Object Detection Evaluation 2012


The object detection and object orientation estimation benchmark consists of 7481 training images and 7518 test images, comprising a total of 80.256 labeled objects. All images are color and saved as png. For evaluation, we compute precision-recall curves for object detection and orientation-similarity-recall curves for joint object detection and orientation estimation. In the latter case not only the object 2D bounding box has to be located correctly, but also the orientation estimate in bird's eye view is evaluated. To rank the methods we compute average precision and average orientation similiarity. We require that all methods use the same parameter set for all test pairs. Our development kit provides details about the data format as well as MATLAB / C++ utility functions for reading and writing the label files.

We evaluate object detection performance using the PASCAL criteria and object detection and orientation estimation performance using the measure discussed in our CVPR 2012 publication. For cars we require an overlap of 70%, while for pedestrians and cyclists we require an overlap of 50% for a detection. Detections in don't care areas or detections which are smaller than the minimum size do not count as false positive. Difficulties are defined as follows:

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

All methods are ranked based on the moderately difficult results. Note that for the hard evaluation ~2 % of the provided bounding boxes have not been recognized by humans, thereby upper bounding recall at 98 %. Hence, the hard evaluation is only given for reference.
Note 1: On 25.04.2017, we have fixed a bug in the object detection evaluation script. As of now, the submitted detections are filtered based on the min. bounding box height for the respective category which we have been done before only for the ground truth detections, thus leading to false positives for the category "Easy" when bounding boxes of height 25-39 Px were submitted (and to false positives for all categories if bounding boxes smaller than 25 Px were submitted). We like to thank Amy Wu, Matt Wilder, Pekka Jänis and Philippe Vandermersch for their feedback. The last leaderboards right before the changes can be found here!

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

Car


Method Setting Code Moderate Easy Hard Runtime Environment
1 ZEEWAIN-AI 96.14 % 95.22 % 88.94 % 0.3 s GPU @ 2.5 Ghz (Python)
2 CLOCs_PVCas code 95.96 % 96.76 % 91.08 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection . 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
3 EA-M-RCNN(BorderAtt) 95.88 % 96.68 % 90.89 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
4 PVGNet 95.80 % 96.87 % 93.05 % 0.05 s 1 core @ >3.5 Ghz (C/C++)
5 ADLAB 95.69 % 96.69 % 90.81 % 0.05 s 1 core @ >3.5 Ghz (C/C++)
6 BANet 95.61 % 98.75 % 90.64 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
7 SE-SSD
This method makes use of Velodyne laser scans.
code 95.60 % 96.69 % 90.53 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, L. Jiang and C. Fu: SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud. CVPR 2021.
8 HUAWEI Octopus 95.50 % 96.30 % 92.81 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
9 SPANet 95.46 % 96.54 % 90.47 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
10 PLNL-3DSSD
This method makes use of Velodyne laser scans.
95.38 % 96.37 % 90.31 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
11 PC-CNN-V2
This method makes use of Velodyne laser scans.
95.20 % 96.06 % 89.37 % 0.5 s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Du, M. Ang, S. Karaman and D. Rus: A General Pipeline for 3D Detection of Vehicles. 2018 IEEE International Conference on Robotics and Automation (ICRA) 2018.
12 F-PointNet
This method makes use of Velodyne laser scans.
code 95.17 % 95.85 % 85.42 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
13 SA-SSD code 95.16 % 97.92 % 90.15 % 0.04 s 1 core @ 2.5 Ghz (Python)
C. He, H. Zeng, J. Huang, X. Hua and L. Zhang: Structure Aware Single-stage 3D Object Detection from Point Cloud. CVPR 2020.
14 Pyramid R-CNN 95.13 % 95.88 % 92.62 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
15 Voxel R-CNN code 95.11 % 96.49 % 92.45 % 0.04 s GPU @ 3.0 Ghz (C/C++)
J. Deng, S. Shi, P. Li, W. Zhou, Y. Zhang and H. Li: Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection . AAAI 2021.
16 TBD 95.10 % 96.48 % 92.62 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
17 3DSSD code 95.10 % 97.69 % 92.18 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
18 3DIoU++ 95.06 % 96.37 % 90.52 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
19 PV-RCNN-v2 95.05 % 96.08 % 92.42 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
20 MVRA + I-FRCNN+ 94.98 % 95.87 % 82.52 % 0.18 s GPU @ 2.5 Ghz (Python)
H. Choi, H. Kang and Y. Hyun: Multi-View Reprojection Architecture for Orientation Estimation. The IEEE International Conference on Computer Vision (ICCV) Workshops 2019.
21 SIENet code 94.97 % 96.02 % 92.40 % 0.08 s 1 core @ 2.5 Ghz (Python)
Z. Li, Y. Yao, Z. Quan, W. Yang and J. Xie: SIENet: Spatial Information Enhancement Network for 3D Object Detection from Point Cloud. 2021.
22 VoTr-2 94.94 % 95.97 % 92.44 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
23 FrustumRCNN 94.90 % 95.98 % 92.39 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
24 SPG_mini
This method makes use of Velodyne laser scans.
94.87 % 97.87 % 92.26 % 0.09 s GPU @ 2.5 Ghz (Python)
25 vb 94.81 % 96.14 % 92.12 % 0.02 s 8 cores @ 2.5 Ghz (Python)
26 E^2-PV-RCNN 94.80 % 95.95 % 92.26 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
27 XView 94.77 % 95.89 % 92.23 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
28 SCIR-Net
This method makes use of Velodyne laser scans.
94.76 % 96.15 % 91.99 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
29 MSG-PGNN 94.75 % 95.86 % 92.16 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
30 Generalized-SIENet 94.72 % 95.76 % 92.19 % 0.08 s 1 core @ 2.5 Ghz (Python)
31 SPG_full
This method makes use of Velodyne laser scans.
94.71 % 97.80 % 92.19 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
32 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 94.70 % 98.17 % 92.04 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
33 3DIoU_v2 94.70 % 96.15 % 92.37 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
34 CVRS VIC-Net 94.69 % 95.79 % 91.89 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
35 HyBrid Feature Det 94.69 % 95.89 % 92.11 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
36 PC-RGNN 94.68 % 95.80 % 92.20 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
37 D3D 94.66 % 95.43 % 89.72 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
38 LZY_RCNN 94.65 % 95.81 % 92.08 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
39 TransCyclistNet 94.64 % 96.08 % 92.10 % 0.08 s 1 core @ 2.5 Ghz (Python)
40 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
code 94.64 % 95.86 % 92.10 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
41 FSA-PVRCNN
This method makes use of Velodyne laser scans.
94.63 % 95.81 % 92.06 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
42 Fast VP-RCNN code 94.62 % 98.00 % 91.91 % 0.05 s 1 core @ 3.5 Ghz (C/C++)
43 nonet 94.62 % 95.86 % 91.86 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
44 TransDet3D 94.61 % 95.83 % 92.07 % 0.08 s 1 core @ 2.5 Ghz (Python)
45 RangeIoUDet
This method makes use of Velodyne laser scans.
94.61 % 95.74 % 91.98 % 0.02 s 1 core @ 2.5 Ghz (Python)
46 Point Image Fusion 94.61 % 95.70 % 92.11 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
47 MSL3D 94.60 % 95.76 % 92.16 % 0.03 s GPU @ 2.5 Ghz (Python)
48 Multi-Sensor3D 94.60 % 95.76 % 92.16 % 0.03 s GPU @ 2.5 Ghz (Python)
49 ReFineNet 94.59 % 95.75 % 92.12 % 0.08 s 1 core @ 2.5 Ghz (Python)
50 MGACNet 94.57 % 95.35 % 91.77 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
51 WHUT-iou_ssd code 94.54 % 95.77 % 91.91 % 0.045s 1 core @ 2.5 Ghz (C/C++)
52 anonymous code 94.53 % 97.51 % 91.80 % 0.05s 1 core @ >3.5 Ghz (python)
53 FPC3D
This method makes use of the epipolar geometry.
94.52 % 96.06 % 91.72 % 33 s 1 core @ 2.5 Ghz (C/C++)
54 FPC-RCNN 94.51 % 96.15 % 91.80 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
55 RangeRCNN-LV 94.51 % 95.93 % 92.07 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
56 TuSimple code 94.47 % 95.12 % 86.45 % 1.6 s GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016.
57 PF-GAP 94.47 % 96.13 % 90.15 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
58 EPNet code 94.44 % 96.15 % 89.99 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
T. Huang, Z. Liu, X. Chen and X. Bai: EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection. ECCV 2020.
59 GNN-RCNN 94.44 % 95.85 % 91.96 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
60 SERCNN
This method makes use of Velodyne laser scans.
94.42 % 96.33 % 89.96 % 0.1 s 1 core @ 2.5 Ghz (Python)
D. Zhou, J. Fang, X. Song, L. Liu, J. Yin, Y. Dai, H. Li and R. Yang: Joint 3D Instance Segmentation and Object Detection for Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020.
61 SAA-SECOND 94.39 % 95.67 % 91.63 % 38m s 1 core @ 2.5 Ghz (C/C++)
62 CVRS VIC-RCNN 94.38 % 95.89 % 91.90 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
63 CVRS_PF 94.37 % 95.56 % 91.43 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
64 CVIS-v2 94.33 % 95.70 % 91.72 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
65 SVGA-Net
This method makes use of Velodyne laser scans.
94.28 % 95.69 % 91.73 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
66 UberATG-MMF
This method makes use of Velodyne laser scans.
94.25 % 97.41 % 89.87 % 0.08 s GPU @ 2.5 Ghz (Python)
M. Liang*, B. Yang*, Y. Chen, R. Hu and R. Urtasun: Multi-Task Multi-Sensor Fusion for 3D Object Detection. CVPR 2019.
67 SRDL
This method uses stereo information.
This method makes use of Velodyne laser scans.
94.24 % 95.86 % 91.80 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
68 Baseline of CA RCNN 94.23 % 95.84 % 91.80 % 0.1 s GPU @ 2.5 Ghz (Python)
69 CVIS-v1 94.23 % 95.84 % 91.80 % 0.1s 1 core @ 2.5 Ghz (Python + C/C++)
70 tbd code 94.21 % 95.68 % 91.49 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
71 TBD 94.21 % 95.51 % 91.69 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
72 GAP-soft-filter 94.20 % 95.81 % 91.53 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
73 HR-faster-rcnn 94.14 % 95.41 % 86.88 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
74 FPCR-CNN 94.13 % 95.95 % 91.20 % 0.05 s 1 core @ 2.5 Ghz (Python)
75 SAA-PV-RCNN 94.11 % 95.01 % 92.50 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
76 RangeRCNN
This method makes use of Velodyne laser scans.
94.03 % 95.48 % 91.74 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, M. Zhang, Z. Zhang, X. Zhao and S. Pu: RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representation. arXiv preprint arXiv:2009.00206 2020.
77 Faraway-Frustum
This method makes use of Velodyne laser scans.
code 93.99 % 95.81 % 91.72 % 0.1 s GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion. arXiv preprint arXiv:2011.01404 2020.
78 SIF 93.95 % 95.51 % 91.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
79 HRI-MSP-L
This method makes use of Velodyne laser scans.
93.92 % 95.51 % 91.42 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
80 AF_V1 93.87 % 94.45 % 86.37 % 0.1 s 1 core @ 2.5 Ghz (Python)
81 LIGA-stereo
This method uses stereo information.
93.77 % 96.66 % 83.97 % 0.375 s Titan Xp
82 Associate-3Ddet_v2 93.77 % 96.83 % 88.57 % 0.04 s 1 core @ 2.5 Ghz (Python)
83 Patches - EMP
This method makes use of Velodyne laser scans.
93.75 % 97.91 % 90.56 % 0.5 s GPU @ 2.5 Ghz (Python)
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
84 CIA-SSD
This method makes use of Velodyne laser scans.
code 93.72 % 96.87 % 86.20 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, S. Chen, L. Jiang and C. Fu: CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud. AAAI 2021.
85 VAL 93.71 % 96.92 % 83.76 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
86 XView-PartA^2 93.71 % 95.42 % 91.26 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
87 HIKVISION-ADLab-HZ 93.69 % 96.70 % 88.66 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
88 deprecated 93.68 % 96.92 % 86.15 % deprecated deprecated
89 modat3D
This is an online method (no batch processing).
93.66 % 94.26 % 83.63 % 0.03 s GPU @ 2.5 Ghz (Python)
90 MVAF-Net code 93.66 % 95.37 % 90.90 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
G. Wang, B. Tian, Y. Zhang, L. Chen, D. Cao and J. Wu: Multi-View Adaptive Fusion Network for 3D Object Detection. arXiv preprint arXiv:2011.00652 2020.
91 TBD 93.64 % 95.31 % 91.21 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
92 CBi-GNN 93.60 % 98.89 % 88.47 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
93 AM-SSD 93.58 % 96.78 % 90.61 % 0.04 s 1 core @ 2.5 Ghz (Python)
94 CenterNet-Boost 93.57 % 96.58 % 86.13 % 0.042 s GPU @ 2.5 Ghz (Python)
95 MonoPair 93.55 % 96.61 % 83.55 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
96 EBM3DOD code 93.54 % 96.81 % 88.33 % 0.12 s 1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.
97 CM3DV 93.53 % 96.79 % 88.35 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
98 CIA-SSD v2
This method makes use of Velodyne laser scans.
93.52 % 96.63 % 88.21 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
99 Deep MANTA 93.50 % 98.89 % 83.21 % 0.7 s GPU @ 2.5 Ghz (Python + C/C++)
F. Chabot, M. Chaouch, J. Rabarisoa, C. Teulière and T. Chateau: Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image. CVPR 2017.
100 Point-GNN
This method makes use of Velodyne laser scans.
code 93.50 % 96.58 % 88.35 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
101 PP-3D 93.50 % 96.58 % 88.35 % 0.1 s 1 core @ 2.5 Ghz (Python)
102 FCY
This method makes use of Velodyne laser scans.
93.49 % 96.74 % 88.39 % 0.02 s GPU @ 2.5 Ghz (Python)
103 Seg-RCNN code 93.49 % 96.74 % 88.10 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
104 CJJ 93.48 % 96.68 % 90.63 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
105 AIMC-RUC 93.47 % 96.75 % 88.35 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
106 PointRes
This method makes use of Velodyne laser scans.
93.47 % 96.69 % 90.46 % 0.013 s 1 core @ 2.5 Ghz (Python + C/C++)
107 dgist_multiDetNet 93.46 % 94.99 % 85.46 % 0.08 s GPU Titanx Pascal (Python)
108 Struc info fusion II 93.45 % 96.72 % 88.31 % 0.05 s GPU @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.
109 EBM3DOD baseline code 93.45 % 96.72 % 88.25 % 0.05 s 1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.
110 Cas-SSD 93.41 % 96.73 % 88.30 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
111 RRC code 93.40 % 95.68 % 87.37 % 3.6 s GPU @ 2.5 Ghz (C/C++)
J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y. Tai and L. Xu: Accurate Single Stage Detector Using Recurrent Rolling Convolution. CVPR 2017.
112 DGIST MT-CNN 93.39 % 95.16 % 85.50 % 0.09 s GPU @ 1.0 Ghz (Python)
113 HR-Cascade-RCNN 93.37 % 95.74 % 87.44 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
114 3D-CVF at SPA
This method makes use of Velodyne laser scans.
93.36 % 96.78 % 86.11 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
J. Yoo, Y. Kim, J. Kim and J. Choi: 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection. ECCV 2020.
115 PSS 93.36 % 96.64 % 90.52 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
116 ISF-v2 93.34 % 96.73 % 90.54 % 0.04 s 1 core @ 2.5 Ghz (Python)
117 Struc info fusion I 93.31 % 96.59 % 88.23 % 0.05 s 1 core @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.
118 RoIFusion code 93.30 % 96.30 % 88.22 % 0.22 s 1 core @ 3.0 Ghz (Python)
119 CityBrainLab-CT3D 93.30 % 96.28 % 90.58 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
120 STD code 93.22 % 96.14 % 90.53 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
121 SARPNET 93.21 % 96.07 % 88.09 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. Ye, H. Chen, C. Zhang, X. Hao and Z. Zhang: SARPNET: Shape Attention Regional Proposal Network for LiDAR-based 3D Object Detection. Neurocomputing 2019.
122 YF 93.21 % 96.16 % 90.23 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
123 H^23D R-CNN 93.20 % 96.20 % 90.55 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
124 Fast Point R-CNN
This method makes use of Velodyne laser scans.
93.18 % 96.13 % 87.68 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, S. Liu, X. Shen and J. Jia: Fast Point R-CNN. Proceedings of the IEEE international conference on computer vision (ICCV) 2019.
125 ASCNet 93.17 % 96.09 % 90.43 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
126 sensekitti code 93.17 % 94.79 % 84.38 % 4.5 s GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.
127 MBDF-Net 93.15 % 96.26 % 90.25 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
128 SJTU-HW 93.11 % 96.30 % 82.21 % 0.85s GPU @ 1.5 Ghz (Python + C/C++)
S. Zhang, X. Zhao, L. Fang, F. Haiping and S. Haitao: LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR. IEEE International Conference on Image Processing 2018.
L. Fang, X. Zhao and S. Zhang: Small-objectness sensitive detection based on shifted single shot detector. Multimedia Tools and Applications 2018.
129 BLPNet_V2 93.11 % 96.07 % 88.06 % 0.04 s 1 core @ 2.5 Ghz (Python)
130 PVF-NET 93.08 % 96.03 % 88.04 % 0.1 s 1 core @ 2.5 Ghz (Python)
131 3DIoU+++ 93.06 % 96.08 % 90.53 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
132 HV 93.04 % 95.91 % 87.88 % 0.02 s GPU @ 2.5 Ghz (Python)
133 NLK-ALL code 92.98 % 95.73 % 88.13 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
134 CLOCs_SecCas 92.95 % 95.43 % 89.21 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
135 MBDF-Net-1 92.85 % 95.98 % 89.91 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
136 YF 92.85 % 96.04 % 89.96 % 0.04 s GPU @ 2.5 Ghz (C/C++)
137 FPGNN 92.83 % 96.26 % 87.69 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
138 TBD 92.82 % 96.06 % 88.00 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
139 HotSpotNet 92.81 % 96.21 % 89.80 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
140 deprecated 92.79 % 95.56 % 91.62 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
141 DPointNet 92.77 % 95.55 % 89.63 % 0.07s 1 core @ 2.5 Ghz (C/C++)
142 SegVoxelNet 92.73 % 96.00 % 87.60 % 0.04 s 1 core @ 2.5 Ghz (Python)
H. Yi, S. Shi, M. Ding, J. Sun, K. Xu, H. Zhou, Z. Wang, S. Li and G. Wang: SegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloud. ICRA 2020.
143 Patches
This method makes use of Velodyne laser scans.
92.72 % 96.34 % 87.63 % 0.15 s GPU @ 2.0 Ghz
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
144 CenterNet3D 92.69 % 95.76 % 89.81 % 0.04 s GPU @ 1.5 Ghz (Python)
G. Wang, B. Tian, Y. Ai, T. Xu, L. Chen and D. Cao: CenterNet3D:An Anchor free Object Detector for Autonomous Driving. 2020.
145 R-GCN 92.67 % 96.19 % 87.66 % 0.16 s GPU @ 2.5 Ghz (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
146 NLK-3D 92.67 % 95.44 % 87.72 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
147 TBD 92.66 % 95.60 % 90.55 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
148 PI-RCNN 92.66 % 96.17 % 87.68 % 0.1 s 1 core @ 2.5 Ghz (Python)
L. Xie, C. Xiang, Z. Yu, G. Xu, Z. Yang, D. Cai and X. He: PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module. AAAI 2020 : The Thirty-Fourth AAAI Conference on Artificial Intelligence 2020.
149 PointPainting
This method makes use of Velodyne laser scans.
92.58 % 98.39 % 89.71 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
150 SIEV-Net 92.56 % 95.56 % 87.40 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
151 DASS 92.53 % 96.23 % 87.75 % 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.
152 3D IoU-Net 92.47 % 96.31 % 87.67 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, S. Krylov, Y. Ding and L. Shao: 3D IoU-Net: IoU Guided 3D Object Detector for Point Clouds. arXiv preprint arXiv:2004.04962 2020.
153 VAR 92.46 % 95.11 % 89.68 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
154 Associate-3Ddet code 92.45 % 95.61 % 87.32 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
L. Du, X. Ye, X. Tan, J. Feng, Z. Xu, E. Ding and S. Wen: Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection. The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
155 Dccnet 92.34 % 96.00 % 86.85 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
156 PointRGCN 92.33 % 97.51 % 87.07 % 0.26 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
157 CCFNET 92.25 % 95.85 % 89.36 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
158 LSNet 92.23 % 96.06 % 87.35 % 0.09 s GPU @ 2.5 Ghz (Python + C/C++)
159 F-ConvNet
This method makes use of Velodyne laser scans.
code 92.19 % 95.85 % 80.09 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
160 MDA 92.17 % 94.88 % 89.54 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
161 PFF3D
This method makes use of Velodyne laser scans.
code 92.15 % 95.37 % 87.54 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
162 yolo4 92.13 % 94.20 % 79.89 % 0.02 s 1 core @ 2.5 Ghz (Python)
163 TBD 92.12 % 93.48 % 89.56 % 0.05 s GPU @ 2.5 Ghz (Python)
164 PVNet 92.12 % 94.84 % 89.27 % 0,1 s 1 core @ 2.5 Ghz (Python)
165 SDP+RPN 92.03 % 95.16 % 79.16 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 2015.
166 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 92.00 % 95.88 % 86.98 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
167 VGCN 91.97 % 94.91 % 89.34 % 0.09 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
168 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 91.90 % 95.92 % 87.11 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
169 MKFFNet 91.88 % 95.29 % 89.21 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
170 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 91.86 % 95.03 % 89.06 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
171 Pointpillar_TV 91.82 % 94.82 % 88.57 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
172 epBRM
This method makes use of Velodyne laser scans.
code 91.77 % 94.59 % 88.45 % 0.1 s GPU @ >3.5 Ghz (Python + C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
173 3DBN_2 91.75 % 95.34 % 89.12 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
174 C-GCN 91.73 % 95.64 % 86.37 % 0.147 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
175 ITVD code 91.73 % 95.85 % 79.31 % 0.3 s GPU @ 2.5 Ghz (C/C++)
Y. Wei Liu: Improving Tiny Vehicle Detection in Complex Scenes. IEEE International Conference on Multimedia and Expo (ICME) 2018.
176 yolo4_5l 91.71 % 93.35 % 79.49 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
177 SINet+ code 91.67 % 94.17 % 78.60 % 0.3 s TITAN X GPU
X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
178 VOXEL_3D 91.61 % 94.50 % 86.37 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
179 Cascade MS-CNN code 91.60 % 94.26 % 78.84 % 0.25 s GPU @ 2.5 Ghz (C/C++)
Z. Cai and N. Vasconcelos: Cascade R-CNN: High Quality Object Detection and Instance Segmentation. arXiv preprint arXiv:1906.09756 2019.
Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A unified multi-scale deep convolutional neural network for fast object detection. European conference on computer vision 2016.
180 tt code 91.59 % 95.15 % 88.72 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
181 MKFFNet 91.54 % 95.32 % 89.02 % 0.01s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
182 V3D 91.52 % 94.46 % 86.34 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
183 MKFFNet 91.51 % 95.19 % 89.01 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
184 MAFF-Net(DAF-Pillar) 91.46 % 94.38 % 83.89 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Z. Zhang, Z. Liang, M. Zhang, X. Zhao, Y. Ming, T. Wenming and S. Pu: MAFF-Net: Filter False Positive for 3D Vehicle Detection with Multi-modal Adaptive Feature Fusion. arXiv preprint arXiv:2009.10945 2020.
185 AIMC-RUC 91.45 % 96.94 % 86.28 % 0.11 s 1 core @ 2.5 Ghz (Python)
186 HRI-VoxelFPN 91.44 % 96.65 % 86.18 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
H. Kuang, B. Wang, J. An, M. Zhang and Z. Zhang: Voxel-FPN:multi-scale voxel feature aggregation in 3D object detection from point clouds. sensors 2020.
187 FPC3D_all
This method makes use of Velodyne laser scans.
91.42 % 95.52 % 86.76 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
188 EgoNet code 91.39 % 96.18 % 81.33 % 0.1 s GPU @ 1.5 Ghz (Python)
S. Li, Z. Yan, H. Li and K. Cheng: Exploring intermediate representation for monocular vehicle pose estimation. The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
189 deprecated 91.31 % 96.90 % 83.91 % 0.06 s GPU @ >3.5 Ghz (Python)
190 SC(DLA34)
This method uses stereo information.
91.27 % 96.61 % 83.50 % 0.04 s GPU @ 2.5 Ghz (Python)
191 GAA 91.20 % 94.50 % 82.97 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
192 PointPillars
This method makes use of Velodyne laser scans.
code 91.19 % 94.00 % 88.17 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
193 LTN 91.18 % 94.68 % 81.51 % 0.4 s GPU @ >3.5 Ghz (Python)
T. Wang, X. He, Y. Cai and G. Xiao: Learning a Layout Transfer Network for Context Aware Object Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
194 WS3D
This method makes use of Velodyne laser scans.
91.15 % 95.13 % 86.52 % 0.1 s GPU @ 2.5 Ghz (Python)
Q. Meng, W. Wang, T. Zhou, J. Shen, L. Van Gool and D. Dai: Weakly Supervised 3D Object Detection from Lidar Point Cloud. 2020.
195 anonymous 91.08 % 96.57 % 82.86 % 1 s 1 core @ 2.5 Ghz (C/C++)
196 KM3D code 91.07 % 96.44 % 81.19 % 0.03 s 1 core @ 2.5 Ghz (Python)
P. Li: Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training. 2020.
197 FII-CenterNet 91.03 % 94.48 % 83.00 % 0.09 s GPU @ 2.5 Ghz (Python)
S. Fan, F. Zhu, S. Chen, H. Zhang, B. Tian, Y. Lv and F. Wang: FII-CenterNet: An Anchor-Free Detector With Foreground Attention for Traffic Object Detection. IEEE Transactions on Vehicular Technology 2021.
198 Aston-EAS 91.02 % 93.91 % 77.93 % 0.24 s GPU @ 2.5 Ghz (Python + C/C++)
J. Wei, J. He, Y. Zhou, K. Chen, Z. Tang and Z. Xiong: Enhanced Object Detection With Deep Convolutional Neural Networks for Advanced Driving Assistance. IEEE Transactions on Intelligent Transportation Systems 2019.
199 MonoFlex 91.02 % 96.01 % 83.38 % 0.03 s GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.
200 ARPNET 90.99 % 94.00 % 83.49 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
201 GA-Aug 90.92 % 93.93 % 83.03 % 0.04 s GPU @ 2.5 Ghz (Python)
202 MonoEF code 90.88 % 96.32 % 83.27 % 0.03 s 1 core @ 2.5 Ghz (Python)
203 PatchNet code 90.87 % 93.82 % 79.62 % 0.4 s 1 core @ 2.5 Ghz (C/C++)
X. Ma, S. Liu, Z. Xia, H. Zhang, X. Zeng and W. Ouyang: Rethinking Pseudo-LiDAR Representation. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
204 MV3D
This method makes use of Velodyne laser scans.
90.83 % 96.47 % 78.63 % 0.36 s GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
205 monodle code 90.81 % 93.83 % 80.93 % 0.04 s GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for Monocular 3D Object Detection. CVPR 2021 .
206 3D IoU Loss
This method makes use of Velodyne laser scans.
90.79 % 95.92 % 85.65 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
D. Zhou, J. Fang, X. Song, C. Guan, J. Yin, Y. Dai and R. Yang: IoU Loss for 2D/3D Object Detection. International Conference on 3D Vision (3DV) 2019.
207 SINet_VGG code 90.79 % 93.59 % 77.53 % 0.2 s TITAN X GPU
X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
208 HNet
This method makes use of Velodyne laser scans.
code 90.77 % 94.55 % 86.03 % 0.05 s GPU @ 2.5 Ghz (Python)
209 APL-Second 90.70 % 95.82 % 85.51 % 0.05 s 1 core @ 2.5 Ghz (Python)
210 OCM3D 90.70 % 94.36 % 84.56 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
211 TANet code 90.67 % 93.67 % 85.31 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
212 yolo4 90.63 % 94.71 % 80.38 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
213 NF2 90.62 % 94.14 % 81.30 % 0.1 s GPU @ 2.5 Ghz (Python)
214 Det3D 90.54 % 94.35 % 84.40 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
215 FADNet code 90.49 % 96.15 % 80.71 % 0.04 s GPU @ >3.5 Ghz (Python)
216 IGRP+ 90.42 % 96.03 % 87.63 % 0.18 s 1 core @ 2.5 Ghz (Python + C/C++)
217 CG-Stereo
This method uses stereo information.
90.38 % 96.31 % 82.80 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
218 yolo4_5l code 90.38 % 91.79 % 80.64 % 0.02 s 1 core @ 2.5 Ghz (Python + C/C++)
219 SCNet
This method makes use of Velodyne laser scans.
90.30 % 95.59 % 85.09 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
220 Deep3DBox 90.19 % 94.71 % 76.82 % 1.5 s GPU @ 2.5 Ghz (C/C++)
A. Mousavian, D. Anguelov, J. Flynn and J. Kosecka: 3D Bounding Box Estimation Using Deep Learning and Geometry. CVPR 2017.
221 FQNet 90.17 % 94.72 % 76.78 % 0.5 s 1 core @ 2.5 Ghz (Python)
L. Liu, J. Lu, C. Xu, Q. Tian and J. Zhou: Deep Fitting Degree Scoring Network for Monocular 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
222 MonoGeo 90.14 % 95.11 % 80.19 % 0.05 s 1 core @ 2.5 Ghz (Python)
223 DeepStereoOP 90.06 % 95.15 % 79.91 % 3.4 s GPU @ 3.5 Ghz (Matlab + C/C++)
C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.
224 SubCNN 89.98 % 94.26 % 79.78 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.
225 MLOD
This method makes use of Velodyne laser scans.
code 89.97 % 94.88 % 84.98 % 0.12 s GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
226 GPP code 89.96 % 94.02 % 81.13 % 0.23 s GPU @ 1.5 Ghz (Python + C/C++)
A. Rangesh and M. Trivedi: Ground plane polling for 6dof pose estimation of objects on the road. IEEE Transactions on Intelligent Vehicles 2020.
227 LCA 89.94 % 93.40 % 82.76 % 0.05 s 1 core @ 2.5 Ghz (Python)
228 AVOD
This method makes use of Velodyne laser scans.
code 89.88 % 95.17 % 82.83 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
229 SINet_PVA code 89.86 % 92.72 % 76.47 % 0.11 s TITAN X GPU
X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
230 MCA 89.72 % 93.42 % 79.96 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
231 UDI-mono3D 89.67 % 94.39 % 80.29 % 0.05 s 1 core @ 2.5 Ghz (Python)
232 3DOP
This method uses stereo information.
code 89.55 % 92.96 % 79.38 % 3s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
233 IAFA 89.46 % 93.08 % 79.83 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
D. Zhou, X. Song, Y. Dai, J. Yin, F. Lu, M. Liao, J. Fang and L. Zhang: IAFA: Instance-Aware Feature Aggregation for 3D Object Detection from a Single Image. Proceedings of the Asian Conference on Computer Vision 2020.
234 Mono3D code 89.37 % 94.52 % 79.15 % 4.2 s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.
235 4d-MSCNN
This method uses stereo information.
code 89.37 % 92.40 % 77.00 % 0.3 min GPU @ 3.0 Ghz (Matlab + C/C++)
P. Ferraz, B. Oliveira, F. Ferreira, C. Silva Martins and others: Three-stage RGBD architecture for vehicle and pedestrian detection using convolutional neural networks and stereo vision. IET Intelligent Transport Systems 2020.
236 R-FCN(FPN) 89.35 % 93.53 % 79.35 % 0.2 s 1 core @ 2.5 Ghz (Python)
237 Scan_YOLO 88.95 % 90.69 % 79.85 % 0.1 s 4 cores @ 3.0 Ghz (Python)
238 AVOD-FPN
This method makes use of Velodyne laser scans.
code 88.92 % 94.70 % 84.13 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
239 AM3D 88.71 % 92.55 % 77.78 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
X. Ma, Z. Wang, H. Li, P. Zhang, W. Ouyang and X. Fan: Accurate Monocular Object Detection via Color- Embedded 3D Reconstruction for Autonomous Driving. Proceedings of the IEEE international Conference on Computer Vision (ICCV) 2019.
240 EACV 88.70 % 94.51 % 81.15 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
241 MS-CNN code 88.68 % 93.87 % 76.11 % 0.4 s GPU @ 2.5 Ghz (C/C++)
Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection. ECCV 2016.
242 PMN 88.65 % 93.64 % 77.94 % 0.2 s 1 core @ 2.5 Ghz (Python)
243 MonoPSR code 88.50 % 93.63 % 73.36 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
244 BFF 88.49 % 90.84 % 78.84 % 8.4 s 4 cores @ 1.5 Ghz (Python)
245 Shift R-CNN (mono) code 88.48 % 94.07 % 78.34 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
246 RCD 88.46 % 92.52 % 83.73 % 0.1 s GPU @ 2.5 Ghz (Python)
A. Bewley, P. Sun, T. Mensink, D. Anguelov and C. Sminchisescu: Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection. Conference on Robot Learning (CoRL) 2020.
247 MM-MRFC
This method uses optical flow information.
This method makes use of Velodyne laser scans.
88.46 % 95.54 % 78.14 % 0.05 s GPU @ 2.5 Ghz (C/C++)
A. Costea, R. Varga and S. Nedevschi: Fast Boosting based Detection using Scale Invariant Multimodal Multiresolution Filtered Features. CVPR 2017.
248 deleted 88.38 % 96.52 % 81.01 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
249 AACL 88.35 % 93.56 % 73.57 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
250 3DBN
This method makes use of Velodyne laser scans.
88.29 % 93.74 % 80.74 % 0.13s 1080Ti (Python+C/C++)
X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.
251 UDI-mono3D 88.16 % 93.93 % 79.57 % 0.05 s 1 core @ 2.5 Ghz (Python)
252 anonymous 88.16 % 96.22 % 75.72 % 1 s 1 core @ 2.5 Ghz (C/C++)
253 tiny-stereo 88.16 % 96.49 % 80.74 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
254 CDI3D 87.97 % 91.46 % 80.14 % 0.03 s GPU @ 2.5 Ghz (Python)
255 MonoRUn 87.91 % 95.48 % 78.10 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
256 Multi-task DG 87.72 % 95.50 % 75.51 % 0.06 s GPU @ 2.5 Ghz (Python)
257 Object Transformer 87.67 % 93.33 % 79.98 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
258 MMCOM 87.58 % 95.08 % 77.48 % 0.04 s 1 core @ 2.5 Ghz (Python)
259 SMOKE code 87.51 % 93.21 % 77.66 % 0.03 s GPU @ 2.5 Ghz (Python)
Z. Liu, Z. Wu and R. Tóth: SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation. 2020.
260 DAMNET code 87.39 % 92.48 % 82.41 % 1 s 1 core @ 2.5 Ghz (C/C++)
261 MA 87.29 % 93.21 % 79.82 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
262 CDN
This method uses stereo information.
code 87.19 % 95.85 % 79.43 % 0.6 s GPU @ 2.5 Ghz (Python)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems (NeurIPS) 2020.
263 IMA 87.17 % 92.67 % 77.46 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
264 RTM3D code 86.93 % 91.82 % 77.41 % 0.05 s GPU @ 1.0 Ghz (Python)
P. Li, H. Zhao, P. Liu and F. Cao: RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving. 2020.
265 yolo_rgb 86.90 % 90.01 % 77.52 % 0.07 s GPU @ 2.5 Ghz (Python)
266 NL_M3D 86.80 % 91.31 % 72.37 % 0.2 s 1 core @ 2.5 Ghz (Python)
267 TBD
This method makes use of Velodyne laser scans.
86.79 % 92.54 % 81.84 % 0.11 s GPU @ 2.5 Ghz (Python + C/C++)
268 MonoRCNN 86.78 % 91.98 % 66.97 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
269 DSGN
This method uses stereo information.
code 86.43 % 95.53 % 78.75 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
270 OSE
This method uses stereo information.
86.21 % 95.64 % 76.83 % 0.1 s GPU @ 2.5 Ghz (C/C++)
271 Stereo R-CNN
This method uses stereo information.
code 85.98 % 93.98 % 71.25 % 0.3 s GPU @ 2.5 Ghz (Python)
P. Li, X. Chen and S. Shen: Stereo R-CNN based 3D Object Detection for Autonomous Driving. CVPR 2019.
272 StereoFENet
This method uses stereo information.
85.70 % 91.48 % 77.62 % 0.15 s 1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
273 PLDet3d 85.51 % 88.65 % 77.30 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
274 ResNet-RRC_Car 85.33 % 91.45 % 74.27 % 0.06 s GPU @ 1.5 Ghz (Python + C/C++)
H. Jeon and others: High-Speed Car Detection Using ResNet- Based Recurrent Rolling Convolution. Proceedings of the IEEE conference on systems, man, and cybernetics 2018.
275 PL++ (SDN+GDC)
This method uses stereo information.
This method makes use of Velodyne laser scans.
code 85.15 % 94.95 % 77.78 % 0.6 s GPU @ 2.5 Ghz (C/C++)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.
276 M3D-RPN code 85.08 % 89.04 % 69.26 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
277 Center3D 85.05 % 95.14 % 73.06 % 0.05 s GPU @ 3.5 Ghz (Python)
278 CDN-PL++
This method uses stereo information.
85.01 % 94.66 % 77.60 % 0.4 s GPU @ 2.5 Ghz (C/C++)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems 2020.
279 SDP+CRC (ft) 85.00 % 92.06 % 71.71 % 0.6 s GPU @ 2.5 Ghz (C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
280 LPCG-M3D 84.95 % 87.35 % 77.05 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
281 bifpn_fsrn 84.93 % 93.68 % 74.45 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
282 ResNet-RRC (pruned) 84.93 % 89.59 % 73.26 % 0.11 s GPU @ 1.5 Ghz (Python + C/C++)
283 SS3D 84.92 % 92.72 % 70.35 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
284 ResNet-RRC 84.81 % 89.43 % 73.18 % 0.11 s GPU @ 1.5 Ghz (Python + C/C++)
285 MonoFENet 84.63 % 91.68 % 76.71 % 0.15 s 1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
286 MV3D (LIDAR)
This method makes use of Velodyne laser scans.
84.39 % 93.08 % 79.27 % 0.24 s GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
287 Complexer-YOLO
This method makes use of Velodyne laser scans.
84.16 % 91.92 % 79.62 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
288 ZoomNet
This method uses stereo information.
code 83.92 % 94.22 % 69.00 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
L. Z. Xu: ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2020.
289 OSE+ 83.92 % 95.20 % 76.69 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
290 LAPNet 83.85 % 90.81 % 65.37 % 0.03 s 1 core @ 2.5 Ghz (Python)
291 D4LCN code 83.67 % 90.34 % 65.33 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
292 Deprecated 83.39 % 89.00 % 64.29 % Deprecated Deprecated
293 DA-Mono3D 83.36 % 88.94 % 64.23 % 0.09s 1 core @ 2.5 Ghz (C/C++)
294 Faster R-CNN code 83.16 % 88.97 % 72.62 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards Real- Time Object Detection with Region Proposal Networks. NIPS 2015.
295 Mag 83.15 % 94.24 % 70.63 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
296 MP-Mono 83.14 % 87.90 % 64.62 % 0.16 s GPU @ 2.5 Ghz (Python)
297 MTMono3d 83.11 % 90.55 % 75.48 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
298 SSL-RTM3D Res18 82.97 % 93.35 % 73.11 % 0.02 s GPU @ 2.5 Ghz (Python)
299 Pseudo-LiDAR++
This method uses stereo information.
code 82.90 % 94.46 % 75.45 % 0.4 s GPU @ 2.5 Ghz (Python)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.
300 Disp R-CNN
This method uses stereo information.
code 82.86 % 93.64 % 68.33 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
301 BS3D 82.72 % 95.35 % 70.01 % 22 ms Titan Xp
N. Gählert, J. Wan, M. Weber, J. Zöllner, U. Franke and J. Denzler: Beyond Bounding Boxes: Using Bounding Shapes for Real-Time 3D Vehicle Detection from Monocular RGB Images. 2019 IEEE Intelligent Vehicles Symposium (IV) 2019.
302 Disp R-CNN (velo)
This method uses stereo information.
code 82.64 % 93.45 % 70.45 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
303 YOLOStereo3D
This method uses stereo information.
code 82.15 % 94.81 % 62.17 % 0.1 s GPU 1080Ti
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.
304 Ground-Aware code 82.05 % 92.33 % 62.08 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
Y. Liu, Y. Yuan and M. Liu: Ground-aware Monocular 3D Object Detection for Autonomous Driving. IEEE Robotics and Automation Letters 2021.
305 FRCNN+Or code 82.00 % 92.91 % 68.79 % 0.09 s Titan Xp GPU
C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine 2018.
C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features. IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.
306 DDMP-3D 81.70 % 91.15 % 63.12 % 0.18 s 1 core @ 2.5 Ghz (Python)
307 ITS-MDPL 81.56 % 92.61 % 74.23 % 0.16 s GPU @ 2.5 Ghz (Python)
308 LCD3D 81.25 % 91.29 % 64.55 % 0.03 s GPU @ 2.5 Ghz (Python)
309 A3DODWTDA (image) code 81.25 % 78.96 % 70.56 % 0.8 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
310 SOD 81.18 % 94.24 % 66.44 % 0.1 s 1 core @ 2.5 Ghz (Python)
311 RefineNet 81.01 % 91.91 % 65.67 % 0.20 s GPU @ 2.5 Ghz (Matlab + C++)
R. Rajaram, E. Bar and M. Trivedi: RefineNet: Refining Object Detectors for Autonomous Driving. IEEE Transactions on Intelligent Vehicles 2016.
R. Rajaram, E. Bar and M. Trivedi: RefineNet: Iterative Refinement for Accurate Object Localization. Intelligent Transportation Systems Conference 2016.
312 CaDDN 80.73 % 93.61 % 71.09 % 0.63 s GPU @ 2.5 Ghz (Python)
C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.
313 UM3D_TUM 80.36 % 92.88 % 65.95 % 0.05 s 1 core @ 2.5 Ghz (Python)
314 GrooMeD-NMS code 80.28 % 90.14 % 63.78 % 0.12 s 1 core @ 2.5 Ghz (Python)
A. Kumar, G. Brazil and X. Liu: GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection. CVPR 2021.
315 3D-GCK 80.19 % 89.55 % 68.08 % 24 ms Tesla V100
N. Gählert, J. Wan, N. Jourdan, J. Finkbeiner, U. Franke and J. Denzler: Single-Shot 3D Detection of Vehicles from Monocular RGB Images via Geometrically Constrained Keypoints in Real-Time. 2020 IEEE Intelligent Vehicles Symposium (IV) 2020.
316 KMC code 79.99 % 89.71 % 73.33 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
317 RelationNet3D_dla34 code 79.78 % 83.69 % 69.35 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
318 YoloMono3D code 79.63 % 92.37 % 59.69 % 0.05 s GPU @ 2.5 Ghz (Python)
Y. Liu, L. Wang and L. Ming: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
319 A3DODWTDA
This method makes use of Velodyne laser scans.
code 79.15 % 82.98 % 68.30 % 0.08 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
320 DFR-Net 78.81 % 90.13 % 60.40 % 0.18 s 1080 Ti (Pytorch)
321 spLBP 78.66 % 81.66 % 61.69 % 1.5 s 8 cores @ 2.5 Ghz (Matlab + C/C++)
Q. Hu, S. Paisitkriangkrai, C. Shen, A. Hengel and F. Porikli: Fast Detection of Multiple Objects in Traffic Scenes With a Common Detection Framework. IEEE Trans. Intelligent Transportation Systems 2016.
322 AEC3D 78.59 % 88.58 % 74.62 % 0.01 s GPU @ 2.5 Ghz (Python)
323 MonoHMOO 78.21 % 92.33 % 61.58 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
324 3D-SSMFCNN code 78.19 % 77.92 % 69.19 % 0.1 s GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.
325 MonoGRNet code 77.94 % 88.65 % 63.31 % 0.04s NVIDIA P40
Z. Qin, J. Wang and Y. Lu: MonoGRNet: A Geometric Reasoning Network for 3D Object Localization. The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) 2019.
326 VN3D 77.90 % 86.89 % 72.05 % 0.02 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
327 Aug3D-RPN 77.88 % 85.57 % 61.16 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
328 Reinspect code 77.48 % 90.27 % 66.73 % 2s 1 core @ 2.5 Ghz (C/C++)
R. Stewart, M. Andriluka and A. Ng: End-to-End People Detection in Crowded Scenes. CVPR 2016.
329 multi-task CNN 77.18 % 86.12 % 68.09 % 25.1 ms GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
330 Regionlets 76.99 % 88.75 % 60.49 % 1 s >8 cores @ 2.5 Ghz (C/C++)
X. Wang, M. Yang, S. Zhu and Y. Lin: Regionlets for Generic Object Detection. T-PAMI 2015.
W. Zou, X. Wang, M. Sun and Y. Lin: Generic Object Detection with Dense Neural Patterns and Regionlets. British Machine Vision Conference 2014.
C. Long, X. Wang, G. Hua, M. Yang and Y. Lin: Accurate Object Detection with Location Relaxation and Regionlets Relocalization. Asian Conference on Computer Vision 2014.
331 3DVP code 76.98 % 84.95 % 65.78 % 40 s 8 cores @ 3.5 Ghz (Matlab + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Data-Driven 3D Voxel Patterns for Object Category Recognition. IEEE Conference on Computer Vision and Pattern Recognition 2015.
332 RelationNet3D_res18 code 76.96 % 87.14 % 67.49 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
333 AutoShape 76.82 % 83.75 % 63.71 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
334 Mobile Stereo R-CNN
This method uses stereo information.
76.73 % 90.08 % 62.23 % 1.8 s NVIDIA Jetson TX2
335 RelationNet3D 76.62 % 81.36 % 68.48 % 0.04 s GPU @ 2.5 Ghz (Python)
336 ICCV 76.45 % 85.48 % 65.52 % 0.04 s GPU @ 2.5 Ghz (Python)
337 SubCat code 76.36 % 84.10 % 60.56 % 0.7 s 6 cores @ 3.5 Ghz (Matlab + C/C++)
E. Ohn-Bar and M. Trivedi: Learning to Detect Vehicles by Clustering Appearance Patterns. T-ITS 2015.
338 GS3D 76.35 % 86.23 % 62.67 % 2 s 1 core @ 2.5 Ghz (C/C++)
B. Li, W. Ouyang, L. Sheng, X. Zeng and X. Wang: GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
339 AOG code 76.24 % 86.08 % 61.51 % 3 s 4 cores @ 2.5 Ghz (Matlab)
T. Wu, B. Li and S. Zhu: Learning And-Or Models to Represent Context and Occlusion for Car Detection and Viewpoint Estimation. TPAMI 2016.
B. Li, T. Wu and S. Zhu: Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model. ECCV 2014.
340 Pose-RCNN 75.83 % 89.59 % 64.06 % 2 s >8 cores @ 2.5 Ghz (Python)
M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on 2016.
341 3D FCN
This method makes use of Velodyne laser scans.
74.65 % 86.74 % 67.85 % >5 s 1 core @ 2.5 Ghz (C/C++)
B. Li: 3D Fully Convolutional Network for Vehicle Detection in Point Cloud. IROS 2017.
342 OC Stereo
This method uses stereo information.
code 74.60 % 87.39 % 62.56 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
343 yolo_depth 74.40 % 88.71 % 65.58 % 0.07 s GPU @ 2.5 Ghz (Python)
344 RTS3D 73.08 % 80.48 % 64.02 % 0.03 s GPU @ 2.5 Ghz (Python)
345 NCL code 71.91 % 64.71 % 71.78 % NA s 1 core @ 2.5 Ghz (Python)
346 Kinematic3D code 71.73 % 89.67 % 54.97 % 0.12 s 1 core @ 1.5 Ghz (C/C++)
G. Brazil, G. Pons-Moll, X. Liu and B. Schiele: Kinematic 3D Object Detection in Monocular Video. ECCV 2020 .
347 AOG-View 71.26 % 85.01 % 55.73 % 3 s 1 core @ 2.5 Ghz (Matlab, C/C++)
B. Li, T. Wu and S. Zhu: Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model. ECCV 2014.
348 GAC3D 70.73 % 83.30 % 52.23 % 0.25 s 1 core @ 2.5 Ghz (Python)
349 MV-RGBD-RF
This method makes use of Velodyne laser scans.
70.70 % 77.89 % 57.41 % 4 s 4 cores @ 2.5 Ghz (C/C++)
A. Gonzalez, D. Vazquez, A. Lopez and J. Amores: On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts.. IEEE Trans. on Cybernetics 2016.
A. Gonzalez, G. Villalonga, J. Xu, D. Vazquez, J. Amores and A. Lopez: Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection. IEEE Intelligent Vehicles Symposium (IV) 2015.
350 Vote3Deep
This method makes use of Velodyne laser scans.
70.30 % 78.95 % 63.12 % 1.5 s 4 cores @ 2.5 Ghz (C/C++)
M. Engelcke, D. Rao, D. Zeng Wang, C. Hay Tong and I. Posner: Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks. ArXiv e-prints 2016.
351 ROI-10D 70.16 % 76.56 % 61.15 % 0.2 s GPU @ 3.5 Ghz (Python)
F. Manhardt, W. Kehl and A. Gaidon: ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape. Computer Vision and Pattern Recognition (CVPR) 2019.
352 RetinaMono code 69.01 % 75.18 % 58.98 % 0.02 s 1 core @ 2.5 Ghz (Python)
353 TBD 68.30 % 88.62 % 59.00 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
354 BirdNet+
This method makes use of Velodyne laser scans.
code 68.05 % 92.10 % 65.61 % 0.1 s Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird’s Eye View. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
355 Decoupled-3D 67.92 % 87.78 % 54.53 % 0.08 s GPU @ 2.5 Ghz (C/C++)
Y. Cai, B. Li, Z. Jiao, H. Li, X. Zeng and X. Wang: Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation. AAAI 2020.
356 SparVox3D 67.88 % 83.76 % 52.56 % 0.05 s GPU @ 2.0 Ghz (Python)
357 MDSNet 67.79 % 90.97 % 53.39 % 0.07 s 1 core @ 2.5 Ghz (Python)
358 Pseudo-Lidar
This method uses stereo information.
code 67.79 % 85.40 % 58.50 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Wang, W. Chao, D. Garg, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
359 OC-DPM 67.06 % 79.07 % 52.61 % 10 s 8 cores @ 2.5 Ghz (Matlab)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Occlusion Patterns for Object Class Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2013.
360 DPM-VOC+VP 66.72 % 82.15 % 49.01 % 8 s 1 core @ 2.5 Ghz (C/C++)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015.
361 BdCost48LDCF code 66.63 % 81.38 % 52.20 % 0.5 s 8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
362 RefinedMPL 65.24 % 88.29 % 53.20 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
363 MDPM-un-BB 64.06 % 79.74 % 49.07 % 60 s 4 core @ 2.5 Ghz (MATLAB)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
364 Y4 code 63.60 % 81.79 % 56.20 % 0.03 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
365 TLNet (Stereo)
This method uses stereo information.
code 63.53 % 76.92 % 54.58 % 0.1 s 1 core @ 2.5 Ghz (Python)
Z. Qin, J. Wang and Y. Lu: Triangulation Learning Network: from Monocular to Stereo 3D Object Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
366 PDV-Subcat 63.24 % 78.27 % 47.67 % 7 s 1 core @ 2.5 Ghz (C/C++)
J. Shen, X. Zuo, J. Li, W. Yang and H. Ling: A novel pixel neighborhood differential statistic feature for pedestrian and face detection . Pattern Recognition 2017.
367 PG-MonoNet 62.75 % 70.87 % 54.34 % 0.19 s GPU @ 2.5 Ghz (Python)
368 MODet
This method makes use of Velodyne laser scans.
62.54 % 66.06 % 60.04 % 0.05 s GTX1080Ti
Y. Zhang, Z. Xiang, C. Qiao and S. Chen: Accurate and Real-Time Object Detection Based on Bird's Eye View on 3D Point Clouds. 2019 International Conference on 3D Vision (3DV) 2019.
369 SubCat48LDCF code 61.16 % 78.86 % 44.69 % 0.5 s 8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
370 DPM-C8B1
This method uses stereo information.
60.21 % 75.24 % 44.73 % 15 s 4 cores @ 2.5 Ghz (Matlab + C/C++)
J. Yebes, L. Bergasa and M. García-Garrido: Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes. Sensors 2015.
J. Yebes, L. Bergasa, R. Arroyo and A. Lázaro: Supervised learning and evaluation of KITTI's cars detector with DPM. IV 2014.
371 FPIOD
This method makes use of Velodyne laser scans.
code 60.04 % 78.81 % 50.13 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
372 SAMME48LDCF code 58.38 % 77.47 % 44.43 % 0.5 s 8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
373 LSVM-MDPM-sv 58.36 % 71.11 % 43.22 % 10 s 4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout. NIPS 2011.
374 BirdNet
This method makes use of Velodyne laser scans.
57.12 % 79.30 % 55.16 % 0.11 s Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
375 ACF-SC 56.60 % 69.90 % 43.61 % <0.3 s 1 core @ >3.5 Ghz (Matlab + C/C++)
C. Cadena, A. Dick and I. Reid: A Fast, Modular Scene Understanding System using Context-Aware Object Detection. Robotics and Automation (ICRA), 2015 IEEE International Conference on 2015.
376 LSVM-MDPM-us code 55.95 % 68.94 % 41.45 % 10 s 4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
377 ACF 54.09 % 63.05 % 41.81 % 0.2 s 1 core @ >3.5 Ghz (Matlab + C/C++)
P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014.
P. Doll\'ar: Piotr's Image and Video Matlab Toolbox (PMT). .
378 Mono3D_PLiDAR code 53.36 % 80.85 % 44.80 % 0.1 s NVIDIA GeForce 1080 (pytorch)
X. Weng and K. Kitani: Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud. arXiv:1903.09847 2019.
379 RT3D-GMP
This method uses stereo information.
51.95 % 62.41 % 39.14 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
380 VeloFCN
This method makes use of Velodyne laser scans.
51.82 % 70.53 % 45.70 % 1 s GPU @ 2.5 Ghz (Python + C/C++)
B. Li, T. Zhang and T. Xia: Vehicle Detection from 3D Lidar Using Fully Convolutional Network. RSS 2016 .
381 Vote3D
This method makes use of Velodyne laser scans.
45.94 % 54.38 % 40.48 % 0.5 s 4 cores @ 2.8 Ghz (C/C++)
D. Wang and I. Posner: Voting for Voting in Online Point Cloud Object Detection. Proceedings of Robotics: Science and Systems 2015.
382 TopNet-HighRes
This method makes use of Velodyne laser scans.
45.85 % 58.04 % 41.11 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
383 RT3DStereo
This method uses stereo information.
45.81 % 56.53 % 37.63 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
384 Multimodal Detection
This method makes use of Velodyne laser scans.
code 45.46 % 63.91 % 37.25 % 0.06 s GPU @ 3.5 Ghz (Matlab + C/C++)
A. Asvadi, L. Garrote, C. Premebida, P. Peixoto and U. Nunes: Multimodal vehicle detection: fusing 3D- LIDAR and color camera data. Pattern Recognition Letters 2017.
385 RT3D
This method makes use of Velodyne laser scans.
39.69 % 50.33 % 40.04 % 0.09 s GPU @ 1.8Ghz
Y. Zeng, Y. Hu, S. Liu, J. Ye, Y. Han, X. Li and N. Sun: RT3D: Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving. IEEE Robotics and Automation Letters 2018.
386 R-AGNO-Net 36.55 % 49.87 % 35.20 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
387 VoxelJones code 36.31 % 43.89 % 34.16 % .18 s 1 core @ 2.5 Ghz (Python + C/C++)
M. Motro and J. Ghosh: Vehicular Multi-object Tracking with Persistent Detector Failures. arXiv preprint arXiv:1907.11306 2019.
388 CSoR
This method makes use of Velodyne laser scans.
code 21.66 % 31.52 % 17.99 % 3.5 s 4 cores @ >3.5 Ghz (Python + C/C++)
L. Plotkin: PyDriver: Entwicklung eines Frameworks für räumliche Detektion und Klassifikation von Objekten in Fahrzeugumgebung. 2015.
389 mBoW
This method makes use of Velodyne laser scans.
21.59 % 35.22 % 16.89 % 10 s 1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
390 DepthCN
This method makes use of Velodyne laser scans.
code 21.18 % 37.45 % 16.08 % 2.3 s GPU @ 3.5 Ghz (Matlab)
A. Asvadi, L. Garrote, C. Premebida, P. Peixoto and U. Nunes: DepthCN: vehicle detection using 3D- LIDAR and convnet. IEEE ITSC 2017.
391 YOLOv2 code 14.31 % 26.74 % 10.94 % 0.02 s GPU @ 3.5 Ghz (C/C++)
J. Redmon, S. Divvala, R. Girshick and A. Farhadi: You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
392 TopNet-UncEst
This method makes use of Velodyne laser scans.
6.24 % 7.24 % 5.42 % 0.09 s NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
393 TopNet-Retina
This method makes use of Velodyne laser scans.
5.00 % 6.82 % 4.52 % 52ms GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
394 TopNet-DecayRate
This method makes use of Velodyne laser scans.
0.01 % 0.00 % 0.01 % 92 ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
395 LaserNet 0.00 % 0.00 % 0.00 % 12 ms GPU @ 2.5 Ghz (C/C++)
G. Meyer, A. Laddha, E. Kee, C. Vallespi-Gonzalez and C. Wellington: LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
396 TBD 0.00 % 0.00 % 0.00 % 0.1 s 1 core @ 2.5 Ghz (Python)
397 TBD 0.00 % 0.00 % 0.00 % 0.1 s 1 core @ 2.5 Ghz (Python)
398 Neighbor-Vote 0.00 % 0.00 % 0.00 % 0.1 s GPU @ 2.5 Ghz (Python)
Table as LaTeX | Only published Methods

Pedestrian


Method Setting Code Moderate Easy Hard Runtime Environment
1 dgist_multiDetNet 80.21 % 89.21 % 75.77 % 0.08 s GPU Titanx Pascal (Python)
2 F-PointNet
This method makes use of Velodyne laser scans.
code 80.13 % 89.83 % 75.05 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
3 NF2 79.59 % 88.28 % 75.47 % 0.1 s GPU @ 2.5 Ghz (Python)
4 DGIST MT-CNN 79.38 % 88.58 % 74.83 % 0.09 s GPU @ 1.0 Ghz (Python)
5 TuSimple code 78.40 % 88.87 % 73.66 % 1.6 s GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016.
6 ZEEWAIN-AI 78.20 % 88.46 % 73.35 % 0.3 s GPU @ 2.5 Ghz (Python)
7 RRC code 76.61 % 85.98 % 71.47 % 3.6 s GPU @ 2.5 Ghz (C/C++)
J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y. Tai and L. Xu: Accurate Single Stage Detector Using Recurrent Rolling Convolution. CVPR 2017.
8 WSSN
This method makes use of Velodyne laser scans.
76.42 % 84.91 % 71.86 % 0.37 s GPU @ >3.5 Ghz (Python + C/C++)
9 ECP Faster R-CNN 76.25 % 85.96 % 70.55 % 0.25 s GPU @ 2.5 Ghz (Python)
M. Braun, S. Krebs, F. Flohr and D. Gavrila: The EuroCity Persons Dataset: A Novel Benchmark for Object Detection. CoRR 2018.
10 Aston-EAS 76.07 % 86.71 % 70.02 % 0.24 s GPU @ 2.5 Ghz (Python + C/C++)
J. Wei, J. He, Y. Zhou, K. Chen, Z. Tang and Z. Xiong: Enhanced Object Detection With Deep Convolutional Neural Networks for Advanced Driving Assistance. IEEE Transactions on Intelligent Transportation Systems 2019.
11 MHN 75.99 % 87.21 % 69.50 % 0.39 s GPU @ 2.5 Ghz (Python)
J. Cao, Y. Pang, S. Zhao and X. Li: High-Level Semantic Networks for Multi- Scale Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2019.
12 FFNet code 75.81 % 87.17 % 69.86 % 1.07 s GPU @ 1.5 Ghz (Python)
C. Zhao, Y. Qian and M. Yang: Monocular Pedestrian Orientation Estimation Based on Deep 2D-3D Feedforward. Pattern Recognition 2019.
13 SJTU-HW 75.81 % 87.17 % 69.86 % 0.85s GPU @ 1.5 Ghz (Python + C/C++)
S. Zhang, X. Zhao, L. Fang, F. Haiping and S. Haitao: LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR. IEEE International Conference on Image Processing 2018.
L. Fang, X. Zhao and S. Zhang: Small-objectness sensitive detection based on shifted single shot detector. Multimedia Tools and Applications 2018.
14 MS-CNN code 74.89 % 85.71 % 68.99 % 0.4 s GPU @ 2.5 Ghz (C/C++)
Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection. ECCV 2016.
15 MMCOM 73.08 % 86.01 % 68.38 % 0.04 s 1 core @ 2.5 Ghz (Python)
16 F-ConvNet
This method makes use of Velodyne laser scans.
code 72.91 % 83.63 % 67.18 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
17 GN 72.29 % 82.93 % 65.56 % 1 s GPU @ 2.5 Ghz (Matlab + C/C++)
S. Jung and K. Hong: Deep network aided by guiding network for pedestrian detection. Pattern Recognition Letters 2017.
18 SubCNN 72.27 % 84.88 % 66.82 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.
19 HR-faster-rcnn 72.26 % 87.65 % 65.71 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
20 VMVS
This method makes use of Velodyne laser scans.
71.82 % 82.80 % 66.85 % 0.25 s GPU @ 2.5 Ghz (Python)
J. Ku, A. Pon, S. Walsh and S. Waslander: Improving 3D object detection for pedestrians with virtual multi-view synthesis orientation estimation. IROS 2019.
21 Multi-task DG 71.64 % 85.34 % 66.76 % 0.06 s GPU @ 2.5 Ghz (Python)
22 IVA code 71.37 % 84.61 % 64.90 % 0.4 s GPU @ 2.5 Ghz (C/C++)
Y. Zhu, J. Wang, C. Zhao, H. Guo and H. Lu: Scale-adaptive Deconvolutional Regression Network for Pedestrian Detection. ACCV 2016.
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 2015.
23 MM-MRFC
This method uses optical flow information.
This method makes use of Velodyne laser scans.
70.76 % 83.79 % 64.81 % 0.05 s GPU @ 2.5 Ghz (C/C++)
A. Costea, R. Varga and S. Nedevschi: Fast Boosting based Detection using Scale Invariant Multimodal Multiresolution Filtered Features. CVPR 2017.
24 SDP+RPN 70.42 % 82.07 % 65.09 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 2015.
25 3DOP
This method uses stereo information.
code 69.57 % 83.17 % 63.48 % 3s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
26 MonoPSR code 68.56 % 85.60 % 63.34 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
27 DeepStereoOP 68.46 % 83.00 % 63.35 % 3.4 s GPU @ 3.5 Ghz (Matlab + C/C++)
C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.
28 sensekitti code 68.41 % 82.72 % 62.72 % 4.5 s GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.
29 FII-CenterNet 67.31 % 81.32 % 61.29 % 0.09 s GPU @ 2.5 Ghz (Python)
S. Fan, F. Zhu, S. Chen, H. Zhang, B. Tian, Y. Lv and F. Wang: FII-CenterNet: An Anchor-Free Detector With Foreground Attention for Traffic Object Detection. IEEE Transactions on Vehicular Technology 2021.
30 Mono3D code 67.29 % 80.30 % 62.23 % 4.2 s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.
31 Faster R-CNN code 66.24 % 79.97 % 61.09 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards Real- Time Object Detection with Region Proposal Networks. NIPS 2015.
32 PMN 66.17 % 82.16 % 60.84 % 0.2 s 1 core @ 2.5 Ghz (Python)
33 CenterNet-Boost 64.96 % 79.86 % 57.80 % 0.042 s GPU @ 2.5 Ghz (Python)
34 SDP+CRC (ft) 64.36 % 79.22 % 59.16 % 0.6 s GPU @ 2.5 Ghz (C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
35 Pose-RCNN 63.54 % 80.07 % 57.02 % 2 s >8 cores @ 2.5 Ghz (Python)
M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on 2016.
36 ADLAB 63.25 % 70.86 % 60.52 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
37 UDI-mono3D 63.24 % 77.94 % 57.35 % 0.05 s 1 core @ 2.5 Ghz (Python)
38 CFM 62.84 % 74.76 % 56.06 % <2 s GPU @ 2.5 Ghz (Matlab + C/C++)
Q. Hu, P. Wang, C. Shen, A. Hengel and F. Porikli: Pushing the Limits of Deep CNNs for Pedestrian Detection. IEEE Transactions on Circuits and Systems for Video Technology 2017.
39 HIKVISION-AFree 62.78 % 73.95 % 60.46 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
40 HotSpotNet 62.31 % 71.43 % 59.24 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
41 EACV 62.29 % 79.38 % 57.16 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
42 UDI-mono3D 62.26 % 77.16 % 56.39 % 0.05 s 1 core @ 2.5 Ghz (Python)
43 GAA 61.92 % 77.67 % 56.56 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
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44 MonoPair 61.57 % 78.81 % 56.51 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
45 SAA-PV-RCNN 61.41 % 70.35 % 58.18 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
46 HIKVISION-ADLab-HZ 61.40 % 71.43 % 57.54 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
47 monodle code 61.29 % 78.66 % 56.18 % 0.04 s GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for Monocular 3D Object Detection. CVPR 2021 .
48 RPN+BF code 61.22 % 77.06 % 55.22 % 0.6 s GPU @ 2.5 Ghz (Matlab + C/C++)
L. Zhang, L. Lin, X. Liang and K. He: Is Faster R-CNN Doing Well for Pedestrian Detection?. ECCV 2016.
49 EA-M-RCNN(BorderAtt) 61.06 % 73.07 % 56.86 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
50 Regionlets 60.83 % 73.79 % 54.72 % 1 s >8 cores @ 2.5 Ghz (C/C++)
X. Wang, M. Yang, S. Zhu and Y. Lin: Regionlets for Generic Object Detection. T-PAMI 2015.
W. Zou, X. Wang, M. Sun and Y. Lin: Generic Object Detection with Dense Neural Patterns and Regionlets. British Machine Vision Conference 2014.
C. Long, X. Wang, G. Hua, M. Yang and Y. Lin: Accurate Object Detection with Location Relaxation and Regionlets Relocalization. Asian Conference on Computer Vision 2014.
51 GA-Aug 60.78 % 76.78 % 55.00 % 0.04 s GPU @ 2.5 Ghz (Python)
52 3DSSD code 60.51 % 72.33 % 56.28 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
53 TBD 60.30 % 70.50 % 57.06 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
54 SIEV-Net 60.07 % 70.41 % 56.29 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
55 Generalized-SIENet 59.54 % 69.16 % 57.33 % 0.08 s 1 core @ 2.5 Ghz (Python)
56 Fast VP-RCNN code 59.32 % 69.51 % 56.66 % 0.05 s 1 core @ 3.5 Ghz (C/C++)
57 modat3D
This is an online method (no batch processing).
59.26 % 78.41 % 54.37 % 0.03 s GPU @ 2.5 Ghz (Python)
58 TANet code 59.07 % 69.90 % 56.44 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
59 anonymous code 59.04 % 69.62 % 56.45 % 0.05s 1 core @ >3.5 Ghz (python)
60 GAP-soft-filter 58.89 % 68.54 % 56.08 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
61 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
code 58.81 % 66.93 % 56.57 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
62 BFF 58.72 % 76.95 % 53.70 % 8.4 s 4 cores @ 1.5 Ghz (Python)
63 SRDL
This method uses stereo information.
This method makes use of Velodyne laser scans.
58.70 % 68.45 % 56.23 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
64 Baseline of CA RCNN 58.68 % 68.44 % 56.22 % 0.1 s GPU @ 2.5 Ghz (Python)
65 CVIS-v1 58.68 % 68.44 % 56.22 % 0.1s 1 core @ 2.5 Ghz (Python + C/C++)
66 PF-GAP 58.65 % 70.40 % 55.02 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
67 MSL3D 58.57 % 69.07 % 55.86 % 0.03 s GPU @ 2.5 Ghz (Python)
68 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 58.37 % 68.88 % 55.38 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
69 SCIR-Net
This method makes use of Velodyne laser scans.
58.37 % 68.18 % 55.78 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
70 Point-GNN
This method makes use of Velodyne laser scans.
code 58.20 % 71.59 % 54.06 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
71 PP-3D 58.20 % 71.59 % 54.06 % 0.1 s 1 core @ 2.5 Ghz (Python)
72 XView-PartA^2 58.17 % 67.12 % 55.81 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
73 DeepParts 58.15 % 71.47 % 51.92 % ~1 s GPU @ 2.5 Ghz (Matlab)
Y. Tian, P. Luo, X. Wang and X. Tang: Deep Learning Strong Parts for Pedestrian Detection. ICCV 2015.
74 CompACT-Deep 58.14 % 70.93 % 52.29 % 1 s 1 core @ 2.5 Ghz (Matlab + C/C++)
Z. Cai, M. Saberian and N. Vasconcelos: Learning Complexity-Aware Cascades for Deep Pedestrian Detection. ICCV 2015.
75 WHUT-iou_ssd code 58.03 % 66.60 % 55.82 % 0.045s 1 core @ 2.5 Ghz (C/C++)
76 E^2-PV-RCNN 58.01 % 67.39 % 55.77 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
77 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 57.96 % 68.78 % 54.01 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
78 Point Image Fusion 57.91 % 66.47 % 55.48 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
79 AVOD-FPN
This method makes use of Velodyne laser scans.
code 57.87 % 67.95 % 55.23 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
80 FSA-PVRCNN
This method makes use of Velodyne laser scans.
57.67 % 65.80 % 55.34 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
81 SVGA-Net
This method makes use of Velodyne laser scans.
57.57 % 67.48 % 55.11 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
82 TBD 57.56 % 66.43 % 55.21 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
83 FPC-RCNN 57.46 % 66.88 % 55.09 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
84 Faraway-Frustum
This method makes use of Velodyne laser scans.
code 57.35 % 67.88 % 54.42 % 0.1 s GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion. arXiv preprint arXiv:2011.01404 2020.
85 SIF 57.32 % 67.78 % 54.86 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
86 GNN-RCNN 57.32 % 66.78 % 55.77 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
87 TBD_IOU2 57.26 % 68.26 % 54.74 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
88 SemanticVoxels 57.22 % 67.62 % 54.90 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
J. Fei, W. Chen, P. Heidenreich, S. Wirges and C. Stiller: SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentation. MFI 2020.
89 CVRS VIC-RCNN 57.20 % 65.43 % 55.17 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
90 CVRS VIC-Net 57.20 % 65.53 % 54.92 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
91 FPCR-CNN 57.14 % 66.59 % 54.43 % 0.05 s 1 core @ 2.5 Ghz (Python)
92 FRCNN+Or code 56.68 % 71.64 % 51.53 % 0.09 s Titan Xp GPU
C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine 2018.
C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features. IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.
93 FilteredICF 56.53 % 69.79 % 50.32 % ~ 2 s >8 cores @ 2.5 Ghz (Matlab + C/C++)
S. Zhang, R. Benenson and B. Schiele: Filtered Channel Features for Pedestrian Detection. CVPR 2015.
94 yolo4_5l 56.46 % 73.14 % 49.57 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
95 ARPNET 56.42 % 69.08 % 52.69 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
96 MonoRUn 56.40 % 73.05 % 51.40 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
97 MV-RGBD-RF
This method makes use of Velodyne laser scans.
56.18 % 72.99 % 49.72 % 4 s 4 cores @ 2.5 Ghz (C/C++)
A. Gonzalez, D. Vazquez, A. Lopez and J. Amores: On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts.. IEEE Trans. on Cybernetics 2016.
A. Gonzalez, G. Villalonga, J. Xu, D. Vazquez, J. Amores and A. Lopez: Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection. IEEE Intelligent Vehicles Symposium (IV) 2015.
98 TBD_IOU1 56.02 % 66.44 % 53.62 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
99 CVIS-v2 56.02 % 65.82 % 53.58 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
100 SAA-SECOND 55.95 % 65.50 % 52.88 % 38m s 1 core @ 2.5 Ghz (C/C++)
101 TBD_IOU 55.90 % 64.94 % 53.60 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
102 AF 55.80 % 66.31 % 52.02 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
103 yolo4 55.78 % 72.49 % 51.11 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
104 Mag 55.74 % 71.91 % 50.92 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
105 MLOD
This method makes use of Velodyne laser scans.
code 55.62 % 68.42 % 51.45 % 0.12 s GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
106 TBD 55.43 % 65.62 % 51.97 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
107 CDI3D 55.16 % 67.35 % 51.17 % 0.03 s GPU @ 2.5 Ghz (Python)
108 PointPillars
This method makes use of Velodyne laser scans.
code 55.10 % 65.29 % 52.39 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
109 FPC3D_all
This method makes use of Velodyne laser scans.
55.08 % 64.74 % 52.59 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
110 STD code 55.04 % 68.33 % 50.85 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
111 Vote3Deep
This method makes use of Velodyne laser scans.
54.80 % 67.99 % 51.17 % 1.5 s 4 cores @ 2.5 Ghz (C/C++)
M. Engelcke, D. Rao, D. Zeng Wang, C. Hay Tong and I. Posner: Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks. ArXiv e-prints 2016.
112 yolo4 54.30 % 73.16 % 49.46 % 0.02 s 1 core @ 2.5 Ghz (Python)
113 epBRM
This method makes use of Velodyne laser scans.
code 54.13 % 62.90 % 51.95 % 0.10 s 1 core @ 2.5 Ghz (C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
114 MGACNet 54.13 % 63.54 % 51.79 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
115 OSE+ 54.12 % 68.48 % 49.93 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
116 XView 53.83 % 62.27 % 51.61 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
117 PointPainting
This method makes use of Velodyne laser scans.
53.76 % 61.86 % 50.61 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
118 MKFFNet 53.64 % 63.25 % 51.07 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
119 MKFFNet 53.55 % 62.18 % 50.80 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
120 PDV2 53.54 % 65.59 % 47.65 % 3.7 s 1 core @ 3.0 Ghz Matlab (C/C++)
J. Shen, X. Zuo, J. Li, W. Yang and H. Ling: A novel pixel neighborhood differential statistic feature for pedestrian and face detection . Pattern Recognition 2017.
121 ASCNet 53.28 % 62.40 % 50.88 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
122 3DBN_2 53.26 % 63.82 % 50.76 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
123 TAFT 53.15 % 67.62 % 47.08 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
J. Shen, X. Zuo, W. Yang, D. Prokhorov, X. Mei and H. Ling: Differential Features for Pedestrian Detection: A Taylor Series Perspective. IEEE Transactions on Intelligent Transportation Systems 2018.
124 Disp R-CNN
This method uses stereo information.
code 52.98 % 71.79 % 48.20 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
125 MTMono3d 52.96 % 69.01 % 46.18 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
126 Disp R-CNN (velo)
This method uses stereo information.
code 52.90 % 71.63 % 48.15 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
127 YF 52.90 % 63.79 % 49.54 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
128 pAUCEnsT 52.88 % 65.84 % 46.97 % 60 s 1 core @ 2.5 Ghz (Matlab + C/C++)
S. Paisitkriangkrai, C. Shen and A. Hengel: Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning. arXiv 2014.
129 SparVox3D 52.84 % 69.33 % 48.49 % 0.05 s GPU @ 2.0 Ghz (Python)
130 VGCN 52.80 % 61.86 % 50.66 % 0.09 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
131 tbd 52.78 % 63.45 % 50.79 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
132 yolo4_5l code 52.74 % 71.89 % 47.90 % 0.02 s 1 core @ 2.5 Ghz (Python + C/C++)
133 deprecated 52.59 % 60.42 % 50.61 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
134 PFF3D
This method makes use of Velodyne laser scans.
code 52.53 % 62.12 % 50.27 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
135 ResNet-RRC 52.09 % 66.44 % 47.51 % 0.11 s GPU @ 1.5 Ghz (Python + C/C++)
136 MKFFNet 51.96 % 60.31 % 49.70 % 0.01s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
137 YF 51.82 % 61.37 % 48.59 % 0.04 s GPU @ 2.5 Ghz (C/C++)
138 TBD 51.31 % 61.14 % 47.82 % 0.05 s GPU @ 2.5 Ghz (Python)
139 Shift R-CNN (mono) code 51.30 % 70.86 % 46.37 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
140 ResNet-RRC (pruned) 51.12 % 65.47 % 46.53 % 0.11 s GPU @ 1.5 Ghz (Python + C/C++)
141 PVNet 50.50 % 60.58 % 48.48 % 0,1 s 1 core @ 2.5 Ghz (Python)
142 AF_MCLS 49.95 % 62.85 % 45.78 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
143 SCNet
This method makes use of Velodyne laser scans.
49.61 % 60.95 % 46.91 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
144 yolo_depth 49.47 % 67.23 % 44.99 % 0.07 s GPU @ 2.5 Ghz (Python)
145 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 49.41 % 58.93 % 46.44 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
146 Y4 code 49.24 % 68.07 % 44.42 % 0.03 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
147 Center3D 48.76 % 67.15 % 44.05 % 0.05 s GPU @ 3.5 Ghz (Python)
148 ACFD
This method makes use of Velodyne laser scans.
code 48.63 % 61.62 % 44.15 % 0.2 s 4 cores @ >3.5 Ghz (C/C++)
M. Dimitrievski, P. Veelaert and W. Philips: Semantically aware multilateral filter for depth upsampling in automotive LiDAR point clouds. IEEE Intelligent Vehicles Symposium, IV 2017, Los Angeles, CA, USA, June 11-14, 2017 2017.
149 R-CNN 48.57 % 62.88 % 43.05 % 4 s GPU @ 3.3 Ghz (C/C++)
J. Hosang, M. Omran, R. Benenson and B. Schiele: Taking a Deeper Look at Pedestrians. arXiv 2015.
150 IGRP+ 48.46 % 59.37 % 44.82 % 0.18 s 1 core @ 2.5 Ghz (Python + C/C++)
151 yolo_rgb 48.45 % 64.50 % 43.95 % 0.07 s GPU @ 2.5 Ghz (Python)
152 RelationNet3D_dla34 code 47.93 % 63.34 % 43.42 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
153 LIGA-stereo
This method uses stereo information.
47.75 % 58.61 % 44.39 % 0.375 s Titan Xp
154 MonoFlex 47.58 % 62.64 % 43.15 % 0.03 s GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.
155 tiny-stereo 47.25 % 60.73 % 43.13 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
156 RoIFusion code 46.81 % 56.26 % 44.58 % 0.22 s 1 core @ 3.0 Ghz (Python)
157 NLK-3D 46.33 % 59.46 % 43.88 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
158 deleted 46.14 % 58.97 % 42.48 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
159 SS3D 45.79 % 61.58 % 41.14 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
160 ACF 45.67 % 59.81 % 40.88 % 1 s 1 core @ 3.5 Ghz (Matlab + C/C++)
P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014.
161 TBD
This method makes use of Velodyne laser scans.
45.64 % 51.65 % 43.00 % 0.11 s GPU @ 2.5 Ghz (Python + C/C++)
162 NLK-ALL code 45.27 % 56.86 % 41.08 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
163 NL_M3D 45.03 % 58.46 % 39.22 % 0.2 s 1 core @ 2.5 Ghz (Python)
164 Fusion-DPM
This method makes use of Velodyne laser scans.
code 44.99 % 58.93 % 40.19 % ~ 30 s 1 core @ 3.5 Ghz (Matlab + C/C++)
C. Premebida, J. Carreira, J. Batista and U. Nunes: Pedestrian Detection Combining RGB and Dense LIDAR Data. IROS 2014.
165 CBi-GNN-persons 44.88 % 58.17 % 40.50 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
166 ACF-MR 44.79 % 58.29 % 39.94 % 0.6 s 1 core @ 3.5 Ghz (C/C++)
R. Rajaram, E. Ohn-Bar and M. Trivedi: Looking at Pedestrians at Different Scales: A Multi-resolution Approach and Evaluations. T-ITS 2016.
167 Deprecated 44.65 % 60.33 % 38.51 % Deprecated Deprecated
168 MonoGeo 44.63 % 58.49 % 40.41 % 0.05 s 1 core @ 2.5 Ghz (Python)
169 HA-SSVM 43.87 % 58.76 % 38.81 % 21 s 1 core @ >3.5 Ghz (Matlab + C/C++)
J. Xu, S. Ramos, D. Vázquez and A. López: Hierarchical Adaptive Structural SVM for Domain Adaptation. IJCV 2016.
170 FCY
This method makes use of Velodyne laser scans.
43.87 % 56.43 % 39.87 % 0.02 s GPU @ 2.5 Ghz (Python)
171 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 43.86 % 54.55 % 40.99 % 0.0047s 1 core @ 2.5 Ghz (python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
172 MonoEF code 43.73 % 58.79 % 39.45 % 0.03 s 1 core @ 2.5 Ghz (Python)
173 DA-Mono3D 43.57 % 59.80 % 39.23 % 0.09s 1 core @ 2.5 Ghz (C/C++)
174 D4LCN code 43.50 % 59.55 % 37.12 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
175 DAMNET code 43.42 % 56.05 % 41.16 % 1 s 1 core @ 2.5 Ghz (C/C++)
176 Pointpillar_TV 43.29 % 53.06 % 41.14 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
177 DPM-VOC+VP 43.26 % 59.21 % 38.12 % 8 s 1 core @ 2.5 Ghz (C/C++)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015.
178 ACF-SC 42.97 % 53.30 % 38.12 % <0.3 s 1 core @ >3.5 Ghz (Matlab + C/C++)
C. Cadena, A. Dick and I. Reid: A Fast, Modular Scene Understanding System using Context-Aware Object Detection. Robotics and Automation (ICRA), 2015 IEEE International Conference on 2015.
179 SquaresICF code 42.61 % 57.08 % 37.85 % 1 s GPU @ >3.5 Ghz (C/C++)
R. Benenson, M. Mathias, T. Tuytelaars and L. Gool: Seeking the strongest rigid detector. CVPR 2013.
180 CG-Stereo
This method uses stereo information.
42.54 % 54.64 % 38.45 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
181 RelationNet3D_res18 code 42.25 % 57.61 % 37.93 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
182 BirdNet+
This method makes use of Velodyne laser scans.
code 41.97 % 51.38 % 40.15 % 0.1 s Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird’s Eye View. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
183 ICCV 41.88 % 55.14 % 37.55 % 0.04 s GPU @ 2.5 Ghz (Python)
184 PLDet3d 41.86 % 55.94 % 37.64 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
185 DDMP-3D 41.54 % 56.73 % 35.52 % 0.18 s 1 core @ 2.5 Ghz (Python)
186 CSW3D
This method makes use of Velodyne laser scans.
41.50 % 53.76 % 37.25 % 0.03 s 4 cores @ 2.5 Ghz (C/C++)
J. Hu, T. Wu, H. Fu, Z. Wang and K. Ding: Cascaded Sliding Window Based Real-Time 3D Region Proposal for Pedestrian Detection. ROBIO 2019.
187 M3D-RPN(S-R) 41.46 % 56.64 % 37.31 % 0.16 s GPU @ 1.5 Ghz (Python)
188 M3D-RPN code 41.46 % 56.64 % 37.31 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
189 YOLOStereo3D
This method uses stereo information.
code 41.46 % 56.20 % 37.07 % 0.1 s GPU 1080Ti
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.
190 SubCat 40.50 % 53.75 % 35.66 % 1.2 s 6 cores @ 2.5 Ghz (Matlab + C/C++)
E. Ohn-Bar and M. Trivedi: Fast and Robust Object Detection Using Visual Subcategories. Computer Vision and Pattern Recognition Workshops Mobile Vision 2014.
191 DSGN
This method uses stereo information.
code 39.93 % 49.28 % 38.13 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
192 RT3D-GMP
This method uses stereo information.
39.83 % 55.56 % 35.18 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
193 MP-Mono 39.60 % 53.59 % 35.73 % 0.16 s GPU @ 2.5 Ghz (Python)
194 SparsePool code 39.59 % 50.81 % 35.91 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
195 SparsePool code 39.43 % 50.94 % 35.78 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
196 AVOD
This method makes use of Velodyne laser scans.
code 39.43 % 50.90 % 35.75 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
197 PG-MonoNet 39.38 % 48.57 % 35.43 % 0.19 s GPU @ 2.5 Ghz (Python)
198 ACF 39.12 % 48.42 % 35.03 % 0.2 s 1 core @ >3.5 Ghz (Matlab + C/C++)
P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014.
P. Doll\'ar: Piotr's Image and Video Matlab Toolbox (PMT). .
199 OSE
This method uses stereo information.
38.62 % 50.26 % 34.87 % 0.1 s GPU @ 2.5 Ghz (C/C++)
200 LSVM-MDPM-sv 37.26 % 50.74 % 33.13 % 10 s 4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout. NIPS 2011.
201 multi-task CNN 37.00 % 49.38 % 33.46 % 25.1 ms GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
202 NCL code 36.89 % 42.81 % 34.76 % NA s 1 core @ 2.5 Ghz (Python)
203 Complexer-YOLO
This method makes use of Velodyne laser scans.
36.45 % 42.16 % 32.91 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
204 LAPNet 36.22 % 48.96 % 32.27 % 0.03 s 1 core @ 2.5 Ghz (Python)
205 LSVM-MDPM-us code 35.92 % 48.73 % 31.70 % 10 s 4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
206 Aug3D-RPN 34.95 % 47.22 % 30.64 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
207 MonoHMOO 34.74 % 49.26 % 30.37 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
208 Vote3D
This method makes use of Velodyne laser scans.
33.04 % 42.66 % 30.59 % 0.5 s 4 cores @ 2.8 Ghz (C/C++)
D. Wang and I. Posner: Voting for Voting in Online Point Cloud Object Detection. Proceedings of Robotics: Science and Systems 2015.
209 FADNet code 32.64 % 42.43 % 29.13 % 0.04 s GPU @ >3.5 Ghz (Python)
210 CaDDN 32.42 % 46.35 % 29.98 % 0.63 s GPU @ 2.5 Ghz (Python)
C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.
211 DFR-Net 31.84 % 45.20 % 27.94 % 0.18 s 1080 Ti (Pytorch)
212 VN3D 31.51 % 41.80 % 29.76 % 0.02 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
213 FPIOD
This method makes use of Velodyne laser scans.
code 30.96 % 45.07 % 28.48 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
214 OC Stereo
This method uses stereo information.
code 30.79 % 43.50 % 28.40 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
215 mBoW
This method makes use of Velodyne laser scans.
30.26 % 41.52 % 26.34 % 10 s 1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
216 BirdNet
This method makes use of Velodyne laser scans.
30.07 % 36.82 % 28.40 % 0.11 s Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
217 SOD 29.47 % 46.61 % 26.97 % 0.1 s 1 core @ 2.5 Ghz (Python)
218 RT3DStereo
This method uses stereo information.
29.30 % 41.12 % 25.25 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
219 DPM-C8B1
This method uses stereo information.
25.34 % 36.40 % 22.00 % 15 s 4 cores @ 2.5 Ghz (Matlab + C/C++)
J. Yebes, L. Bergasa and M. García-Garrido: Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes. Sensors 2015.
J. Yebes, L. Bergasa, R. Arroyo and A. Lázaro: Supervised learning and evaluation of KITTI's cars detector with DPM. IV 2014.
220 AEC3D 22.20 % 29.92 % 20.63 % 0.01 s GPU @ 2.5 Ghz (Python)
221 RefinedMPL 20.81 % 30.41 % 18.72 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
222 TopNet-Retina
This method makes use of Velodyne laser scans.
16.45 % 22.37 % 15.43 % 52ms GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
223 TopNet-HighRes
This method makes use of Velodyne laser scans.
15.28 % 21.22 % 13.89 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
224 YOLOv2 code 11.46 % 15.37 % 9.67 % 0.02 s GPU @ 3.5 Ghz (C/C++)
J. Redmon, S. Divvala, R. Girshick and A. Farhadi: You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
225 TopNet-UncEst
This method makes use of Velodyne laser scans.
8.58 % 13.00 % 7.38 % 0.09 s NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
226 BIP-HETERO 7.05 % 8.51 % 6.30 % ~2 s 1 core @ 2.5 Ghz (C/C++)
A. Mekonnen, F. Lerasle, A. Herbulot and C. Briand: People Detection with Heterogeneous Features and Explicit Optimization on Computation Time. Pattern Recognition (ICPR), 2014 22nd International Conference on 2014.
227 TopNet-DecayRate
This method makes use of Velodyne laser scans.
0.01 % 0.01 % 0.01 % 92 ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
228 UM3D_TUM 0.00 % 0.00 % 0.00 % 0.05 s 1 core @ 2.5 Ghz (Python)
Table as LaTeX | Only published Methods

Cyclist


Method Setting Code Moderate Easy Hard Runtime Environment
1 HRI-MSP-L
This method makes use of Velodyne laser scans.
83.08 % 92.12 % 75.62 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
2 HIKVISION-AFree 82.54 % 91.47 % 75.88 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
3 NF2 81.75 % 89.67 % 71.43 % 0.1 s GPU @ 2.5 Ghz (Python)
4 RangeIoUDet
This method makes use of Velodyne laser scans.
81.67 % 90.43 % 74.90 % 0.02 s 1 core @ 2.5 Ghz (Python)
5 anonymous code 81.52 % 89.31 % 74.71 % 0.05s 1 core @ >3.5 Ghz (python)
6 Fast VP-RCNN code 81.41 % 89.29 % 74.88 % 0.05 s 1 core @ 3.5 Ghz (C/C++)
7 Point Image Fusion 80.73 % 87.99 % 74.32 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
8 SAA-PV-RCNN 80.71 % 88.94 % 73.79 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
9 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
code 80.57 % 88.65 % 74.81 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
10 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 80.42 % 86.62 % 73.64 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
11 HIKVISION-ADLab-HZ 80.36 % 89.70 % 73.36 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
12 GNN-RCNN 80.35 % 89.53 % 73.85 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
13 XView-PartA^2 80.16 % 88.15 % 73.92 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
14 Generalized-SIENet 80.07 % 89.23 % 73.76 % 0.08 s 1 core @ 2.5 Ghz (Python)
15 WHUT-iou_ssd code 79.98 % 87.31 % 74.30 % 0.045s 1 core @ 2.5 Ghz (C/C++)
16 E^2-PV-RCNN 79.94 % 87.22 % 73.58 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
17 FPC-RCNN 79.89 % 88.62 % 73.18 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
18 PV-RCNN-v2 79.22 % 85.76 % 72.35 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
19 HotSpotNet 78.81 % 86.06 % 71.74 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
20 TBD 78.73 % 88.55 % 71.87 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
21 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 78.29 % 88.90 % 71.19 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
22 CVRS VIC-RCNN 78.27 % 88.66 % 72.05 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
23 F-ConvNet
This method makes use of Velodyne laser scans.
code 78.05 % 86.75 % 68.12 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
24 PointPainting
This method makes use of Velodyne laser scans.
78.04 % 87.70 % 69.27 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
25 CBi-GNN-persons 77.89 % 88.05 % 69.54 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
26 SAA-SECOND 77.47 % 88.50 % 70.23 % 38m s 1 core @ 2.5 Ghz (C/C++)
27 MMCOM 77.43 % 85.83 % 68.34 % 0.04 s 1 core @ 2.5 Ghz (Python)
28 TBD 77.34 % 87.15 % 70.53 % 0.05 s GPU @ 2.5 Ghz (Python)
29 TBD 77.31 % 87.22 % 70.53 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
30 CVIS-v2 76.98 % 86.55 % 70.29 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
31 MSL3D 76.96 % 85.93 % 70.41 % 0.03 s GPU @ 2.5 Ghz (Python)
32 FSA-PVRCNN
This method makes use of Velodyne laser scans.
76.95 % 84.41 % 71.00 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
33 ASCNet 76.95 % 83.81 % 70.84 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
34 SVGA-Net
This method makes use of Velodyne laser scans.
76.83 % 86.14 % 70.98 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
35 RRC code 76.81 % 86.81 % 66.59 % 3.6 s GPU @ 2.5 Ghz (C/C++)
J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y. Tai and L. Xu: Accurate Single Stage Detector Using Recurrent Rolling Convolution. CVPR 2017.
36 CVRS VIC-Net 76.47 % 85.99 % 70.98 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
37 MKFFNet 76.34 % 86.90 % 69.83 % 0.01s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
38 deprecated 76.14 % 84.03 % 70.81 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
39 FPCR-CNN 75.83 % 87.74 % 68.98 % 0.05 s 1 core @ 2.5 Ghz (Python)
40 MS-CNN code 75.30 % 84.88 % 65.27 % 0.4 s GPU @ 2.5 Ghz (C/C++)
Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection. ECCV 2016.
41 TuSimple code 75.22 % 83.68 % 65.22 % 1.6 s GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016.
42 Point-GNN
This method makes use of Velodyne laser scans.
code 75.08 % 85.75 % 68.69 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
43 PP-3D 75.08 % 85.75 % 68.69 % 0.1 s 1 core @ 2.5 Ghz (Python)
44 SCIR-Net
This method makes use of Velodyne laser scans.
74.88 % 84.92 % 68.13 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
45 EA-M-RCNN(BorderAtt) 74.85 % 88.69 % 68.01 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
46 NLK-ALL code 74.83 % 87.37 % 68.05 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
47 Deep3DBox 74.78 % 84.36 % 64.05 % 1.5 s GPU @ 2.5 Ghz (C/C++)
A. Mousavian, D. Anguelov, J. Flynn and J. Kosecka: 3D Bounding Box Estimation Using Deep Learning and Geometry. CVPR 2017.
48 MKFFNet 74.69 % 84.92 % 68.29 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
49 MKFFNet 74.39 % 85.89 % 68.02 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
50 3DBN_2 74.34 % 88.48 % 67.66 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
51 RoIFusion code 74.27 % 85.15 % 68.29 % 0.22 s 1 core @ 3.0 Ghz (Python)
52 3DSSD code 74.12 % 87.09 % 67.67 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
53 SDP+RPN 73.85 % 82.59 % 64.87 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 2015.
54 TBD_IOU 73.84 % 87.97 % 66.36 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
55 Baseline of CA RCNN 73.69 % 85.39 % 66.94 % 0.1 s GPU @ 2.5 Ghz (Python)
56 CVIS-v1 73.69 % 85.39 % 66.94 % 0.1s 1 core @ 2.5 Ghz (Python + C/C++)
57 SRDL
This method uses stereo information.
This method makes use of Velodyne laser scans.
73.68 % 85.44 % 66.94 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
58 MGACNet 73.66 % 85.45 % 67.10 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
59 Faraway-Frustum
This method makes use of Velodyne laser scans.
code 73.63 % 85.43 % 66.64 % 0.1 s GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion. arXiv preprint arXiv:2011.01404 2020.
60 dgist_multiDetNet 73.57 % 87.95 % 64.65 % 0.08 s GPU Titanx Pascal (Python)
61 sensekitti code 73.48 % 82.90 % 64.03 % 4.5 s GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.
62 TBD_IOU1 73.46 % 87.05 % 66.14 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
63 GAP-soft-filter 73.45 % 85.13 % 66.08 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
64 tbd 73.43 % 84.88 % 65.72 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
65 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 73.42 % 86.21 % 66.45 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
66 FPC3D_all
This method makes use of Velodyne laser scans.
73.25 % 84.12 % 66.56 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
67 SIF 73.19 % 85.18 % 65.41 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
68 F-PointNet
This method makes use of Velodyne laser scans.
code 73.16 % 86.86 % 65.21 % 0.17 s GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
69 TBD_IOU2 73.16 % 87.07 % 65.98 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
70 XView 73.16 % 88.02 % 65.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
71 YF 72.90 % 87.20 % 66.56 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
72 H^23D R-CNN 72.73 % 85.50 % 65.81 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
73 DGIST MT-CNN 72.57 % 86.82 % 63.47 % 0.09 s GPU @ 1.0 Ghz (Python)
74 VGCN 72.28 % 86.81 % 65.68 % 0.09 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
75 ZEEWAIN-AI 72.25 % 83.84 % 63.80 % 0.3 s GPU @ 2.5 Ghz (Python)
76 MonoPSR code 72.08 % 82.06 % 62.43 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
77 ARPNET 71.95 % 84.96 % 65.21 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
78 SubCNN 71.72 % 79.36 % 62.74 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.
79 STD code 71.63 % 83.99 % 64.92 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
80 PVNet 71.10 % 83.89 % 65.08 % 0,1 s 1 core @ 2.5 Ghz (Python)
81 Multi-task DG 70.98 % 80.96 % 62.18 % 0.06 s GPU @ 2.5 Ghz (Python)
82 NLK-3D 70.55 % 85.92 % 63.76 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
83 AF 70.41 % 86.42 % 63.26 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
84 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 70.18 % 82.86 % 63.55 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
85 TBD 70.12 % 81.15 % 63.79 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
86 PF-GAP 70.11 % 86.24 % 63.26 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
87 YF 70.10 % 80.50 % 64.21 % 0.04 s GPU @ 2.5 Ghz (C/C++)
88 FCY
This method makes use of Velodyne laser scans.
70.05 % 83.02 % 63.63 % 0.02 s GPU @ 2.5 Ghz (Python)
89 CCFNET 69.17 % 83.76 % 62.87 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
90 PointPillars
This method makes use of Velodyne laser scans.
code 68.98 % 83.97 % 62.17 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
91 Vote3Deep
This method makes use of Velodyne laser scans.
68.82 % 78.41 % 62.50 % 1.5 s 4 cores @ 2.5 Ghz (C/C++)
M. Engelcke, D. Rao, D. Zeng Wang, C. Hay Tong and I. Posner: Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks. ArXiv e-prints 2016.
92 3DOP
This method uses stereo information.
code 68.71 % 80.52 % 61.07 % 3s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
93 Pose-RCNN 68.40 % 81.53 % 59.43 % 2 s >8 cores @ 2.5 Ghz (Python)
M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on 2016.
94 TANet code 68.20 % 82.24 % 62.13 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
95 SIEV-Net 68.07 % 86.63 % 61.03 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
96 AF_MCLS 67.97 % 85.45 % 60.95 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
97 IVA code 67.57 % 78.48 % 58.83 % 0.4 s GPU @ 2.5 Ghz (C/C++)
Y. Zhu, J. Wang, C. Zhao, H. Guo and H. Lu: Scale-adaptive Deconvolutional Regression Network for Pedestrian Detection. ACCV 2016.
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 2015.
98 IGRP+ 67.56 % 83.94 % 61.15 % 0.18 s 1 core @ 2.5 Ghz (Python + C/C++)
99 DeepStereoOP 67.22 % 79.35 % 58.60 % 3.4 s GPU @ 3.5 Ghz (Matlab + C/C++)
C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.
100 FII-CenterNet 66.54 % 79.04 % 57.76 % 0.09 s GPU @ 2.5 Ghz (Python)
S. Fan, F. Zhu, S. Chen, H. Zhang, B. Tian, Y. Lv and F. Wang: FII-CenterNet: An Anchor-Free Detector With Foreground Attention for Traffic Object Detection. IEEE Transactions on Vehicular Technology 2021.
101 epBRM
This method makes use of Velodyne laser scans.
code 66.51 % 79.65 % 60.31 % 0.10 s 1 core @ 2.5 Ghz (C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
102 PFF3D
This method makes use of Velodyne laser scans.
code 66.25 % 79.44 % 60.11 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
103 Pointpillar_TV 66.20 % 79.86 % 59.73 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
104 HR-faster-rcnn 65.53 % 83.49 % 58.13 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
105 Mono3D code 65.15 % 77.19 % 57.88 % 4.2 s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.
106 TBD
This method makes use of Velodyne laser scans.
64.04 % 72.04 % 59.43 % 0.11 s GPU @ 2.5 Ghz (Python + C/C++)
107 Faster R-CNN code 62.86 % 72.40 % 54.97 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards Real- Time Object Detection with Region Proposal Networks. NIPS 2015.
108 SCNet
This method makes use of Velodyne laser scans.
62.50 % 78.48 % 56.34 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
109 UDI-mono3D 62.26 % 79.20 % 53.95 % 0.05 s 1 core @ 2.5 Ghz (Python)
110 AVOD-FPN
This method makes use of Velodyne laser scans.
code 60.79 % 70.38 % 55.37 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
111 SDP+CRC (ft) 60.72 % 75.63 % 53.00 % 0.6 s GPU @ 2.5 Ghz (C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
112 Complexer-YOLO
This method makes use of Velodyne laser scans.
59.78 % 66.94 % 55.63 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
113 UDI-mono3D 59.44 % 77.70 % 51.49 % 0.05 s 1 core @ 2.5 Ghz (Python)
114 Regionlets 58.52 % 71.12 % 50.83 % 1 s >8 cores @ 2.5 Ghz (C/C++)
X. Wang, M. Yang, S. Zhu and Y. Lin: Regionlets for Generic Object Detection. T-PAMI 2015.
W. Zou, X. Wang, M. Sun and Y. Lin: Generic Object Detection with Dense Neural Patterns and Regionlets. British Machine Vision Conference 2014.
C. Long, X. Wang, G. Hua, M. Yang and Y. Lin: Accurate Object Detection with Location Relaxation and Regionlets Relocalization. Asian Conference on Computer Vision 2014.
115 PMN 57.22 % 74.63 % 49.82 % 0.2 s 1 core @ 2.5 Ghz (Python)
116 FRCNN+Or code 57.01 % 70.99 % 50.14 % 0.09 s Titan Xp GPU
C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine 2018.
C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features. IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.
117 modat3D
This is an online method (no batch processing).
56.51 % 75.55 % 49.70 % 0.03 s GPU @ 2.5 Ghz (Python)
118 MonoPair 56.37 % 74.77 % 48.37 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
119 MLOD
This method makes use of Velodyne laser scans.
code 56.04 % 75.35 % 49.11 % 0.12 s GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
120 LIGA-stereo
This method uses stereo information.
55.77 % 74.25 % 49.68 % 0.375 s Titan Xp
121 yolo4_5l 55.42 % 75.21 % 48.57 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
122 EACV 55.01 % 73.41 % 48.75 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
123 MonoFlex 54.76 % 72.41 % 46.21 % 0.03 s GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.
124 BirdNet+
This method makes use of Velodyne laser scans.
code 54.61 % 74.97 % 50.29 % 0.1 s Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird’s Eye View. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
125 GAA 54.24 % 71.23 % 47.41 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
126 NL_M3D 53.51 % 71.09 % 47.07 % 0.2 s 1 core @ 2.5 Ghz (Python)
127 monodle code 53.29 % 70.78 % 45.01 % 0.04 s GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for Monocular 3D Object Detection. CVPR 2021 .
128 DAMNET code 52.93 % 71.01 % 48.56 % 1 s 1 core @ 2.5 Ghz (C/C++)
129 GA-Aug 52.71 % 67.62 % 46.37 % 0.04 s GPU @ 2.5 Ghz (Python)
130 AVOD
This method makes use of Velodyne laser scans.
code 52.60 % 66.45 % 46.39 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
131 yolo4 50.62 % 71.71 % 44.18 % 0.02 s 1 core @ 2.5 Ghz (Python)
132 MonoGeo 50.48 % 65.42 % 42.48 % 0.05 s 1 core @ 2.5 Ghz (Python)
133 CDI3D 50.29 % 63.72 % 43.95 % 0.03 s GPU @ 2.5 Ghz (Python)
134 deleted 50.22 % 68.25 % 44.84 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
135 CenterNet-Boost 49.84 % 69.64 % 43.05 % 0.042 s GPU @ 2.5 Ghz (Python)
136 MP-Mono 49.65 % 70.29 % 41.07 % 0.16 s GPU @ 2.5 Ghz (Python)
137 MonoRUn 49.13 % 67.47 % 43.41 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
138 Mag 48.91 % 68.41 % 42.87 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
139 tiny-stereo 48.90 % 68.84 % 42.97 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
140 yolo4 48.67 % 67.33 % 43.00 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
141 CG-Stereo
This method uses stereo information.
48.46 % 69.98 % 42.41 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
142 yolo4_5l code 48.38 % 69.14 % 42.16 % 0.02 s 1 core @ 2.5 Ghz (Python + C/C++)
143 MTMono3d 47.71 % 67.12 % 38.84 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
144 BirdNet
This method makes use of Velodyne laser scans.
47.64 % 64.91 % 44.59 % 0.11 s Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
145 Disp R-CNN (velo)
This method uses stereo information.
code 46.37 % 63.22 % 40.15 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
146 Disp R-CNN
This method uses stereo information.
code 46.37 % 63.24 % 40.15 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
147 SparsePool code 44.57 % 60.53 % 40.37 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
148 Deprecated 44.26 % 63.15 % 37.38 % Deprecated Deprecated
149 DA-Mono3D 43.98 % 63.35 % 39.14 % 0.09s 1 core @ 2.5 Ghz (C/C++)
150 FADNet code 43.40 % 59.77 % 37.28 % 0.04 s GPU @ >3.5 Ghz (Python)
151 Scan_YOLO 43.39 % 64.82 % 37.77 % 0.1 s 4 cores @ 3.0 Ghz (Python)
152 ResNet-RRC (pruned) 43.35 % 58.81 % 37.68 % 0.11 s GPU @ 1.5 Ghz (Python + C/C++)
153 Shift R-CNN (mono) code 42.96 % 63.24 % 38.22 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
154 ResNet-RRC 42.88 % 58.72 % 37.74 % 0.11 s GPU @ 1.5 Ghz (Python + C/C++)
155 D4LCN code 42.86 % 65.29 % 36.29 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
156 PLDet3d 41.84 % 60.16 % 37.65 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
157 yolo_rgb 41.59 % 62.22 % 37.32 % 0.07 s GPU @ 2.5 Ghz (Python)
158 M3D-RPN code 41.54 % 61.54 % 35.23 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
159 MonoEF code 41.19 % 51.06 % 35.70 % 0.03 s 1 core @ 2.5 Ghz (Python)
160 Center3D 40.99 % 65.34 % 36.50 % 0.05 s GPU @ 3.5 Ghz (Python)
161 SOD 40.95 % 60.07 % 34.02 % 0.1 s 1 core @ 2.5 Ghz (Python)
162 MV-RGBD-RF
This method makes use of Velodyne laser scans.
40.94 % 51.10 % 34.83 % 4 s 4 cores @ 2.5 Ghz (C/C++)
A. Gonzalez, D. Vazquez, A. Lopez and J. Amores: On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts.. IEEE Trans. on Cybernetics 2016.
A. Gonzalez, G. Villalonga, J. Xu, D. Vazquez, J. Amores and A. Lopez: Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection. IEEE Intelligent Vehicles Symposium (IV) 2015.
163 RelationNet3D_dla34 code 39.52 % 59.56 % 34.51 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
164 OSE+ 39.26 % 58.13 % 34.35 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
165 DDMP-3D 38.62 % 58.70 % 34.10 % 0.18 s 1 core @ 2.5 Ghz (Python)
166 RelationNet3D_res18 code 37.41 % 56.22 % 32.56 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
167 yolo_depth 36.89 % 50.88 % 32.64 % 0.07 s GPU @ 2.5 Ghz (Python)
168 ICCV 36.70 % 53.31 % 31.94 % 0.04 s GPU @ 2.5 Ghz (Python)
169 Aug3D-RPN 36.69 % 51.49 % 30.04 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
170 SparsePool code 36.26 % 44.21 % 32.57 % 0.13 s 8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
171 PG-MonoNet 36.09 % 47.28 % 32.15 % 0.19 s GPU @ 2.5 Ghz (Python)
172 Y4 code 35.92 % 53.50 % 31.89 % 0.03 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
173 LAPNet 35.62 % 53.46 % 29.27 % 0.03 s 1 core @ 2.5 Ghz (Python)
174 SS3D 35.48 % 52.97 % 31.07 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
175 DSGN
This method uses stereo information.
code 35.15 % 49.10 % 31.41 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
176 pAUCEnsT 34.90 % 50.51 % 30.35 % 60 s 1 core @ 2.5 Ghz (Matlab + C/C++)
S. Paisitkriangkrai, C. Shen and A. Hengel: Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning. arXiv 2014.
177 TopNet-Retina
This method makes use of Velodyne laser scans.
31.98 % 47.51 % 29.84 % 52ms GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
178 DFR-Net 31.93 % 48.34 % 27.95 % 0.18 s 1080 Ti (Pytorch)
179 OSE
This method uses stereo information.
28.78 % 45.05 % 25.66 % 0.1 s GPU @ 2.5 Ghz (C/C++)
180 OC Stereo
This method uses stereo information.
code 28.76 % 43.18 % 24.80 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
181 VN3D 28.05 % 38.28 % 26.98 % 0.02 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
182 Vote3D
This method makes use of Velodyne laser scans.
27.99 % 39.81 % 25.19 % 0.5 s 4 cores @ 2.8 Ghz (C/C++)
D. Wang and I. Posner: Voting for Voting in Online Point Cloud Object Detection. Proceedings of Robotics: Science and Systems 2015.
183 LSVM-MDPM-us code 27.81 % 37.66 % 24.83 % 10 s 4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
184 DPM-VOC+VP 27.73 % 41.58 % 24.61 % 8 s 1 core @ 2.5 Ghz (C/C++)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015.
185 RefinedMPL 27.17 % 44.47 % 22.84 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
186 CaDDN 27.13 % 40.03 % 23.23 % 0.63 s GPU @ 2.5 Ghz (Python)
C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.
187 LSVM-MDPM-sv 26.05 % 35.70 % 23.56 % 10 s 4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout. NIPS 2011.
188 DPM-C8B1
This method uses stereo information.
25.57 % 41.47 % 21.93 % 15 s 4 cores @ 2.5 Ghz (Matlab + C/C++)
J. Yebes, L. Bergasa and M. García-Garrido: Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes. Sensors 2015.
J. Yebes, L. Bergasa, R. Arroyo and A. Lázaro: Supervised learning and evaluation of KITTI's cars detector with DPM. IV 2014.
189 MonoHMOO 23.59 % 37.41 % 21.20 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
190 FPIOD
This method makes use of Velodyne laser scans.
code 23.10 % 37.02 % 19.66 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
191 RT3D-GMP
This method uses stereo information.
22.90 % 33.64 % 19.87 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
192 mBoW
This method makes use of Velodyne laser scans.
17.63 % 26.66 % 16.02 % 10 s 1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
193 AEC3D 16.63 % 26.21 % 15.52 % 0.01 s GPU @ 2.5 Ghz (Python)
194 TopNet-HighRes
This method makes use of Velodyne laser scans.
13.98 % 22.86 % 14.52 % 101ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
195 RT3DStereo
This method uses stereo information.
12.96 % 19.58 % 11.47 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
196 TopNet-UncEst
This method makes use of Velodyne laser scans.
12.00 % 18.14 % 11.85 % 0.09 s NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
197 YOLOv2 code 0.06 % 0.15 % 0.07 % 0.02 s GPU @ 3.5 Ghz (C/C++)
J. Redmon, S. Divvala, R. Girshick and A. Farhadi: You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016.
J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
198 TopNet-DecayRate
This method makes use of Velodyne laser scans.
0.04 % 0.00 % 0.04 % 92 ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
199 UM3D_TUM 0.00 % 0.00 % 0.00 % 0.05 s 1 core @ 2.5 Ghz (Python)
Table as LaTeX | Only published Methods

Object Detection and Orientation Estimation Evaluation

Cars


Method Setting Code Moderate Easy Hard Runtime Environment
1 CLOCs_PVCas code 95.79 % 96.74 % 90.81 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection . 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
2 EA-M-RCNN(BorderAtt) 95.44 % 96.37 % 90.31 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
3 HUAWEI Octopus 95.40 % 96.29 % 92.67 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
4 BANet 95.34 % 98.65 % 90.28 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
5 SE-SSD
This method makes use of Velodyne laser scans.
code 95.17 % 96.55 % 90.00 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, L. Jiang and C. Fu: SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud. CVPR 2021.
6 SPANet 95.03 % 96.31 % 89.99 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
7 TBD 95.03 % 96.47 % 92.50 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
8 Pyramid R-CNN 95.03 % 95.87 % 92.46 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
9 3DIoU++ 94.97 % 96.36 % 90.40 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
10 Voxel R-CNN code 94.96 % 96.47 % 92.24 % 0.04 s GPU @ 3.0 Ghz (C/C++)
J. Deng, S. Shi, P. Li, W. Zhou, Y. Zhang and H. Li: Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection . AAAI 2021.
11 PV-RCNN-v2 94.90 % 96.07 % 92.22 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
12 SIENet code 94.85 % 96.01 % 92.23 % 0.08 s 1 core @ 2.5 Ghz (Python)
Z. Li, Y. Yao, Z. Quan, W. Yang and J. Xie: SIENet: Spatial Information Enhancement Network for 3D Object Detection from Point Cloud. 2021.
13 VoTr-2 94.81 % 95.95 % 92.24 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
14 FrustumRCNN 94.79 % 95.97 % 92.23 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
15 E^2-PV-RCNN 94.69 % 95.94 % 92.09 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
16 XView 94.66 % 95.88 % 92.07 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
17 MSG-PGNN 94.65 % 95.85 % 92.01 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
18 vb 94.64 % 96.12 % 91.85 % 0.02 s 8 cores @ 2.5 Ghz (Python)
19 Generalized-SIENet 94.59 % 95.74 % 91.99 % 0.08 s 1 core @ 2.5 Ghz (Python)
20 HyBrid Feature Det 94.59 % 95.87 % 91.97 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
21 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 94.57 % 98.15 % 91.85 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
22 3DIoU_v2 94.57 % 96.14 % 92.18 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
23 LZY_RCNN 94.56 % 95.80 % 91.94 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
24 CVRS VIC-Net 94.55 % 95.78 % 91.67 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
25 PC-RGNN 94.55 % 95.79 % 92.03 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
26 SCIR-Net
This method makes use of Velodyne laser scans.
94.55 % 96.11 % 91.68 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
27 TransCyclistNet 94.52 % 96.07 % 91.94 % 0.08 s 1 core @ 2.5 Ghz (Python)
28 Fast VP-RCNN code 94.52 % 97.99 % 91.74 % 0.05 s 1 core @ 3.5 Ghz (C/C++)
29 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
code 94.52 % 95.84 % 91.93 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
30 TransDet3D 94.50 % 95.82 % 91.89 % 0.08 s 1 core @ 2.5 Ghz (Python)
31 ReFineNet 94.49 % 95.74 % 91.97 % 0.08 s 1 core @ 2.5 Ghz (Python)
32 Point Image Fusion 94.49 % 95.69 % 91.92 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
33 nonet 94.48 % 95.85 % 91.64 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
34 MSL3D 94.46 % 95.74 % 91.94 % 0.03 s GPU @ 2.5 Ghz (Python)
35 Multi-Sensor3D 94.46 % 95.74 % 91.94 % 0.03 s GPU @ 2.5 Ghz (Python)
36 MVRA + I-FRCNN+ 94.46 % 95.66 % 81.74 % 0.18 s GPU @ 2.5 Ghz (Python)
H. Choi, H. Kang and Y. Hyun: Multi-View Reprojection Architecture for Orientation Estimation. The IEEE International Conference on Computer Vision (ICCV) Workshops 2019.
37 WHUT-iou_ssd code 94.45 % 95.76 % 91.75 % 0.045s 1 core @ 2.5 Ghz (C/C++)
38 MGACNet 94.44 % 95.33 % 91.55 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
39 anonymous code 94.43 % 97.50 % 91.66 % 0.05s 1 core @ >3.5 Ghz (python)
40 FSA-PVRCNN
This method makes use of Velodyne laser scans.
94.43 % 95.76 % 91.77 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
41 RangeIoUDet
This method makes use of Velodyne laser scans.
94.42 % 95.69 % 91.70 % 0.02 s 1 core @ 2.5 Ghz (Python)
42 FPC-RCNN 94.40 % 96.13 % 91.63 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
43 FPC3D
This method makes use of the epipolar geometry.
94.39 % 96.04 % 91.51 % 33 s 1 core @ 2.5 Ghz (C/C++)
44 RangeRCNN-LV 94.37 % 95.92 % 91.85 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
45 GNN-RCNN 94.32 % 95.84 % 91.79 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
46 PF-GAP 94.31 % 96.10 % 89.92 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
47 CVRS VIC-RCNN 94.29 % 95.88 % 91.77 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
48 SAA-SECOND 94.24 % 95.64 % 91.40 % 38m s 1 core @ 2.5 Ghz (C/C++)
49 SERCNN
This method makes use of Velodyne laser scans.
94.24 % 96.31 % 89.71 % 0.1 s 1 core @ 2.5 Ghz (Python)
D. Zhou, J. Fang, X. Song, L. Liu, J. Yin, Y. Dai, H. Li and R. Yang: Joint 3D Instance Segmentation and Object Detection for Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020.
50 CVRS_PF 94.23 % 95.55 % 91.21 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
51 EPNet code 94.22 % 96.13 % 89.68 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
T. Huang, Z. Liu, X. Chen and X. Bai: EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection. ECCV 2020.
52 D3D 94.18 % 95.22 % 89.14 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
53 CVIS-v2 94.16 % 95.68 % 91.45 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
54 SVGA-Net
This method makes use of Velodyne laser scans.
94.13 % 95.68 % 91.48 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
55 SRDL
This method uses stereo information.
This method makes use of Velodyne laser scans.
94.08 % 95.83 % 91.55 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
56 TBD 94.07 % 95.49 % 91.44 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
57 Baseline of CA RCNN 94.07 % 95.82 % 91.54 % 0.1 s GPU @ 2.5 Ghz (Python)
58 CVIS-v1 94.07 % 95.82 % 91.54 % 0.1s 1 core @ 2.5 Ghz (Python + C/C++)
59 GAP-soft-filter 94.04 % 95.78 % 91.27 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
60 tbd code 94.03 % 95.66 % 91.20 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
61 SAA-PV-RCNN 94.02 % 95.00 % 92.34 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
62 FPCR-CNN 93.99 % 95.94 % 90.99 % 0.05 s 1 core @ 2.5 Ghz (Python)
63 RangeRCNN
This method makes use of Velodyne laser scans.
93.90 % 95.47 % 91.53 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, M. Zhang, Z. Zhang, X. Zhao and S. Pu: RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representation. arXiv preprint arXiv:2009.00206 2020.
64 HRI-MSP-L
This method makes use of Velodyne laser scans.
93.82 % 95.50 % 91.27 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
65 SIF 93.79 % 95.48 % 91.30 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
66 AF_V1 93.77 % 94.45 % 86.25 % 0.1 s 1 core @ 2.5 Ghz (Python)
67 XView-PartA^2 93.59 % 95.41 % 91.09 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
68 HIKVISION-ADLab-HZ 93.58 % 96.68 % 88.53 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
69 Patches - EMP
This method makes use of Velodyne laser scans.
93.58 % 97.88 % 90.31 % 0.5 s GPU @ 2.5 Ghz (Python)
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
70 LIGA-stereo
This method uses stereo information.
93.54 % 96.63 % 83.68 % 0.375 s Titan Xp
71 MVAF-Net code 93.54 % 95.35 % 90.70 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
G. Wang, B. Tian, Y. Zhang, L. Chen, D. Cao and J. Wu: Multi-View Adaptive Fusion Network for 3D Object Detection. arXiv preprint arXiv:2011.00652 2020.
72 TBD 93.53 % 95.30 % 91.03 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
73 Associate-3Ddet_v2 93.46 % 96.66 % 88.20 % 0.04 s 1 core @ 2.5 Ghz (Python)
74 VAL 93.45 % 96.83 % 83.46 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
75 CIA-SSD
This method makes use of Velodyne laser scans.
code 93.34 % 96.65 % 85.76 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, S. Chen, L. Jiang and C. Fu: CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud. AAAI 2021.
76 Deep MANTA 93.31 % 98.83 % 82.95 % 0.7 s GPU @ 2.5 Ghz (Python + C/C++)
F. Chabot, M. Chaouch, J. Rabarisoa, C. Teulière and T. Chateau: Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image. CVPR 2017.
77 CIA-SSD v2
This method makes use of Velodyne laser scans.
93.22 % 96.53 % 87.81 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
78 deprecated 93.22 % 96.75 % 85.64 % deprecated deprecated
79 CityBrainLab-CT3D 93.20 % 96.26 % 90.44 % 0.06 s 1 core @ 2.5 Ghz (Python + C/C++)
80 AM-SSD 93.18 % 96.56 % 90.13 % 0.04 s 1 core @ 2.5 Ghz (Python)
81 CBi-GNN 93.16 % 98.70 % 87.97 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
82 PointRes
This method makes use of Velodyne laser scans.
93.15 % 96.57 % 90.04 % 0.013 s 1 core @ 2.5 Ghz (Python + C/C++)
83 YF 93.15 % 96.15 % 90.08 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
84 AIMC-RUC 93.14 % 96.64 % 87.92 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
85 CJJ 93.14 % 96.59 % 90.16 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
86 FCY
This method makes use of Velodyne laser scans.
93.08 % 96.51 % 87.90 % 0.02 s GPU @ 2.5 Ghz (Python)
87 PSS 93.05 % 96.52 % 90.08 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
88 H^23D R-CNN 93.03 % 96.13 % 90.33 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
89 ASCNet 93.01 % 96.05 % 90.21 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
90 Seg-RCNN code 92.99 % 96.50 % 87.54 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
91 3DIoU+++ 92.98 % 96.07 % 90.40 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
92 CM3DV 92.98 % 96.47 % 87.68 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
93 HV 92.89 % 95.89 % 87.65 % 0.02 s GPU @ 2.5 Ghz (Python)
94 EBM3DOD code 92.88 % 96.39 % 87.58 % 0.12 s 1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.
95 Struc info fusion II 92.88 % 96.44 % 87.67 % 0.05 s GPU @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.
96 Cas-SSD 92.83 % 96.38 % 87.64 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
97 MBDF-Net 92.77 % 96.19 % 89.70 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
98 YF 92.74 % 96.02 % 89.76 % 0.04 s GPU @ 2.5 Ghz (C/C++)
99 HotSpotNet 92.74 % 96.20 % 89.68 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
100 Struc info fusion I 92.71 % 96.24 % 87.55 % 0.05 s 1 core @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.
101 EBM3DOD baseline code 92.70 % 96.31 % 87.44 % 0.05 s 1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.
102 deprecated 92.66 % 95.52 % 91.39 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
103 SARPNET 92.58 % 95.82 % 87.33 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
Y. Ye, H. Chen, C. Zhang, X. Hao and Z. Zhang: SARPNET: Shape Attention Regional Proposal Network for LiDAR-based 3D Object Detection. Neurocomputing 2019.
104 Patches
This method makes use of Velodyne laser scans.
92.57 % 96.31 % 87.41 % 0.15 s GPU @ 2.0 Ghz
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
105 R-GCN 92.53 % 96.16 % 87.45 % 0.16 s GPU @ 2.5 Ghz (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
106 PI-RCNN 92.52 % 96.15 % 87.47 % 0.1 s 1 core @ 2.5 Ghz (Python)
L. Xie, C. Xiang, Z. Yu, G. Xu, Z. Yang, D. Cai and X. He: PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module. AAAI 2020 : The Thirty-Fourth AAAI Conference on Artificial Intelligence 2020.
107 CenterNet3D 92.48 % 95.71 % 89.54 % 0.04 s GPU @ 1.5 Ghz (Python)
G. Wang, B. Tian, Y. Ai, T. Xu, L. Chen and D. Cao: CenterNet3D:An Anchor free Object Detector for Autonomous Driving. 2020.
108 PointPainting
This method makes use of Velodyne laser scans.
92.43 % 98.36 % 89.49 % 0.4 s GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
109 3D IoU-Net 92.42 % 96.31 % 87.60 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, S. Krylov, Y. Ding and L. Shao: 3D IoU-Net: IoU Guided 3D Object Detector for Point Clouds. arXiv preprint arXiv:2004.04962 2020.
110 MBDF-Net-1 92.37 % 95.87 % 89.26 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
111 CLOCs_SecCas 92.37 % 95.16 % 88.43 % 0.1 s 1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
112 VAR 92.28 % 95.08 % 89.42 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
113 NLK-ALL code 92.27 % 95.49 % 87.34 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
114 DASS 92.25 % 96.20 % 87.26 % 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.
115 SegVoxelNet 92.16 % 95.86 % 86.90 % 0.04 s 1 core @ 2.5 Ghz (Python)
H. Yi, S. Shi, M. Ding, J. Sun, K. Xu, H. Zhou, Z. Wang, S. Li and G. Wang: SegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloud. ICRA 2020.
116 PointRGCN 92.15 % 97.48 % 86.83 % 0.26 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
117 NLK-3D 92.15 % 95.20 % 87.13 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
118 MDA 92.01 % 94.87 % 89.31 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
119 PVNet 92.00 % 94.82 % 89.08 % 0,1 s 1 core @ 2.5 Ghz (Python)
120 F-ConvNet
This method makes use of Velodyne laser scans.
code 91.98 % 95.81 % 79.83 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
121 TBD 91.97 % 93.46 % 89.36 % 0.05 s GPU @ 2.5 Ghz (Python)
122 CCFNET 91.90 % 95.79 % 88.82 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
123 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 91.87 % 95.86 % 86.78 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
124 VGCN 91.80 % 94.88 % 89.06 % 0.09 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
125 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 91.77 % 95.90 % 86.92 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
126 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 91.73 % 95.00 % 88.86 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
127 MKFFNet 91.72 % 95.26 % 88.92 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
128 CenterNet-Boost 91.65 % 95.56 % 84.24 % 0.042 s GPU @ 2.5 Ghz (Python)
129 Pointpillar_TV 91.61 % 94.80 % 88.25 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
130 C-GCN 91.57 % 95.63 % 86.13 % 0.147 s GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
131 VOXEL_3D 91.52 % 94.49 % 86.23 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
132 V3D 91.44 % 94.45 % 86.22 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
133 TBD 91.42 % 95.56 % 86.05 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
134 tt code 91.38 % 95.14 % 88.39 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
135 MKFFNet 91.38 % 95.30 % 88.77 % 0.01s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
136 MKFFNet 91.29 % 95.17 % 88.69 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
137 EgoNet code 91.23 % 96.11 % 80.96 % 0.1 s GPU @ 1.5 Ghz (Python)
S. Li, Z. Yan, H. Li and K. Cheng: Exploring intermediate representation for monocular vehicle pose estimation. The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
138 AIMC-RUC 91.18 % 96.84 % 85.94 % 0.11 s 1 core @ 2.5 Ghz (Python)
139 PFF3D
This method makes use of Velodyne laser scans.
code 91.06 % 94.86 % 86.28 % 0.05 s GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
140 3DBN_2 91.05 % 94.89 % 88.42 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
141 SC(DLA34)
This method uses stereo information.
91.02 % 96.54 % 83.15 % 0.04 s GPU @ 2.5 Ghz (Python)
142 SIEV-Net 91.00 % 94.66 % 85.74 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
143 MonoFlex 90.82 % 95.95 % 83.11 % 0.03 s GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.
144 MAFF-Net(DAF-Pillar) 90.78 % 94.17 % 83.17 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
Z. Zhang, Z. Liang, M. Zhang, X. Zhao, Y. Ming, T. Wenming and S. Pu: MAFF-Net: Filter False Positive for 3D Vehicle Detection with Multi-modal Adaptive Feature Fusion. arXiv preprint arXiv:2009.10945 2020.
145 HRI-VoxelFPN 90.76 % 96.35 % 85.37 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
H. Kuang, B. Wang, J. An, M. Zhang and Z. Zhang: Voxel-FPN:multi-scale voxel feature aggregation in 3D object detection from point clouds. sensors 2020.
146 KM3D code 90.70 % 96.34 % 80.72 % 0.03 s 1 core @ 2.5 Ghz (Python)
P. Li: Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training. 2020.
147 anonymous 90.70 % 96.46 % 82.39 % 1 s 1 core @ 2.5 Ghz (C/C++)
148 PointPillars
This method makes use of Velodyne laser scans.
code 90.70 % 93.84 % 87.47 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
149 WS3D
This method makes use of Velodyne laser scans.
90.69 % 94.85 % 85.94 % 0.1 s GPU @ 2.5 Ghz (Python)
Q. Meng, W. Wang, T. Zhou, J. Shen, L. Van Gool and D. Dai: Weakly Supervised 3D Object Detection from Lidar Point Cloud. 2020.
150 MonoEF code 90.65 % 96.19 % 82.95 % 0.03 s 1 core @ 2.5 Ghz (Python)
151 FPC3D_all
This method makes use of Velodyne laser scans.
90.60 % 95.35 % 85.79 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
152 modat3D
This is an online method (no batch processing).
90.49 % 92.61 % 80.32 % 0.03 s GPU @ 2.5 Ghz (Python)
153 DPointNet 90.38 % 93.61 % 87.34 % 0.07s 1 core @ 2.5 Ghz (C/C++)
154 IGRP+ 90.31 % 96.01 % 87.41 % 0.18 s 1 core @ 2.5 Ghz (Python + C/C++)
155 monodle code 90.23 % 93.46 % 80.11 % 0.04 s GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for Monocular 3D Object Detection. CVPR 2021 .
156 3D IoU Loss
This method makes use of Velodyne laser scans.
90.21 % 95.60 % 84.96 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
D. Zhou, J. Fang, X. Song, C. Guan, J. Yin, Y. Dai and R. Yang: IoU Loss for 2D/3D Object Detection. International Conference on 3D Vision (3DV) 2019.
157 ARPNET 90.11 % 93.42 % 82.56 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
158 TANet code 90.11 % 93.52 % 84.61 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
159 OCM3D 90.03 % 94.18 % 83.29 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
160 CG-Stereo
This method uses stereo information.
89.98 % 96.28 % 82.21 % 0.57 s GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
161 Det3D 89.92 % 94.21 % 83.18 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
162 Deep3DBox 89.88 % 94.62 % 76.40 % 1.5 s GPU @ 2.5 Ghz (C/C++)
A. Mousavian, D. Anguelov, J. Flynn and J. Kosecka: 3D Bounding Box Estimation Using Deep Learning and Geometry. CVPR 2017.
163 FADNet code 89.84 % 95.89 % 79.98 % 0.04 s GPU @ >3.5 Ghz (Python)
164 GPP code 89.68 % 93.94 % 80.60 % 0.23 s GPU @ 1.5 Ghz (Python + C/C++)
A. Rangesh and M. Trivedi: Ground plane polling for 6dof pose estimation of objects on the road. IEEE Transactions on Intelligent Vehicles 2020.
165 SubCNN 89.53 % 94.11 % 79.14 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.
166 MonoGeo 89.44 % 94.67 % 79.27 % 0.05 s 1 core @ 2.5 Ghz (Python)
167 SCNet
This method makes use of Velodyne laser scans.
89.36 % 95.23 % 84.03 % 0.04 s GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
168 GA-Aug 89.24 % 92.66 % 81.31 % 0.04 s GPU @ 2.5 Ghz (Python)
169 AVOD
This method makes use of Velodyne laser scans.
code 89.22 % 94.98 % 82.14 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
170 GAA 89.21 % 93.59 % 80.80 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
171 IAFA 89.14 % 92.96 % 79.40 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
D. Zhou, X. Song, Y. Dai, J. Yin, F. Lu, M. Liao, J. Fang and L. Zhang: IAFA: Instance-Aware Feature Aggregation for 3D Object Detection from a Single Image. Proceedings of the Asian Conference on Computer Vision 2020.
172 MCA 88.91 % 92.91 % 79.11 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
173 AVOD-FPN
This method makes use of Velodyne laser scans.
code 88.61 % 94.65 % 83.71 % 0.1 s Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
174 deleted 88.13 % 96.48 % 80.66 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
175 AACL 88.00 % 93.36 % 73.17 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
176 DeepStereoOP 87.81 % 93.68 % 77.60 % 3.4 s GPU @ 3.5 Ghz (Matlab + C/C++)
C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.
177 tiny-stereo 87.74 % 96.38 % 80.24 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
178 MonoRUn 87.64 % 95.44 % 77.75 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
179 3DBN
This method makes use of Velodyne laser scans.
87.59 % 93.34 % 79.91 % 0.13s 1080Ti (Python+C/C++)
X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.
180 ISF-v2 87.49 % 93.15 % 84.13 % 0.04 s 1 core @ 2.5 Ghz (Python)
181 FQNet 87.49 % 93.66 % 73.61 % 0.5 s 1 core @ 2.5 Ghz (Python)
L. Liu, J. Lu, C. Xu, Q. Tian and J. Zhou: Deep Fitting Degree Scoring Network for Monocular 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
182 Shift R-CNN (mono) code 87.47 % 93.75 % 77.19 % 0.25 s GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
183 MonoPSR code 87.45 % 93.29 % 72.26 % 0.2 s GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
184 Mono3D code 87.28 % 93.13 % 77.00 % 4.2 s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.
185 Object Transformer 87.23 % 93.00 % 79.42 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
186 MA 87.08 % 93.12 % 79.50 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
187 SMOKE code 87.02 % 92.94 % 77.12 % 0.03 s GPU @ 2.5 Ghz (Python)
Z. Liu, Z. Wu and R. Tóth: SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation. 2020.
188 3DOP
This method uses stereo information.
code 86.93 % 91.31 % 76.72 % 3s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
189 CDN
This method uses stereo information.
code 86.90 % 95.79 % 79.05 % 0.6 s GPU @ 2.5 Ghz (Python)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems (NeurIPS) 2020.
190 DAMNET code 86.83 % 92.37 % 81.73 % 1 s 1 core @ 2.5 Ghz (C/C++)
191 RTM3D code 86.73 % 91.75 % 77.18 % 0.05 s GPU @ 1.0 Ghz (Python)
P. Li, H. Zhao, P. Liu and F. Cao: RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving. 2020.
192 IMA 86.71 % 92.51 % 76.95 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
193 MonoRCNN 86.48 % 91.90 % 66.71 % 0.07 s GPU @ 2.5 Ghz (Python + C/C++)
194 TBD
This method makes use of Velodyne laser scans.
86.19 % 92.45 % 81.09 % 0.11 s GPU @ 2.5 Ghz (Python + C/C++)
195 MonoPair 86.11 % 91.65 % 76.45 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
196 DSGN
This method uses stereo information.
code 86.03 % 95.42 % 78.27 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
197 UDI-mono3D 85.76 % 92.25 % 76.23 % 0.05 s 1 core @ 2.5 Ghz (Python)
198 NL_M3D 85.32 % 90.88 % 70.87 % 0.2 s 1 core @ 2.5 Ghz (Python)
199 StereoFENet
This method uses stereo information.
85.14 % 91.28 % 76.80 % 0.15 s 1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
200 OSE
This method uses stereo information.
84.75 % 95.15 % 75.34 % 0.1 s GPU @ 2.5 Ghz (C/C++)
201 PL++ (SDN+GDC)
This method uses stereo information.
This method makes use of Velodyne laser scans.
code 84.42 % 94.83 % 76.95 % 0.6 s GPU @ 2.5 Ghz (C/C++)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.
202 SS3D 84.38 % 92.57 % 69.82 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
203 CDN-PL++
This method uses stereo information.
84.21 % 94.45 % 76.69 % 0.4 s GPU @ 2.5 Ghz (C/C++)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems 2020.
204 UDI-mono3D 84.20 % 91.88 % 75.38 % 0.05 s 1 core @ 2.5 Ghz (Python)
205 MonoFENet 84.09 % 91.42 % 75.93 % 0.15 s 1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
206 Complexer-YOLO
This method makes use of Velodyne laser scans.
83.89 % 91.77 % 79.24 % 0.06 s GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
207 ZoomNet
This method uses stereo information.
code 83.79 % 94.14 % 68.78 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
L. Z. Xu: ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2020.
208 PLDet3d 83.76 % 88.25 % 75.11 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
209 LPCG-M3D 83.39 % 86.97 % 75.09 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
210 Deprecated 83.08 % 88.95 % 64.00 % Deprecated Deprecated
211 DA-Mono3D 83.00 % 88.87 % 63.87 % 0.09s 1 core @ 2.5 Ghz (C/C++)
212 M3D-RPN code 82.81 % 88.38 % 67.08 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
213 MTMono3d 82.65 % 90.34 % 74.98 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
214 Center3D 82.51 % 93.10 % 70.79 % 0.05 s GPU @ 3.5 Ghz (Python)
215 OSE+ 82.44 % 94.33 % 75.05 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
216 SSL-RTM3D Res18 82.43 % 93.13 % 72.47 % 0.02 s GPU @ 2.5 Ghz (Python)
217 Disp R-CNN (velo)
This method uses stereo information.
code 82.09 % 93.31 % 69.78 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
218 D4LCN code 82.08 % 90.01 % 63.98 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
219 Disp R-CNN
This method uses stereo information.
code 81.96 % 93.49 % 67.35 % 0.387 s GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
220 Pseudo-LiDAR++
This method uses stereo information.
code 81.87 % 94.14 % 74.29 % 0.4 s GPU @ 2.5 Ghz (Python)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.
221 LAPNet 81.63 % 90.16 % 63.47 % 0.03 s 1 core @ 2.5 Ghz (Python)
222 BS3D 81.22 % 94.66 % 68.39 % 22 ms Titan Xp
N. Gählert, J. Wan, M. Weber, J. Zöllner, U. Franke and J. Denzler: Beyond Bounding Boxes: Using Bounding Shapes for Real-Time 3D Vehicle Detection from Monocular RGB Images. 2019 IEEE Intelligent Vehicles Symposium (IV) 2019.
223 LCD3D 81.01 % 91.20 % 64.29 % 0.03 s GPU @ 2.5 Ghz (Python)
224 YOLOStereo3D
This method uses stereo information.
code 80.88 % 93.65 % 61.17 % 0.1 s GPU 1080Ti
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.
225 MP-Mono 80.86 % 87.24 % 62.57 % 0.16 s GPU @ 2.5 Ghz (Python)
226 SOD 80.62 % 94.15 % 65.94 % 0.1 s 1 core @ 2.5 Ghz (Python)
227 FRCNN+Or code 80.57 % 91.50 % 67.49 % 0.09 s Titan Xp GPU
C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine 2018.
C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features. IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.
228 ITS-MDPL 80.56 % 92.45 % 73.05 % 0.16 s GPU @ 2.5 Ghz (Python)
229 DDMP-3D 80.20 % 90.73 % 61.82 % 0.18 s 1 core @ 2.5 Ghz (Python)
230 UM3D_TUM 80.15 % 92.80 % 65.77 % 0.05 s 1 core @ 2.5 Ghz (Python)
231 Ground-Aware code 80.05 % 90.98 % 60.51 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
Y. Liu, Y. Yuan and M. Liu: Ground-aware Monocular 3D Object Detection for Autonomous Driving. IEEE Robotics and Automation Letters 2021.
232 GrooMeD-NMS code 79.93 % 90.05 % 63.43 % 0.12 s 1 core @ 2.5 Ghz (Python)
A. Kumar, G. Brazil and X. Liu: GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection. CVPR 2021.
233 RelationNet3D_dla34 code 79.59 % 83.64 % 69.13 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
234 KMC code 79.09 % 89.31 % 72.31 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
235 YoloMono3D code 78.50 % 91.43 % 58.80 % 0.05 s GPU @ 2.5 Ghz (Python)
Y. Liu, L. Wang and L. Ming: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
236 3D-GCK 78.44 % 88.59 % 66.28 % 24 ms Tesla V100
N. Gählert, J. Wan, N. Jourdan, J. Finkbeiner, U. Franke and J. Denzler: Single-Shot 3D Detection of Vehicles from Monocular RGB Images via Geometrically Constrained Keypoints in Real-Time. 2020 IEEE Intelligent Vehicles Symposium (IV) 2020.
237 3D-SSMFCNN code 77.82 % 77.84 % 68.67 % 0.1 s GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.
238 AEC3D 77.75 % 88.40 % 73.70 % 0.01 s GPU @ 2.5 Ghz (Python)
239 DFR-Net 77.41 % 89.79 % 59.20 % 0.18 s 1080 Ti (Pytorch)
240 Aug3D-RPN 76.89 % 84.89 % 60.21 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
241 VN3D 76.83 % 86.60 % 70.95 % 0.02 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
242 AutoShape 76.61 % 83.71 % 63.47 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
243 RelationNet3D_res18 code 76.45 % 86.98 % 66.83 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
244 RelationNet3D 76.44 % 81.31 % 68.25 % 0.04 s GPU @ 2.5 Ghz (Python)
245 MonoHMOO 75.95 % 91.51 % 59.55 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
246 ICCV 75.90 % 85.36 % 64.93 % 0.04 s GPU @ 2.5 Ghz (Python)
247 3DVP code 75.71 % 84.44 % 64.41 % 40 s 8 cores @ 3.5 Ghz (Matlab + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Data-Driven 3D Voxel Patterns for Object Category Recognition. IEEE Conference on Computer Vision and Pattern Recognition 2015.
248 GS3D 75.63 % 85.79 % 61.85 % 2 s 1 core @ 2.5 Ghz (C/C++)
B. Li, W. Ouyang, L. Sheng, X. Zeng and X. Wang: GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
249 Pose-RCNN 75.41 % 89.49 % 63.57 % 2 s >8 cores @ 2.5 Ghz (Python)
M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on 2016.
250 SubCat code 75.26 % 83.31 % 59.55 % 0.7 s 6 cores @ 3.5 Ghz (Matlab + C/C++)
E. Ohn-Bar and M. Trivedi: Learning to Detect Vehicles by Clustering Appearance Patterns. T-ITS 2015.
251 3D FCN
This method makes use of Velodyne laser scans.
74.54 % 86.65 % 67.73 % >5 s 1 core @ 2.5 Ghz (C/C++)
B. Li: 3D Fully Convolutional Network for Vehicle Detection in Point Cloud. IROS 2017.
252 Mobile Stereo R-CNN
This method uses stereo information.
74.13 % 88.80 % 59.84 % 1.8 s NVIDIA Jetson TX2
253 OC Stereo
This method uses stereo information.
code 73.34 % 86.86 % 61.37 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
254 RTS3D 72.74 % 80.36 % 63.65 % 0.03 s GPU @ 2.5 Ghz (Python)
255 GAC3D 70.49 % 83.27 % 52.04 % 0.25 s 1 core @ 2.5 Ghz (Python)
256 ROI-10D 68.14 % 75.32 % 58.98 % 0.2 s GPU @ 3.5 Ghz (Python)
F. Manhardt, W. Kehl and A. Gaidon: ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape. Computer Vision and Pattern Recognition (CVPR) 2019.
257 BirdNet+
This method makes use of Velodyne laser scans.
code 67.65 % 91.82 % 65.11 % 0.1 s Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird’s Eye View. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
258 multi-task CNN 67.51 % 79.00 % 58.80 % 25.1 ms GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
259 CaDDN 67.31 % 78.28 % 59.52 % 0.63 s GPU @ 2.5 Ghz (Python)
C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.
260 Decoupled-3D 67.23 % 87.34 % 53.84 % 0.08 s GPU @ 2.5 Ghz (C/C++)
Y. Cai, B. Li, Z. Jiao, H. Li, X. Zeng and X. Wang: Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation. AAAI 2020.
261 BdCost48LDCF code 65.50 % 80.44 % 51.24 % 0.5 s 8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
262 OC-DPM 65.32 % 77.35 % 51.00 % 10 s 8 cores @ 2.5 Ghz (Matlab)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Occlusion Patterns for Object Class Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2013.
263 RefinedMPL 64.02 % 87.95 % 52.06 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
264 TBD 63.75 % 85.52 % 54.42 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
265 DPM-VOC+VP 63.58 % 79.09 % 46.59 % 8 s 1 core @ 2.5 Ghz (C/C++)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015.
266 AOG-View 62.62 % 77.62 % 48.27 % 3 s 1 core @ 2.5 Ghz (Matlab, C/C++)
B. Li, T. Wu and S. Zhu: Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model. ECCV 2014.
267 PG-MonoNet 61.20 % 70.34 % 52.59 % 0.19 s GPU @ 2.5 Ghz (Python)
268 LSVM-MDPM-sv 57.48 % 70.23 % 42.54 % 10 s 4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout. NIPS 2011.
269 SAMME48LDCF code 57.26 % 76.28 % 43.55 % 0.5 s 8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
270 BirdNet
This method makes use of Velodyne laser scans.
56.94 % 79.20 % 54.88 % 0.11 s Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
271 VeloFCN
This method makes use of Velodyne laser scans.
51.05 % 70.03 % 44.82 % 1 s GPU @ 2.5 Ghz (Python + C/C++)
B. Li, T. Zhang and T. Xia: Vehicle Detection from 3D Lidar Using Fully Convolutional Network. RSS 2016 .
272 Mono3D_PLiDAR code 49.39 % 76.90 % 41.13 % 0.1 s NVIDIA GeForce 1080 (pytorch)
X. Weng and K. Kitani: Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud. arXiv:1903.09847 2019.
273 DPM-C8B1
This method uses stereo information.
48.00 % 57.76 % 35.52 % 15 s 4 cores @ 2.5 Ghz (Matlab + C/C++)
J. Yebes, L. Bergasa and M. García-Garrido: Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes. Sensors 2015.
J. Yebes, L. Bergasa, R. Arroyo and A. Lázaro: Supervised learning and evaluation of KITTI's cars detector with DPM. IV 2014.
274 LTN 46.54 % 48.96 % 41.58 % 0.4 s GPU @ >3.5 Ghz (Python)
T. Wang, X. He, Y. Cai and G. Xiao: Learning a Layout Transfer Network for Context Aware Object Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
275 sensekitti code 46.12 % 49.16 % 42.79 % 4.5 s GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.
276 Kinematic3D code 45.50 % 58.33 % 34.81 % 0.12 s 1 core @ 1.5 Ghz (C/C++)
G. Brazil, G. Pons-Moll, X. Liu and B. Schiele: Kinematic 3D Object Detection in Monocular Video. ECCV 2020 .
277 PVGNet 40.79 % 43.04 % 39.42 % 0.05 s 1 core @ >3.5 Ghz (C/C++)
278 anonymous 40.75 % 45.00 % 34.48 % 1 s 1 core @ 2.5 Ghz (C/C++)
279 Dccnet 40.44 % 37.79 % 38.54 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
280 3D-CVF at SPA
This method makes use of Velodyne laser scans.
39.79 % 40.44 % 36.10 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
J. Yoo, Y. Kim, J. Kim and J. Choi: 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection. ECCV 2020.
281 CDI3D 39.62 % 41.27 % 34.88 % 0.03 s GPU @ 2.5 Ghz (Python)
282 HR-faster-rcnn 39.35 % 39.78 % 36.47 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
283 SPG_mini
This method makes use of Velodyne laser scans.
38.90 % 40.04 % 38.62 % 0.09 s GPU @ 2.5 Ghz (Python)
284 deprecated 38.89 % 40.49 % 35.13 % 0.06 s GPU @ >3.5 Ghz (Python)
285 dgist_multiDetNet 38.76 % 39.75 % 35.38 % 0.08 s GPU Titanx Pascal (Python)
286 SPG_full
This method makes use of Velodyne laser scans.
38.73 % 40.02 % 38.52 % 0.09 s 1 core @ 2.5 Ghz (Python + C/C++)
287 BLPNet_V2 38.66 % 39.39 % 38.36 % 0.04 s 1 core @ 2.5 Ghz (Python)
288 PVF-NET 38.53 % 39.57 % 38.23 % 0.1 s 1 core @ 2.5 Ghz (Python)
289 DGIST MT-CNN 38.47 % 39.69 % 35.22 % 0.09 s GPU @ 1.0 Ghz (Python)
290 SA-SSD code 38.30 % 39.40 % 37.07 % 0.04 s 1 core @ 2.5 Ghz (Python)
C. He, H. Zeng, J. Huang, X. Hua and L. Zhang: Structure Aware Single-stage 3D Object Detection from Point Cloud. CVPR 2020.
291 HR-Cascade-RCNN 38.25 % 39.72 % 35.92 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
292 NF2 37.91 % 38.81 % 34.27 % 0.1 s GPU @ 2.5 Ghz (Python)
293 yolo4 37.27 % 38.19 % 32.45 % 0.02 s 1 core @ 2.5 Ghz (Python)
294 Point-GNN
This method makes use of Velodyne laser scans.
code 37.20 % 38.66 % 36.29 % 0.6 s GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
295 PP-3D 37.20 % 38.66 % 36.29 % 0.1 s 1 core @ 2.5 Ghz (Python)
296 yolo4_5l 37.14 % 37.92 % 32.31 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
297 FPGNN 36.87 % 38.36 % 36.08 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
298 yolo4_5l code 36.81 % 37.14 % 33.24 % 0.02 s 1 core @ 2.5 Ghz (Python + C/C++)
299 RT3D-GMP
This method uses stereo information.
36.31 % 44.06 % 27.32 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
300 PMN 36.23 % 38.08 % 32.05 % 0.2 s 1 core @ 2.5 Ghz (Python)
301 MMCOM 36.08 % 39.58 % 32.06 % 0.04 s 1 core @ 2.5 Ghz (Python)
302 Scan_YOLO 36.02 % 36.78 % 32.65 % 0.1 s 4 cores @ 3.0 Ghz (Python)
303 Multi-task DG 35.50 % 38.34 % 30.85 % 0.06 s GPU @ 2.5 Ghz (Python)
304 yolo_rgb 35.23 % 36.60 % 31.70 % 0.07 s GPU @ 2.5 Ghz (Python)
305 bifpn_fsrn 33.84 % 37.56 % 29.98 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
306 Mag 33.74 % 38.34 % 28.76 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
307 yolo_depth 30.33 % 36.32 % 26.80 % 0.07 s GPU @ 2.5 Ghz (Python)
308 AOG code 29.81 % 33.28 % 23.91 % 3 s 4 cores @ 2.5 Ghz (Matlab)
T. Wu, B. Li and S. Zhu: Learning And-Or Models to Represent Context and Occlusion for Car Detection and Viewpoint Estimation. TPAMI 2016.
B. Li, T. Wu and S. Zhu: Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model. ECCV 2014.
309 NCL code 29.49 % 26.49 % 29.89 % NA s 1 core @ 2.5 Ghz (Python)
310 RetinaMono code 28.68 % 31.39 % 24.70 % 0.02 s 1 core @ 2.5 Ghz (Python)
311 SubCat48LDCF code 26.68 % 34.33 % 19.44 % 0.5 s 8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
312 Y4 code 25.53 % 32.98 % 22.95 % 0.03 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
313 RT3DStereo
This method uses stereo information.
21.41 % 25.58 % 17.52 % 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.
314 CSoR
This method makes use of Velodyne laser scans.
code 20.82 % 30.65 % 17.14 % 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.
315 R-AGNO-Net 19.00 % 24.71 % 18.36 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
316 RT3D
This method makes use of Velodyne laser scans.
18.96 % 24.41 % 19.85 % 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.
317 VoxelJones code 15.41 % 17.83 % 14.13 % .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.
318 Associate-3Ddet code 1.20 % 0.52 % 1.38 % 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.
319 APL-Second 1.16 % 0.50 % 1.50 % 0.05 s 1 core @ 2.5 Ghz (Python)
320 HNet
This method makes use of Velodyne laser scans.
code 0.49 % 0.07 % 0.83 % 0.05 s GPU @ 2.5 Ghz (Python)
Table as LaTeX | Only published Methods


Pedestrians


Method Setting Code Moderate Easy Hard Runtime Environment
1 VMVS
This method makes use of Velodyne laser scans.
68.19 % 79.98 % 63.18 % 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.
2 SubCNN 66.70 % 79.65 % 61.35 % 2 s GPU @ 3.5 Ghz (Python + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.
3 F-ConvNet
This method makes use of Velodyne laser scans.
code 63.87 % 75.19 % 58.57 % 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.
4 3DOP
This method uses stereo information.
code 61.48 % 74.22 % 55.89 % 3s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
5 HotSpotNet 60.65 % 70.36 % 57.42 % 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.
6 HIKVISION-AFree 60.39 % 72.27 % 57.76 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
7 DeepStereoOP 60.15 % 73.76 % 55.30 % 3.4 s GPU @ 3.5 Ghz (Matlab + C/C++)
C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.
8 Pose-RCNN 59.84 % 76.24 % 53.59 % 2 s >8 cores @ 2.5 Ghz (Python)
M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on 2016.
9 HIKVISION-ADLab-HZ 59.14 % 69.90 % 55.15 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
10 FFNet code 58.87 % 69.24 % 53.75 % 1.07 s GPU @ 1.5 Ghz (Python)
C. Zhao, Y. Qian and M. Yang: Monocular Pedestrian Orientation Estimation Based on Deep 2D-3D Feedforward. Pattern Recognition 2019.
11 Mono3D code 58.66 % 71.19 % 53.94 % 4.2 s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.
12 SAA-PV-RCNN 57.56 % 66.99 % 54.15 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
13 Fast VP-RCNN code 55.00 % 65.69 % 52.09 % 0.05 s 1 core @ 3.5 Ghz (C/C++)
14 anonymous code 54.85 % 66.07 % 51.96 % 0.05s 1 core @ >3.5 Ghz (python)
15 TBD_IOU2 54.80 % 66.21 % 52.03 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
16 MonoPSR code 54.65 % 68.98 % 50.07 % 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.
17 Generalized-SIENet 54.48 % 64.46 % 51.93 % 0.08 s 1 core @ 2.5 Ghz (Python)
18 XView-PartA^2 54.47 % 64.10 % 51.83 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
19 WHUT-iou_ssd code 54.44 % 63.62 % 51.89 % 0.045s 1 core @ 2.5 Ghz (C/C++)
20 DSA-PV-RCNN
This method makes use of Velodyne laser scans.
code 54.38 % 63.12 % 51.98 % 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.
21 E^2-PV-RCNN 54.15 % 64.15 % 51.62 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
22 FPC-RCNN 54.09 % 64.06 % 51.49 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
23 monodle code 53.78 % 69.94 % 48.98 % 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 .
24 AF 53.73 % 64.69 % 49.84 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
25 TBD 53.58 % 62.81 % 50.86 % 0.07 s 1 core @ 2.5 Ghz (Python + C/C++)
26 GNN-RCNN 53.54 % 63.34 % 51.61 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
27 GAP-soft-filter 53.53 % 63.48 % 50.30 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
28 TBD_IOU 53.49 % 63.07 % 50.94 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
29 PF-GAP 53.38 % 65.13 % 49.49 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
30 TBD_IOU1 53.37 % 64.25 % 50.70 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
31 SRDL
This method uses stereo information.
This method makes use of Velodyne laser scans.
53.36 % 63.39 % 50.43 % 0.15 s GPU @ 2.5 Ghz (Python + C/C++)
32 Baseline of CA RCNN 53.35 % 63.39 % 50.42 % 0.1 s GPU @ 2.5 Ghz (Python)
33 CVIS-v1 53.35 % 63.39 % 50.42 % 0.1s 1 core @ 2.5 Ghz (Python + C/C++)
34 FPCR-CNN 53.33 % 63.22 % 50.41 % 0.05 s 1 core @ 2.5 Ghz (Python)
35 Point Image Fusion 53.29 % 62.67 % 50.59 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
36 SAA-SECOND 53.19 % 63.23 % 49.95 % 38m s 1 core @ 2.5 Ghz (C/C++)
37 CVRS VIC-Net 52.63 % 61.60 % 50.07 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
38 MSL3D 52.49 % 63.54 % 49.53 % 0.03 s GPU @ 2.5 Ghz (Python)
39 FSA-PVRCNN
This method makes use of Velodyne laser scans.
52.49 % 60.99 % 49.92 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
40 SCIR-Net
This method makes use of Velodyne laser scans.
52.45 % 62.55 % 49.62 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
41 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
code 52.42 % 63.45 % 49.23 % 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.
42 CVRS VIC-RCNN 52.40 % 61.31 % 50.15 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
43 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 52.20 % 63.51 % 48.27 % 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.
44 FRCNN+Or code 52.15 % 67.03 % 47.14 % 0.09 s Titan Xp GPU
C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine 2018.
C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features. IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.
45 SIF 52.10 % 62.72 % 49.19 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
46 SVGA-Net
This method makes use of Velodyne laser scans.
51.69 % 61.84 % 49.00 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
47 TBD 51.49 % 62.02 % 47.97 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
48 modat3D
This is an online method (no batch processing).
51.46 % 68.64 % 47.00 % 0.03 s GPU @ 2.5 Ghz (Python)
49 FPC3D_all
This method makes use of Velodyne laser scans.
51.20 % 61.48 % 48.42 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
50 MGACNet 50.52 % 60.32 % 47.92 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
51 CVIS-v2 50.51 % 60.74 % 47.69 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
52 tbd 50.36 % 61.48 % 47.94 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
53 EA-M-RCNN(BorderAtt) 50.35 % 60.54 % 46.78 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
54 PointPainting
This method makes use of Velodyne laser scans.
50.22 % 59.25 % 46.95 % 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.
55 YF 49.55 % 60.50 % 46.17 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
56 XView 49.30 % 58.39 % 46.81 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
57 CenterNet-Boost 49.09 % 62.03 % 43.52 % 0.042 s GPU @ 2.5 Ghz (Python)
58 GA-Aug 48.81 % 62.74 % 43.91 % 0.04 s GPU @ 2.5 Ghz (Python)
59 deprecated 48.72 % 57.13 % 46.45 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
60 YF 48.71 % 58.68 % 45.46 % 0.04 s GPU @ 2.5 Ghz (C/C++)
61 ARPNET 48.49 % 60.47 % 45.02 % 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.
62 3DBN_2 48.43 % 59.19 % 45.73 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
63 VGCN 48.42 % 57.79 % 45.87 % 0.09 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
64 AF_MCLS 48.39 % 61.41 % 44.22 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
65 TBD 48.34 % 58.57 % 44.85 % 0.05 s GPU @ 2.5 Ghz (Python)
66 PointPillars
This method makes use of Velodyne laser scans.
code 48.05 % 57.47 % 45.40 % 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.
67 MKFFNet 47.99 % 57.39 % 45.41 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
68 MonoRUn 47.82 % 63.28 % 43.23 % 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.
69 MKFFNet 47.50 % 56.15 % 44.58 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
70 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 47.33 % 57.19 % 44.31 % 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.
71 PVNet 46.68 % 57.18 % 44.38 % 0,1 s 1 core @ 2.5 Ghz (Python)
72 Shift R-CNN (mono) code 46.56 % 64.73 % 41.86 % 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.
73 IGRP+ 46.34 % 57.61 % 42.75 % 0.18 s 1 core @ 2.5 Ghz (Python + C/C++)
74 Disp R-CNN
This method uses stereo information.
code 45.80 % 63.23 % 41.32 % 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.
75 Disp R-CNN (velo)
This method uses stereo information.
code 45.66 % 63.16 % 41.14 % 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.
76 UDI-mono3D 45.37 % 57.89 % 40.78 % 0.05 s 1 core @ 2.5 Ghz (Python)
77 MKFFNet 44.94 % 53.14 % 42.71 % 0.01s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
78 UDI-mono3D 44.75 % 57.42 % 40.21 % 0.05 s 1 core @ 2.5 Ghz (Python)
79 MonoFlex 44.20 % 58.96 % 39.89 % 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.
80 AVOD-FPN
This method makes use of Velodyne laser scans.
code 43.99 % 53.48 % 41.56 % 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.
81 NF2 43.94 % 49.13 % 41.55 % 0.1 s GPU @ 2.5 Ghz (Python)
82 dgist_multiDetNet 43.48 % 49.02 % 40.97 % 0.08 s GPU Titanx Pascal (Python)
83 GAA 43.31 % 56.36 % 39.16 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
84 DGIST MT-CNN 43.26 % 48.67 % 40.74 % 0.09 s GPU @ 1.0 Ghz (Python)
85 RelationNet3D_dla34 code 42.98 % 57.56 % 38.77 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
86 MonoPair 42.38 % 55.26 % 38.53 % 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.
87 SIEV-Net 42.35 % 49.90 % 39.38 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
88 CBi-GNN-persons 41.73 % 54.55 % 37.59 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
89 NLK-3D 41.71 % 54.22 % 39.32 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
90 MTMono3d 41.63 % 54.28 % 36.32 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
91 ASCNet 41.06 % 49.17 % 38.90 % 0.09 s 1 core @ 2.5 Ghz (C/C++)
92 PFF3D
This method makes use of Velodyne laser scans.
code 40.99 % 48.75 % 38.99 % 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.
93 MonoGeo 39.76 % 52.87 % 35.83 % 0.05 s 1 core @ 2.5 Ghz (Python)
94 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 39.76 % 50.30 % 36.90 % 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 FCY
This method makes use of Velodyne laser scans.
39.67 % 51.30 % 35.90 % 0.02 s GPU @ 2.5 Ghz (Python)
96 SS3D 39.60 % 53.72 % 35.40 % 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.
97 NLK-ALL code 39.31 % 49.20 % 35.60 % 0.04 s 1 core @ 2.5 Ghz (Python + C/C++)
98 MMCOM 39.20 % 46.12 % 36.81 % 0.04 s 1 core @ 2.5 Ghz (Python)
99 HR-faster-rcnn 39.02 % 47.41 % 35.57 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
100 SemanticVoxels 38.95 % 45.59 % 37.21 % 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.
101 Multi-task DG 38.79 % 46.21 % 36.07 % 0.06 s GPU @ 2.5 Ghz (Python)
102 Center3D 38.59 % 53.15 % 34.77 % 0.05 s GPU @ 3.5 Ghz (Python)
103 Deprecated 38.08 % 52.18 % 32.76 % Deprecated Deprecated
104 DAMNET code 37.88 % 49.72 % 35.52 % 1 s 1 core @ 2.5 Ghz (C/C++)
105 DPM-VOC+VP 37.79 % 52.91 % 33.27 % 8 s 1 core @ 2.5 Ghz (C/C++)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015.
106 M3D-RPN(S-R) 37.78 % 51.90 % 33.95 % 0.16 s GPU @ 1.5 Ghz (Python)
107 DA-Mono3D 37.29 % 51.66 % 33.37 % 0.09s 1 core @ 2.5 Ghz (C/C++)
108 CG-Stereo
This method uses stereo information.
36.47 % 48.23 % 32.77 % 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.
109 TANet code 36.21 % 42.54 % 34.39 % 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.
110 RelationNet3D_res18 code 35.64 % 49.09 % 31.78 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
111 YOLOStereo3D
This method uses stereo information.
code 35.62 % 48.99 % 31.58 % 0.1 s GPU 1080Ti
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.
112 PMN 35.57 % 44.42 % 32.68 % 0.2 s 1 core @ 2.5 Ghz (Python)
113 SCNet
This method makes use of Velodyne laser scans.
35.49 % 44.50 % 33.38 % 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.
114 ICCV 35.33 % 47.10 % 31.54 % 0.04 s GPU @ 2.5 Ghz (Python)
115 NL_M3D 35.20 % 46.64 % 30.56 % 0.2 s 1 core @ 2.5 Ghz (Python)
116 MonoEF code 34.63 % 47.45 % 31.01 % 0.03 s 1 core @ 2.5 Ghz (Python)
117 ADLAB 34.58 % 39.13 % 32.97 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
118 tiny-stereo 34.27 % 46.17 % 31.04 % 0.4 s 1 core @ 2.5 Ghz (Python + C/C++)
119 sensekitti code 34.26 % 41.03 % 31.51 % 4.5 s GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.
120 Pointpillar_TV 34.24 % 42.95 % 32.08 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
121 D4LCN code 33.62 % 46.73 % 28.71 % 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.
122 DDMP-3D 33.35 % 46.12 % 28.45 % 0.18 s 1 core @ 2.5 Ghz (Python)
123 SparsePool code 33.35 % 43.86 % 29.99 % 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.
124 SparsePool code 33.29 % 43.52 % 30.01 % 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.
125 PLDet3d 33.24 % 45.55 % 29.71 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
126 LSVM-MDPM-sv 33.01 % 45.60 % 29.27 % 10 s 4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout. NIPS 2011.
127 TBD
This method makes use of Velodyne laser scans.
32.96 % 38.63 % 30.74 % 0.11 s GPU @ 2.5 Ghz (Python + C/C++)
128 AVOD
This method makes use of Velodyne laser scans.
code 32.19 % 42.54 % 29.09 % 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.
129 Complexer-YOLO
This method makes use of Velodyne laser scans.
32.13 % 37.32 % 28.94 % 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.
130 RPN+BF code 32.12 % 41.19 % 28.83 % 0.6 s GPU @ 2.5 Ghz (Matlab + C/C++)
L. Zhang, L. Lin, X. Liang and K. He: Is Faster R-CNN Doing Well for Pedestrian Detection?. ECCV 2016.
131 M3D-RPN code 31.88 % 44.33 % 28.55 % 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 .
132 Point-GNN
This method makes use of Velodyne laser scans.
code 31.86 % 39.16 % 29.65 % 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.
133 PP-3D 31.86 % 39.16 % 29.65 % 0.1 s 1 core @ 2.5 Ghz (Python)
134 yolo4_5l 31.53 % 40.97 % 27.63 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
135 SubCat 31.26 % 42.31 % 27.39 % 1.2 s 6 cores @ 2.5 Ghz (Matlab + C/C++)
E. Ohn-Bar and M. Trivedi: Fast and Robust Object Detection Using Visual Subcategories. Computer Vision and Pattern Recognition Workshops Mobile Vision 2014.
136 LIGA-stereo
This method uses stereo information.
30.30 % 37.79 % 27.97 % 0.375 s Titan Xp
137 Mag 30.28 % 39.29 % 27.59 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
138 yolo4 30.09 % 40.84 % 27.35 % 0.02 s 1 core @ 2.5 Ghz (Python)
139 OSE
This method uses stereo information.
30.02 % 40.27 % 26.86 % 0.1 s GPU @ 2.5 Ghz (C/C++)
140 Aug3D-RPN 29.75 % 40.50 % 25.96 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
141 MP-Mono 29.60 % 40.63 % 26.57 % 0.16 s GPU @ 2.5 Ghz (Python)
142 PG-MonoNet 29.56 % 37.28 % 26.48 % 0.19 s GPU @ 2.5 Ghz (Python)
143 BirdNet+