.72 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
6 BtcDet
This method makes use of Velodyne laser scans.
82.86 % 90.64 % 78.09 % 0.09 s GPU @ 2.5 Ghz (Python + C/C++)
7 sfd 82.79 % 91.30 % 78.07 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
8 PE-RCVN 82.69 % 91.51 % 77.75 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
9 SPG_mini
This method makes use of Velodyne laser scans.
82.66 % 90.64 % 77.91 % 0.09 s GPU @ 2.5 Ghz (Python)
10 SE-SSD
This method makes use of Velodyne laser scans.
code 82.54 % 91.49 % 77.15 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, L. Jiang and C. Fu: SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud. CVPR 2021.
11 DFNet-V 82.45 % 89.40 % 77.56 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
12 DFNet-PV 82.40 % 90.99 % 77.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
13 EA-M-RCNN(BorderAtt) 82.33 % 87.77 % 77.37 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
14 NFAF3D-light 82.30 % 90.88 % 76.89 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
15 CLOCs code 82.28 % 89.16 % 77.23 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection . 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
16 PPAF
This method makes use of Velodyne laser scans.
82.23 % 88.76 % 77.48 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
17 CityBrainLab 82.19 % 90.51 % 77.17 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
18 SPG
This method makes use of Velodyne laser scans.
code 82.13 % 90.50 % 78.90 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Xu, Y. Zhou, W. Wang, C. Qi and D. Anguelov: SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation. Proceedings of the IEEE conference on computer vision and pattern recognition (ICCV) 2021.
19 VoTr-TSD 82.09 % 89.90 % 79.14 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
J. Mao, Y. Xue, M. Niu, H. Bai, J. Feng, X. Liang, H. Xu and C. Xu: Voxel Transformer for 3D Object Detection. ICCV 2021.
20 Pyramid R-CNN 82.08 % 88.39 % 77.49 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
J. Mao, M. Niu, H. Bai, X. Liang, H. Xu and C. Xu: Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection. ICCV 2021.
21 SRIF-RCNN 82.04 % 88.45 % 77.54 % 0.0947 s 1 core @ 2.5 Ghz (C/C++)
22 Anonymous 81.96 % 89.90 % 77.20 % 0.1s 1 core @ 2.5 Ghz (C/C++)
23 EPNet++ 81.96 % 91.37 % 76.71 % 0.1 s GPU @ 2.5 Ghz (Python)
24 PV-RCNN-v2 81.88 % 90.14 % 77.15 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
25 SGNet 81.85 % 88.83 % 77.47 % 0.09 s GPU @ 2.5 Ghz (Python)
26 Anonymous 81.85 % 89.96 % 76.51 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
27 ST-RCNN
This method makes use of Velodyne laser scans.
81.84 % 90.50 % 77.22 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
28 ST-RCNN (SNLW-RCNN)
This method makes use of Velodyne laser scans.
code 81.84 % 90.50 % 77.22 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
29 ISE-RCNN 81.83 % 89.12 % 77.29 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
30 SqueezeRCNN 81.80 % 88.72 % 77.10 % 0.08 s 1 core @ 2.5 Ghz (Python)
31 CityBrainLab-CT3D code 81.77 % 87.83 % 77.16 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
H. Sheng, S. Cai, Y. Liu, B. Deng, J. Huang, X. Hua and M. Zhao: Improving 3D Object Detection with Channel- wise Transformer. ICCV 2021.
32 JPVNet 81.73 % 88.66 % 76.94 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
33 M3DeTR code 81.73 % 90.28 % 76.96 % n/a s GPU @ 1.0 Ghz (Python)
T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis and D. Manocha: M3DeTR: Multi-representation, Multi- scale, Mutual-relation 3D Object Detection with Transformers. 2021.
34 SIENet code 81.71 % 88.22 % 77.22 % 0.08 s 1 core @ 2.5 Ghz (Python)
Z. Li, Y. Yao, Z. Quan, W. Yang and J. Xie: SIENet: Spatial Information Enhancement Network for 3D Object Detection from Point Cloud. 2021.
35 E^2-PV-RCNN 81.70 % 88.33 % 77.20 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
36 PLNL-3DSSD
This method makes use of Velodyne laser scans.
81.69 % 88.98 % 74.90 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
37 VCRCNN 81.68 % 90.52 % 77.26 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
38 TBD 81.68 % 87.93 % 76.92 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
39 ASCNet 81.67 % 88.48 % 76.93 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
40 Fast VP-RCNN code 81.62 % 90.97 % 76.90 % 0.05 s 1 core @ 3.5 Ghz (C/C++)
41 Voxel R-CNN code 81.62 % 90.90 % 77.06 % 0.04 s GPU @ 3.0 Ghz (C/C++)
J. Deng, S. Shi, P. Li, W. Zhou, Y. Zhang and H. Li: Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection . AAAI 2021.
42 BANet code 81.61 % 89.28 % 76.58 % 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.
43 SARFE 81.59 % 88.88 % 76.74 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
44 HyBrid Feature Det 81.59 % 88.77 % 76.92 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
45 FromVoxelToPoint code 81.58 % 88.53 % 77.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to- Point Decoder. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
46 H^23D R-CNN code 81.55 % 90.43 % 77.22 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
J. Deng, W. Zhou, Y. Zhang and H. Li: From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2021.
47 anonymous code 81.55 % 90.94 % 76.74 % 0.05s 1 core @ >3.5 Ghz (python)
48 LZY_RCNN 81.52 % 88.77 % 78.59 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
49 TBD 81.51 % 88.96 % 77.27 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
50 FrustumRCNN 81.50 % 87.83 % 77.08 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
51 MSG-PGNN 81.50 % 88.70 % 76.88 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
52 TransCyclistNet 81.46 % 88.47 % 76.87 % 0.08 s 1 core @ 2.5 Ghz (Python)
53 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
code 81.46 % 88.25 % 76.96 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
54 P2V-RCNN 81.45 % 88.34 % 77.20 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.
55 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 81.43 % 90.25 % 76.82 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
56 TPCG 81.41 % 89.16 % 76.90 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
57 PC-RGNN 81.38 % 87.94 % 76.88 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
58 DDet 81.38 % 89.63 % 78.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
59 WHUT-iou_ssd code 81.37 % 89.84 % 76.83 % 0.045s 1 core @ 2.5 Ghz (C/C++)
60 XView 81.35 % 89.21 % 76.87 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
61 ISE-RCNN-PV 81.34 % 88.05 % 76.99 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
62 RangeRCNN
This method makes use of Velodyne laser scans.
81.33 % 88.47 % 77.09 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, M. Zhang, Z. Zhang, X. Zhao and S. Pu: RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representation. arXiv preprint arXiv:2009.00206 2020.
63 FSA-PVRCNN
This method makes use of Velodyne laser scans.
81.31 % 88.01 % 76.75 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
64 TransDet3D 81.28 % 88.11 % 76.73 % 0.08 s 1 core @ 2.5 Ghz (Python)
65 Generalized-SIENet 81.24 % 87.70 % 76.79 % 0.08 s 1 core @ 2.5 Ghz (Python)
66 Point Image Fusion 81.23 % 89.01 % 76.77 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
67 MSL3D 81.15 % 87.27 % 76.56 % 0.03 s GPU @ 2.5 Ghz (Python)
68 Multi-Sensor3D 81.15 % 87.27 % 76.56 % 0.03 s GPU @ 2.5 Ghz (Python)
69 SAA-PV-RCNN 81.09 % 87.24 % 78.05 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
70 FPC-RCNN 81.08 % 88.68 % 76.46 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
71 VPFNet code 80.97 % 88.51 % 76.74 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
72 CSVoxel-RCNN 80.97 % 87.66 % 76.29 % 0.03 s GPU @ 1.0 Ghz (Python)
73 VueronNet code 80.96 % 90.06 % 73.72 % 0.06 s 1 core @ 2.0 Ghz (Python)
74 FusionDetv2-v4 80.93 % 87.75 % 76.12 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
75 AIMC-RUC 80.83 % 90.14 % 73.59 % 0.11 s 1 core @ 2.5 Ghz (Python)
76 SVGA-Net
This method makes use of Velodyne laser scans.
80.82 % 87.40 % 76.23 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
77 GNN-RCNN 80.81 % 87.94 % 76.53 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
78 sa-voxel-centernet code 80.77 % 87.39 % 76.45 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
79 SA-voxel-centernet code 80.77 % 87.28 % 76.51 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
80 Associate-3Ddet_v2 80.77 % 91.53 % 75.23 % 0.04 s 1 core @ 2.5 Ghz (Python)
81 FusionDetv2-v3 80.70 % 88.05 % 76.10 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
82 StructuralIF 80.69 % 87.15 % 76.26 % 0.02 s 8 cores @ 2.5 Ghz (Python)
J. Pei An: Deep structural information fusion for 3D object detection on LiDAR-camera system. Accepted in CVIU 2021.
83 CLOCs_PVCas code 80.67 % 88.94 % 77.15 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection . 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
84 SCIR-Net
This method makes use of Velodyne laser scans.
80.62 % 87.53 % 76.00 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
85 XView-PartA^2 80.41 % 87.72 % 76.22 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
86 Sem-Aug v1 code 80.40 % 88.92 % 77.37 % 0.04 s GPU @ 3.5 Ghz (Python)
87 SRDL
This method uses stereo information.
This method makes use of Velodyne laser scans.
80.38 % 87.73 % 76.27 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
88 Fast-CLOCs 80.35 % 89.10 % 76.99 % 0.1 s GPU @ 2.5 Ghz (Python)
89 SPANet 80.34 % 91.05 % 74.89 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
90 IA-SSD (single) 80.32 % 88.87 % 75.10 % 0.013 s 1 core @ 2.5 Ghz (C/C++)
91 AM-SSD 80.30 % 89.58 % 75.02 % 0.04 s 1 core @ 2.5 Ghz (Python)
92 CIA-SSD
This method makes use of Velodyne laser scans.
code 80.28 % 89.59 % 72.87 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, S. Chen, L. Jiang and C. Fu: CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud. AAAI 2021.
93 FusionDetv1 80.28 % 87.45 % 76.21 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
94 TBD 80.24 % 87.67 % 76.27 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
95 VPV 80.21 % 88.97 % 75.22 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
96 VCT 80.19 % 89.12 % 77.19 % 0.2 s 1 core @ 2.5 Ghz (Python)
97 TBD 80.17 % 86.83 % 75.96 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
98 IA-SSD (multi) 80.13 % 88.34 % 75.04 % 0.014 s 1 core @ 2.5 Ghz (C/C++)
99 TBD 80.12 % 88.30 % 75.29 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
100 EBM3DOD code 80.12 % 91.05 % 72.78 % 0.12 s 1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.
101 3D-CVF at SPA
This method makes use of Velodyne laser scans.
80.05 % 89.20 % 73.11 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
J. Yoo, Y. Kim, J. Kim and J. Choi: 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection. ECCV 2020.
102 TBD 80.02 % 88.45 % 74.85 % TBD GPU @ 2.5 Ghz (Python + C/C++)
103 MVOD 80.01 % 88.53 % 77.24 % 0.16 s 1 core @ 2.5 Ghz (C/C++)
104 MBDF-Net 80.00 % 90.87 % 75.04 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
105 SIF 79.88 % 86.84 % 75.89 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
106 CM3DV 79.87 % 89.00 % 72.59 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
107 RangeIoUDet
This method makes use of Velodyne laser scans.
79.80 % 88.60 % 76.76 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, Z. Zhang, M. Zhang, X. Zhao and S. Pu: RangeIoUDet: Range Image Based Real-Time 3D Object Detector Optimized by Intersection Over Union. CVPR 2021.
108 SA-SSD code 79.79 % 88.75 % 74.16 % 0.04 s 1 core @ 2.5 Ghz (Python)
C. He, H. Zeng, J. Huang, X. Hua and L. Zhang: Structure Aware Single-stage 3D Object Detection from Point Cloud. CVPR 2020.
109 KpNet 79.75 % 88.92 % 72.17 % 0.42 s 1 core @ 2.5 Ghz (C/C++)
110 KpNet 79.74 % 88.88 % 72.13 % 42 s 1 core @ 2.5 Ghz (C/C++)
111 Seg-RCNN code 79.73 % 89.16 % 72.28 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
112 STD code 79.71 % 87.95 % 75.09 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
113 MGAF-3DSSD code 79.68 % 88.16 % 72.39 % 0.1 s 1 core @ 2.5 Ghz (Python)
J. Li, H. Dai, L. Shao and Y. Ding: Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
114 MBDF-Net-1 79.65 % 90.43 % 74.72 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
115 Struc info fusion II 79.59 % 88.97 % 72.51 % 0.05 s GPU @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.
116 3DSSD code 79.57 % 88.36 % 74.55 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
117 EBM3DOD baseline code 79.52 % 88.80 % 72.30 % 0.05 s 1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.
118 Struc info fusion I 79.49 % 88.70 % 74.25 % 0.05 s 1 core @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.
119 Point-GNN
This method makes use of Velodyne laser scans.
code 79.47 % 88.33 % 72.29 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
120 SECOND 79.46 % 87.44 % 73.97 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
121 RoIFusion code 79.36 % 88.09 % 72.51 % 0.22 s 1 core @ 3.0 Ghz (Python)
122 NV-RCNN 79.32 % 87.58 % 74.74 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
123 3DIoU_v2 79.30 % 88.22 % 76.96 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
124 EPNet code 79.28 % 89.81 % 74.59 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
T. Huang, Z. Liu, X. Chen and X. Bai: EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection. ECCV 2020.
125 FPCR-CNN 79.25 % 88.45 % 75.69 % 0.05 s 1 core @ 2.5 Ghz (Python)
126 3DIoU++ 79.22 % 87.49 % 76.68 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
127 DVFENet 79.18 % 86.20 % 74.58 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection. Neurocomputing 2021.
128 Faraway-Frustum
This method makes use of Velodyne laser scans.
code 79.05 % 87.45 % 76.14 % 0.1 s GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion. arXiv preprint arXiv:2011.01404 2020.
129 3D IoU-Net 79.03 % 87.96 % 72.78 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, S. Krylov, Y. Ding and L. Shao: 3D IoU-Net: IoU Guided 3D Object Detector for Point Clouds. arXiv preprint arXiv:2004.04962 2020.
130 CCFNET 78.97 % 88.20 % 74.14 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
131 SERCNN
This method makes use of Velodyne laser scans.
78.96 % 87.74 % 74.30 % 0.1 s 1 core @ 2.5 Ghz (Python)
D. Zhou, J. Fang, X. Song, L. Liu, J. Yin, Y. Dai, H. Li and R. Yang: Joint 3D Instance Segmentation and Object Detection for Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020.
132 NV2P-RCNN 78.92 % 87.36 % 74.16 % 0.1 s GPU @ 2.5 Ghz (Python)
133 demo 78.85 % 87.50 % 72.05 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
134 FPC3D
This method makes use of the epipolar geometry.
78.81 % 87.61 % 75.49 % 33 s 1 core @ 2.5 Ghz (C/C++)
135 MVAF-Net code 78.71 % 87.87 % 75.48 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
G. Wang, B. Tian, Y. Zhang, L. Chen, D. Cao and J. Wu: Multi-View Adaptive Fusion Network for 3D Object Detection. arXiv preprint arXiv:2011.00652 2020.
136 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 78.49 % 87.81 % 73.51 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
137 CLOCs_SecCas 78.45 % 86.38 % 72.45 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
138 FusionDetv2-v2 78.42 % 86.59 % 73.87 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
139 Patches - EMP
This method makes use of Velodyne laser scans.
78.41 % 89.84 % 73.15 % 0.5 s GPU @ 2.5 Ghz (Python)
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
140 MKFFNet 78.40 % 85.25 % 73.75 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
141 HotSpotNet 78.31 % 87.60 % 73.34 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
142 FusionDetv2-v5 78.30 % 86.94 % 73.44 % 0.05 s 1 core @ 2.5 Ghz (Java + C/C++)
143 MKFFNet 78.30 % 87.25 % 73.66 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
144 MKFFNet 78.30 % 86.86 % 73.80 % 0.01s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
145 SAA-SECOND 78.13 % 86.13 % 73.34 % 38m s 1 core @ 2.5 Ghz (C/C++)
146 3D-VDNet 78.05 % 87.13 % 72.90 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
147 VPN 77.93 % 85.02 % 72.97 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
148 HVPR 77.92 % 86.38 % 73.04 % 0.02 s GPU @ 2.5 Ghz (Python)
149 CenterNet3D 77.90 % 86.20 % 73.03 % 0.04 s GPU @ 1.5 Ghz (Python)
G. Wang, B. Tian, Y. Ai, T. Xu, L. Chen and D. Cao: CenterNet3D:An Anchor free Object Detector for Autonomous Driving. 2020.
150 CVFNet 77.70 % 88.75 % 71.95 % 28.1ms 1 core @ 2.5 Ghz (Python)
151 VOXEL_3D 77.69 % 86.45 % 72.20 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
152 VGCN 77.65 % 84.47 % 73.36 % 0.09 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
153 AutoAlign 77.58 % 86.84 % 73.23 % 0.1 s 1 core @ 2.5 Ghz (Python)
154 UberATG-MMF
This method makes use of Velodyne laser scans.
77.43 % 88.40 % 70.22 % 0.08 s GPU @ 2.5 Ghz (Python)
M. Liang*, B. Yang*, Y. Chen, R. Hu and R. Urtasun: Multi-Task Multi-Sensor Fusion for 3D Object Detection. CVPR 2019.
155 Associate-3Ddet code 77.40 % 85.99 % 70.53 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
L. Du, X. Ye, X. Tan, J. Feng, Z. Xu, E. Ding and S. Wen: Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection. The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
156 Fast Point R-CNN
This method makes use of Velodyne laser scans.
77.40 % 85.29 % 70.24 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, S. Liu, X. Shen and J. Jia: Fast Point R-CNN. Proceedings of the IEEE international conference on computer vision (ICCV) 2019.
157 Patches
This method makes use of Velodyne laser scans.
77.20 % 88.67 % 71.82 % 0.15 s GPU @ 2.0 Ghz
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
158 Sem-Aug-PointRCNN code 77.04 % 82.75 % 73.21 % 0.1 s GPU @ 3.5 Ghz (C/C++)
159 HRI-VoxelFPN 76.70 % 85.64 % 69.44 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
H. Kuang, B. Wang, J. An, M. Zhang and Z. Zhang: Voxel-FPN:multi-scale voxel feature aggregation in 3D object detection from point clouds. sensors 2020.
160 SARPNET 76.64 % 85.63 % 71.31 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. Ye, H. Chen, C. Zhang, X. Hao and Z. Zhang: SARPNET: Shape Attention Regional Proposal Network for LiDAR-based 3D Object Detection. Neurocomputing 2019.
161 YF 76.57 % 87.15 % 71.23 % 0.04 s GPU @ 2.5 Ghz (C/C++)
162 3D IoU Loss
This method makes use of Velodyne laser scans.
76.50 % 86.16 % 71.39 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
D. Zhou, J. Fang, X. Song, C. Guan, J. Yin, Y. Dai and R. Yang: IoU Loss for 2D/3D Object Detection. International Conference on 3D Vision (3DV) 2019.
163 HNet-3DSSD
This method makes use of Velodyne laser scans.
code 76.48 % 86.06 % 69.71 % 0.05 s GPU @ 2.5 Ghz (Python)
164 F-ConvNet
This method makes use of Velodyne laser scans.
code 76.39 % 87.36 % 66.69 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
165 DPointNet 76.34 % 81.67 % 70.34 % 0.07s 1 core @ 2.5 Ghz (C/C++)
166 SegVoxelNet 76.13 % 86.04 % 70.76 % 0.04 s 1 core @ 2.5 Ghz (Python)
H. Yi, S. Shi, M. Ding, J. Sun, K. Xu, H. Zhou, Z. Wang, S. Li and G. Wang: SegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloud. ICRA 2020.
167 S-AT GCN 76.04 % 83.20 % 71.17 % 0.02 s GPU @ 2.0 Ghz (Python)
L. Wang, C. Wang, X. Zhang, T. Lan and J. Li: S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection. CoRR 2021.
168 TANet code 75.94 % 84.39 % 68.82 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
169 APL-Second 75.75 % 84.26 % 70.65 % 0.05 s 1 core @ 2.5 Ghz (Python)
170 PointRGCN 75.73 % 85.97 % 70.60 % 0.26 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
171 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 75.64 % 86.96 % 70.70 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
172 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 75.43 % 86.10 % 68.88 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
173 R-GCN 75.26 % 83.42 % 68.73 % 0.16 s GPU @ 2.5 Ghz (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
174 epBRM
This method makes use of Velodyne laser scans.
code 75.15 % 85.00 % 69.84 % 0.1 s GPU @ >3.5 Ghz (Python + C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
175 MAFF-Net(DAF-Pillar) 75.04 % 85.52 % 67.61 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Z. Zhang, Z. Liang, M. Zhang, X. Zhao, Y. Ming, T. Wenming and S. Pu: MAFF-Net: Filter False Positive for 3D Vehicle Detection with Multi-modal Adaptive Feature Fusion. arXiv preprint arXiv:2009.10945 2020.
176 sscl-20p 74.82 % 86.06 % 69.87 % 0.02 s 1 core @ 2.5 Ghz (Python)
177 PI-RCNN 74.82 % 84.37 % 70.03 % 0.1 s 1 core @ 2.5 Ghz (Python)
L. Xie, C. Xiang, Z. Yu, G. Xu, Z. Yang, D. Cai and X. He: PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module. AAAI 2020 : The Thirty-Fourth AAAI Conference on Artificial Intelligence 2020.
178 FPGNN 74.77 % 83.82 % 67.93 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
179 FPC3D_all
This method makes use of Velodyne laser scans.
74.55 % 85.50 % 69.91 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
180 PointPillars
This method makes use of Velodyne laser scans.
code 74.31 % 82.58 % 68.99 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
181 ARPNET 74.04 % 84.69 % 68.64 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
182 PC-CNN-V2
This method makes use of Velodyne laser scans.
73.79 % 85.57 % 65.65 % 0.5 s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Du, M. Ang, S. Karaman and D. Rus: A General Pipeline for 3D Detection of Vehicles. 2018 IEEE International Conference on Robotics and Automation (ICRA) 2018.
183 C-GCN 73.62 % 83.49 % 67.01 % 0.147 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
184 LSNet 73.55 % 86.13 % 68.58 % 0.09 s GPU @ 2.5 Ghz (Python + C/C++)
185 3DBN
This method makes use of Velodyne laser scans.
73.53 % 83.77 % 66.23 % 0.13s 1080Ti (Python+C/C++)
X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.
186 PointRGBNet 73.49 % 83.99 % 68.56 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
187 RangeDet code 73.44 % 80.53 % 67.28 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
188 SCNet
This method makes use of Velodyne laser scans.
73.17 % 83.34 % 67.93 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
189 PFF3D
This method makes use of Velodyne laser scans.
code 72.93 % 81.11 % 67.24 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
190 DASS 72.31 % 81.85 % 65.99 % 0.09 s 1 core @ 2.0 Ghz (Python)
O. Unal, L. Van Gool and D. Dai: Improving Point Cloud Semantic Segmentation by Learning 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2021.
191 HS3D code 72.25 % 83.57 % 67.49 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
192 AVOD-FPN
This method makes use of Velodyne laser scans.
code 71.76 % 83.07 % 65.73 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
193 PointPainting
This method makes use of Velodyne laser scans.
71.70 % 82.11 % 67.08 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
194 WS3D
This method makes use of Velodyne laser scans.
70.59 % 80.99 % 64.23 % 0.1 s GPU @ 2.5 Ghz (Python)
Q. Meng, W. Wang, T. Zhou, J. Shen, L. Van Gool and D. Dai: Weakly Supervised 3D Object Detection from Lidar Point Cloud. 2020.
195 F-PointNet
This method makes use of Velodyne laser scans.
code 69.79 % 82.19 % 60.59 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
196 FusionDetv2-baseline 68.87 % 79.05 % 63.68 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
197 UberATG-ContFuse
This method makes use of Velodyne laser scans.
68.78 % 83.68 % 61.67 % 0.06 s GPU @ 2.5 Ghz (Python)
M. Liang, B. Yang, S. Wang and R. Urtasun: Deep Continuous Fusion for Multi-Sensor 3D Object Detection. ECCV 2018.
198 MLOD
This method makes use of Velodyne laser scans.
code 67.76 % 77.24 % 62.05 % 0.12 s GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
199 AVOD
This method makes use of Velodyne laser scans.
code 66.47 % 76.39 % 60.23 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
200 FusionDetv2-v1 65.65 % 75.21 % 60.65 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
201 MMLAB LIGA-Stereo
This method uses stereo information.
code 64.66 % 81.39 % 57.22 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
202 BirdNet+
This method makes use of Velodyne laser scans.
code 64.04 % 76.15 % 59.79 % 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.
203 MV3D
This method makes use of Velodyne laser scans.
63.63 % 74.97 % 54.00 % 0.36 s GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
204 KMC code 62.74 % 74.45 % 56.76 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
205 LIGA-Stereo-old
This method uses stereo information.
62.65 % 81.76 % 55.24 % 0.375 s Titan Xp
206 AEC3D 61.99 % 72.16 % 57.11 % 18 ms GPU @ 2.5 Ghz (Python)
207 VN3D 61.41 % 72.37 % 56.86 % 0.02 s 1 core @ 2.5 Ghz (Python)
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208 RCD 60.56 % 70.54 % 55.58 % 0.1 s GPU @ 2.5 Ghz (Python)
A. Bewley, P. Sun, T. Mensink, D. Anguelov and C. Sminchisescu: Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection. Conference on Robot Learning (CoRL) 2020.
209 deleted 57.11 % 76.87 % 50.05 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
210 A3DODWTDA
This method makes use of Velodyne laser scans.
code 56.82 % 62.84 % 48.12 % 0.08 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
211 PL++ (SDN+GDC)
This method uses stereo information.
This method makes use of Velodyne laser scans.
code 54.88 % 68.38 % 49.16 % 0.6 s GPU @ 2.5 Ghz (C/C++)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.
212 MV3D (LIDAR)
This method makes use of Velodyne laser scans.
54.54 % 68.35 % 49.16 % 0.24 s GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
213 CDN
This method uses stereo information.
code 54.22 % 74.52 % 46.36 % 0.6 s GPU @ 2.5 Ghz (Python)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems (NeurIPS) 2020.
214 CG-Stereo
This method uses stereo information.
53.58 % 74.39 % 46.50 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
215 DSGN
This method uses stereo information.
code 52.18 % 73.50 % 45.14 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
216 BirdNet+ (legacy)
This method makes use of Velodyne laser scans.
code 51.85 % 70.14 % 50.03 % 0.1 s Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird’s Eye View. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
217 NCL code 50.07 % 46.58 % 50.33 % NA s 1 core @ 2.5 Ghz (Python)
218 SOD 48.69 % 70.90 % 40.12 % 0.1 s 1 core @ 2.5 Ghz (Python)
219 Complexer-YOLO
This method makes use of Velodyne laser scans.
47.34 % 55.93 % 42.60 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
220 Disp R-CNN (velo)
This method uses stereo information.
code 45.78 % 68.21 % 37.73 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
221 CDN-PL++
This method uses stereo information.
44.86 % 64.31 % 38.11 % 0.4 s GPU @ 2.5 Ghz (C/C++)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems 2020.
222 Disp R-CNN
This method uses stereo information.
code 43.27 % 67.02 % 36.43 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
223 R-AGNO-Net 42.79 % 49.49 % 39.31 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
224 Pseudo-LiDAR++
This method uses stereo information.
code 42.43 % 61.11 % 36.99 % 0.4 s GPU @ 2.5 Ghz (Python)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.
225 OSE+ 41.60 % 62.67 % 35.82 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
226 YOLOStereo3D
This method uses stereo information.
code 41.25 % 65.68 % 30.42 % 0.1 s GPU 1080Ti
Y. Liu, L. Wang and M. Liu: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
227 BEVC 40.72 % 50.05 % 36.42 % 35ms GPU @ 1.5 Ghz (Python)
228 RT3D-GMP
This method uses stereo information.
38.76 % 45.79 % 30.00 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
229 ZoomNet
This method uses stereo information.
code 38.64 % 55.98 % 30.97 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
L. Z. Xu: ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2020.
230 OC Stereo
This method uses stereo information.
code 37.60 % 55.15 % 30.25 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
231 Pseudo-Lidar
This method uses stereo information.
code 34.05 % 54.53 % 28.25 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Wang, W. Chao, D. Garg, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
232 SC(DLA34)
This method uses stereo information.
31.30 % 49.94 % 25.62 % 0.04 s GPU @ 2.5 Ghz (Python)
233 Stereo R-CNN
This method uses stereo information.
code 30.23 % 47.58 % 23.72 % 0.3 s GPU @ 2.5 Ghz (Python)
P. Li, X. Chen and S. Shen: Stereo R-CNN based 3D Object Detection for Autonomous Driving. CVPR 2019.
234 BirdNet
This method makes use of Velodyne laser scans.
27.26 % 40.99 % 25.32 % 0.11 s Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
235 TBD 24.87 % 33.30 % 21.96 % 0.1 s 1 core @ 2.5 Ghz (Python)
236 RT3DStereo
This method uses stereo information.
23.28 % 29.90 % 18.96 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
237 Digging_M3D 21.24 % 29.15 % 19.18 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
238 RT3D
This method makes use of Velodyne laser scans.
19.14 % 23.74 % 18.86 % 0.09 s GPU @ 1.8Ghz
Y. Zeng, Y. Hu, S. Liu, J. Ye, Y. Han, X. Li and N. Sun: RT3D: Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving. IEEE Robotics and Automation Letters 2018.
239 StereoFENet
This method uses stereo information.
18.41 % 29.14 % 14.20 % 0.15 s 1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
240 SCSTSV-MonoFlex 17.91 % 27.38 % 15.58 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
241 LPCG-Monoflex 17.80 % 25.56 % 15.38 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
242 MonoDDE 17.14 % 24.93 % 15.10 % 0.04 s 1 core @ 2.5 Ghz (Python)
243 Mobile Stereo R-CNN
This method uses stereo information.
17.04 % 26.97 % 13.26 % 1.8 s NVIDIA Jetson TX2
244 CMKD 16.99 % 27.20 % 15.08 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
245 MonoCon 16.46 % 22.50 % 13.95 % 0.02 s GPU @ 2.5 Ghz (Python)
246 DD3D code 16.34 % 23.22 % 14.20 % n/a s 1 core @ 2.5 Ghz (C/C++)
D. Park, R. Ambrus, V. Guizilini, J. Li and A. Gaidon: Is Pseudo-Lidar needed for Monocular 3D Object detection?. IEEE/CVF International Conference on Computer Vision (ICCV) .
247 mono3d 15.26 % 23.41 % 12.80 % 0.03 s GPU @ 2.5 Ghz (Python)
248 ZongmuMono3d code 15.08 % 23.79 % 13.25 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
249 GUPNet code 15.02 % 22.26 % 13.12 % NA s 1 core @ 2.5 Ghz (Python + C/C++)
Y. Lu, X. Ma, L. Yang, T. Zhang, Y. Liu, Q. Chu, J. Yan and W. Ouyang: Geometry Uncertainty Projection Network for Monocular 3D Object Detection. arXiv preprint arXiv:2107.13774 2021.
250 vadin-TBD 14.94 % 21.75 % 13.07 % 0.04 s 1 core @ 2.5 Ghz (Python)
251 LPCG-M3D 14.82 % 22.73 % 12.88 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
252 M3DSSD++ 14.75 % 23.61 % 11.80 % 0.16s 1 core @ 2.5 Ghz (C/C++)
253 MonoFlex 14.73 % 22.29 % 12.77 % 0.03 s 1 core @ 2.5 Ghz (Python)
254 CA3D 14.49 % 20.89 % 12.19 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
255 MM 14.38 % 21.26 % 11.87 % 1 s 1 core @ 2.5 Ghz (C/C++)
256 ANM 14.33 % 20.84 % 11.61 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
257 DLE code 14.33 % 24.23 % 10.30 % 0.06 s NVIDIA Tesla V100
C. Liu, S. Gu, L. Gool and R. Timofte: Deep Line Encoding for Monocular 3D Object Detection and Depth Prediction. Proceedings of the British Machine Vision Conference (BMVC) 2021.
258 ITS-MDPL 14.28 % 24.67 % 12.13 % 0.16 s GPU @ 2.5 Ghz (Python)
259 SwinMono3D 14.24 % 22.61 % 10.11 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
260 AutoShape code 14.17 % 22.47 % 11.36 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
Z. Liu, D. Zhou, F. Lu, J. Fang and L. Zhang: AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.
261 MAOLoss code 14.00 % 20.05 % 11.81 % 0.05 s 1 core @ 2.5 Ghz (Python)
262 E2E-DA 13.97 % 19.73 % 11.82 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
263 GAC3D++ 13.90 % 19.53 % 11.77 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
264 MonoFlex 13.89 % 19.94 % 12.07 % 0.03 s GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.
265 MonoEF code 13.87 % 21.29 % 11.71 % 0.03 s 1 core @ 2.5 Ghz (Python)
Y. Zhou, Y. He, H. Zhu, C. Wang, H. Li and Q. Jiang: Monocular 3D Object Detection: An Extrinsic Parameter Free Approach. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
266 MonoGeo 13.81 % 18.85 % 11.52 % 0.05 s 1 core @ 2.5 Ghz (Python)
267 K3D 13.80 % 20.04 % 11.67 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
268 none 13.79 % 18.84 % 11.52 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
269 DFR-Net 13.63 % 19.40 % 10.35 % 0.18 s 1080 Ti (Pytorch)
Z. Zou, X. Ye, L. Du, X. Cheng, X. Tan, L. Zhang, J. Feng, X. Xue and E. Ding: The devil is in the task: Exploiting reciprocal appearance-localization features for monocular 3d object detection . ICCV 2021.
270 MonoLCD 13.52 % 18.08 % 11.58 % 0.04 s 1 core @ 2.5 Ghz (Python)
271 CaDDN code 13.41 % 19.17 % 11.46 % 0.63 s GPU @ 2.5 Ghz (Python)
C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.
272 MDSNet 13.40 % 22.80 % 10.27 % 0.07 s 1 core @ 2.5 Ghz (Python)
273 PCT code 13.37 % 21.00 % 11.31 % 0.045 s 1 core @ 2.5 Ghz (C/C++)
L. Wang, L. Zhang, Y. Zhu, Z. Zhang, T. He, M. Li and X. Xue: Progressive Coordinate Transforms for Monocular 3D Object Detection. NeurIPS 2021.
274 KAIST-VDCLab 13.33 % 19.06 % 11.90 % 0.04 s 1 core @ 2.5 Ghz (Python)
275 Ground-Aware code 13.25 % 21.65 % 9.91 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
Y. Liu, Y. Yuan and M. Liu: Ground-aware Monocular 3D Object Detection for Autonomous Driving. IEEE Robotics and Automation Letters 2021.
276 MonoHMOO 13.12 % 20.28 % 9.56 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
277 Aug3D-RPN 12.99 % 17.82 % 9.78 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth. 2021.
278 vadin-TBD2 code 12.99 % 20.10 % 10.50 % 0.20 s 1 core @ 2.5 Ghz (Python)
279 RelationNet3D_dla34 code 12.88 % 17.67 % 11.01 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
280 PLDet3d 12.85 % 20.72 % 11.11 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
281 DDMP-3D 12.78 % 19.71 % 9.80 % 0.18 s 1 core @ 2.5 Ghz (Python)
L. Wang, L. Du, X. Ye, Y. Fu, G. Guo, X. Xue, J. Feng and L. Zhang: Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection. CVPR 2020.
282 RetinaMono 12.73 % 19.41 % 10.45 % 0.02 s 1 core @ 2.5 Ghz (Python)
283 Kinematic3D code 12.72 % 19.07 % 9.17 % 0.12 s 1 core @ 1.5 Ghz (C/C++)
G. Brazil, G. Pons-Moll, X. Liu and B. Schiele: Kinematic 3D Object Detection in Monocular Video. ECCV 2020 .
284 DA-Mono3D 12.66 % 16.99 % 9.97 % 0.09s 1 core @ 2.5 Ghz (C/C++)
285 MonoRCNN code 12.65 % 18.36 % 10.03 % 0.07 s GPU @ 2.5 Ghz (Python)
X. Shi, Q. Ye, X. Chen, C. Chen, Z. Chen and T. Kim: Geometry-based Distance Decomposition for Monocular 3D Object Detection. ICCV 2021.
286 RelationNet3D 12.60 % 17.57 % 10.95 % 0.04 s GPU @ 2.5 Ghz (Python)
287 Object Transformer 12.58 % 17.87 % 10.87 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
288 TBD 12.53 % 22.40 % 10.64 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
289 AutoShape 12.42 % 20.35 % 9.70 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
290 MP-Mono 12.37 % 17.89 % 9.58 % 0.16 s GPU @ 2.5 Ghz (Python)
291 GrooMeD-NMS code 12.32 % 18.10 % 9.65 % 0.12 s 1 core @ 2.5 Ghz (Python)
A. Kumar, G. Brazil and X. Liu: GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection. CVPR 2021.
292 MonoRUn code 12.30 % 19.65 % 10.58 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
293 Deprecated 12.30 % 16.48 % 9.14 % Deprecated Deprecated
294 monodle code 12.26 % 17.23 % 10.29 % 0.04 s GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for Monocular 3D Object Detection. CVPR 2021 .
295 PPTrans 12.06 % 19.79 % 10.48 % 0.2 s GPU @ 2.5 Ghz (Python)
296 YoloMono3D code 12.06 % 18.28 % 8.42 % 0.05 s GPU @ 2.5 Ghz (Python)
Y. Liu, L. Wang and L. Ming: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
297 IAFA 12.01 % 17.81 % 10.61 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
D. Zhou, X. Song, Y. Dai, J. Yin, F. Lu, M. Liao, J. Fang and L. Zhang: IAFA: Instance-Aware Feature Aggregation for 3D Object Detection from a Single Image. Proceedings of the Asian Conference on Computer Vision 2020.
298 GAC3D 12.00 % 17.75 % 9.15 % 0.25 s 1 core @ 2.5 Ghz (Python)
M. Bui, D. Ngo, H. Pham and D. Nguyen: GAC3D: improving monocular 3D object detection with ground-guide model and adaptive convolution. 2021.
299 PGD-FCOS3D code 11.76 % 19.05 % 9.39 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
T. Wang, X. Zhu, J. Pang and D. Lin: Probabilistic and Geometric Depth: Detecting Objects in Perspective. Conference on Robot Learning 2021.
300 D4LCN code 11.72 % 16.65 % 9.51 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
301 RetinaMono code 11.61 % 16.68 % 9.57 % 0.02 s 1 core @ 2.5 Ghz (Python)
302 KM3D code 11.45 % 16.73 % 9.92 % 0.03 s 1 core @ 2.5 Ghz (Python)
P. Li: Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training. 2020.
303 RefinedMPL 11.14 % 18.09 % 8.94 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
304 PatchNet code 11.12 % 15.68 % 10.17 % 0.4 s 1 core @ 2.5 Ghz (C/C++)
X. Ma, S. Liu, Z. Xia, H. Zhang, X. Zeng and W. Ouyang: Rethinking Pseudo-LiDAR Representation. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
305 ImVoxelNet code 10.97 % 17.15 % 9.15 % 0.2 s GPU @ 2.5 Ghz (Python)
D. Rukhovich, A. Vorontsova and A. Konushin: ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection. arXiv preprint arXiv:2106.01178 2021.
306 COF3D 10.91 % 17.86 % 8.20 % 200 s 1 core @ 2.5 Ghz (Python + C/C++)
307 AM3D 10.74 % 16.50 % 9.52 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
X. Ma, Z. Wang, H. Li, P. Zhang, W. Ouyang and X. Fan: Accurate Monocular Object Detection via Color- Embedded 3D Reconstruction for Autonomous Driving. Proceedings of the IEEE international Conference on Computer Vision (ICCV) 2019.
308 Lite-FPN 10.64 % 15.32 % 8.59 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
309 TBD 10.61 % 15.71 % 8.66 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
310 Keypoint-3D 10.42 % 15.97 % 7.91 % 14 s 1 core @ 2.5 Ghz (C/C++)
311 RTM3D code 10.34 % 14.41 % 8.77 % 0.05 s GPU @ 1.0 Ghz (Python)
P. Li, H. Zhao, P. Liu and F. Cao: RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving. 2020.
312 E2E-DA-Lite (Res18) 10.32 % 15.56 % 8.89 % 0.01 s GPU @ 2.5 Ghz (Python)
313 MonoPair 9.99 % 13.04 % 8.65 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
314 RelationNet3D_res18 code 9.93 % 14.27 % 8.43 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
315 FADNet code 9.92 % 16.37 % 8.05 % 0.04 s GPU @ >3.5 Ghz (Python)
316 Neighbor-Vote 9.90 % 15.57 % 8.89 % 0.1 s GPU @ 2.5 Ghz (Python)
X. Chu, J. Deng, Y. Li, Z. Yuan, Y. Zhang, J. Ji and Y. Zhang: Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance Voting. ACM MM 2021.
317 SMOKE code 9.76 % 14.03 % 7.84 % 0.03 s GPU @ 2.5 Ghz (Python)
Z. Liu, Z. Wu and R. Tóth: SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation. 2020.
318 M3D-RPN code 9.71 % 14.76 % 7.42 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
319 QD-3DT
This is an online method (no batch processing).
code 9.33 % 12.81 % 7.86 % 0.03 s GPU @ 2.5 Ghz (Python)
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.
320 ICCV 9.31 % 13.37 % 8.29 % 0.04 s GPU @ 2.5 Ghz (Python)
321 TopNet-HighRes
This method makes use of Velodyne laser scans.
9.28 % 12.67 % 7.95 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
322 MonoCInIS 7.94 % 15.82 % 6.68 % 0,13 s GPU @ 2.5 Ghz (C/C++)
J. Heylen, M. De Wolf, B. Dawagne, M. Proesmans, L. Van Gool, W. Abbeloos, H. Abdelkawy and D. Reino: MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.
323 Geo3D 7.70 % 11.52 % 6.80 % 0.04 s GPU @ 2.5 Ghz (Python)
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324 SS3D 7.68 % 10.78 % 6.51 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
325 MonoCInIS 7.66 % 15.21 % 6.24 % 0,14 s GPU @ 2.5 Ghz (Python)
J. Heylen, M. De Wolf, B. Dawagne, M. Proesmans, L. Van Gool, W. Abbeloos, H. Abdelkawy and D. Reino: MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.
326 Mono3D_PLiDAR code 7.50 % 10.76 % 6.10 % 0.1 s NVIDIA GeForce 1080 (pytorch)
X. Weng and K. Kitani: Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud. arXiv:1903.09847 2019.
327 MonoPSR code 7.25 % 10.76 % 5.85 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
328 Decoupled-3D 7.02 % 11.08 % 5.63 % 0.08 s GPU @ 2.5 Ghz (C/C++)
Y. Cai, B. Li, Z. Jiao, H. Li, X. Zeng and X. Wang: Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation. AAAI 2020.
329 VoxelJones code 6.35 % 7.39 % 5.80 % .18 s 1 core @ 2.5 Ghz (Python + C/C++)
M. Motro and J. Ghosh: Vehicular Multi-object Tracking with Persistent Detector Failures. arXiv preprint arXiv:1907.11306 2019.
330 MonoGRNet code 5.74 % 9.61 % 4.25 % 0.04s NVIDIA P40
Z. Qin, J. Wang and Y. Lu: MonoGRNet: A Geometric Reasoning Network for 3D Object Localization. The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) 2019.
331 A3DODWTDA (image) code 5.27 % 6.88 % 4.45 % 0.8 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
332 MonoFENet 5.14 % 8.35 % 4.10 % 0.15 s 1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
333 TLNet (Stereo)
This method uses stereo information.
code 4.37 % 7.64 % 3.74 % 0.1 s 1 core @ 2.5 Ghz (Python)
Z. Qin, J. Wang and Y. Lu: Triangulation Learning Network: from Monocular to Stereo 3D Object Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
334 CSoR
This method makes use of Velodyne laser scans.
4.06 % 5.61 % 3.17 % 3.5 s 4 cores @ >3.5 Ghz (Python + C/C++)
L. Plotkin: PyDriver: Entwicklung eines Frameworks für räumliche Detektion und Klassifikation von Objekten in Fahrzeugumgebung. 2015.
335 Shift R-CNN (mono) code 3.87 % 6.88 % 2.83 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
336 MVRA + I-FRCNN+ 3.27 % 5.19 % 2.49 % 0.18 s GPU @ 2.5 Ghz (Python)
H. Choi, H. Kang and Y. Hyun: Multi-View Reprojection Architecture for Orientation Estimation. The IEEE International Conference on Computer Vision (ICCV) Workshops 2019.
337 SparVox3D 3.20 % 5.27 % 2.56 % 0.05 s GPU @ 2.0 Ghz (Python)
E. Balatkan and F. Kıraç: Improving Regression Performance on Monocular 3D Object Detection Using Bin-Mixing and Sparse Voxel Data. 2021 6th International Conference on Computer Science and Engineering (UBMK) 2021.
338 TopNet-UncEst
This method makes use of Velodyne laser scans.
3.02 % 3.24 % 2.26 % 0.09 s NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
339 GS3D 2.90 % 4.47 % 2.47 % 2 s 1 core @ 2.5 Ghz (C/C++)
B. Li, W. Ouyang, L. Sheng, X. Zeng and X. Wang: GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
340 3D-GCK 2.52 % 3.27 % 2.11 % 24 ms Tesla V100
N. Gählert, J. Wan, N. Jourdan, J. Finkbeiner, U. Franke and J. Denzler: Single-Shot 3D Detection of Vehicles from Monocular RGB Images via Geometrically Constrained Keypoints in Real-Time. 2020 IEEE Intelligent Vehicles Symposium (IV) 2020.
341 weakm3d 2.26 % 5.03 % 1.63 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
342 ROI-10D 2.02 % 4.32 % 1.46 % 0.2 s GPU @ 3.5 Ghz (Python)
F. Manhardt, W. Kehl and A. Gaidon: ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape. Computer Vision and Pattern Recognition (CVPR) 2019.
343 FQNet 1.51 % 2.77 % 1.01 % 0.5 s 1 core @ 2.5 Ghz (Python)
L. Liu, J. Lu, C. Xu, Q. Tian and J. Zhou: Deep Fitting Degree Scoring Network for Monocular 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
344 3D-SSMFCNN code 1.41 % 1.88 % 1.11 % 0.1 s GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.
345 mBoW
This method makes use of Velodyne laser scans.
0.00 % 0.00 % 0.00 % 10 s 1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
Table as LaTeX | Only published Methods

Pedestrian


Method Setting Code Moderate Easy Hard Runtime Environment
1 VPFNet code 48.36 % 54.65 % 44.98 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
2 HIKVISION-AFree 46.88 % 52.75 % 43.50 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
3 ADLAB 46.18 % 53.59 % 43.28 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
4 PiFeNet 45.89 % 54.84 % 42.02 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
5 ISE-RCNN 45.66 % 51.44 % 42.43 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
6 HotSpotNet 45.37 % 53.10 % 41.47 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
7 H^23D R-CNN 45.26 % 52.75 % 41.56 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
8 PE-RCVN 45.01 % 50.29 % 41.85 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
9 SAA-PV-RCNN 45.00 % 52.55 % 41.82 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
10 VPN 44.56 % 54.13 % 41.73 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
11 EPNet++ 44.38 % 52.79 % 41.29 % 0.1 s GPU @ 2.5 Ghz (Python)
12 TANet code 44.34 % 53.72 % 40.49 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
13 TBD 44.32 % 49.37 % 41.24 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
14 3DSSD code 44.27 % 54.64 % 40.23 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
15 AutoAlign 44.08 % 53.99 % 40.82 % 0.1 s 1 core @ 2.5 Ghz (Python)
16 ISE-RCNN-PV 43.78 % 50.03 % 40.50 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
17 Point-GNN
This method makes use of Velodyne laser scans.
code 43.77 % 51.92 % 40.14 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
18 VCT 43.65 % 50.27 % 41.43 % 0.2 s 1 core @ 2.5 Ghz (Python)
19 EA-M-RCNN(BorderAtt) 43.44 % 51.81 % 39.85 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
20 F-ConvNet
This method makes use of Velodyne laser scans.
code 43.38 % 52.16 % 38.80 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
21 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 43.35 % 53.10 % 40.06 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
22 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 43.29 % 52.17 % 40.29 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
23 FromVoxelToPoint code 43.28 % 51.80 % 40.71 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to- Point Decoder. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
24 VMVS
This method makes use of Velodyne laser scans.
43.27 % 53.44 % 39.51 % 0.25 s GPU @ 2.5 Ghz (Python)
J. Ku, A. Pon, S. Walsh and S. Waslander: Improving 3D object detection for pedestrians with virtual multi-view synthesis orientation estimation. IROS 2019.
25 P2V-RCNN 43.19 % 50.91 % 40.81 % 0.1 s 2 cores @ 2.5 Ghz (Python)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.
26 MGAF-3DSSD code 43.09 % 50.65 % 39.65 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
27 SGNet 43.00 % 49.68 % 40.45 % 0.09 s GPU @ 2.5 Ghz (Python)
28 Frustum-PointPillars 42.89 % 51.22 % 39.28 % 0.06 s 4 cores @ 3.0 Ghz (Python)
A. Paigwar, D. Sierra-Gonzalez, \. Erkent and C. Laugier: Frustum-PointPillars: A Multi-Stage Approach for 3D Object Detection using RGB Camera and LiDAR. International Conference on Computer Vision, ICCV, Workshop on Autonomous Vehicle Vision 2021.
29 Fast-CLOCs 42.72 % 52.10 % 39.08 % 0.1 s GPU @ 2.5 Ghz (Python)
30 STD code 42.47 % 53.29 % 38.35 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
31 AVOD-FPN
This method makes use of Velodyne laser scans.
code 42.27 % 50.46 % 39.04 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
32 SemanticVoxels 42.19 % 50.90 % 39.52 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
J. Fei, W. Chen, P. Heidenreich, S. Wirges and C. Stiller: SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentation. MFI 2020.
33 TBD 42.19 % 49.89 % 39.34 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
34 TBD 42.19 % 49.89 % 39.34 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
35 F-PointNet
This method makes use of Velodyne laser scans.
code 42.15 % 50.53 % 38.08 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
36 PPAF
This method makes use of Velodyne laser scans.
42.05 % 48.66 % 38.94 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
37 PointPillars
This method makes use of Velodyne laser scans.
code 41.92 % 51.45 % 38.89 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
38 TBD_IOU1 41.65 % 49.00 % 39.31 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
39 epBRM
This method makes use of Velodyne laser scans.
code 41.52 % 49.17 % 39.08 % 0.10 s 1 core @ 2.5 Ghz (C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
40 TBD_IOU 41.45 % 48.25 % 39.17 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
41 GNN-RCNN 41.32 % 47.48 % 38.97 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
42 tbd 41.10 % 50.56 % 37.49 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
43 IA-SSD (single) 41.03 % 47.90 % 37.98 % 0.013 s 1 core @ 2.5 Ghz (C/C++)
44 Generalized-SIENet 40.97 % 47.01 % 38.88 % 0.08 s 1 core @ 2.5 Ghz (Python)
45 PointPainting
This method makes use of Velodyne laser scans.
40.97 % 50.32 % 37.87 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
46 SCIR-Net
This method makes use of Velodyne laser scans.
40.95 % 49.23 % 38.47 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
47 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
code 40.89 % 46.97 % 38.80 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
48 SARFE 40.79 % 47.29 % 38.25 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
49 XView-PartA^2 40.71 % 47.73 % 38.47 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
50 SAA-SECOND 40.57 % 48.73 % 37.77 % 38m s 1 core @ 2.5 Ghz (C/C++)
51 WHUT-iou_ssd code 40.53 % 46.41 % 38.48 % 0.045s 1 core @ 2.5 Ghz (C/C++)
52 E^2-PV-RCNN 40.47 % 46.61 % 38.60 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
53 SA-voxel-centernet code 40.43 % 46.10 % 38.32 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
54 FusionDetv2-v3 40.38 % 46.86 % 37.41 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
55 FPCR-CNN 40.32 % 48.33 % 37.66 % 0.05 s 1 core @ 2.5 Ghz (Python)
56 P2V_PCV1 40.27 % 45.43 % 38.24 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
57 sa-voxel-centernet code 40.24 % 46.08 % 38.07 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
58 FPC-RCNN 40.13 % 46.41 % 37.84 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
59 TPCG 39.97 % 46.35 % 37.66 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
60 M3DeTR code 39.94 % 45.70 % 37.66 % n/a s GPU @ 1.0 Ghz (Python)
T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis and D. Manocha: M3DeTR: Multi-representation, Multi- scale, Mutual-relation 3D Object Detection with Transformers. 2021.
61 FusionDetv2-v5 39.91 % 47.50 % 37.39 % 0.05 s 1 core @ 2.5 Ghz (Java + C/C++)
62 SVGA-Net
This method makes use of Velodyne laser scans.
39.88 % 47.59 % 37.57 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
63 DDet 39.87 % 45.82 % 38.00 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
64 MVOD 39.82 % 46.22 % 37.56 % 0.16 s 1 core @ 2.5 Ghz (C/C++)
65 Point Image Fusion 39.79 % 45.04 % 37.62 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
66 anonymous code 39.74 % 46.09 % 37.41 % 0.05s 1 core @ >3.5 Ghz (python)
67 FusionDetv2-v4 39.68 % 46.93 % 37.31 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
68 Fast VP-RCNN code 39.65 % 45.95 % 37.29 % 0.05 s 1 core @ 3.5 Ghz (C/C++)
69 VCRCNN 39.64 % 45.19 % 37.55 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
70 TBD 39.48 % 45.46 % 37.35 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
71 SRDL
This method uses stereo information.
This method makes use of Velodyne laser scans.
39.43 % 47.30 % 36.99 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
72 FusionDetv1 39.42 % 47.30 % 36.97 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
73 FSA-PVRCNN
This method makes use of Velodyne laser scans.
39.39 % 44.14 % 37.13 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
74 demo 39.38 % 47.69 % 36.06 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
75 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 39.37 % 47.98 % 36.01 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
76 ST-RCNN
This method makes use of Velodyne laser scans.
39.36 % 44.96 % 37.09 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
77 FusionDetv2-v2 39.31 % 44.98 % 37.22 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
78 ARPNET 39.31 % 48.32 % 35.93 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
79 TBD 39.31 % 46.85 % 36.11 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
80 IA-SSD (multi) 39.03 % 46.51 % 35.61 % 0.014 s 1 core @ 2.5 Ghz (C/C++)
81 tbd 38.89 % 45.98 % 35.94 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
82 NV-RCNN 38.75 % 47.05 % 36.52 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
83 SIF 38.74 % 46.23 % 36.06 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
84 SCNet
This method makes use of Velodyne laser scans.
38.66 % 47.83 % 35.70 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
85 Faraway-Frustum
This method makes use of Velodyne laser scans.
code 38.58 % 46.33 % 35.71 % 0.1 s GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion. arXiv preprint arXiv:2011.01404 2020.
86 MSL3D 38.58 % 45.00 % 35.72 % 0.03 s GPU @ 2.5 Ghz (Python)
87 AF_MCLS 38.29 % 47.07 % 34.67 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
88 MKFFNet 38.05 % 46.01 % 35.72 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
89 FPC3D_all
This method makes use of Velodyne laser scans.
37.95 % 45.49 % 35.60 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
90 VGCN 37.60 % 45.28 % 34.96 % 0.09 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
91 DVFENet 37.50 % 43.55 % 35.33 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection. Neurocomputing 2021.
92 MLOD
This method makes use of Velodyne laser scans.
code 37.47 % 47.58 % 35.07 % 0.12 s GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
93 S-AT GCN 37.37 % 44.63 % 34.92 % 0.02 s GPU @ 2.0 Ghz (Python)
L. Wang, C. Wang, X. Zhang, T. Lan and J. Li: S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection. CoRR 2021.
94 YF 36.99 % 44.43 % 34.40 % 0.04 s GPU @ 2.5 Ghz (C/C++)
95 HS3D code 36.86 % 45.62 % 33.67 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
96 XView 36.79 % 42.44 % 34.96 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
97 MKFFNet 36.66 % 43.94 % 34.56 % 0.01s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
98 FusionDetv2-baseline 36.66 % 41.34 % 34.60 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
99 MKFFNet 36.65 % 44.00 % 34.59 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
100 TBD 36.53 % 44.11 % 34.30 % TBD GPU @ 2.5 Ghz (Python + C/C++)
101 PFF3D
This method makes use of Velodyne laser scans.
code 36.07 % 43.93 % 32.86 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
102 NV2P-RCNN 35.98 % 43.18 % 33.88 % 0.1 s GPU @ 2.5 Ghz (Python)
103 ASCNet 35.76 % 42.00 % 33.69 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
104 RoIFusion code 35.14 % 42.22 % 32.92 % 0.22 s 1 core @ 3.0 Ghz (Python)
105 BirdNet+
This method makes use of Velodyne laser scans.
code 35.06 % 41.55 % 32.93 % 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.
106 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 34.59 % 42.27 % 31.37 % 0.0047s 1 core @ 2.5 Ghz (python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
107 CBi-GNN-persons 32.92 % 41.65 % 29.19 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
108 BirdNet+ (legacy)
This method makes use of Velodyne laser scans.
code 31.46 % 37.99 % 29.46 % 0.1 s Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird’s Eye View. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
109 SparsePool code 30.38 % 37.84 % 26.94 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
110 MMLAB LIGA-Stereo
This method uses stereo information.
code 30.00 % 40.46 % 27.07 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
111 SparsePool code 27.92 % 35.52 % 25.87 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
112 AVOD
This method makes use of Velodyne laser scans.
code 27.86 % 36.10 % 25.76 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
113 CSW3D
This method makes use of Velodyne laser scans.
26.64 % 33.75 % 23.34 % 0.03 s 4 cores @ 2.5 Ghz (C/C++)
J. Hu, T. Wu, H. Fu, Z. Wang and K. Ding: Cascaded Sliding Window Based Real-Time 3D Region Proposal for Pedestrian Detection. ROBIO 2019.
114 PointRGBNet 26.40 % 34.77 % 24.03 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
115 Disp R-CNN (velo)
This method uses stereo information.
code 25.80 % 37.12 % 22.04 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
116 Disp R-CNN
This method uses stereo information.
code 25.40 % 35.75 % 21.79 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
117 deleted 25.13 % 35.02 % 22.36 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
118 FusionDetv2-v1 24.55 % 30.58 % 23.64 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
119 CG-Stereo
This method uses stereo information.
24.31 % 33.22 % 20.95 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
120 NCL code 23.33 % 27.75 % 21.66 % NA s 1 core @ 2.5 Ghz (Python)
121 LIGA-Stereo-old
This method uses stereo information.
23.23 % 30.14 % 20.58 % 0.375 s Titan Xp
122 YOLOStereo3D
This method uses stereo information.
code 19.75 % 28.49 % 16.48 % 0.1 s GPU 1080Ti
Y. Liu, L. Wang and M. Liu: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
123 OSE+ 19.67 % 28.30 % 17.17 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
124 AEC3D 19.00 % 24.39 % 17.43 % 18 ms GPU @ 2.5 Ghz (Python)
125 BEVC 17.65 % 23.49 % 15.92 % 35ms GPU @ 1.5 Ghz (Python)
126 OC Stereo
This method uses stereo information.
code 17.58 % 24.48 % 15.60 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
127 BirdNet
This method makes use of Velodyne laser scans.
17.08 % 22.04 % 15.82 % 0.11 s Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
128 VN3D 15.69 % 19.56 % 13.17 % 0.02 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
129 DSGN
This method uses stereo information.
code 15.55 % 20.53 % 14.15 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
130 SOD 14.68 % 21.13 % 12.67 % 0.1 s 1 core @ 2.5 Ghz (Python)
131 Complexer-YOLO
This method makes use of Velodyne laser scans.
13.96 % 17.60 % 12.70 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
132 RT3D-GMP
This method uses stereo information.
11.41 % 16.23 % 10.12 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
133 CMKD 10.39 % 16.89 % 9.29 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
134 GUPNet code 9.76 % 14.95 % 8.41 % NA s 1 core @ 2.5 Ghz (Python + C/C++)
Y. Lu, X. Ma, L. Yang, T. Zhang, Y. Liu, Q. Chu, J. Yan and W. Ouyang: Geometry Uncertainty Projection Network for Monocular 3D Object Detection. arXiv preprint arXiv:2107.13774 2021.
135 DD3D code 9.30 % 13.91 % 8.05 % 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) .
136 ZongmuMono3d code 9.18 % 14.23 % 7.82 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
137 MM 8.81 % 13.99 % 7.26 % 1 s 1 core @ 2.5 Ghz (C/C++)
138 SCSTSV-MonoFlex 8.75 % 13.10 % 7.38 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
139 SwinMono3D 8.54 % 12.96 % 7.19 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
140 MonoCon 8.41 % 13.10 % 6.94 % 0.02 s GPU @ 2.5 Ghz (Python)
141 MonoFlex 8.16 % 11.89 % 6.81 % 0.03 s 1 core @ 2.5 Ghz (Python)
142 CaDDN code 8.14 % 12.87 % 6.76 % 0.63 s GPU @ 2.5 Ghz (Python)
C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.
143 vadin-TBD 7.66 % 11.87 % 6.82 % 0.04 s 1 core @ 2.5 Ghz (Python)
144 MonoLCD 7.62 % 11.21 % 6.47 % 0.04 s 1 core @ 2.5 Ghz (Python)
145 K3D 7.60 % 12.58 % 6.73 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
146 ANM 7.54 % 11.92 % 6.37 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
147 LPCG-Monoflex 7.33 % 10.82 % 6.18 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
148 MonoDDE 7.32 % 11.13 % 6.67 % 0.04 s 1 core @ 2.5 Ghz (Python)
149 RefinedMPL 7.18 % 11.14 % 5.84 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
150 TopNet-HighRes
This method makes use of Velodyne laser scans.
6.92 % 10.40 % 6.63 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
151 MonoRUn code 6.78 % 10.88 % 5.83 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
152 MonoPair 6.68 % 10.02 % 5.53 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
153 mono3d 6.62 % 10.10 % 5.46 % 0.03 s GPU @ 2.5 Ghz (Python)
154 monodle code 6.55 % 9.64 % 5.44 % 0.04 s GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for Monocular 3D Object Detection. CVPR 2021 .
155 MonoFlex 6.31 % 9.43 % 5.26 % 0.03 s GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.
156 GAC3D++ 6.29 % 9.29 % 5.20 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
157 RelationNet3D_dla34 code 6.22 % 9.28 % 5.23 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
158 E2E-DA 5.95 % 8.79 % 5.72 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
159 DA-Mono3D 5.68 % 7.86 % 4.81 % 0.09s 1 core @ 2.5 Ghz (C/C++)
160 M3DSSD++ 5.65 % 8.10 % 4.72 % 0.16s 1 core @ 2.5 Ghz (C/C++)
161 MonoGeo 5.63 % 8.00 % 4.71 % 0.05 s 1 core @ 2.5 Ghz (Python)
162 Deprecated 5.62 % 7.52 % 4.71 % Deprecated Deprecated
163 ICCV 5.25 % 8.34 % 4.72 % 0.04 s GPU @ 2.5 Ghz (Python)
164 MonoHMOO 5.23 % 7.62 % 4.28 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
165 RelationNet3D_res18 code 5.19 % 7.95 % 4.21 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
166 Aug3D-RPN 4.71 % 6.01 % 3.87 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth. 2021.
167 Shift R-CNN (mono) code 4.66 % 7.95 % 4.16 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
168 Lite-FPN 4.38 % 6.57 % 3.56 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
169 COF3D 4.37 % 6.02 % 3.55 % 200 s 1 core @ 2.5 Ghz (Python + C/C++)
170 PLDet3d 4.25 % 6.31 % 3.49 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
171 MAOLoss code 4.18 % 5.81 % 3.67 % 0.05 s 1 core @ 2.5 Ghz (Python)
172 M3D-RPN(S-R) 4.11 % 5.70 % 3.37 % 0.16 s GPU @ 1.5 Ghz (Python)
173 MonoPSR code 4.00 % 6.12 % 3.30 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
174 MP-Mono 3.75 % 5.09 % 3.50 % 0.16 s GPU @ 2.5 Ghz (Python)
175 Geo3D 3.65 % 5.74 % 3.01 % 0.04 s GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
176 DFR-Net 3.62 % 6.09 % 3.39 % 0.18 s 1080 Ti (Pytorch)
Z. Zou, X. Ye, L. Du, X. Cheng, X. Tan, L. Zhang, J. Feng, X. Xue and E. Ding: The devil is in the task: Exploiting reciprocal appearance-localization features for monocular 3d object detection . ICCV 2021.
177 DDMP-3D 3.55 % 4.93 % 3.01 % 0.18 s 1 core @ 2.5 Ghz (Python)
L. Wang, L. Du, X. Ye, Y. Fu, G. Guo, X. Xue, J. Feng and L. Zhang: Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection. CVPR 2020.
178 FADNet code 3.53 % 5.40 % 3.31 % 0.04 s GPU @ >3.5 Ghz (Python)
179 E2E-DA-Lite (Res18) 3.51 % 5.82 % 3.42 % 0.01 s GPU @ 2.5 Ghz (Python)
180 M3D-RPN code 3.48 % 4.92 % 2.94 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
181 D4LCN code 3.42 % 4.55 % 2.83 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
182 QD-3DT
This is an online method (no batch processing).
code 3.37 % 5.53 % 3.02 % 0.03 s GPU @ 2.5 Ghz (Python)
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.
183 MonoEF code 2.79 % 4.27 % 2.21 % 0.03 s 1 core @ 2.5 Ghz (Python)
Y. Zhou, Y. He, H. Zhu, C. Wang, H. Li and Q. Jiang: Monocular 3D Object Detection: An Extrinsic Parameter Free Approach. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
184 KAIST-VDCLab 2.47 % 3.27 % 2.43 % 0.04 s 1 core @ 2.5 Ghz (Python)
185 RT3DStereo
This method uses stereo information.
2.45 % 3.28 % 2.35 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
186 TopNet-UncEst
This method makes use of Velodyne laser scans.
1.87 % 3.42 % 1.73 % 0.09 s NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
187 PPTrans 1.85 % 2.68 % 1.44 % 0.2 s GPU @ 2.5 Ghz (Python)
188 TBD 1.81 % 3.00 % 1.59 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
189 SS3D 1.78 % 2.31 % 1.48 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
190 PGD-FCOS3D code 1.49 % 2.28 % 1.38 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
T. Wang, X. Zhu, J. Pang and D. Lin: Probabilistic and Geometric Depth: Detecting Objects in Perspective. Conference on Robot Learning 2021.
191 SparVox3D 1.35 % 1.93 % 1.04 % 0.05 s GPU @ 2.0 Ghz (Python)
E. Balatkan and F. Kıraç: Improving Regression Performance on Monocular 3D Object Detection Using Bin-Mixing and Sparse Voxel Data. 2021 6th International Conference on Computer Science and Engineering (UBMK) 2021.
192 mBoW
This method makes use of Velodyne laser scans.
0.00 % 0.00 % 0.00 % 10 s 1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
Table as LaTeX | Only published Methods

Cyclist


Method Setting Code Moderate Easy Hard Runtime Environment
1 ISE-RCNN-PV 71.94 % 84.94 % 64.09 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
2 SGNet 70.40 % 86.75 % 62.73 % 0.09 s GPU @ 2.5 Ghz (Python)
3 HIKVISION-AFree 69.92 % 84.65 % 63.33 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
4 SARFE 69.67 % 84.88 % 62.26 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
5 ISE-RCNN 69.18 % 82.62 % 62.77 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
6 anonymous code 69.13 % 83.09 % 61.35 % 0.05s 1 core @ >3.5 Ghz (python)
7 sa-voxel-centernet code 69.03 % 81.88 % 61.66 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
8 Fast VP-RCNN code 69.02 % 83.81 % 61.51 % 0.05 s 1 core @ 3.5 Ghz (C/C++)
9 SAA-PV-RCNN 68.96 % 82.06 % 61.54 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
10 BtcDet
This method makes use of Velodyne laser scans.
68.68 % 82.81 % 61.81 % 0.09 s GPU @ 2.5 Ghz (Python + C/C++)
11 SA-voxel-centernet code 68.67 % 81.47 % 61.40 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
12 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
code 68.54 % 82.19 % 61.33 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
13 TPCG 68.15 % 82.13 % 61.56 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
14 PE-RCVN 68.13 % 84.96 % 60.34 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
15 E^2-PV-RCNN 68.03 % 81.55 % 60.51 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
16 RangeIoUDet
This method makes use of Velodyne laser scans.
67.77 % 83.12 % 60.26 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, Z. Zhang, M. Zhang, X. Zhao and S. Pu: RangeIoUDet: Range Image Based Real-Time 3D Object Detector Optimized by Intersection Over Union. CVPR 2021.
17 Point Image Fusion 67.69 % 83.15 % 60.62 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
18 Generalized-SIENet 67.61 % 83.00 % 60.09 % 0.08 s 1 core @ 2.5 Ghz (Python)
19 FPC-RCNN 67.57 % 82.79 % 60.69 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
20 DDet 67.55 % 82.03 % 60.23 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
21 GNN-RCNN 67.49 % 81.25 % 61.15 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
22 PPAF
This method makes use of Velodyne laser scans.
67.37 % 80.50 % 61.18 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
23 PV-RCNN-v2 67.33 % 82.22 % 60.04 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
24 SPG_mini
This method makes use of Velodyne laser scans.
66.96 % 80.21 % 60.50 % 0.09 s GPU @ 2.5 Ghz (Python)
25 VCRCNN 66.78 % 81.29 % 59.32 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
26 M3DeTR code 66.74 % 83.83 % 59.03 % n/a s GPU @ 1.0 Ghz (Python)
T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis and D. Manocha: M3DeTR: Multi-representation, Multi- scale, Mutual-relation 3D Object Detection with Transformers. 2021.
27 TBD 66.63 % 85.08 % 60.36 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
28 CBi-GNN-persons 66.49 % 79.95 % 59.12 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
29 VCT 66.38 % 82.37 % 60.01 % 0.2 s 1 core @ 2.5 Ghz (Python)
30 XView-PartA^2 66.33 % 80.65 % 59.72 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
31 IA-SSD (single) 66.25 % 82.36 % 59.70 % 0.013 s 1 core @ 2.5 Ghz (C/C++)
32 EA-M-RCNN(BorderAtt) 66.04 % 82.39 % 58.19 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
33 WHUT-iou_ssd code 65.98 % 79.38 % 59.56 % 0.045s 1 core @ 2.5 Ghz (C/C++)
34 HotSpotNet 65.95 % 82.59 % 59.00 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
35 TBD 65.64 % 82.29 % 57.98 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
36 ST-RCNN
This method makes use of Velodyne laser scans.
65.61 % 78.82 % 58.44 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
37 JPVNet 65.41 % 80.66 % 59.26 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
38 Fast-CLOCs 65.31 % 82.83 % 57.43 % 0.1 s GPU @ 2.5 Ghz (Python)
39 FSA-PVRCNN
This method makes use of Velodyne laser scans.
65.20 % 80.68 % 59.14 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
40 ASCNet 65.10 % 78.41 % 57.87 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
41 F-ConvNet
This method makes use of Velodyne laser scans.
code 65.07 % 81.98 % 56.54 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
42 MVOD 64.95 % 79.52 % 57.53 % 0.16 s 1 core @ 2.5 Ghz (C/C++)
43 TBD 64.60 % 80.49 % 57.18 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
44 AutoAlign 64.36 % 80.41 % 56.88 % 0.1 s 1 core @ 2.5 Ghz (Python)
45 FusionDetv2-v5 64.28 % 78.57 % 57.02 % 0.05 s 1 core @ 2.5 Ghz (Java + C/C++)
46 3DSSD code 64.10 % 82.48 % 56.90 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
47 VPFNet code 64.10 % 77.64 % 58.00 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
48 RoIFusion code 64.05 % 80.84 % 58.37 % 0.22 s 1 core @ 3.0 Ghz (Python)
49 PointPainting
This method makes use of Velodyne laser scans.
63.78 % 77.63 % 55.89 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
50 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 63.71 % 78.60 % 57.65 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
51 TBD_IOU 63.68 % 79.74 % 56.63 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
52 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 63.52 % 79.17 % 56.93 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
53 Point-GNN
This method makes use of Velodyne laser scans.
code 63.48 % 78.60 % 57.08 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
54 MGAF-3DSSD code 63.43 % 80.64 % 55.15 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
55 FromVoxelToPoint code 63.41 % 81.49 % 56.40 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to- Point Decoder. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
56 FusionDetv2-v4 63.38 % 79.65 % 56.61 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
57 P2V-RCNN 63.13 % 78.62 % 56.81 % 0.1 s 2 cores @ 2.5 Ghz (Python)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.
58 tbd 62.75 % 78.45 % 56.30 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
59 H^23D R-CNN code 62.74 % 78.67 % 55.78 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
J. Deng, W. Zhou, Y. Zhang and H. Li: From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2021.
60 TBD_IOU1 62.67 % 80.32 % 55.99 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
61 FPCR-CNN 62.56 % 79.61 % 55.82 % 0.05 s 1 core @ 2.5 Ghz (Python)
62 VGCN 62.36 % 78.47 % 55.88 % 0.09 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
63 MSL3D 62.27 % 76.74 % 56.20 % 0.03 s GPU @ 2.5 Ghz (Python)
64 SRDL
This method uses stereo information.
This method makes use of Velodyne laser scans.
62.02 % 77.35 % 55.52 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
65 FusionDetv1 62.02 % 77.33 % 55.52 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
66 Faraway-Frustum
This method makes use of Velodyne laser scans.
code 62.00 % 77.36 % 55.40 % 0.1 s GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion. arXiv preprint arXiv:2011.01404 2020.
67 DVFENet 62.00 % 78.73 % 55.18 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection. Neurocomputing 2021.
68 FusionDetv2-v3 61.96 % 79.43 % 55.28 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
69 NV2P-RCNN 61.95 % 73.58 % 55.62 % 0.1 s GPU @ 2.5 Ghz (Python)
70 IA-SSD (multi) 61.94 % 78.35 % 55.70 % 0.014 s 1 core @ 2.5 Ghz (C/C++)
71 SVGA-Net
This method makes use of Velodyne laser scans.
61.86 % 75.45 % 54.68 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
72 VPN 61.82 % 77.81 % 55.33 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
73 MKFFNet 61.80 % 78.08 % 54.89 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
74 FusionDetv2-v2 61.78 % 76.70 % 54.75 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
75 S-AT GCN 61.70 % 75.24 % 55.32 % 0.02 s GPU @ 2.0 Ghz (Python)
L. Wang, C. Wang, X. Zhang, T. Lan and J. Li: S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection. CoRR 2021.
76 SIF 61.61 % 77.13 % 55.11 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
77 STD code 61.59 % 78.69 % 55.30 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
78 SCIR-Net
This method makes use of Velodyne laser scans.
60.89 % 76.32 % 54.48 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
79 AF_MCLS 60.89 % 78.82 % 54.13 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
80 P2V_PCV1 60.84 % 75.25 % 54.70 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
81 NV-RCNN 60.66 % 78.34 % 54.39 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
82 SAA-SECOND 60.50 % 75.65 % 53.81 % 38m s 1 core @ 2.5 Ghz (C/C++)
83 MKFFNet 60.48 % 76.68 % 54.20 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
84 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 60.30 % 75.42 % 53.81 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
85 CCFNET 60.18 % 78.05 % 53.42 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
86 EPNet++ 59.71 % 76.15 % 53.67 % 0.1 s GPU @ 2.5 Ghz (Python)
87 TBD 59.61 % 74.98 % 53.52 % TBD GPU @ 2.5 Ghz (Python + C/C++)
88 XView 59.55 % 77.24 % 53.47 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
89 FPC3D_all
This method makes use of Velodyne laser scans.
59.45 % 74.75 % 52.71 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
90 TANet code 59.44 % 75.70 % 52.53 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
91 MKFFNet 59.14 % 75.64 % 52.97 % 0.01s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
92 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 58.82 % 74.96 % 52.53 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
93 HS3D code 58.65 % 74.75 % 52.98 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
94 PointPillars
This method makes use of Velodyne laser scans.
code 58.65 % 77.10 % 51.92 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
95 demo 58.20 % 72.48 % 52.69 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
96 ARPNET 58.20 % 74.21 % 52.13 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
97 YF 56.86 % 71.37 % 52.18 % 0.04 s GPU @ 2.5 Ghz (C/C++)
98 FusionDetv2-baseline 56.34 % 71.16 % 50.70 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
99 epBRM
This method makes use of Velodyne laser scans.
code 56.13 % 72.08 % 49.91 % 0.10 s 1 core @ 2.5 Ghz (C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
100 F-PointNet
This method makes use of Velodyne laser scans.
code 56.12 % 72.27 % 49.01 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
101 TBD 53.95 % 70.44 % 47.22 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
102 TBD 53.95 % 70.44 % 47.22 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
103 BirdNet+
This method makes use of Velodyne laser scans.
code 53.84 % 65.67 % 49.06 % 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.
104 tbd 53.00 % 68.71 % 46.32 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
105 PiFeNet 52.66 % 69.63 % 46.02 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
106 PointRGBNet 52.15 % 67.05 % 46.78 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
107 SCNet
This method makes use of Velodyne laser scans.
50.79 % 67.98 % 45.15 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
108 AVOD-FPN
This method makes use of Velodyne laser scans.
code 50.55 % 63.76 % 44.93 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
109 MLOD
This method makes use of Velodyne laser scans.
code 49.43 % 68.81 % 42.84 % 0.12 s GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
110 BirdNet+ (legacy)
This method makes use of Velodyne laser scans.
code 47.72 % 67.38 % 42.89 % 0.1 s Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird’s Eye View. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
111 PFF3D
This method makes use of Velodyne laser scans.
code 46.78 % 63.27 % 41.37 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
112 AVOD
This method makes use of Velodyne laser scans.
code 42.08 % 57.19 % 38.29 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
113 SparsePool code 37.33 % 52.61 % 33.39 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
114 LIGA-Stereo-old
This method uses stereo information.
37.21 % 53.35 % 32.92 % 0.375 s Titan Xp
115 MMLAB LIGA-Stereo
This method uses stereo information.
code 36.86 % 54.44 % 32.06 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
116 FusionDetv2-v1 36.58 % 51.38 % 32.88 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
117 SparsePool code 32.61 % 40.87 % 29.05 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
118 deleted 31.31 % 46.37 % 27.66 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
119 CG-Stereo
This method uses stereo information.
30.89 % 47.40 % 27.23 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
120 BirdNet
This method makes use of Velodyne laser scans.
30.25 % 43.98 % 27.21 % 0.11 s Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
121 SOD 25.29 % 40.51 % 21.32 % 0.1 s 1 core @ 2.5 Ghz (Python)
122 Disp R-CNN (velo)
This method uses stereo information.
code 24.40 % 40.05 % 21.12 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
123 Disp R-CNN
This method uses stereo information.
code 24.40 % 40.04 % 21.12 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
124 AEC3D 22.41 % 31.40 % 21.56 % 18 ms GPU @ 2.5 Ghz (Python)
125 VN3D 21.53 % 30.76 % 21.03 % 0.02 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
126 OSE+ 20.75 % 32.62 % 17.23 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
127 Complexer-YOLO
This method makes use of Velodyne laser scans.
18.53 % 24.27 % 17.31 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
128 DSGN
This method uses stereo information.
code 18.17 % 27.76 % 16.21 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
129 OC Stereo
This method uses stereo information.
code 16.63 % 29.40 % 14.72 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
130 BEVC 14.08 % 22.30 % 13.44 % 35ms GPU @ 1.5 Ghz (Python)
131 RT3D-GMP
This method uses stereo information.
12.99 % 18.31 % 10.63 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
132 CMKD 5.68 % 9.27 % 4.99 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
133 MonoPSR code 4.74 % 8.37 % 3.68 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
134 TopNet-UncEst
This method makes use of Velodyne laser scans.
4.54 % 7.13 % 3.81 % 0.09 s NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
135 RelationNet3D_dla34 code 4.49 % 8.07 % 3.98 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
136 E2E-DA 4.42 % 7.36 % 3.34 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
137 LPCG-Monoflex 4.38 % 6.98 % 3.56 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
138 TBD 4.32 % 7.79 % 3.98 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
139 MAOLoss code 4.06 % 6.71 % 3.16 % 0.05 s 1 core @ 2.5 Ghz (Python)
140 E2E-DA-Lite (Res18) 3.99 % 6.87 % 3.04 % 0.01 s GPU @ 2.5 Ghz (Python)
141 SCSTSV-MonoFlex 3.82 % 6.65 % 3.06 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
142 DA-Mono3D 3.80 % 5.65 % 3.23 % 0.09s 1 core @ 2.5 Ghz (C/C++)
143 MonoDDE 3.78 % 5.94 % 3.33 % 0.04 s 1 core @ 2.5 Ghz (Python)
144 ZongmuMono3d code 3.77 % 7.21 % 3.15 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
145 DFR-Net 3.58 % 5.69 % 3.10 % 0.18 s 1080 Ti (Pytorch)
Z. Zou, X. Ye, L. Du, X. Cheng, X. Tan, L. Zhang, J. Feng, X. Xue and E. Ding: The devil is in the task: Exploiting reciprocal appearance-localization features for monocular 3d object detection . ICCV 2021.
146 vadin-TBD 3.50 % 5.48 % 2.99 % 0.04 s 1 core @ 2.5 Ghz (Python)
147 CaDDN code 3.41 % 7.00 % 3.30 % 0.63 s GPU @ 2.5 Ghz (Python)
C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.
148 RT3DStereo
This method uses stereo information.
3.37 % 5.29 % 2.57 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
149 GUPNet code 3.21 % 5.58 % 2.66 % NA s 1 core @ 2.5 Ghz (Python + C/C++)
Y. Lu, X. Ma, L. Yang, T. Zhang, Y. Liu, Q. Chu, J. Yan and W. Ouyang: Geometry Uncertainty Projection Network for Monocular 3D Object Detection. arXiv preprint arXiv:2107.13774 2021.
150 M3DSSD++ 2.94 % 5.18 % 2.43 % 0.16s 1 core @ 2.5 Ghz (C/C++)
151 MonoGeo 2.93 % 4.73 % 2.58 % 0.05 s 1 core @ 2.5 Ghz (Python)
152 MM 2.92 % 5.49 % 2.64 % 1 s 1 core @ 2.5 Ghz (C/C++)
153 K3D 2.81 % 5.17 % 2.57 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
154 MonoLCD 2.75 % 4.46 % 2.64 % 0.04 s 1 core @ 2.5 Ghz (Python)
155 Deprecated 2.71 % 3.89 % 2.27 % Deprecated Deprecated
156 ANM 2.69 % 4.69 % 2.68 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
157 monodle code 2.66 % 4.59 % 2.45 % 0.04 s GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for Monocular 3D Object Detection. CVPR 2021 .
158 RelationNet3D_res18 code 2.55 % 4.85 % 2.33 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
159 SwinMono3D 2.54 % 3.76 % 2.31 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
160 mono3d 2.53 % 4.71 % 2.22 % 0.03 s GPU @ 2.5 Ghz (Python)
161 DDMP-3D 2.50 % 4.18 % 2.32 % 0.18 s 1 core @ 2.5 Ghz (Python)
L. Wang, L. Du, X. Ye, Y. Fu, G. Guo, X. Xue, J. Feng and L. Zhang: Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection. CVPR 2020.
162 Aug3D-RPN 2.43 % 4.36 % 2.55 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth. 2021.
163 QD-3DT
This is an online method (no batch processing).
code 2.39 % 4.16 % 1.85 % 0.03 s GPU @ 2.5 Ghz (Python)
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.
164 MonoFlex 2.35 % 4.17 % 2.04 % 0.03 s GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.
165 ICCV 2.33 % 4.51 % 2.22 % 0.04 s GPU @ 2.5 Ghz (Python)
166 MonoPair 2.12 % 3.79 % 1.83 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
167 GAC3D++ 2.12 % 3.82 % 2.23 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
168 MonoFlex 2.10 % 3.39 % 1.67 % 0.03 s 1 core @ 2.5 Ghz (Python)
169 Geo3D 2.00 % 3.47 % 1.52 % 0.04 s GPU @ 2.5 Ghz (Python)
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170 MonoCon 1.92 % 2.80 % 1.55 % 0.02 s GPU @ 2.5 Ghz (Python)
171 RefinedMPL 1.82 % 3.23 % 1.77 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
172 TopNet-HighRes
This method makes use of Velodyne laser scans.
1.67 % 2.49 % 1.88 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
173 D4LCN code 1.67 % 2.45 % 1.36 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
174 MonoHMOO 1.60 % 1.87 % 1.66 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
175 MP-Mono 1.58 % 2.36 % 1.69 % 0.16 s GPU @ 2.5 Ghz (Python)
176 KAIST-VDCLab 1.56 % 2.34 % 1.61 % 0.04 s 1 core @ 2.5 Ghz (Python)
177 DD3D code 1.52 % 2.39 % 1.31 % 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) .
178 COF3D 1.46 % 2.34 % 1.28 % 200 s 1 core @ 2.5 Ghz (Python + C/C++)
179 SS3D 1.45 % 2.80 % 1.35 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
180 PGD-FCOS3D code 1.38 % 2.81 % 1.20 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
T. Wang, X. Zhu, J. Pang and D. Lin: Probabilistic and Geometric Depth: Detecting Objects in Perspective. Conference on Robot Learning 2021.
181 PPTrans 1.38 % 2.31 % 1.20 % 0.2 s GPU @ 2.5 Ghz (Python)
182 MonoEF code 0.92 % 1.80 % 0.71 % 0.03 s 1 core @ 2.5 Ghz (Python)
Y. Zhou, Y. He, H. Zhu, C. Wang, H. Li and Q. Jiang: Monocular 3D Object Detection: An Extrinsic Parameter Free Approach. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
183 PLDet3d 0.80 % 1.24 % 0.89 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
184 M3D-RPN code 0.65 % 0.94 % 0.47 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
185 FADNet code 0.64 % 1.44 % 0.67 % 0.04 s GPU @ >3.5 Ghz (Python)
186 MonoRUn code 0.61 % 1.01 % 0.48 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
187 Lite-FPN 0.41 % 0.50 % 0.24 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
188 Shift R-CNN (mono) code 0.29 % 0.48 % 0.31 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
189 mBoW
This method makes use of Velodyne laser scans.
0.00 % 0.00 % 0.00 % 10 s 1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
Table as LaTeX | Only published Methods

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Citation

When using this dataset in your research, we will be happy if you cite us:
@INPROCEEDINGS{Geiger2012CVPR,
  author = {Andreas Geiger and Philip Lenz and Raquel Urtasun},
  title = {Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite},
  booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2012}
}



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