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 VPFNet 83.21 % 91.02 % 78.20 % 0.06 s 2 cores @ 2.5 Ghz (Python)
2 Anonymous 82.99 % 91.64 % 78.02 % 0.1 s GPU @ 2.5 Ghz (Python)
3 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++)
4 HIKVISION-ADLab-HZ 82.83 % 89.00 % 76.00 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
5 SFD 82.79 % 91.30 % 78.07 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
6 SPG_mini
This method makes use of Velodyne laser scans.
82.66 % 90.64 % 77.91 % 0.09 s GPU @ 2.5 Ghz (Python)
7 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.
8 EA-M-RCNN(BorderAtt) 82.33 % 87.77 % 77.37 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
9 TBD
This method makes use of Velodyne laser scans.
82.23 % 88.76 % 77.48 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
10 HUAWEI Octopus 82.13 % 88.26 % 77.41 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
11 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.
12 VoTr-2 82.09 % 89.90 % 79.14 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
13 ADLAB 82.08 % 90.92 % 77.36 % 0.05 s 1 core @ >3.5 Ghz (C/C++)
14 Pyramid R-CNN 82.08 % 88.39 % 77.49 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
15 SRF^2-RCNN 82.04 % 88.45 % 77.54 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
16 Anonymous 81.96 % 89.90 % 77.20 % 0.1s 1 core @ 2.5 Ghz (C/C++)
17 PV-RCNN-v2 81.88 % 90.14 % 77.15 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
18 RangeRCNN-LV 81.85 % 88.76 % 77.18 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
19 L RCNN++ 81.85 % 89.96 % 76.51 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
20 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++)
21 PVGNet 81.81 % 89.94 % 77.09 % 0.05 s 1 core @ >3.5 Ghz (C/C++)
22 SqueezeRCNN 81.80 % 88.72 % 77.10 % 0.08 s 1 core @ 2.5 Ghz (Python)
23 CityBrainLab-CT3D 81.77 % 87.83 % 77.16 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
24 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.
25 E^2-PV-RCNN 81.70 % 88.33 % 77.20 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
26 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++)
27 TBD 81.68 % 87.93 % 76.92 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
28 ASCNet 81.67 % 88.48 % 76.93 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
29 Fast VP-RCNN code 81.62 % 90.97 % 76.90 % 0.05 s 1 core @ 3.5 Ghz (C/C++)
30 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.
31 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.
32 HyBrid Feature Det 81.59 % 88.77 % 76.92 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
33 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.
34 CVRS VIC-RCNN 81.57 % 88.60 % 77.09 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
35 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.
36 anonymous code 81.55 % 90.94 % 76.74 % 0.05s 1 core @ >3.5 Ghz (python)
37 LZY_RCNN 81.52 % 88.77 % 78.59 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
38 TBD 81.51 % 88.96 % 77.27 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
39 FrustumRCNN 81.50 % 87.83 % 77.08 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
40 MSG-PGNN 81.50 % 88.70 % 76.88 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
41 TransCyclistNet 81.46 % 88.47 % 76.87 % 0.08 s 1 core @ 2.5 Ghz (Python)
42 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.
43 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.
44 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.
45 TPCG 81.41 % 89.16 % 76.90 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
46 PC-RGNN 81.38 % 87.94 % 76.88 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
47 WHUT-iou_ssd code 81.37 % 89.84 % 76.83 % 0.045s 1 core @ 2.5 Ghz (C/C++)
48 XView 81.35 % 89.21 % 76.87 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
49 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.
50 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++)
51 TransDet3D 81.28 % 88.11 % 76.73 % 0.08 s 1 core @ 2.5 Ghz (Python)
52 Generalized-SIENet 81.24 % 87.70 % 76.79 % 0.08 s 1 core @ 2.5 Ghz (Python)
53 ReFineNet 81.24 % 87.70 % 76.77 % 0.08 s 1 core @ 2.5 Ghz (Python)
54 Point Image Fusion 81.23 % 89.01 % 76.77 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
55 MSL3D 81.15 % 87.27 % 76.56 % 0.03 s GPU @ 2.5 Ghz (Python)
56 Multi-Sensor3D 81.15 % 87.27 % 76.56 % 0.03 s GPU @ 2.5 Ghz (Python)
57 SAA-PV-RCNN 81.09 % 87.24 % 78.05 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
58 FPC-RCNN 81.08 % 88.68 % 76.46 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
59 VPFNet code 80.97 % 88.51 % 76.74 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
60 VueronNet code 80.96 % 90.06 % 73.72 % 0.06 s 1 core @ 2.0 Ghz (Python)
61 FusionDetv2-v4 80.93 % 87.75 % 76.12 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
62 AIMC-RUC 80.83 % 90.14 % 73.59 % 0.11 s 1 core @ 2.5 Ghz (Python)
63 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++)
64 GNN-RCNN 80.81 % 87.94 % 76.53 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
65 sa-voxel-centernet code 80.77 % 87.39 % 76.45 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
66 SA-voxel-centernet code 80.77 % 87.28 % 76.51 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
67 Associate-3Ddet_v2 80.77 % 91.53 % 75.23 % 0.04 s 1 core @ 2.5 Ghz (Python)
68 CIA-SSD v2
This method makes use of Velodyne laser scans.
80.71 % 89.61 % 75.06 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
69 FusionDetv2-v3 80.70 % 88.05 % 76.10 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
70 StructuralIF 80.69 % 87.15 % 76.26 % 0.02 s 8 cores @ 2.5 Ghz (Python)
71 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.
72 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++)
73 CVRS VIC-Net 80.61 % 88.25 % 75.83 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
74 FusionDetv1 80.48 % 87.20 % 76.01 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
75 XView-PartA^2 80.41 % 87.72 % 76.22 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
76 SRDL
This method uses stereo information.
This method makes use of Velodyne laser scans.
80.38 % 87.73 % 76.27 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
77 Fast 80.35 % 89.10 % 76.99 % 0.1 s GPU @ 2.5 Ghz (Python)
78 AM-SSD 80.30 % 89.58 % 75.02 % 0.04 s 1 core @ 2.5 Ghz (Python)
79 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.
80 Baseline of CA RCNN 80.28 % 87.45 % 76.21 % 0.1 s GPU @ 2.5 Ghz (Python)
81 TBD 80.24 % 87.67 % 76.27 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
82 VCT 80.19 % 89.12 % 77.19 % 0.2 s 1 core @ 2.5 Ghz (Python)
83 GAP-soft-filter 80.18 % 87.43 % 76.21 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
84 CBi-GNN 80.18 % 91.50 % 74.76 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
85 TBD 80.17 % 86.83 % 75.96 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
86 deprecated 80.16 % 89.48 % 72.75 % deprecated deprecated
87 TBD 80.12 % 88.30 % 75.29 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
88 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.
89 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.
90 IA-SSD 80.02 % 88.45 % 74.85 % <0.02 s GPU @ 2.5 Ghz (Python + C/C++)
91 MVOD 80.01 % 88.53 % 77.24 % 0.16 s 1 core @ 2.5 Ghz (C/C++)
92 MBDF-Net 80.00 % 90.87 % 75.04 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
93 SIF 79.88 % 86.84 % 75.89 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
94 CM3DV 79.87 % 89.00 % 72.59 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
95 RangeIoUDet
This method makes use of Velodyne laser scans.
79.80 % 88.60 % 76.76 % 0.02 s 1 core @ 2.5 Ghz (Python)
96 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.
97 Seg-RCNN code 79.73 % 89.16 % 72.28 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
98 CJJ 79.72 % 88.98 % 74.71 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
99 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.
100 PSS 79.71 % 89.13 % 74.78 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
101 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.
102 FCY
This method makes use of Velodyne laser scans.
79.67 % 89.19 % 74.35 % 0.02 s GPU @ 2.5 Ghz (Python)
103 MBDF-Net-1 79.65 % 90.43 % 74.72 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
104 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.
105 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.
106 PointRes
This method makes use of Velodyne laser scans.
79.55 % 88.73 % 74.17 % 0.013 s 1 core @ 2.5 Ghz (Python + C/C++)
107 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.
108 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.
109 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.
110 PP-3D 79.47 % 88.33 % 72.29 % 0.1 s 1 core @ 2.5 Ghz (Python)
111 SECOND 79.46 % 87.44 % 73.97 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
112 RoIFusion code 79.36 % 88.09 % 72.51 % 0.22 s 1 core @ 3.0 Ghz (Python)
113 3DIoU_v2 79.30 % 88.22 % 76.96 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
114 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.
115 PF-GAP 79.27 % 87.65 % 76.43 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
116 FPCR-CNN 79.25 % 88.45 % 75.69 % 0.05 s 1 core @ 2.5 Ghz (Python)
117 3DIoU++ 79.22 % 87.49 % 76.68 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
118 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.
119 NLK-ALL code 79.13 % 87.23 % 74.30 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
120 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.
121 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.
122 CCFNET 78.97 % 88.20 % 74.14 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
123 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.
124 YF 78.85 % 87.50 % 72.05 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
125 deprecated 78.83 % 87.89 % 73.52 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
126 FPC3D
This method makes use of the epipolar geometry.
78.81 % 87.61 % 75.49 % 33 s 1 core @ 2.5 Ghz (C/C++)
127 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.
128 ISF-v2 78.67 % 87.54 % 74.03 % 0.04 s 1 core @ 2.5 Ghz (Python)
129 BLPNet_V2 78.57 % 87.10 % 71.67 % 0.04 s 1 core @ 2.5 Ghz (Python)
130 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.
131 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.
132 FusionDetv2-v2 78.42 % 86.59 % 73.87 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
133 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.
134 MKFFNet 78.40 % 85.25 % 73.75 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
135 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.
136 FusionDetv2-v5 78.30 % 86.94 % 73.44 % 0.05 s 1 core @ 2.5 Ghz (Java + C/C++)
137 MKFFNet 78.30 % 87.25 % 73.66 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
138 MKFFNet 78.30 % 86.86 % 73.80 % 0.01s 1 core @ 2.5 Ghz (Python)
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139 SAA-SECOND 78.13 % 86.13 % 73.34 % 38m s 1 core @ 2.5 Ghz (C/C++)
140 3D-VDNet 78.05 % 87.13 % 72.90 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
141 HV 77.92 % 86.38 % 73.04 % 0.02 s GPU @ 2.5 Ghz (Python)
142 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.
143 V3D 77.87 % 86.58 % 72.52 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
144 CVFNet 77.70 % 88.75 % 71.95 % 28.1ms 1 core @ 2.5 Ghz (Python)
145 VOXEL_3D 77.69 % 86.45 % 72.20 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
146 VGCN 77.65 % 84.47 % 73.36 % 0.09 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
147 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.
148 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.
149 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.
150 Dccnet 77.22 % 86.67 % 69.97 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
151 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.
152 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.
153 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.
154 YF 76.57 % 87.15 % 71.23 % 0.04 s GPU @ 2.5 Ghz (C/C++)
155 IGRP+ 76.54 % 86.90 % 71.77 % 0.18 s 1 core @ 2.5 Ghz (Python + C/C++)
156 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.
157 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)
158 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.
159 DPointNet 76.34 % 81.67 % 70.34 % 0.07s 1 core @ 2.5 Ghz (C/C++)
160 SIEV-Net 76.18 % 85.21 % 70.60 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
161 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.
162 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.
163 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.
164 APL-Second 75.75 % 84.26 % 70.65 % 0.05 s 1 core @ 2.5 Ghz (Python)
165 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.
166 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.
167 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.
168 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.
169 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.
170 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.
171 sscl-20p 74.82 % 86.06 % 69.87 % 0.02 s 1 core @ 2.5 Ghz (Python)
172 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.
173 FPGNN 74.77 % 83.82 % 67.93 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
174 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++)
175 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.
176 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.
177 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.
178 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.
179 LSNet 73.55 % 86.13 % 68.58 % 0.09 s GPU @ 2.5 Ghz (Python + C/C++)
180 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.
181 PointRGBNet 73.49 % 83.99 % 68.56 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
182 RangeDet code 73.44 % 80.53 % 67.28 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
183 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.
184 TBD 73.02 % 82.74 % 67.97 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
185 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.
186 DASS 72.31 % 81.85 % 65.99 % 0.09 s 1 core @ 2.0 Ghz (Python)
O. Unal, L. Gool and D. Dai: Improving Point Cloud Semantic Segmentation by Learning 3D Object Detection. 2020.
187 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.
188 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.
189 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.
190 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.
191 FusionDetv2-baseline 68.87 % 79.05 % 63.68 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
192 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.
193 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.
194 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.
195 FusionDetv2-v1 65.65 % 75.21 % 60.65 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
196 DAMNET code 65.52 % 76.25 % 59.54 % 1 s 1 core @ 2.5 Ghz (C/C++)
197 MMLAB LIGA-Stereo
This method uses stereo information.
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.
198 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.
199 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.
200 KMC code 62.74 % 74.45 % 56.76 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
201 LIGA-Stereo-old
This method uses stereo information.
62.65 % 81.76 % 55.24 % 0.375 s Titan Xp
202 VN3D 61.41 % 72.37 % 56.86 % 0.02 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
203 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.
204 AEC3D 59.47 % 71.76 % 54.72 % 18 ms GPU @ 2.5 Ghz (Python)
205 deleted 57.11 % 76.87 % 50.05 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
206 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.
207 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.
208 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.
209 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.
210 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.
211 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.
212 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.
213 NCL code 50.07 % 46.58 % 50.33 % NA s 1 core @ 2.5 Ghz (Python)
214 SOD 48.69 % 70.90 % 40.12 % 0.1 s 1 core @ 2.5 Ghz (Python)
215 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.
216 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.
217 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.
218 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.
219 R-AGNO-Net 42.79 % 49.49 % 39.31 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
220 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.
221 OSE+ 41.60 % 62.67 % 35.82 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
222 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.
223 BEVC 40.72 % 50.05 % 36.42 % 35ms GPU @ 1.5 Ghz (Python)
224 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.
225 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.
226 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.
227 RTS3D 37.38 % 58.51 % 31.12 % 0.03 s GPU @ 2.5 Ghz (Python)
228 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.
229 SC(DLA34)
This method uses stereo information.
31.30 % 49.94 % 25.62 % 0.04 s GPU @ 2.5 Ghz (Python)
230 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.
231 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.
232 TBD 24.87 % 33.30 % 21.96 % 0.1 s 1 core @ 2.5 Ghz (Python)
233 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.
234 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.
235 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.
236 Mobile Stereo R-CNN
This method uses stereo information.
17.04 % 26.97 % 13.26 % 1.8 s NVIDIA Jetson TX2
237 LPCG-M3D 14.82 % 22.73 % 12.88 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
238 CA3D 14.49 % 20.89 % 12.19 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
239 ITS-MDPL 14.28 % 24.67 % 12.13 % 0.16 s GPU @ 2.5 Ghz (Python)
240 AutoShape 14.17 % 22.47 % 11.36 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
241 E2E-DA 13.97 % 19.73 % 11.82 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
242 MM 13.92 % 20.82 % 12.29 % 1 s 1 core @ 2.5 Ghz (C/C++)
243 GAC3D++ 13.90 % 19.53 % 11.77 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
244 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.
245 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.
246 MonoGeo 13.81 % 18.85 % 11.52 % 0.05 s 1 core @ 2.5 Ghz (Python)
247 none 13.79 % 18.84 % 11.52 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
248 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.
249 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.
250 MDSNet 13.40 % 22.80 % 10.27 % 0.07 s 1 core @ 2.5 Ghz (Python)
251 PCT 13.37 % 21.00 % 11.31 % 0.045 s 1 core @ 2.5 Ghz (C/C++)
252 CenterNet-Boost 13.33 % 19.06 % 11.90 % 0.042 s GPU @ 2.5 Ghz (Python)
253 Det3D 13.26 % 24.00 % 9.94 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
254 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.
255 MonoHMOO 13.12 % 20.28 % 9.56 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
256 Aug3D-RPN 12.99 % 17.82 % 9.78 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
257 RelationNet3D_dla34 code 12.88 % 17.67 % 11.01 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
258 PLDet3d 12.85 % 20.72 % 11.11 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
259 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.
260 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 .
261 DA-Mono3D 12.66 % 16.99 % 9.97 % 0.09s 1 core @ 2.5 Ghz (C/C++)
262 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.
263 RelationNet3D 12.60 % 17.57 % 10.95 % 0.04 s GPU @ 2.5 Ghz (Python)
264 Object Transformer 12.58 % 17.87 % 10.87 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
265 TBD 12.53 % 22.40 % 10.64 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
266 AutoShape 12.42 % 20.35 % 9.70 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
267 MP-Mono 12.37 % 17.89 % 9.58 % 0.16 s GPU @ 2.5 Ghz (Python)
268 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.
269 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.
270 Deprecated 12.30 % 16.48 % 9.14 % Deprecated Deprecated
271 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 .
272 PPTrans 12.06 % 19.79 % 10.48 % 0.2 s GPU @ 2.5 Ghz (Python)
273 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.
274 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.
275 GAC3D 12.00 % 17.75 % 9.15 % 0.25 s 1 core @ 2.5 Ghz (Python)
276 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.
277 RetinaMono code 11.61 % 16.68 % 9.57 % 0.02 s 1 core @ 2.5 Ghz (Python)
278 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.
279 CDI3D 11.32 % 15.70 % 9.26 % 0.03 s GPU @ 2.5 Ghz (Python)
280 LAPNet 11.29 % 18.02 % 8.50 % 0.03 s 1 core @ 2.5 Ghz (Python)
281 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.
282 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.
283 ImVoxelNet code 10.97 % 17.15 % 9.15 % 0.2 s GPU @ 2.5 Ghz (Python)
284 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.
285 Lite-FPN 10.64 % 15.32 % 8.59 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
286 TBD 10.61 % 15.71 % 8.66 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
287 OCM3D 10.44 % 17.48 % 7.87 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
288 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.
289 E2E-DA-Lite (Res18) 10.32 % 15.56 % 8.89 % 0.01 s GPU @ 2.5 Ghz (Python)
290 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.
291 RelationNet3D_res18 code 9.93 % 14.27 % 8.43 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
292 FADNet code 9.92 % 16.37 % 8.05 % 0.04 s GPU @ >3.5 Ghz (Python)
293 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.
294 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.
295 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 .
296 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.
297 ICCV 9.31 % 13.37 % 8.29 % 0.04 s GPU @ 2.5 Ghz (Python)
298 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.
299 Geo3D 7.70 % 11.52 % 6.80 % 0.04 s GPU @ 2.5 Ghz (Python)
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300 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.
301 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.
302 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.
303 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.
304 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.
305 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.
306 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.
307 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.
308 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.
309 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.
310 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.
311 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.
312 SparVox3D 3.20 % 5.27 % 2.56 % 0.05 s GPU @ 2.0 Ghz (Python)
313 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.
314 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.
315 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.
316 weakm3d 2.32 % 3.80 % 1.66 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
317 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.
318 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.
319 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.
320 UDI-mono3D 0.72 % 0.62 % 0.53 % 0.05 s 1 core @ 2.5 Ghz (Python)
321 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 % 0.25 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 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.
5 H^23D R-CNN 45.26 % 52.75 % 41.56 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
6 SAA-PV-RCNN 45.00 % 52.55 % 41.82 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
7 SIEV-Net 44.80 % 54.00 % 41.11 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
8 HIKVISION-ADLab-HZ 44.78 % 52.09 % 42.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
9 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.
10 TBD 44.32 % 49.37 % 41.24 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
11 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.
12 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.
13 PP-3D 43.77 % 51.92 % 40.14 % 0.1 s 1 core @ 2.5 Ghz (Python)
14 VCT 43.65 % 50.27 % 41.43 % 0.2 s 1 core @ 2.5 Ghz (Python)
15 EA-M-RCNN(BorderAtt) 43.44 % 51.81 % 39.85 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
16 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.
17 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.
18 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.
19 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.
20 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.
21 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.
22 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.
23 Fast 42.72 % 52.10 % 39.08 % 0.1 s GPU @ 2.5 Ghz (Python)
24 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.
25 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.
26 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.
27 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.
28 TBD
This method makes use of Velodyne laser scans.
42.05 % 48.66 % 38.94 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
29 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.
30 TBD_IOU1 41.65 % 49.00 % 39.31 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
31 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.
32 TBD_IOU 41.45 % 48.25 % 39.17 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
33 GNN-RCNN 41.32 % 47.48 % 38.97 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
34 Generalized-SIENet 40.97 % 47.01 % 38.88 % 0.08 s 1 core @ 2.5 Ghz (Python)
35 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.
36 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++)
37 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.
38 XView-PartA^2 40.71 % 47.73 % 38.47 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
39 SAA-SECOND 40.57 % 48.73 % 37.77 % 38m s 1 core @ 2.5 Ghz (C/C++)
40 WHUT-iou_ssd code 40.53 % 46.41 % 38.48 % 0.045s 1 core @ 2.5 Ghz (C/C++)
41 E^2-PV-RCNN 40.47 % 46.61 % 38.60 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
42 SA-voxel-centernet code 40.43 % 46.10 % 38.32 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
43 FusionDetv2-v3 40.38 % 46.86 % 37.41 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
44 FPCR-CNN 40.32 % 48.33 % 37.66 % 0.05 s 1 core @ 2.5 Ghz (Python)
45 P2V_PCV1 40.27 % 45.43 % 38.24 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
46 sa-voxel-centernet code 40.24 % 46.08 % 38.07 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
47 FPC-RCNN 40.13 % 46.41 % 37.84 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
48 TPCG 39.97 % 46.35 % 37.66 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
49 FusionDetv2-v5 39.91 % 47.50 % 37.39 % 0.05 s 1 core @ 2.5 Ghz (Java + C/C++)
50 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++)
51 MVOD 39.82 % 46.22 % 37.56 % 0.16 s 1 core @ 2.5 Ghz (C/C++)
52 Point Image Fusion 39.79 % 45.04 % 37.62 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
53 anonymous code 39.74 % 46.09 % 37.41 % 0.05s 1 core @ >3.5 Ghz (python)
54 FusionDetv2-v4 39.68 % 46.93 % 37.31 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
55 Fast VP-RCNN code 39.65 % 45.95 % 37.29 % 0.05 s 1 core @ 3.5 Ghz (C/C++)
56 PF-GAP 39.53 % 47.63 % 36.44 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
57 TBD 39.48 % 45.46 % 37.35 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
58 GAP-soft-filter 39.47 % 46.93 % 36.99 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
59 CVRS VIC-RCNN 39.46 % 45.19 % 37.16 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
60 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++)
61 Baseline of CA RCNN 39.42 % 47.30 % 36.97 % 0.1 s GPU @ 2.5 Ghz (Python)
62 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++)
63 YF 39.38 % 47.69 % 36.06 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
64 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.
65 FusionDetv2-v2 39.31 % 44.98 % 37.22 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
66 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.
67 TBD 39.31 % 46.85 % 36.11 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
68 tbd 38.89 % 45.98 % 35.94 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
69 SIF 38.74 % 46.23 % 36.06 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
70 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.
71 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.
72 MSL3D 38.58 % 45.00 % 35.72 % 0.03 s GPU @ 2.5 Ghz (Python)
73 FusionDetv1 38.31 % 45.10 % 36.15 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
74 AF_MCLS 38.29 % 47.07 % 34.67 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
75 MKFFNet 38.05 % 46.01 % 35.72 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
76 IGRP+ 38.05 % 46.26 % 34.53 % 0.18 s 1 core @ 2.5 Ghz (Python + C/C++)
77 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++)
78 VGCN 37.60 % 45.28 % 34.96 % 0.09 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
79 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.
80 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.
81 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.
82 CVRS VIC-Net 37.18 % 43.82 % 35.35 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
83 YF 36.99 % 44.43 % 34.40 % 0.04 s GPU @ 2.5 Ghz (C/C++)
84 XView 36.79 % 42.44 % 34.96 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
85 MKFFNet 36.66 % 43.94 % 34.56 % 0.01s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
86 FusionDetv2-baseline 36.66 % 41.34 % 34.60 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
87 MKFFNet 36.65 % 44.00 % 34.59 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
88 IA-SSD 36.53 % 44.11 % 34.30 % <0.02 s GPU @ 2.5 Ghz (Python + C/C++)
89 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.
90 ASCNet 35.76 % 42.00 % 33.69 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
91 deprecated 35.21 % 41.32 % 33.32 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
92 RoIFusion code 35.14 % 42.22 % 32.92 % 0.22 s 1 core @ 3.0 Ghz (Python)
93 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.
94 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.
95 NLK-ALL code 34.46 % 44.30 % 30.83 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
96 DAMNET code 33.66 % 43.32 % 30.12 % 1 s 1 core @ 2.5 Ghz (C/C++)
97 CBi-GNN-persons 32.92 % 41.65 % 29.19 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
98 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.
99 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.
100 MMLAB LIGA-Stereo
This method uses stereo information.
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.
101 FCY
This method makes use of Velodyne laser scans.
29.38 % 37.28 % 26.19 % 0.02 s GPU @ 2.5 Ghz (Python)
102 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.
103 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.
104 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.
105 PointRGBNet 26.40 % 34.77 % 24.03 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
106 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.
107 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.
108 deleted 25.13 % 35.02 % 22.36 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
109 FusionDetv2-v1 24.55 % 30.58 % 23.64 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
110 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.
111 NCL code 23.33 % 27.75 % 21.66 % NA s 1 core @ 2.5 Ghz (Python)
112 LIGA-Stereo-old
This method uses stereo information.
23.23 % 30.14 % 20.58 % 0.375 s Titan Xp
113 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.
114 OSE+ 19.67 % 28.30 % 17.17 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
115 BEVC 17.65 % 23.49 % 15.92 % 35ms GPU @ 1.5 Ghz (Python)
116 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.
117 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.
118 AEC3D 16.81 % 20.98 % 15.22 % 18 ms GPU @ 2.5 Ghz (Python)
119 VN3D 15.69 % 19.56 % 13.17 % 0.02 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
120 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.
121 SOD 14.68 % 21.13 % 12.67 % 0.1 s 1 core @ 2.5 Ghz (Python)
122 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.
123 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.
124 MM 8.38 % 13.22 % 6.99 % 1 s 1 core @ 2.5 Ghz (C/C++)
125 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.
126 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.
127 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.
128 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.
129 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.
130 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 .
131 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.
132 GAC3D++ 6.29 % 9.29 % 5.20 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
133 RelationNet3D_dla34 code 6.22 % 9.28 % 5.23 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
134 E2E-DA 5.95 % 8.79 % 5.72 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
135 DA-Mono3D 5.68 % 7.86 % 4.81 % 0.09s 1 core @ 2.5 Ghz (C/C++)
136 MonoGeo 5.63 % 8.00 % 4.71 % 0.05 s 1 core @ 2.5 Ghz (Python)
137 Deprecated 5.62 % 7.52 % 4.71 % Deprecated Deprecated
138 ICCV 5.25 % 8.34 % 4.72 % 0.04 s GPU @ 2.5 Ghz (Python)
139 MonoHMOO 5.23 % 7.62 % 4.28 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
140 RelationNet3D_res18 code 5.19 % 7.95 % 4.21 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
141 Aug3D-RPN 4.71 % 6.01 % 3.87 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
142 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.
143 Lite-FPN 4.38 % 6.57 % 3.56 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
144 PLDet3d 4.25 % 6.31 % 3.49 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
145 M3D-RPN(S-R) 4.11 % 5.70 % 3.37 % 0.16 s GPU @ 1.5 Ghz (Python)
146 CDI3D 4.03 % 5.64 % 3.29 % 0.03 s GPU @ 2.5 Ghz (Python)
147 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.
148 MP-Mono 3.75 % 5.09 % 3.50 % 0.16 s GPU @ 2.5 Ghz (Python)
149 Geo3D 3.65 % 5.74 % 3.01 % 0.04 s GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
150 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.
151 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.
152 FADNet code 3.53 % 5.40 % 3.31 % 0.04 s GPU @ >3.5 Ghz (Python)
153 E2E-DA-Lite (Res18) 3.51 % 5.82 % 3.42 % 0.01 s GPU @ 2.5 Ghz (Python)
154 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 .
155 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.
156 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.
157 LAPNet 3.16 % 4.41 % 2.70 % 0.03 s 1 core @ 2.5 Ghz (Python)
158 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.
159 CenterNet-Boost 2.47 % 3.27 % 2.43 % 0.042 s GPU @ 2.5 Ghz (Python)
160 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.
161 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.
162 PPTrans 1.85 % 2.68 % 1.44 % 0.2 s GPU @ 2.5 Ghz (Python)
163 TBD 1.81 % 3.00 % 1.59 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
164 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.
165 UDI-mono3D 1.45 % 2.18 % 1.12 % 0.05 s 1 core @ 2.5 Ghz (Python)
166 SparVox3D 1.35 % 1.93 % 1.04 % 0.05 s GPU @ 2.0 Ghz (Python)
167 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 HIKVISION-AFree 69.92 % 84.65 % 63.33 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
2 anonymous code 69.13 % 83.09 % 61.35 % 0.05s 1 core @ >3.5 Ghz (python)
3 sa-voxel-centernet code 69.03 % 81.88 % 61.66 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
4 Fast VP-RCNN code 69.02 % 83.81 % 61.51 % 0.05 s 1 core @ 3.5 Ghz (C/C++)
5 SAA-PV-RCNN 68.96 % 82.06 % 61.54 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
6 HIKVISION-ADLab-HZ 68.83 % 84.82 % 60.75 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
7 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++)
8 SA-voxel-centernet code 68.67 % 81.47 % 61.40 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
9 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.
10 TPCG 68.15 % 82.13 % 61.56 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
11 E^2-PV-RCNN 68.03 % 81.55 % 60.51 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
12 RangeIoUDet
This method makes use of Velodyne laser scans.
67.77 % 83.12 % 60.26 % 0.02 s 1 core @ 2.5 Ghz (Python)
13 Point Image Fusion 67.69 % 83.15 % 60.62 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
14 Generalized-SIENet 67.61 % 83.00 % 60.09 % 0.08 s 1 core @ 2.5 Ghz (Python)
15 FPC-RCNN 67.57 % 82.79 % 60.69 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
16 GNN-RCNN 67.49 % 81.25 % 61.15 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
17 TBD
This method makes use of Velodyne laser scans.
67.37 % 80.50 % 61.18 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
18 PV-RCNN-v2 67.33 % 82.22 % 60.04 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
19 SPG_mini
This method makes use of Velodyne laser scans.
66.96 % 80.21 % 60.50 % 0.09 s GPU @ 2.5 Ghz (Python)
20 TBD 66.63 % 85.08 % 60.36 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
21 CBi-GNN-persons 66.49 % 79.95 % 59.12 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
22 VCT 66.38 % 82.37 % 60.01 % 0.2 s 1 core @ 2.5 Ghz (Python)
23 XView-PartA^2 66.33 % 80.65 % 59.72 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
24 EA-M-RCNN(BorderAtt) 66.04 % 82.39 % 58.19 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
25 WHUT-iou_ssd code 65.98 % 79.38 % 59.56 % 0.045s 1 core @ 2.5 Ghz (C/C++)
26 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.
27 TBD 65.64 % 82.29 % 57.98 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
28 Fast 65.31 % 82.83 % 57.43 % 0.1 s GPU @ 2.5 Ghz (Python)
29 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++)
30 ASCNet 65.10 % 78.41 % 57.87 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
31 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.
32 CVRS VIC-RCNN 64.99 % 81.47 % 58.62 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
33 MVOD 64.95 % 79.52 % 57.53 % 0.16 s 1 core @ 2.5 Ghz (C/C++)
34 TBD 64.60 % 80.49 % 57.18 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
35 FusionDetv2-v5 64.28 % 78.57 % 57.02 % 0.05 s 1 core @ 2.5 Ghz (Java + C/C++)
36 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.
37 VPFNet code 64.10 % 77.64 % 58.00 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
38 RoIFusion code 64.05 % 80.84 % 58.37 % 0.22 s 1 core @ 3.0 Ghz (Python)
39 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.
40 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.
41 TBD_IOU 63.68 % 79.74 % 56.63 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
42 CVRS VIC-Net 63.65 % 78.29 % 57.27 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
43 NLK-ALL code 63.65 % 79.94 % 57.28 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
44 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.
45 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.
46 PP-3D 63.48 % 78.60 % 57.08 % 0.1 s 1 core @ 2.5 Ghz (Python)
47 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.
48 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.
49 FusionDetv2-v4 63.38 % 79.65 % 56.61 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
50 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.
51 FusionDetv1 63.05 % 77.46 % 55.43 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
52 tbd 62.75 % 78.45 % 56.30 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
53 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.
54 TBD_IOU1 62.67 % 80.32 % 55.99 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
55 FPCR-CNN 62.56 % 79.61 % 55.82 % 0.05 s 1 core @ 2.5 Ghz (Python)
56 VGCN 62.36 % 78.47 % 55.88 % 0.09 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
57 MSL3D 62.27 % 76.74 % 56.20 % 0.03 s GPU @ 2.5 Ghz (Python)
58 deprecated 62.16 % 75.45 % 56.00 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
59 GAP-soft-filter 62.04 % 77.06 % 55.09 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
60 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++)
61 Baseline of CA RCNN 62.02 % 77.33 % 55.52 % 0.1 s GPU @ 2.5 Ghz (Python)
62 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.
63 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.
64 FusionDetv2-v3 61.96 % 79.43 % 55.28 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
65 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++)
66 MKFFNet 61.80 % 78.08 % 54.89 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
67 FusionDetv2-v2 61.78 % 76.70 % 54.75 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
68 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.
69 SIF 61.61 % 77.13 % 55.11 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
70 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.
71 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++)
72 AF_MCLS 60.89 % 78.82 % 54.13 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
73 P2V_PCV1 60.84 % 75.25 % 54.70 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
74 SAA-SECOND 60.50 % 75.65 % 53.81 % 38m s 1 core @ 2.5 Ghz (C/C++)
75 MKFFNet 60.48 % 76.68 % 54.20 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
76 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.
77 CCFNET 60.18 % 78.05 % 53.42 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
78 SIEV-Net 59.99 % 78.75 % 52.37 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
79 PF-GAP 59.92 % 77.88 % 53.48 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
80 IA-SSD 59.61 % 74.98 % 53.52 % <0.02 s GPU @ 2.5 Ghz (Python + C/C++)
81 XView 59.55 % 77.24 % 53.47 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
82 FCY
This method makes use of Velodyne laser scans.
59.54 % 76.30 % 52.29 % 0.02 s GPU @ 2.5 Ghz (Python)
83 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++)
84 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.
85 MKFFNet 59.14 % 75.64 % 52.97 % 0.01s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
86 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.
87 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.
88 YF 58.20 % 72.48 % 52.69 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
89 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.
90 YF 56.86 % 71.37 % 52.18 % 0.04 s GPU @ 2.5 Ghz (C/C++)
91 FusionDetv2-baseline 56.34 % 71.16 % 50.70 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
92 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.
93 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.
94 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.
95 IGRP+ 53.22 % 69.87 % 47.55 % 0.18 s 1 core @ 2.5 Ghz (Python + C/C++)
96 PointRGBNet 52.15 % 67.05 % 46.78 % 0.08 s 4 cores @ 2.5 Ghz (Python + C/C++)
97 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.
98 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.
99 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.
100 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.
101 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.
102 DAMNET code 42.82 % 58.71 % 38.74 % 1 s 1 core @ 2.5 Ghz (C/C++)
103 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.
104 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.
105 LIGA-Stereo-old
This method uses stereo information.
37.21 % 53.35 % 32.92 % 0.375 s Titan Xp
106 MMLAB LIGA-Stereo
This method uses stereo information.
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.
107 FusionDetv2-v1 36.58 % 51.38 % 32.88 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
108 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.
109 deleted 31.31 % 46.37 % 27.66 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
110 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.
111 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.
112 SOD 25.29 % 40.51 % 21.32 % 0.1 s 1 core @ 2.5 Ghz (Python)
113 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.
114 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.
115 VN3D 21.53 % 30.76 % 21.03 % 0.02 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
116 OSE+ 20.75 % 32.62 % 17.23 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
117 AEC3D 19.75 % 28.45 % 18.49 % 18 ms GPU @ 2.5 Ghz (Python)
118 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.
119 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.
120 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.
121 BEVC 14.08 % 22.30 % 13.44 % 35ms GPU @ 1.5 Ghz (Python)
122 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.
123 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.
124 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.
125 RelationNet3D_dla34 code 4.49 % 8.07 % 3.98 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
126 E2E-DA 4.42 % 7.36 % 3.34 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
127 TBD 4.32 % 7.79 % 3.98 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
128 E2E-DA-Lite (Res18) 3.99 % 6.87 % 3.04 % 0.01 s GPU @ 2.5 Ghz (Python)
129 DA-Mono3D 3.80 % 5.65 % 3.23 % 0.09s 1 core @ 2.5 Ghz (C/C++)
130 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.
131 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.
132 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.
133 MonoGeo 2.93 % 4.73 % 2.58 % 0.05 s 1 core @ 2.5 Ghz (Python)
134 Deprecated 2.71 % 3.89 % 2.27 % Deprecated Deprecated
135 CDI3D 2.69 % 4.15 % 2.45 % 0.03 s GPU @ 2.5 Ghz (Python)
136 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 .
137 RelationNet3D_res18 code 2.55 % 4.85 % 2.33 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
138 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.
139 Aug3D-RPN 2.43 % 4.36 % 2.55 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
140 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.
141 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.
142 ICCV 2.33 % 4.51 % 2.22 % 0.04 s GPU @ 2.5 Ghz (Python)
143 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.
144 GAC3D++ 2.12 % 3.82 % 2.23 % 0.25 s 1 core @ 2.5 Ghz (C/C++)
145 Geo3D 2.00 % 3.47 % 1.52 % 0.04 s GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
146 MM 1.89 % 3.49 % 1.82 % 1 s 1 core @ 2.5 Ghz (C/C++)
147 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.
148 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.
149 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.
150 MonoHMOO 1.60 % 1.87 % 1.66 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
151 MP-Mono 1.58 % 2.36 % 1.69 % 0.16 s GPU @ 2.5 Ghz (Python)
152 CenterNet-Boost 1.56 % 2.34 % 1.61 % 0.042 s GPU @ 2.5 Ghz (Python)
153 UDI-mono3D 1.47 % 3.01 % 1.47 % 0.05 s 1 core @ 2.5 Ghz (Python)
154 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.
155 PPTrans 1.38 % 2.31 % 1.20 % 0.2 s GPU @ 2.5 Ghz (Python)
156 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.
157 LAPNet 0.89 % 1.37 % 0.62 % 0.03 s 1 core @ 2.5 Ghz (Python)
158 PLDet3d 0.80 % 1.24 % 0.89 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
159 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 .
160 FADNet code 0.64 % 1.44 % 0.67 % 0.04 s GPU @ >3.5 Ghz (Python)
161 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.
162 Lite-FPN 0.41 % 0.50 % 0.24 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
163 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.
164 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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