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 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.
2 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.
3 SA-SSD 95.16 % 97.92 % 90.15 % 0.04 s 1 core @ 2.5 Ghz (Python)
4 3DSSD 95.10 % 97.69 % 92.18 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
5 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.
6 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
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. arXiv preprint arXiv:1912.13192 2019.
7 BM-NET 94.49 % 95.09 % 85.06 % 0.5 s GPU @ 2.5 Ghz (Python + C/C++)
8 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.
9 EPNet 94.44 % 96.15 % 89.99 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
10 CPRCCNN 94.42 % 96.33 % 89.96 % 0.1 s 1 core @ 2.5 Ghz (Python)
11 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.
12 DGIST-CellBox 93.90 % 95.86 % 88.26 % 0.1 s GPU @ 2.5 Ghz (Java + C/C++)
13 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.
14 Noah CV Lab - SSL 93.65 % 94.02 % 86.02 % 0.1 s GPU @ 2.5 Ghz (Python)
15 THICV-YDM 93.60 % 96.26 % 81.08 % 0.06 s GPU @ 2.5 Ghz (Python)
16 MLF_PointCas 93.55 % 96.69 % 86.16 % 0.1 s GPU @ 2.5 Ghz (Python)
17 MonoPair 93.55 % 96.61 % 83.55 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
18 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.
19 Point-GNN
This method makes use of Velodyne laser scans.
93.50 % 96.58 % 88.35 % 0.6 s GPU @ 2.5 Ghz (Python)
20 FichaDL 93.46 % 96.00 % 84.39 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
21 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.
22 ORP 93.27 % 96.76 % 85.86 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
23 CFENet 93.26 % 93.91 % 86.99 % 4 s GPU @ 2.5 Ghz (Python + C/C++)
24 STD 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.
25 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.
26 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.
27 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.
28 ELE 93.14 % 98.44 % 90.32 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
29 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.
30 RGB3D
This method makes use of Velodyne laser scans.
93.07 % 96.54 % 88.04 % 0.39 s GPU @ 2.5 Ghz (Python)
31 PointRCNN-deprecated
This method makes use of Velodyne laser scans.
92.96 % 96.72 % 85.81 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
32 SerialR-FCN+SG-NMS 92.93 % 95.72 % 82.92 % 0.2 s 1 core @ 2.5 Ghz (Python)
33 PointCSE 92.78 % 95.99 % 87.66 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
34 Mono3CN 92.76 % 95.51 % 84.80 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
35 MuRF 92.74 % 95.74 % 87.64 % 0.05 s GPU @ 1.5 Ghz (Python + C/C++)
36 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.
37 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.
38 PPFNet code 92.68 % 96.32 % 87.66 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
39 R-GCN 92.67 % 96.19 % 87.66 % 0.16 s GPU @ 2.5 Ghz (Python)
40 PI-RCNN 92.66 % 96.17 % 87.68 % 0.1 s 1 core @ 2.5 Ghz (Python)
41 OHS-Direct 92.65 % 96.09 % 89.72 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots. 2019.
42 NU-optim 92.63 % 95.67 % 87.37 % 0.04 s GPU @ >3.5 Ghz (Python)
43 3D-CVF
This method makes use of Velodyne laser scans.
92.60 % 96.20 % 89.60 % 0.05 s GPU @ >3.5 Ghz (Python)
44 deprecated 92.59 % 96.21 % 89.58 % 0.05 s GPU @ >3.5 Ghz (Python)
45 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. arXiv preprint arXiv:1911.10150 2019.
46 SPA 92.56 % 95.96 % 87.60 % 0.1 s 1 core @ 2.5 Ghz (Python)
47 DEFT 92.55 % 96.17 % 89.51 % 1 s GPU @ 2.5 Ghz (Python)
48 CAASS-3D 92.47 % 96.31 % 87.67 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
49 PPBA 92.46 % 95.22 % 87.53 % NA s GPU @ 2.5 Ghz (Python)
50 TBU 92.46 % 95.22 % 87.53 % NA s GPU @ 2.5 Ghz (Python)
51 Associate-3Ddet
This method makes use of Velodyne laser scans.
92.45 % 95.61 % 87.32 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
52 CP
This method makes use of Velodyne laser scans.
92.44 % 96.14 % 87.58 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
53 YOLOv3.5 92.42 % 95.22 % 82.32 % 0.05 s GPU @ 2.5 Ghz (Python)
54 OHS-Dense 92.39 % 95.84 % 89.51 % 0.03 s 1 core @ 2.5 Ghz (Python/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots. 2019.
55 PointRGCN 92.33 % 97.51 % 87.07 % 0.26 s GPU @ V100 (Python)
56 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.
57 IE-PointRCNN 92.08 % 96.01 % 87.05 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
58 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.
59 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.
60 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.
61 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. arXiv preprint arXiv:1907.03670 2019.
62 MBR-SSD 91.83 % 93.46 % 84.97 % 4.0 s GPU @ 2.5 Ghz (Python + C/C++)
63 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.
64 MLF_SecCas 91.76 % 96.53 % 83.90 % 0.05 s 1 core @ 2.5 Ghz (Python)
65 C-GCN 91.73 % 95.64 % 86.37 % 0.147 s GPU @ V100 (Python)
66 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.
67 HRI-FusionRCNN 91.70 % 94.61 % 84.10 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
68 PiP 91.67 % 94.35 % 88.35 % 0.05 s 1 core @ 2.5 Ghz (Python)
69 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.
70 Faster RCNN + A 91.60 % 94.77 % 81.43 % 0.19 s GPU @ 2.5 Ghz (Python + C/C++)
71 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.
72 deprecated 91.59 % 94.34 % 79.14 % 0.05 s GPU @ 2.0 Ghz (Python)
73 Det-RGBD
This method uses stereo information.
91.49 % 94.30 % 79.41 % 0.58 s GPU @ 2.5 Ghz (Python + C/C++)
74 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.
75 TBA 91.43 % 93.99 % 88.51 % 0.07 s 1 core @ 2.5 Ghz (Python)
76 RUC 91.40 % 95.02 % 88.41 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
77 Faster RCNN + G 91.28 % 94.34 % 81.02 % 1.1 s GPU @ 2.5 Ghz (Python)
78 Faster RCNN + Gr + A 91.25 % 94.09 % 81.25 % 1.29 s GPU @ 2.5 Ghz (Python)
79 Alibaba-AILabsX
This method makes use of Velodyne laser scans.
91.23 % 96.33 % 83.75 % 0.05 s 1 core @ >3.5 Ghz (C/C++)
80 OACV 91.21 % 94.23 % 83.07 % 0.23 s GPU @ 2.5 Ghz (Python)
81 CentrNet-v1
This method makes use of Velodyne laser scans.
91.21 % 94.22 % 88.36 % 0.03 s GPU @ 2.5 Ghz (Python)
82 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.
83 Faster RCNN + A 91.19 % 94.43 % 80.99 % 0.19 s GPU @ 2.5 Ghz (Python)
84 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.
85 autonet 91.17 % 93.70 % 88.10 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
86 PointPiallars_SECA 91.12 % 93.66 % 87.94 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
87 DDB
This method makes use of Velodyne laser scans.
91.12 % 93.71 % 87.34 % 0.05 s GPU @ 2.5 Ghz (Python)
88 PCSC-Net 91.11 % 94.31 % 88.02 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
89 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.
90 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.
91 Bit 90.96 % 93.84 % 87.47 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
92 MMV 90.91 % 94.16 % 83.36 % 0.4 s GPU @ 2.5 Ghz (C/C++)
93 JSU-NET 90.90 % 96.41 % 80.67 % 0.1 s 1 core @ 2.5 Ghz (Python)
94 GAFM 90.90 % 96.46 % 80.70 % 0.5 s 1 core @ 2.5 Ghz (Python)
95 GA_BALANCE 90.86 % 96.19 % 78.40 % 1 s 1 core @ 2.5 Ghz (Python)
96 A-VoxelNet 90.86 % 93.84 % 83.27 % 0.029 s GPU @ 2.5 Ghz (Python)
97 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.
98 MVSLN 90.81 % 96.12 % 83.39 % 0.1s s 1 core @ 2.5 Ghz (C/C++)
99 MPNet
This method makes use of Velodyne laser scans.
90.80 % 94.68 % 87.30 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
100 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.
101 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.
102 Tencent_ADlab_Lidar
This method makes use of Velodyne laser scans.
90.74 % 93.80 % 86.75 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
103 GA_FULLDATA 90.73 % 96.31 % 78.22 % 1 s 4 cores @ 2.5 Ghz (Python)
104 SRF 90.69 % 95.88 % 85.52 % 0.05 s GPU @ 2.5 Ghz (Python + C/C++)
105 HR-SECOND code 90.68 % 93.72 % 85.63 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
106 GA2500 90.68 % 95.86 % 80.29 % 0.2 s 1 core @ 2.5 Ghz (Python)
107 GA_rpn500 90.68 % 95.86 % 80.29 % 1 s 1 core @ 2.5 Ghz (Python)
108 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.
109 SFB-SECOND 90.67 % 96.17 % 85.43 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
110 SECOND-V1.5
This method makes use of Velodyne laser scans.
code 90.65 % 95.96 % 85.35 % 0.04 s GPU @ 2.0 Ghz (Python + C/C++)
111 PTS
This method makes use of Velodyne laser scans.
code 90.64 % 95.74 % 85.41 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
112 VOXEL_FPN_HR 90.55 % 93.76 % 85.42 % 0.12 s 8 cores @ 2.5 Ghz (Python)
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113 FOFNet
This method makes use of Velodyne laser scans.
90.52 % 94.00 % 85.20 % 0.04 s GPU @ 2.5 Ghz (Python)
114 MP 90.50 % 93.86 % 85.17 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
115 Sogo_MM 90.46 % 94.31 % 80.62 % 1.5 s GPU @ 2.5 Ghz (C/C++)
116 bigger_ga 90.38 % 95.76 % 77.92 % 1 s 1 core @ 2.5 Ghz (Python)
117 AtrousDet 90.35 % 95.94 % 77.94 % 0.05 s TITAN X
118 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.
119 RUC code 90.24 % 92.60 % 86.55 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
120 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.
121 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.
122 BVVF 90.15 % 95.65 % 84.95 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
123 FCY
This method makes use of Velodyne laser scans.
90.15 % 93.27 % 86.60 % 0.02 s GPU @ 2.5 Ghz (Python)
124 SAANet 90.14 % 95.93 % 82.95 % 0.10 s 1 core @ 2.5 Ghz (Python)
125 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.
126 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.
127 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.
128 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. arXiv preprint arXiv:1811.06666 2018.
129 RUC code 89.93 % 93.12 % 85.44 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
130 ZRNet(ResNet-50) 89.92 % 95.24 % 79.69 % 0.04 s GPU @ 2.5 Ghz (Python)
131 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.
132 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.
133 ZRNet 89.72 % 93.97 % 79.47 % 0.04 s GPU @ 2.5 Ghz (Python)
134 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.
135 PAD 89.49 % 93.43 % 85.85 % 0.15 s 1 core @ 2.5 Ghz (Python)
136 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.
137 4D-MSCNN+CRL
This method uses stereo information.
89.37 % 92.40 % 77.00 % 0.2 s GPU @ 2.5 Ghz (Matlab + C/C++)
138 MonoDIS 89.15 % 94.61 % 78.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
139 cas+res+soft 89.14 % 94.54 % 78.37 % 0.2 s 4 cores @ 2.5 Ghz (Python)
140 merge12-12 88.96 % 94.58 % 78.22 % 0.2 s 4 cores @ 2.5 Ghz (Python)
141 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.
142 autoRUC 88.88 % 94.23 % 81.35 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
143 Prune 88.85 % 94.20 % 81.31 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
144 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.
145 SS3D_HW 88.68 % 94.49 % 68.79 % 0.4 s GPU @ 2.5 Ghz (Python)
146 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.
147 CRCNNA 88.59 % 94.82 % 76.74 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
148 3DNN 88.56 % 94.52 % 81.51 % 0.09 s GPU @ 2.5 Ghz (Python)
149 CSFADet 88.54 % 93.75 % 78.62 % 0.05 s GPU @ 2.5 Ghz (Python)
150 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.
151 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.
152 DPSM 88.47 % 93.67 % 75.62 % 0.1 s GPU @ 2.5 Ghz (Python)
153 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.
154 PointRes
This method makes use of Velodyne laser scans.
This method makes use of GPS/IMU information.
This is an online method (no batch processing).
88.41 % 95.38 % 84.22 % 0.013 s 1 core @ 2.5 Ghz (Python + C/C++)
155 TridentNet 88.37 % 90.33 % 80.57 % 0.2 s GPU @ 2.5 Ghz (Python)
156 PP-3D 88.35 % 93.71 % 80.84 % 0.1 s 1 core @ 2.5 Ghz (Python)
157 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.
158 NLK 87.89 % 91.65 % 83.32 % 0.02 s 1 core @ 2.5 Ghz (Python)
159 Multi-3D
This method makes use of Velodyne laser scans.
87.87 % 93.70 % 76.07 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
160 ga50 87.65 % 95.76 % 75.14 % 1 s 1 core @ 2.5 Ghz (Python)
161 cas_retina 87.64 % 93.87 % 75.30 % 0.2 s 4 cores @ 2.5 Ghz (Python)
162 SMOKE 87.51 % 93.21 % 77.66 % 0.03 s GPU @ 2.5 Ghz (Python)
163 MonoSS 87.46 % 93.15 % 77.58 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
164 cascadercnn 87.36 % 89.37 % 73.42 % 0.36 s 4 cores @ 2.5 Ghz (Python)
165 SCANet 87.28 % 92.91 % 81.99 % 0.17 s >8 cores @ 2.5 Ghz (Python)
166 RTM3D code 86.92 % 91.85 % 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.
167 anm 86.52 % 94.88 % 76.46 % 3 s 1 core @ 2.5 Ghz (C/C++)
168 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. arXiv preprint arXiv:2001.03398 2020.
169 ReSqueeze 86.12 % 90.35 % 76.53 % 0.03 s GPU @ >3.5 Ghz (Python)
170 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.
171 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.
172 ResNet-RRC w/RGBD 85.58 % 91.32 % 74.80 % 0.057 s GPU @ 1.5 Ghz (Python + C/C++)
173 cas_retina_1_13 85.48 % 91.54 % 74.60 % 0.03 s 4 cores @ 2.5 Ghz (Python)
174 NEUAV 85.42 % 89.67 % 77.28 % 0.06 s GPU @ 2.5 Ghz (Python)
175 ResNet-RRC 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.
176 Cmerge 85.32 % 93.40 % 70.57 % 0.2 s 4 cores @ 2.5 Ghz (Python)
177 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.
178 RAR-Net 85.08 % 89.04 % 69.26 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
179 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 .
180 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.
181 IDA-3D
This method uses stereo information.
84.92 % 92.79 % 74.75 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
182 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.
183 LPN 84.77 % 89.19 % 74.08 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
184 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.
185 SECA 84.60 % 92.51 % 79.53 % 1 s GPU @ 2.5 Ghz (Python)
186 PG-MonoNet 84.42 % 88.61 % 68.59 % 0.19 s GPU @ 2.5 Ghz (Python)
187 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.
188 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.
189 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.
190 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. arXiv preprint arXiv:1912.04799 2019.
191 ASOD 83.52 % 94.09 % 68.68 % 0.28 s GPU @ 2.5 Ghz (Python)
192 softretina 83.30 % 93.55 % 70.59 % 0.16 s 4 cores @ 2.5 Ghz (Python)
193 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.
194 ZKNet 82.96 % 92.17 % 72.43 % 0.01 s GPU @ 2.0 Ghz (Python)
195 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.
196 Retinanet100 82.73 % 93.97 % 68.37 % 0.2 s 4 cores @ 2.5 Ghz (Python)
197 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.
198 Pseudo-LiDAR E2E
This method uses stereo information.
82.54 % 94.00 % 75.31 % 0.4 s GPU @ 2.5 Ghz (Python)
199 Disp R-CNN
This method uses stereo information.
82.47 % 93.15 % 70.35 % 0.42 s GPU @ 2.5 Ghz (Python + C/C++)
200 Disp R-CNN (velo)
This method uses stereo information.
82.40 % 93.11 % 70.26 % 0.42 s GPU @ 2.5 Ghz (Python + C/C++)
201 cascade_gw 82.35 % 85.98 % 71.60 % 0.2 s 4 cores @ 2.5 Ghz (Python)
202 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.
203 CBNet 81.70 % 91.47 % 72.02 % 1 s 4 cores @ 2.5 Ghz (Python)
204 Resnet101Faster rcnn 81.44 % 91.08 % 71.52 % 1 s 1 core @ 2.5 Ghz (Python)
205 yyyyolo 81.33 % 94.36 % 68.72 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
206 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.
207 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.
208 MTDP 80.97 % 89.03 % 66.91 % 0.15 s GPU @ 2.0 Ghz (Python)
209 RFCN_RFB 80.89 % 88.07 % 69.66 % 0.2 s 4 cores @ 2.5 Ghz (Python)
210 Manhnet 80.85 % 89.06 % 64.29 % 26 ms 1 core @ 2.5 Ghz (C/C++)
211 centernet 80.78 % 90.29 % 70.53 % 0.01 s GPU @ 2.5 Ghz (Python)
212 3D-GCK 80.19 % 89.55 % 68.08 % 24 ms Tesla V100
213 RADNet-Fusion
This method makes use of Velodyne laser scans.
80.04 % 76.72 % 76.78 % 0.1 s 1 core @ 2.5 Ghz (Python)
214 NM code 79.98 % 90.71 % 68.98 % 0.01 s GPU @ 2.5 Ghz (Python)
215 RADNet-LIDAR
This method makes use of Velodyne laser scans.
79.59 % 75.20 % 76.03 % 0.1 s 1 core @ 2.5 Ghz (Python)
216 MMRetina
This method uses stereo information.
This method uses optical flow information.
This method makes use of Velodyne laser scans.
79.53 % 89.66 % 69.52 % 0.38 s GPU @ 2.5 Ghz (Python)
217 SceneNet 79.26 % 90.70 % 67.98 % 0.03 s GPU @ 2.5 Ghz (C/C++)
218 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.
219 MTNAS 78.82 % 88.96 % 67.07 % 0.02 s 1 core @ 2.5 Ghz (python)
220 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.
221 dgist_multiDetNet 78.26 % 93.58 % 70.04 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
222 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.
223 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.
224 yolov3_warp 77.61 % 92.24 % 65.70 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
225 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.
226 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.
227 YoloMono3D 77.02 % 92.32 % 57.09 % 0.05 s GPU @ 2.5 Ghz (Python)
228 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.
229 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.
230 FailNet-Fusion
This method makes use of Velodyne laser scans.
76.90 % 74.55 % 71.94 % 0.1 s 1 core @ 2.5 Ghz (Python)
231 RTL3D 76.74 % 79.68 % 72.56 % 0.02 s GPU @ 2.5 Ghz (Python)
232 avodC 76.58 % 87.30 % 71.65 % 0.1 s GPU @ 2.5 Ghz (Python)
233 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.
234 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.
235 FailNet-LIDAR
This method makes use of Velodyne laser scans.
76.26 % 74.16 % 71.24 % 0.1 s 1 core @ 2.5 Ghz (Python)
236 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.
237 bin 76.16 % 78.73 % 63.39 % 15ms s GPU @ >3.5 Ghz (Python)
238 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.
239 VoxelNet(Unofficial) 75.22 % 81.37 % 68.74 % 0.5 s GPU @ 2.0 Ghz (Python)
240 RFCN 75.14 % 83.04 % 61.55 % 0.2 s 4 cores @ 2.5 Ghz (Python)
241 myfaster-rcnn-v1.5 74.93 % 89.85 % 62.56 % 0.1 s 1 core @ 2.5 Ghz (Python)
242 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.
243 OC Stereo
This method uses stereo information.
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. arXiv preprint arXiv:1909.07566 2019.
244 yolo800 74.31 % 78.93 % 63.83 % 0.13 s 4 cores @ 2.5 Ghz (Python)
245 3DVSSD 74.11 % 86.99 % 63.57 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
246 Multi-task DG 74.07 % 91.06 % 64.48 % 0.06 s GPU @ 2.5 Ghz (Python)
247 FD2 73.93 % 88.65 % 64.62 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
248 BdCost+DA+BB+MS 73.72 % 85.18 % 57.79 % TBD s 4 cores @ 2.5 Ghz (Matlab + C/C++)
249 m-prcnn
This method uses stereo information.
73.64 % 87.64 % 57.03 % 0.43 s 1 core @ 2.5 Ghz (Python)
250 BdCost+DA+MS 73.62 % 85.03 % 58.94 % TBD s 4 cores @ 2.5 Ghz (Matlab/C++)
251 Int-YOLO code 73.23 % 75.81 % 63.59 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
252 stereo_sa
This method uses stereo information.
72.99 % 87.88 % 63.49 % 0.3 s GPU @ 2.5 Ghz (Python)
253 RuiRUC 72.08 % 87.48 % 55.28 % 0.12 s 1 core @ 2.5 Ghz (Python)
254 ANM 71.97 % 87.17 % 55.19 % 0.12 s 1 core @ 2.5 Ghz (Python)
255 RFBnet 71.66 % 87.25 % 63.00 % 0.2 s 4 cores @ 2.5 Ghz (Python)
256 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.
257 GPVL 71.06 % 81.67 % 54.96 % 10 s 1 core @ 2.5 Ghz (C/C++)
258 BdCost+DA+BB 70.86 % 85.52 % 56.19 % TBD s 4 cores @ 2.5 Ghz (Matlab + C/C++)
259 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.
260 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.
261 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.
262 fasterrcnn 69.45 % 74.76 % 60.20 % 0.2 s 4 cores @ 2.5 Ghz (Python)
263 myfaster-rcnn 68.38 % 90.54 % 55.97 % 0.01 s 1 core @ 2.5 Ghz (Python)
264 Decoupled-3D v2 68.17 % 88.64 % 54.74 % 0.08 s GPU @ 2.5 Ghz (C/C++)
265 Decoupled-3D 67.92 % 87.78 % 54.53 % 0.08 s GPU @ 2.5 Ghz (C/C++)
266 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.
267 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.
268 mymask-rcnn 66.82 % 88.60 % 52.18 % 0.3 s 1 core @ 2.5 Ghz (Python)
269 Fast-SSD 66.79 % 85.19 % 57.89 % 0.06 s GTX650Ti
270 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.
271 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.
272 E-VoxelNet 65.33 % 68.00 % 57.84 % 0.1 s GPU @ 2.5 Ghz (Python)
273 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.
274 BdCost48-25C 64.63 % 81.42 % 52.22 % 4 s 1 core @ 2.5 Ghz (C/C++)
275 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.
276 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.
277 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.
278 yl_net 61.78 % 66.00 % 60.36 % 0.03 s GPU @ 2.5 Ghz (Python)
279 Lidar_ROI+Yolo(UJS) 61.71 % 73.32 % 53.65 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
280 GNN 61.48 % 79.09 % 51.06 % 0.2 s 1 core @ 2.5 Ghz (Python)
281 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.
282 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.
283 SDP-Net-s 59.94 % 65.51 % 57.20 % 0.01 s GPU @ 2.5 Ghz (Python)
284 RADNet-Mono 59.85 % 67.47 % 54.14 % 0.1 s 1 core @ 2.5 Ghz (Python)
285 monoref3d 58.97 % 78.11 % 47.72 % 0.1 s 1 core @ 2.5 Ghz (Python)
286 ref3D 58.97 % 78.11 % 47.72 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
287 100Frcnn 58.92 % 82.09 % 49.04 % 2 s 4 cores @ 2.5 Ghz (Python + C/C++)
288 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.
289 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.
290 ref3D 57.16 % 77.96 % 45.99 % 0.1 s 1 core @ 2.5 Ghz (Python)
291 BirdNet
This method makes use of Velodyne laser scans.
57.12 % 79.30 % 55.16 % 0.11 s Titan Xp GPU
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.
292 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.
293 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.
294 mylsi-faster-rcnn 55.81 % 80.45 % 47.38 % 0.3 s 1 core @ 2.5 Ghz (Python)
295 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). .
296 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.
297 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 .
298 FailNet-Mono 47.95 % 59.59 % 41.33 % 0.1 s 1 core @ 2.5 Ghz (Python)
299 SF
This method uses stereo information.
This method makes use of Velodyne laser scans.
46.68 % 60.62 % 38.22 % 0.5 s 1 core @ 2.5 Ghz (Python)
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300 softyolo 45.97 % 66.08 % 38.02 % 0.16 s 4 cores @ 2.5 Ghz (Python)
301 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.
302 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.
303 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.
304 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.
305 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.
306 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.
307 Licar
This method makes use of Velodyne laser scans.
35.19 % 42.34 % 33.97 % 0.09 s GPU @ 2.0 Ghz (Python)
308 KD53-20 34.76 % 51.76 % 29.39 % 0.19 s 4 cores @ 2.5 Ghz (Python)
309 SAIC-SA-3D
This method makes use of Velodyne laser scans.
31.16 % 41.51 % 29.83 % 0.05 s GPU @ 2.5 Ghz (Python)
310 FCN-Depth code 25.05 % 52.32 % 18.07 % 1 s GPU @ 1.5 Ghz (Matlab + C/C++)
311 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.
312 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.
313 R-CNN_VGG 21.36 % 29.38 % 16.61 % 10 s GPU @ 2.5 Ghz (Matlab + C/C++)
314 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.
315 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.
316 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.
317 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.
318 FCPP 0.07 % 0.01 % 0.07 % 0.02 s 1 core @ 2.0 Ghz (Python + C/C++)
319 ANM 0.01 % 0.01 % 0.02 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
320 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.
321 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.
322 JSyolo 0.00 % 0.00 % 0.00 % 0.16 s 4 cores @ 2.5 Ghz (Python)
Table as LaTeX | Only published Methods

Pedestrian


Method Setting Code Moderate Easy Hard Runtime Environment
1 FichaDL 82.50 % 90.75 % 75.66 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
2 DGIST-CellBox 81.29 % 90.04 % 76.92 % 0.1 s GPU @ 2.5 Ghz (Java + C/C++)
3 Alibaba-CityBrain 81.19 % 90.99 % 74.68 % 1.5 s GPU @ 2.5 Ghz (Python + C/C++)
4 ExtAtt 81.05 % 90.60 % 76.08 % 1.2 s GPU @ 2.5 Ghz (Python + C/C++)
5 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.
6 HWFD 79.06 % 88.94 % 73.98 % 0.21 s one 1080Ti
7 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.
8 Argus_detection_v1 77.01 % 84.86 % 72.15 % 0.25 s GPU @ 1.5 Ghz (C/C++)
9 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.
10 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.
11 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.
12 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.
13 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.
14 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.
15 THICV-YDM 75.65 % 89.04 % 68.72 % 0.06 s GPU @ 2.5 Ghz (Python)
16 Noah CV Lab - SSL 75.64 % 86.57 % 70.53 % 0.1 s GPU @ 2.5 Ghz (Python)
17 Multi-3D
This method makes use of Velodyne laser scans.
75.32 % 86.00 % 69.55 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
18 Faster RCNN + Gr + A 74.95 % 86.95 % 69.50 % 1.29 s GPU @ 2.5 Ghz (Python)
19 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.
20 Faster RCNN + G 73.75 % 85.51 % 68.54 % 1.1 s GPU @ 2.5 Ghz (Python)
21 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.
22 Sogo_MM 72.82 % 84.99 % 67.42 % 1.5 s GPU @ 2.5 Ghz (C/C++)
23 Faster RCNN + A 72.67 % 86.21 % 67.55 % 0.19 s GPU @ 2.5 Ghz (Python)
24 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.
25 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.
26 FRCNN-WS 72.26 % 84.20 % 67.47 % 0.22 s 1 core @ 3.0 Ghz (Python)
27 Faster RCNN + A 72.09 % 85.35 % 66.87 % 0.19 s GPU @ 2.5 Ghz (Python + C/C++)
28 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.
29 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.
30 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.
31 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.
32 TridentNet 70.07 % 83.42 % 65.28 % 0.2 s GPU @ 2.5 Ghz (Python)
33 CSFADet 70.07 % 84.72 % 64.81 % 0.05 s GPU @ 2.5 Ghz (Python)
34 Mono3CN 69.75 % 83.47 % 63.15 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
35 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.
36 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.
37 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.
38 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.
39 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.
40 dgist_multiDetNet 67.03 % 81.96 % 61.39 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
41 YOLOv3.5 66.54 % 83.87 % 61.45 % 0.05 s GPU @ 2.5 Ghz (Python)
42 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.
43 AtrousDet 64.97 % 80.79 % 58.36 % 0.05 s TITAN X
44 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.
45 CRCNNA 63.69 % 78.10 % 58.41 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
46 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.
47 PCN 63.41 % 80.08 % 58.55 % 0.6 s
48 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.
49 merge12-12 62.84 % 80.27 % 56.08 % 0.2 s 4 cores @ 2.5 Ghz (Python)
50 cas+res+soft 62.71 % 80.11 % 55.99 % 0.2 s 4 cores @ 2.5 Ghz (Python)
51 cas_retina 62.37 % 79.82 % 57.15 % 0.2 s 4 cores @ 2.5 Ghz (Python)
52 cas_retina_1_13 61.87 % 79.09 % 56.70 % 0.03 s 4 cores @ 2.5 Ghz (Python)
53 MonoPair 61.57 % 78.81 % 56.51 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
54 ReSqueeze 61.33 % 73.69 % 56.65 % 0.03 s GPU @ >3.5 Ghz (Python)
55 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.
56 JSU-NET 61.19 % 83.17 % 56.20 % 0.1 s 1 core @ 2.5 Ghz (Python)
57 Regionlets 60.83 % 73.79 % 54.72 % 1 s >8 cores @ 2.5 Ghz (C/C++)
X. Wang, M. Yang, S. Zhu and Y. Lin: Regionlets for Generic Object Detection. T-PAMI 2015.
W. Zou, X. Wang, M. Sun and Y. Lin: Generic Object Detection with Dense Neural Patterns and Regionlets. British Machine Vision Conference 2014.
C. Long, X. Wang, G. Hua, M. Yang and Y. Lin: Accurate Object Detection with Location Relaxation and Regionlets Relocalization. Asian Conference on Computer Vision 2014.
58 bin 60.73 % 71.43 % 55.78 % 15ms s GPU @ >3.5 Ghz (Python)
59 OHS-Direct 60.63 % 69.37 % 57.64 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots. 2019.
60 3DSSD 60.51 % 72.33 % 56.28 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
61 anm 60.35 % 76.02 % 55.46 % 3 s 1 core @ 2.5 Ghz (C/C++)
62 PiP 59.94 % 70.52 % 56.51 % 0.05 s 1 core @ 2.5 Ghz (Python)
63 cascadercnn 59.50 % 78.79 % 54.44 % 0.36 s 4 cores @ 2.5 Ghz (Python)
64 A-VoxelNet 59.07 % 69.90 % 56.49 % 0.029 s GPU @ 2.5 Ghz (Python)
65 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.
66 mymask-rcnn 58.89 % 72.55 % 52.58 % 0.3 s 1 core @ 2.5 Ghz (Python)
67 OHS-Dense 58.70 % 68.18 % 54.68 % 0.03 s 1 core @ 2.5 Ghz (Python/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots. 2019.
68 DDB
This method makes use of Velodyne laser scans.
58.53 % 69.03 % 55.90 % 0.05 s GPU @ 2.5 Ghz (Python)
69 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
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. arXiv preprint arXiv:1912.13192 2019.
70 Point-GNN
This method makes use of Velodyne laser scans.
58.20 % 71.59 % 54.06 % 0.6 s GPU @ 2.5 Ghz (Python)
71 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.
72 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.
73 PPBA 58.06 % 67.73 % 55.69 % NA s GPU @ 2.5 Ghz (Python)
74 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. arXiv preprint arXiv:1907.03670 2019.
75 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.
76 LPN 57.69 % 71.87 % 53.21 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
77 TBU 57.44 % 67.29 % 54.00 % NA s GPU @ 2.5 Ghz (Python)
78 LDAM 56.68 % 64.73 % 54.21 % 24 ms GTX 1080 ti GPU
79 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.
80 yolo800 56.67 % 71.26 % 50.91 % 0.13 s 4 cores @ 2.5 Ghz (Python)
81 ZKNet 56.58 % 71.15 % 51.87 % 0.01 s GPU @ 2.0 Ghz (Python)
82 CentrNet-v1
This method makes use of Velodyne laser scans.
56.57 % 66.27 % 54.19 % 0.03 s GPU @ 2.5 Ghz (Python)
83 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.
84 Tencent_ADlab_Lidar
This method makes use of Velodyne laser scans.
56.49 % 66.91 % 54.06 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
85 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.
86 FD2 56.35 % 71.37 % 51.08 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
87 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.
88 RFCN 55.96 % 72.32 % 49.66 % 0.2 s 4 cores @ 2.5 Ghz (Python)
89 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.
90 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.
91 STD 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.
92 RFCN_RFB 54.98 % 70.61 % 48.75 % 0.2 s 4 cores @ 2.5 Ghz (Python)
93 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.
94 CHTTL MMF 54.28 % 72.79 % 49.31 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
95 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.
96 NM code 54.05 % 69.81 % 49.32 % 0.01 s GPU @ 2.5 Ghz (Python)
97 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. arXiv preprint arXiv:1911.10150 2019.
98 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.
99 fasterrcnn 53.42 % 69.29 % 48.76 % 0.2 s 4 cores @ 2.5 Ghz (Python)
100 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.
101 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.
102 Multi-task DG 52.87 % 71.27 % 48.10 % 0.06 s GPU @ 2.5 Ghz (Python)
103 NEUAV 52.53 % 68.50 % 47.99 % 0.06 s GPU @ 2.5 Ghz (Python)
104 deprecated 52.32 % 67.93 % 47.77 % 0.05 s GPU @ 2.0 Ghz (Python)
105 PP-3D 52.11 % 63.07 % 49.79 % 0.1 s 1 core @ 2.5 Ghz (Python)
106 SF
This method uses stereo information.
This method makes use of Velodyne laser scans.
51.83 % 67.73 % 47.45 % 0.5 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
107 MTDP 51.81 % 68.12 % 46.95 % 0.15 s GPU @ 2.0 Ghz (Python)
108 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.
109 SCANet 51.23 % 65.06 % 47.06 % 0.17 s >8 cores @ 2.5 Ghz (Python)
110 centernet 51.09 % 69.27 % 45.40 % 0.01 s GPU @ 2.5 Ghz (Python)
111 myfaster-rcnn-v1.5 50.95 % 67.68 % 46.29 % 0.1 s 1 core @ 2.5 Ghz (Python)
112 FCY
This method makes use of Velodyne laser scans.
50.88 % 59.73 % 48.61 % 0.02 s GPU @ 2.5 Ghz (Python)
113 PPFNet code 50.52 % 57.82 % 47.44 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
114 FOFNet
This method makes use of Velodyne laser scans.
50.08 % 62.64 % 46.27 % 0.04 s GPU @ 2.5 Ghz (Python)
115 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.
116 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.
117 Resnet101Faster rcnn 49.12 % 64.72 % 44.60 % 1 s 1 core @ 2.5 Ghz (Python)
118 VOXEL_FPN_HR 49.09 % 60.28 % 45.47 % 0.12 s 8 cores @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
119 SS3D_HW 49.01 % 64.67 % 42.86 % 0.4 s GPU @ 2.5 Ghz (Python)
120 cascade_gw 48.99 % 67.35 % 44.25 % 0.2 s 4 cores @ 2.5 Ghz (Python)
121 Int-YOLO code 48.76 % 64.09 % 44.31 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
122 MP 48.73 % 60.26 % 45.05 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
123 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.
124 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.
125 mylsi-faster-rcnn 47.99 % 65.03 % 43.43 % 0.3 s 1 core @ 2.5 Ghz (Python)
126 HR-SECOND code 46.69 % 58.68 % 42.93 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
127 Cmerge 46.51 % 63.68 % 41.60 % 0.2 s 4 cores @ 2.5 Ghz (Python)
128 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.
129 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.
130 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.
131 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.
132 yyyyolo 44.55 % 60.74 % 39.96 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
133 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.
134 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.
135 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. arXiv preprint arXiv:1912.04799 2019.
136 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.
137 PG-MonoNet 43.12 % 58.51 % 38.92 % 0.19 s GPU @ 2.5 Ghz (Python)
138 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.
139 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.
140 GNN 42.28 % 58.09 % 37.81 % 0.2 s 1 core @ 2.5 Ghz (Python)
141 MMRetina
This method uses stereo information.
This method uses optical flow information.
This method makes use of Velodyne laser scans.
41.63 % 59.63 % 36.97 % 0.38 s GPU @ 2.5 Ghz (Python)
142 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.
143 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 .
144 RTM3D code 41.43 % 58.14 % 37.09 % 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.
145 myfaster-rcnn 41.22 % 56.87 % 36.99 % 0.01 s 1 core @ 2.5 Ghz (Python)
146 yolov3_warp 40.64 % 55.04 % 36.33 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
147 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.
148 SAANet 40.43 % 51.16 % 38.38 % 0.10 s 1 core @ 2.5 Ghz (Python)
149 Retinanet100 40.03 % 54.30 % 35.33 % 0.2 s 4 cores @ 2.5 Ghz (Python)
150 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. arXiv preprint arXiv:2001.03398 2020.
151 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.
152 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.
153 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.
154 softyolo 39.30 % 54.49 % 36.66 % 0.16 s 4 cores @ 2.5 Ghz (Python)
155 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). .
156 pedestrian_cnn 37.90 % 52.07 % 33.58 % 1 s 1 core @ 2.5 Ghz (C/C++)
157 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.
158 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.
159 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.
160 KD53-20 36.03 % 45.78 % 32.79 % 0.19 s 4 cores @ 2.5 Ghz (Python)
161 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.
162 Lidar_ROI+Yolo(UJS) 35.58 % 47.74 % 31.48 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
163 34.81 % 44.38 % 32.10 %
164 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.
165 OC Stereo
This method uses stereo information.
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. arXiv preprint arXiv:1909.07566 2019.
166 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.
167 BirdNet
This method makes use of Velodyne laser scans.
30.07 % 36.82 % 28.40 % 0.11 s Titan Xp GPU
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.
168 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.
169 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.
170 100Frcnn 21.92 % 34.07 % 19.48 % 2 s 4 cores @ 2.5 Ghz (Python + C/C++)
171 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.
172 R-CNN_VGG 19.97 % 26.62 % 17.96 % 10 s GPU @ 2.5 Ghz (Matlab + C/C++)
173 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.
174 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.
175 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.
176 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.
177 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.
178 CBNet 1.33 % 1.03 % 1.41 % 1 s 4 cores @ 2.5 Ghz (Python)
179 softretina 0.26 % 0.19 % 0.26 % 0.16 s 4 cores @ 2.5 Ghz (Python)
180 JSyolo 0.12 % 0.19 % 0.12 % 0.16 s 4 cores @ 2.5 Ghz (Python)
181 TopNet-DecayRate
This method makes use of Velodyne laser scans.
0.01 % 0.01 % 0.01 % 92 ms NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
Table as LaTeX | Only published Methods

Cyclist


Method Setting Code Moderate Easy Hard Runtime Environment
1 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
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. arXiv preprint arXiv:1912.13192 2019.
2 FichaDL 80.38 % 88.41 % 69.72 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
3 Noah CV Lab - SSL 79.10 % 86.71 % 69.66 % 0.1 s GPU @ 2.5 Ghz (Python)
4 OHS-Dense 78.42 % 85.79 % 71.80 % 0.03 s 1 core @ 2.5 Ghz (Python/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots. 2019.
5 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. arXiv preprint arXiv:1907.03670 2019.
6 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.
7 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. arXiv preprint arXiv:1911.10150 2019.
8 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.
9 HWFD 76.17 % 87.02 % 66.45 % 0.21 s one 1080Ti
10 OHS-Direct 75.98 % 83.71 % 68.80 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots. 2019.
11 Multi-3D
This method makes use of Velodyne laser scans.
75.77 % 84.71 % 65.95 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
12 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.
13 VOXEL_FPN_HR 75.24 % 87.73 % 68.60 % 0.12 s 8 cores @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
14 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.
15 Point-GNN
This method makes use of Velodyne laser scans.
75.08 % 85.75 % 68.69 % 0.6 s GPU @ 2.5 Ghz (Python)
16 ExtAtt 75.08 % 86.09 % 65.30 % 1.2 s GPU @ 2.5 Ghz (Python + C/C++)
17 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.
18 PPBA 74.46 % 86.45 % 69.15 % NA s GPU @ 2.5 Ghz (Python)
19 TBU 74.46 % 86.45 % 69.15 % NA s GPU @ 2.5 Ghz (Python)
20 3DSSD 74.12 % 87.09 % 67.67 % 0.04 s GPU @ 2.5 Ghz (Python + C/C++)
21 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.
22 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.
23 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.
24 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.
25 FOFNet
This method makes use of Velodyne laser scans.
72.96 % 87.12 % 66.37 % 0.04 s GPU @ 2.5 Ghz (Python)
26 HR-SECOND code 72.77 % 84.21 % 66.25 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
27 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.
28 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.
29 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.
30 STD 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.
31 Sogo_MM 71.57 % 79.35 % 62.22 % 1.5 s GPU @ 2.5 Ghz (C/C++)
32 PiP 71.52 % 82.97 % 65.52 % 0.05 s 1 core @ 2.5 Ghz (Python)
33 Faster RCNN + Gr + A 70.78 % 83.99 % 63.36 % 1.29 s GPU @ 2.5 Ghz (Python)
34 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.
35 TridentNet 69.63 % 81.97 % 59.52 % 0.2 s GPU @ 2.5 Ghz (Python)
36 MP 69.52 % 85.05 % 63.17 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
37 LDAM 69.31 % 80.20 % 63.85 % 24 ms GTX 1080 ti GPU
38 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.
39 DGIST-CellBox 68.92 % 83.72 % 61.32 % 0.1 s GPU @ 2.5 Ghz (Java + C/C++)
40 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.
41 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.
42 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.
43 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.
44 Faster RCNN + G 68.09 % 83.51 % 60.60 % 1.1 s GPU @ 2.5 Ghz (Python)
45 Faster RCNN + A 67.84 % 82.06 % 60.52 % 0.19 s GPU @ 2.5 Ghz (Python)
46 Tencent_ADlab_Lidar
This method makes use of Velodyne laser scans.
67.82 % 82.74 % 61.06 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
47 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.
48 A-VoxelNet 67.37 % 81.32 % 60.27 % 0.029 s GPU @ 2.5 Ghz (Python)
49 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.
50 Faster RCNN + A 67.15 % 83.77 % 59.85 % 0.19 s GPU @ 2.5 Ghz (Python + C/C++)
51 SAANet 66.58 % 83.07 % 59.88 % 0.10 s 1 core @ 2.5 Ghz (Python)
52 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.
53 FCY
This method makes use of Velodyne laser scans.
65.50 % 81.33 % 59.04 % 0.02 s GPU @ 2.5 Ghz (Python)
54 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.
55 deprecated 63.34 % 83.91 % 53.78 % 0.05 s GPU @ 2.0 Ghz (Python)
56 Mono3CN 63.29 % 81.46 % 56.27 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
57 CentrNet-v1
This method makes use of Velodyne laser scans.
62.99 % 78.90 % 56.46 % 0.03 s GPU @ 2.5 Ghz (Python)
58 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.
59 AtrousDet 62.50 % 79.02 % 53.87 % 0.05 s TITAN X
60 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.
61 SCANet 62.31 % 76.50 % 56.06 % 0.17 s >8 cores @ 2.5 Ghz (Python)
62 DDB
This method makes use of Velodyne laser scans.
61.41 % 78.04 % 55.37 % 0.05 s GPU @ 2.5 Ghz (Python)
63 PP-3D 61.29 % 77.75 % 54.59 % 0.1 s 1 core @ 2.5 Ghz (Python)
64 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.
65 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.
66 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.
67 merge12-12 59.48 % 77.66 % 51.41 % 0.2 s 4 cores @ 2.5 Ghz (Python)
68 cas+res+soft 59.43 % 77.85 % 51.34 % 0.2 s 4 cores @ 2.5 Ghz (Python)
69 YOLOv3.5 58.57 % 79.16 % 51.74 % 0.05 s GPU @ 2.5 Ghz (Python)
70 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.
71 cascadercnn 58.08 % 77.24 % 51.13 % 0.36 s 4 cores @ 2.5 Ghz (Python)
72 GA_rpn500 57.82 % 76.06 % 49.00 % 1 s 1 core @ 2.5 Ghz (Python)
73 GA2500 57.82 % 76.06 % 48.99 % 0.2 s 1 core @ 2.5 Ghz (Python)
74 bin 57.62 % 64.36 % 50.70 % 15ms s GPU @ >3.5 Ghz (Python)
75 dgist_multiDetNet 57.44 % 78.42 % 49.84 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
76 GA_FULLDATA 57.20 % 75.50 % 50.26 % 1 s 4 cores @ 2.5 Ghz (Python)
77 cas_retina 57.14 % 73.97 % 50.32 % 0.2 s 4 cores @ 2.5 Ghz (Python)
78 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.
79 CSFADet 56.88 % 73.82 % 50.22 % 0.05 s GPU @ 2.5 Ghz (Python)
80 cas_retina_1_13 56.39 % 72.80 % 49.71 % 0.03 s 4 cores @ 2.5 Ghz (Python)
81 MonoPair 56.37 % 74.77 % 48.37 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
82 GA_BALANCE 56.07 % 78.33 % 49.02 % 1 s 1 core @ 2.5 Ghz (Python)
83 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.
84 bigger_ga 55.66 % 73.05 % 47.31 % 1 s 1 core @ 2.5 Ghz (Python)
85 Multi-task DG 55.30 % 75.48 % 48.22 % 0.06 s GPU @ 2.5 Ghz (Python)
86 ReSqueeze 54.50 % 69.64 % 48.24 % 0.03 s GPU @ >3.5 Ghz (Python)
87 CRCNNA 53.41 % 69.81 % 46.29 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
88 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.
89 GAFM 51.40 % 73.43 % 44.61 % 0.5 s 1 core @ 2.5 Ghz (Python)
90 JSU-NET 51.10 % 72.92 % 44.26 % 0.1 s 1 core @ 2.5 Ghz (Python)
91 ZKNet 49.48 % 66.29 % 42.81 % 0.01 s GPU @ 2.0 Ghz (Python)
92 anm 49.05 % 66.96 % 43.44 % 3 s 1 core @ 2.5 Ghz (C/C++)
93 ga50 49.02 % 70.25 % 42.52 % 1 s 1 core @ 2.5 Ghz (Python)
94 NEUAV 48.65 % 69.50 % 42.64 % 0.06 s GPU @ 2.5 Ghz (Python)
95 LPN 48.57 % 65.77 % 42.66 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
96 mylsi-faster-rcnn 47.90 % 69.04 % 41.72 % 0.3 s 1 core @ 2.5 Ghz (Python)
97 fasterrcnn 47.87 % 64.39 % 42.03 % 0.2 s 4 cores @ 2.5 Ghz (Python)
98 BirdNet
This method makes use of Velodyne laser scans.
47.64 % 64.91 % 44.59 % 0.11 s Titan Xp GPU
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.
99 yolo800 47.31 % 63.22 % 42.28 % 0.13 s 4 cores @ 2.5 Ghz (Python)
100 RTM3D code 46.79 % 66.89 % 40.09 % 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.
101 RFCN 46.70 % 62.09 % 40.71 % 0.2 s 4 cores @ 2.5 Ghz (Python)
102 NM code 45.82 % 60.69 % 40.83 % 0.01 s GPU @ 2.5 Ghz (Python)
103 SS3D_HW 45.53 % 61.79 % 39.03 % 0.4 s GPU @ 2.5 Ghz (Python)
104 PG-MonoNet 45.40 % 63.75 % 37.14 % 0.19 s GPU @ 2.5 Ghz (Python)
105 RFCN_RFB 45.28 % 60.06 % 39.66 % 0.2 s 4 cores @ 2.5 Ghz (Python)
106 Cmerge 44.87 % 64.38 % 37.80 % 0.2 s 4 cores @ 2.5 Ghz (Python)
107 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.
108 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.
109 myfaster-rcnn-v1.5 42.89 % 59.60 % 38.07 % 0.1 s 1 core @ 2.5 Ghz (Python)
110 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. arXiv preprint arXiv:1912.04799 2019.
111 cascade_gw 42.84 % 63.58 % 36.94 % 0.2 s 4 cores @ 2.5 Ghz (Python)
112 FD2 42.67 % 62.54 % 38.41 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
113 centernet 42.45 % 58.95 % 37.56 % 0.01 s GPU @ 2.5 Ghz (Python)
114 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 .
115 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.
116 MTDP 40.46 % 53.83 % 35.74 % 0.15 s GPU @ 2.0 Ghz (Python)
117 Int-YOLO code 39.83 % 53.34 % 34.16 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
118 GNN 39.80 % 58.30 % 34.56 % 0.2 s 1 core @ 2.5 Ghz (Python)
119 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.
120 myfaster-rcnn 35.81 % 54.28 % 31.82 % 0.01 s 1 core @ 2.5 Ghz (Python)
121 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.
122 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. arXiv preprint arXiv:2001.03398 2020.
123 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.
124 Retinanet100 32.30 % 46.60 % 28.29 % 0.2 s 4 cores @ 2.5 Ghz (Python)
125 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.
126 yolov3_warp 29.48 % 44.46 % 25.84 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
127 OC Stereo
This method uses stereo information.
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. arXiv preprint arXiv:1909.07566 2019.
128 MMRetina
This method uses stereo information.
This method uses optical flow information.
This method makes use of Velodyne laser scans.
28.00 % 43.71 % 24.62 % 0.38 s GPU @ 2.5 Ghz (Python)
129 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.
130 softyolo 27.90 % 41.90 % 24.74 % 0.16 s 4 cores @ 2.5 Ghz (Python)
131 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.
132 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.
133 100Frcnn 27.69 % 43.23 % 23.91 % 2 s 4 cores @ 2.5 Ghz (Python + C/C++)
134 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.
135 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.
136 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.
137 BdCost+DA+BB+MS 25.52 % 33.92 % 21.14 % TBD s 4 cores @ 2.5 Ghz (C/C++)
138 R-CNN_VGG 25.14 % 34.28 % 22.17 % 10 s GPU @ 2.5 Ghz (Matlab + C/C++)
139 Lidar_ROI+Yolo(UJS) 24.42 % 36.43 % 21.78 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
140 BdCost+DA+BB 20.00 % 26.87 % 16.76 % TBD s 4 cores @ 2.5 Ghz (C/C++)
141 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.
142 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.
143 mymask-rcnn 13.58 % 18.03 % 12.42 % 0.3 s 1 core @ 2.5 Ghz (Python)
144 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.
145 KD53-20 12.81 % 20.05 % 11.99 % 0.19 s 4 cores @ 2.5 Ghz (Python)
146 yyyyolo 12.52 % 16.29 % 11.07 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
147 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.
148 CBNet 0.39 % 0.24 % 0.44 % 1 s 4 cores @ 2.5 Ghz (Python)
149 softretina 0.25 % 0.16 % 0.18 % 0.16 s 4 cores @ 2.5 Ghz (Python)
150 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.
151 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.
152 JSyolo 0.03 % 0.02 % 0.04 % 0.16 s 4 cores @ 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 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
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. arXiv preprint arXiv:1912.13192 2019.
2 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.
3 CPRCCNN 94.24 % 96.31 % 89.71 % 0.1 s 1 core @ 2.5 Ghz (Python)
4 EPNet 94.22 % 96.13 % 89.68 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
5 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.
6 MLF_PointCas 93.34 % 96.66 % 85.87 % 0.1 s GPU @ 2.5 Ghz (Python)
7 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.
8 THICV-YDM 93.11 % 96.07 % 80.52 % 0.06 s GPU @ 2.5 Ghz (Python)
9 ELE 93.07 % 98.42 % 90.17 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
10 RGB3D
This method makes use of Velodyne laser scans.
92.94 % 96.52 % 87.83 % 0.39 s GPU @ 2.5 Ghz (Python)
11 PointRCNN-deprecated
This method makes use of Velodyne laser scans.
92.74 % 96.70 % 85.51 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
12 OHS-Direct 92.58 % 96.08 % 89.60 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots. 2019.
13 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.
14 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.
15 ORP 92.57 % 96.22 % 85.06 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
16 R-GCN 92.53 % 96.16 % 87.45 % 0.16 s GPU @ 2.5 Ghz (Python)
17 PPFNet code 92.52 % 96.30 % 87.44 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
18 PI-RCNN 92.52 % 96.15 % 87.47 % 0.1 s 1 core @ 2.5 Ghz (Python)
19 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. arXiv preprint arXiv:1911.10150 2019.
20 CAASS-3D 92.42 % 96.31 % 87.60 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
21 OHS-Dense 92.32 % 95.83 % 89.39 % 0.03 s 1 core @ 2.5 Ghz (Python/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots. 2019.
22 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.
23 CP
This method makes use of Velodyne laser scans.
92.16 % 96.05 % 87.22 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
24 PointRGCN 92.15 % 97.48 % 86.83 % 0.26 s GPU @ V100 (Python)
25 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.
26 PointCSE 91.95 % 95.52 % 86.75 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
27 IE-PointRCNN 91.94 % 96.00 % 86.84 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
28 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.
29 NU-optim 91.87 % 95.17 % 86.54 % 0.04 s GPU @ >3.5 Ghz (Python)
30 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.
31 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. arXiv preprint arXiv:1907.03670 2019.
32 C-GCN 91.57 % 95.63 % 86.13 % 0.147 s GPU @ V100 (Python)
33 RUC 91.25 % 95.01 % 88.14 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
34 MLF_SecCas 91.18 % 96.19 % 83.25 % 0.05 s 1 core @ 2.5 Ghz (Python)
35 HRI-FusionRCNN 91.03 % 94.30 % 83.31 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
36 deprecated 91.02 % 94.06 % 78.56 % 0.05 s GPU @ 2.0 Ghz (Python)
37 Mono3CN 90.96 % 94.22 % 82.86 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
38 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.
39 Alibaba-AILabsX
This method makes use of Velodyne laser scans.
90.75 % 96.06 % 83.22 % 0.05 s 1 core @ >3.5 Ghz (C/C++)
40 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.
41 TBA 90.69 % 93.55 % 87.56 % 0.07 s 1 core @ 2.5 Ghz (Python)
42 SRF 90.54 % 95.86 % 85.30 % 0.05 s GPU @ 2.5 Ghz (Python + C/C++)
43 CentrNet-v1
This method makes use of Velodyne laser scans.
90.48 % 93.79 % 87.43 % 0.03 s GPU @ 2.5 Ghz (Python)
44 MMV 90.41 % 93.93 % 82.79 % 0.4 s GPU @ 2.5 Ghz (C/C++)
45 DDB
This method makes use of Velodyne laser scans.
90.38 % 93.21 % 86.42 % 0.05 s GPU @ 2.5 Ghz (Python)
46 OACV 90.35 % 93.95 % 81.90 % 0.23 s GPU @ 2.5 Ghz (Python)
47 autonet 90.31 % 93.30 % 87.00 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
48 MVSLN 90.26 % 95.95 % 82.75 % 0.1s s 1 core @ 2.5 Ghz (C/C++)
49 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.
50 Bit 90.19 % 93.42 % 86.48 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
51 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.
52 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.
53 PCSC-Net 90.09 % 93.83 % 86.76 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
54 FOFNet
This method makes use of Velodyne laser scans.
90.05 % 93.87 % 84.52 % 0.04 s GPU @ 2.5 Ghz (Python)
55 SFB-SECOND 90.04 % 95.99 % 84.70 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
56 PTS
This method makes use of Velodyne laser scans.
code 90.03 % 95.41 % 84.73 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
57 A-VoxelNet 90.00 % 93.24 % 82.31 % 0.029 s GPU @ 2.5 Ghz (Python)
58 Sogo_MM 89.97 % 94.15 % 79.94 % 1.5 s GPU @ 2.5 Ghz (C/C++)
59 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.
60 SECOND-V1.5
This method makes use of Velodyne laser scans.
code 89.88 % 95.53 % 84.46 % 0.04 s GPU @ 2.0 Ghz (Python + C/C++)
61 PointPiallars_SECA 89.86 % 92.96 % 86.46 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
62 Tencent_ADlab_Lidar
This method makes use of Velodyne laser scans.
89.82 % 93.37 % 85.67 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
63 VOXEL_FPN_HR 89.81 % 93.52 % 84.59 % 0.12 s 8 cores @ 2.5 Ghz (Python)
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64 BVVF 89.77 % 95.55 % 84.48 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
65 MPNet
This method makes use of Velodyne laser scans.
89.75 % 94.31 % 86.07 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
66 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. arXiv preprint arXiv:1811.06666 2018.
67 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.
68 FCY
This method makes use of Velodyne laser scans.
89.49 % 93.02 % 85.72 % 0.02 s GPU @ 2.5 Ghz (Python)
69 SAANet 89.46 % 95.64 % 82.12 % 0.10 s 1 core @ 2.5 Ghz (Python)
70 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.
71 RUC code 89.26 % 92.28 % 85.38 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
72 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.
73 RUC code 88.90 % 92.68 % 84.04 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
74 PAD 88.71 % 93.09 % 84.86 % 0.15 s 1 core @ 2.5 Ghz (Python)
75 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.
76 SS3D_HW 88.50 % 94.45 % 68.61 % 0.4 s GPU @ 2.5 Ghz (Python)
77 DPSM 88.29 % 93.59 % 75.35 % 0.1 s GPU @ 2.5 Ghz (Python)
78 Prune 88.10 % 93.86 % 80.41 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
79 autoRUC 88.03 % 93.80 % 80.36 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
80 PointRes
This method makes use of Velodyne laser scans.
This method makes use of GPS/IMU information.
This is an online method (no batch processing).
87.83 % 95.24 % 83.39 % 0.013 s 1 core @ 2.5 Ghz (Python + C/C++)
81 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.
82 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.
83 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.
84 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.
85 PP-3D 87.46 % 93.09 % 79.88 % 0.1 s 1 core @ 2.5 Ghz (Python)
86 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.
87 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.
88 3DNN 87.08 % 93.78 % 79.72 % 0.09 s GPU @ 2.5 Ghz (Python)
89 SMOKE 87.02 % 92.94 % 77.12 % 0.03 s GPU @ 2.5 Ghz (Python)
90 MonoSS 86.95 % 92.88 % 77.04 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
91 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.
92 RTM3D code 86.76 % 91.80 % 77.20 % 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.
93 MBR-SSD 86.57 % 90.97 % 78.03 % 4.0 s GPU @ 2.5 Ghz (Python + C/C++)
94 SCANet 86.51 % 92.47 % 81.09 % 0.17 s >8 cores @ 2.5 Ghz (Python)
95 MonoPair 86.11 % 91.65 % 76.45 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
96 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. arXiv preprint arXiv:2001.03398 2020.
97 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.
98 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.
99 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.
100 IDA-3D
This method uses stereo information.
84.32 % 92.63 % 73.98 % 0.08 s 1 core @ 2.5 Ghz (Python + C/C++)
101 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.
102 SECA 83.99 % 92.34 % 78.85 % 1 s GPU @ 2.5 Ghz (Python)
103 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.
104 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.
105 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 .
106 RAR-Net 82.63 % 88.40 % 66.90 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
107 ASOD 82.13 % 93.56 % 67.32 % 0.28 s GPU @ 2.5 Ghz (Python)
108 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. arXiv preprint arXiv:1912.04799 2019.
109 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.
110 PG-MonoNet 81.77 % 87.61 % 66.06 % 0.19 s GPU @ 2.5 Ghz (Python)
111 Disp R-CNN
This method uses stereo information.
81.61 % 92.91 % 69.20 % 0.42 s GPU @ 2.5 Ghz (Python + C/C++)
112 Pseudo-LiDAR E2E
This method uses stereo information.
81.56 % 93.74 % 74.23 % 0.4 s GPU @ 2.5 Ghz (Python)
113 HR-SECOND code 81.23 % 88.32 % 74.89 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
114 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.
115 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.
116 Manhnet 79.97 % 88.71 % 63.47 % 26 ms 1 core @ 2.5 Ghz (C/C++)
117 3D-GCK 78.44 % 88.59 % 66.28 % 24 ms Tesla V100
118 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.
119 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.
120 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.
121 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.
122 avodC 75.35 % 86.76 % 70.17 % 0.1 s GPU @ 2.5 Ghz (Python)
123 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.
124 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.
125 OC Stereo
This method uses stereo information.
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. arXiv preprint arXiv:1909.07566 2019.
126 BdCost+DA+BB+MS 72.87 % 84.39 % 57.07 % TBD s 4 cores @ 2.5 Ghz (Matlab + C/C++)
127 BdCost+DA+MS 72.65 % 84.06 % 58.08 % TBD s 4 cores @ 2.5 Ghz (Matlab/C++)
128 BdCost+DA+BB 70.07 % 84.66 % 55.50 % TBD s 4 cores @ 2.5 Ghz (Matlab + C/C++)
129 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.
130 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.
131 Decoupled-3D v2 67.47 % 88.23 % 54.04 % 0.08 s GPU @ 2.5 Ghz (C/C++)
132 Decoupled-3D 67.23 % 87.34 % 53.84 % 0.08 s GPU @ 2.5 Ghz (C/C++)
133 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.
134 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.
135 deprecated 65.30 % 69.02 % 63.66 % 0.05 s GPU @ >3.5 Ghz (Python)
136 3DVSSD 65.28 % 79.56 % 55.73 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
137 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.
138 BdCost48-25C 63.90 % 80.69 % 51.54 % 4 s 1 core @ 2.5 Ghz (C/C++)
139 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.
140 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.
141 monoref3d 58.30 % 77.65 % 46.90 % 0.1 s 1 core @ 2.5 Ghz (Python)
142 ref3D 58.30 % 77.65 % 46.90 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
143 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.
144 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.
145 3D-CVF
This method makes use of Velodyne laser scans.
57.01 % 62.54 % 54.94 % 0.05 s GPU @ >3.5 Ghz (Python)
146 BirdNet
This method makes use of Velodyne laser scans.
56.94 % 79.20 % 54.88 % 0.11 s Titan Xp GPU
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.
147 ref3D 56.49 % 77.52 % 45.17 % 0.1 s 1 core @ 2.5 Ghz (Python)
148 DEFT 51.66 % 57.41 % 50.02 % 1 s GPU @ 2.5 Ghz (Python)
149 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 .
150 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.
151 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.
152 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.
153 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.
154 ReSqueeze 45.58 % 49.08 % 41.33 % 0.03 s GPU @ >3.5 Ghz (Python)
155 Resnet101Faster rcnn 44.01 % 51.21 % 39.19 % 1 s 1 core @ 2.5 Ghz (Python)
156 FD2 38.89 % 48.29 % 34.35 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
157 bin 38.58 % 43.36 % 32.42 % 15ms s GPU @ >3.5 Ghz (Python)
158 DGIST-CellBox 38.36 % 39.11 % 36.15 % 0.1 s GPU @ 2.5 Ghz (Java + C/C++)
159 SA-SSD 38.30 % 39.40 % 37.07 % 0.04 s 1 core @ 2.5 Ghz (Python)
160 Faster RCNN + A 37.92 % 39.50 % 33.85 % 0.19 s GPU @ 2.5 Ghz (Python + C/C++)
161 JSU-NET 37.60 % 41.33 % 33.41 % 0.1 s 1 core @ 2.5 Ghz (Python)
162 Faster RCNN + G 37.49 % 39.05 % 33.40 % 1.1 s GPU @ 2.5 Ghz (Python)
163 Faster RCNN + A 37.35 % 38.75 % 33.38 % 0.19 s GPU @ 2.5 Ghz (Python)
164 Point-GNN
This method makes use of Velodyne laser scans.
37.20 % 38.66 % 36.29 % 0.6 s GPU @ 2.5 Ghz (Python)
165 GAFM 37.08 % 40.28 % 33.08 % 0.5 s 1 core @ 2.5 Ghz (Python)
166 CRCNNA 37.04 % 40.19 % 32.03 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
167 Faster RCNN + Gr + A 36.95 % 38.22 % 33.16 % 1.29 s GPU @ 2.5 Ghz (Python)
168 CSFADet 36.83 % 39.76 % 32.73 % 0.05 s GPU @ 2.5 Ghz (Python)
169 cas_retina 36.63 % 39.70 % 31.52 % 0.2 s 4 cores @ 2.5 Ghz (Python)
170 GA_BALANCE 36.62 % 38.44 % 31.94 % 1 s 1 core @ 2.5 Ghz (Python)
171 GA_rpn500 36.54 % 38.33 % 32.67 % 1 s 1 core @ 2.5 Ghz (Python)
172 GA2500 36.54 % 38.33 % 32.67 % 0.2 s 1 core @ 2.5 Ghz (Python)
173 cas+res+soft 36.53 % 38.82 % 32.26 % 0.2 s 4 cores @ 2.5 Ghz (Python)
174 merge12-12 36.47 % 38.83 % 32.20 % 0.2 s 4 cores @ 2.5 Ghz (Python)
175 GA_FULLDATA 36.43 % 38.90 % 31.61 % 1 s 4 cores @ 2.5 Ghz (Python)
176 AtrousDet 36.36 % 38.86 % 31.79 % 0.05 s TITAN X
177 bigger_ga 36.21 % 38.41 % 31.58 % 1 s 1 core @ 2.5 Ghz (Python)
178 cas_retina_1_13 35.89 % 39.02 % 31.33 % 0.03 s 4 cores @ 2.5 Ghz (Python)
179 cascadercnn 35.61 % 36.22 % 30.16 % 0.36 s 4 cores @ 2.5 Ghz (Python)
180 Cmerge 35.02 % 38.33 % 29.06 % 0.2 s 4 cores @ 2.5 Ghz (Python)
181 ga50 34.95 % 38.21 % 30.29 % 1 s 1 core @ 2.5 Ghz (Python)
182 softretina 34.57 % 39.31 % 29.27 % 0.16 s 4 cores @ 2.5 Ghz (Python)
183 Retinanet100 34.37 % 39.15 % 28.43 % 0.2 s 4 cores @ 2.5 Ghz (Python)
184 ZKNet 34.27 % 38.09 % 29.93 % 0.01 s GPU @ 2.0 Ghz (Python)
185 LPN 33.61 % 34.57 % 29.72 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
186 cascade_gw 33.53 % 34.76 % 29.71 % 0.2 s 4 cores @ 2.5 Ghz (Python)
187 RADNet-Fusion
This method makes use of Velodyne laser scans.
33.31 % 31.96 % 32.72 % 0.1 s 1 core @ 2.5 Ghz (Python)
188 RADNet-LIDAR
This method makes use of Velodyne laser scans.
33.08 % 31.30 % 32.31 % 0.1 s 1 core @ 2.5 Ghz (Python)
189 NM code 32.78 % 37.21 % 28.36 % 0.01 s GPU @ 2.5 Ghz (Python)
190 SceneNet 32.78 % 37.79 % 28.30 % 0.03 s GPU @ 2.5 Ghz (C/C++)
191 MTDP 32.68 % 36.06 % 27.12 % 0.15 s GPU @ 2.0 Ghz (Python)
192 CBNet 32.63 % 36.51 % 29.26 % 1 s 4 cores @ 2.5 Ghz (Python)
193 Fast-SSD 32.51 % 41.41 % 28.45 % 0.06 s GTX650Ti
194 centernet 32.22 % 35.79 % 28.50 % 0.01 s GPU @ 2.5 Ghz (Python)
195 RTL3D 32.16 % 33.73 % 30.58 % 0.02 s GPU @ 2.5 Ghz (Python)
196 RFCN_RFB 32.06 % 35.39 % 27.94 % 0.2 s 4 cores @ 2.5 Ghz (Python)
197 dgist_multiDetNet 32.01 % 38.16 % 28.72 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
198 FailNet-Fusion
This method makes use of Velodyne laser scans.
31.68 % 30.84 % 30.56 % 0.1 s 1 core @ 2.5 Ghz (Python)
199 MTNAS 31.15 % 35.43 % 27.02 % 0.02 s 1 core @ 2.5 Ghz (python)
200 yolo800 31.13 % 32.49 % 26.76 % 0.13 s 4 cores @ 2.5 Ghz (Python)
201 FailNet-LIDAR
This method makes use of Velodyne laser scans.
31.10 % 30.32 % 29.89 % 0.1 s 1 core @ 2.5 Ghz (Python)
202 VoxelNet(Unofficial) 31.08 % 34.54 % 28.79 % 0.5 s GPU @ 2.0 Ghz (Python)
203 SAIC-SA-3D
This method makes use of Velodyne laser scans.
31.02 % 41.38 % 29.60 % 0.05 s GPU @ 2.5 Ghz (Python)
204 RFCN 30.93 % 34.24 % 25.27 % 0.2 s 4 cores @ 2.5 Ghz (Python)
205 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.
206 m-prcnn
This method uses stereo information.
29.62 % 34.80 % 22.79 % 0.43 s 1 core @ 2.5 Ghz (Python)
207 Multi-task DG 29.49 % 36.06 % 26.06 % 0.06 s GPU @ 2.5 Ghz (Python)
208 fasterrcnn 28.42 % 30.28 % 24.95 % 0.2 s 4 cores @ 2.5 Ghz (Python)
209 RFBnet 27.91 % 34.44 % 25.24 % 0.2 s 4 cores @ 2.5 Ghz (Python)
210 E-VoxelNet 26.87 % 27.66 % 24.05 % 0.1 s GPU @ 2.5 Ghz (Python)
211 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.
212 Lidar_ROI+Yolo(UJS) 25.33 % 30.36 % 22.20 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
213 RADNet-Mono 24.78 % 28.55 % 22.84 % 0.1 s 1 core @ 2.5 Ghz (Python)
214 100Frcnn 23.32 % 32.81 % 19.45 % 2 s 4 cores @ 2.5 Ghz (Python + C/C++)
215 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.
216 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.
217 FailNet-Mono 19.63 % 25.13 % 17.19 % 0.1 s 1 core @ 2.5 Ghz (Python)
218 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.
219 softyolo 18.31 % 26.80 % 15.28 % 0.16 s 4 cores @ 2.5 Ghz (Python)
220 Licar
This method makes use of Velodyne laser scans.
16.16 % 18.56 % 15.59 % 0.09 s GPU @ 2.0 Ghz (Python)
221 YoloMono3D 15.72 % 18.27 % 11.85 % 0.05 s GPU @ 2.5 Ghz (Python)
222 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.
223 KD53-20 13.76 % 20.58 % 11.91 % 0.19 s 4 cores @ 2.5 Ghz (Python)
224 MuRF 1.75 % 0.63 % 2.14 % 0.05 s GPU @ 1.5 Ghz (Python + C/C++)
225 MP 1.51 % 0.63 % 2.03 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
226 PiP 1.45 % 0.56 % 1.85 % 0.05 s 1 core @ 2.5 Ghz (Python)
227 SPA 1.25 % 0.59 % 1.64 % 0.1 s 1 core @ 2.5 Ghz (Python)
228 Associate-3Ddet
This method makes use of Velodyne laser scans.
1.20 % 0.52 % 1.38 % 0.05 s 1 core @ 2.5 Ghz (Python + C/C++)
229 FCPP 0.06 % 0.00 % 0.07 % 0.02 s 1 core @ 2.0 Ghz (Python + C/C++)
230 JSyolo 0.00 % 0.00 % 0.00 % 0.16 s 4 cores @ 2.5 Ghz (Python)
Table as LaTeX | Only published Methods


Pedestrians


Method Setting Code Moderate Easy Hard Runtime Environment
1 THICV-YDM 69.07 % 83.00 % 62.54 % 0.06 s GPU @ 2.5 Ghz (Python)
2 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.
3 Sogo_MM 67.31 % 80.02 % 61.99 % 1.5 s GPU @ 2.5 Ghz (C/C++)
4 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.
5 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.
6 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.
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 Mono3CN 59.17 % 72.16 % 53.22 % 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 OHS-Direct 58.45 % 67.82 % 55.33 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots. 2019.
13 OHS-Dense 56.89 % 66.97 % 52.75 % 0.03 s 1 core @ 2.5 Ghz (Python/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots. 2019.
14 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.
15 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
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. arXiv preprint arXiv:1912.13192 2019.
16 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. arXiv preprint arXiv:1907.03670 2019.
17 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.
18 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. arXiv preprint arXiv:1911.10150 2019.
19 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.
20 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.
21 PPFNet code 47.73 % 55.78 % 44.56 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
22 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.
23 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.
24 VOXEL_FPN_HR 45.65 % 56.17 % 42.10 % 0.12 s 8 cores @ 2.5 Ghz (Python)
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25 SS3D_HW 44.43 % 59.56 % 38.77 % 0.4 s GPU @ 2.5 Ghz (Python)
26 FOFNet
This method makes use of Velodyne laser scans.
44.33 % 55.61 % 40.85 % 0.04 s GPU @ 2.5 Ghz (Python)
27 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.
28 DGIST-CellBox 43.86 % 48.68 % 41.52 % 0.1 s GPU @ 2.5 Ghz (Java + C/C++)
29 DDB
This method makes use of Velodyne laser scans.
43.21 % 52.02 % 40.81 % 0.05 s GPU @ 2.5 Ghz (Python)
30 PiP 42.76 % 51.23 % 40.06 % 0.05 s 1 core @ 2.5 Ghz (Python)
31 HWFD 42.45 % 48.31 % 39.66 % 0.21 s one 1080Ti
32 MonoPair 42.38 % 55.26 % 38.53 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
33 SCANet 42.12 % 54.48 % 38.64 % 0.17 s >8 cores @ 2.5 Ghz (Python)
34 Faster RCNN + Gr + A 40.92 % 47.81 % 37.89 % 1.29 s GPU @ 2.5 Ghz (Python)
35 HR-SECOND code 40.81 % 51.12 % 37.48 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
36 Faster RCNN + G 40.49 % 47.16 % 37.57 % 1.1 s GPU @ 2.5 Ghz (Python)
37 Faster RCNN + A 39.95 % 47.52 % 37.08 % 0.19 s GPU @ 2.5 Ghz (Python)
38 CentrNet-v1
This method makes use of Velodyne laser scans.
39.83 % 46.21 % 38.05 % 0.03 s GPU @ 2.5 Ghz (Python)
39 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.
40 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.
41 Faster RCNN + A 39.44 % 46.80 % 36.46 % 0.19 s GPU @ 2.5 Ghz (Python + C/C++)
42 RTM3D code 38.75 % 54.73 % 34.52 % 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.
43 CSFADet 38.41 % 46.75 % 35.44 % 0.05 s GPU @ 2.5 Ghz (Python)
44 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.
45 Tencent_ADlab_Lidar
This method makes use of Velodyne laser scans.
37.23 % 44.01 % 35.54 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
46 dgist_multiDetNet 37.10 % 45.90 % 33.80 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
47 A-VoxelNet 36.24 % 42.48 % 34.36 % 0.029 s GPU @ 2.5 Ghz (Python)
48 PP-3D 36.22 % 44.49 % 34.19 % 0.1 s 1 core @ 2.5 Ghz (Python)
49 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.
50 SAANet 36.08 % 46.09 % 34.14 % 0.10 s 1 core @ 2.5 Ghz (Python)
51 AtrousDet 35.85 % 44.79 % 32.12 % 0.05 s TITAN X
52 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.
53 CRCNNA 34.88 % 43.18 % 31.90 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
54 FCY
This method makes use of Velodyne laser scans.
34.67 % 40.75 % 33.00 % 0.02 s GPU @ 2.5 Ghz (Python)
55 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.
56 merge12-12 34.10 % 43.60 % 30.43 % 0.2 s 4 cores @ 2.5 Ghz (Python)
57 cas+res+soft 34.01 % 43.51 % 30.28 % 0.2 s 4 cores @ 2.5 Ghz (Python)
58 cas_retina 33.98 % 43.80 % 31.12 % 0.2 s 4 cores @ 2.5 Ghz (Python)
59 cas_retina_1_13 33.87 % 43.55 % 30.99 % 0.03 s 4 cores @ 2.5 Ghz (Python)
60 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. arXiv preprint arXiv:1912.04799 2019.
61 JSU-NET 33.55 % 45.79 % 30.72 % 0.1 s 1 core @ 2.5 Ghz (Python)
62 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.
63 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.
64 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.
65 PG-MonoNet 32.67 % 44.75 % 29.33 % 0.19 s GPU @ 2.5 Ghz (Python)
66 cascadercnn 32.59 % 43.37 % 29.73 % 0.36 s 4 cores @ 2.5 Ghz (Python)
67 ReSqueeze 32.47 % 38.49 % 30.04 % 0.03 s GPU @ >3.5 Ghz (Python)
68 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.
69 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.
70 yolo800 32.12 % 40.53 % 28.83 % 0.13 s 4 cores @ 2.5 Ghz (Python)
71 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.
72 bin 31.94 % 36.94 % 29.50 % 15ms s GPU @ >3.5 Ghz (Python)
73 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 .
74 Point-GNN
This method makes use of Velodyne laser scans.
31.86 % 39.16 % 29.65 % 0.6 s GPU @ 2.5 Ghz (Python)
75 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.
76 ZKNet 31.21 % 39.55 % 28.61 % 0.01 s GPU @ 2.0 Ghz (Python)
77 RFCN 30.97 % 40.51 % 27.45 % 0.2 s 4 cores @ 2.5 Ghz (Python)
78 LPN 30.84 % 38.60 % 28.49 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
79 CHTTL MMF 30.45 % 41.08 % 27.57 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
80 RFCN_RFB 29.91 % 38.71 % 26.50 % 0.2 s 4 cores @ 2.5 Ghz (Python)
81 deprecated 29.74 % 37.71 % 27.25 % 0.05 s GPU @ 2.0 Ghz (Python)
82 NM code 29.60 % 38.81 % 26.99 % 0.01 s GPU @ 2.5 Ghz (Python)
83 fasterrcnn 29.48 % 38.63 % 26.89 % 0.2 s 4 cores @ 2.5 Ghz (Python)
84 Multi-task DG 28.71 % 38.97 % 26.13 % 0.06 s GPU @ 2.5 Ghz (Python)
85 FD2 28.40 % 35.59 % 25.75 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
86 MTDP 28.24 % 37.49 % 25.57 % 0.15 s GPU @ 2.0 Ghz (Python)
87 centernet 27.53 % 37.41 % 24.35 % 0.01 s GPU @ 2.5 Ghz (Python)
88 cascade_gw 26.32 % 36.41 % 23.73 % 0.2 s 4 cores @ 2.5 Ghz (Python)
89 Cmerge 25.09 % 34.53 % 22.43 % 0.2 s 4 cores @ 2.5 Ghz (Python)
90 DSGN
This method uses stereo information.
code 24.32 % 31.21 % 23.09 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. arXiv preprint arXiv:2001.03398 2020.
91 ACF 24.31 % 32.23 % 21.70 % 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.
92 Resnet101Faster rcnn 23.70 % 30.19 % 21.55 % 1 s 1 core @ 2.5 Ghz (Python)
93 multi-task CNN 22.80 % 30.30 % 20.47 % 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.
94 ACF-MR 22.61 % 29.23 % 20.08 % 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.
95 OC Stereo
This method uses stereo information.
22.02 % 31.36 % 20.20 % 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. arXiv preprint arXiv:1909.07566 2019.
96 BirdNet
This method makes use of Velodyne laser scans.
21.83 % 27.12 % 20.56 % 0.11 s Titan Xp GPU
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.
97 Retinanet100 21.71 % 29.72 % 19.12 % 0.2 s 4 cores @ 2.5 Ghz (Python)
98 softyolo 21.56 % 30.46 % 20.01 % 0.16 s 4 cores @ 2.5 Ghz (Python)
99 Lidar_ROI+Yolo(UJS) 19.43 % 26.83 % 17.14 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
100 KD53-20 19.36 % 25.10 % 17.54 % 0.19 s 4 cores @ 2.5 Ghz (Python)
101 DPM-C8B1
This method uses stereo information.
19.17 % 27.79 % 16.48 % 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.
102 RefinedMPL 17.26 % 25.83 % 15.41 % 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.
103 RT3DStereo
This method uses stereo information.
15.34 % 21.41 % 13.23 % 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.
104 100Frcnn 12.37 % 19.41 % 10.92 % 2 s 4 cores @ 2.5 Ghz (Python + C/C++)
105 MP 5.39 % 6.41 % 5.14 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
106 CBNet 0.72 % 0.56 % 0.75 % 1 s 4 cores @ 2.5 Ghz (Python)
107 softretina 0.13 % 0.10 % 0.14 % 0.16 s 4 cores @ 2.5 Ghz (Python)
108 JSyolo 0.06 % 0.11 % 0.07 % 0.16 s 4 cores @ 2.5 Ghz (Python)
Table as LaTeX | Only published Methods


Cyclists


Method Setting Code Moderate Easy Hard Runtime Environment
1 MMLab PV-RCNN
This method makes use of Velodyne laser scans.
79.70 % 86.43 % 72.96 % 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. arXiv preprint arXiv:1912.13192 2019.
2 OHS-Dense 78.23 % 85.65 % 71.60 % 0.03 s 1 core @ 2.5 Ghz (Python/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots. 2019.
3 MMLab-PartA^2
This method makes use of Velodyne laser scans.
code 77.52 % 88.70 % 70.41 % 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. arXiv preprint arXiv:1907.03670 2019.
4 PointPainting
This method makes use of Velodyne laser scans.
76.92 % 87.33 % 68.21 % 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. arXiv preprint arXiv:1911.10150 2019.
5 F-ConvNet
This method makes use of Velodyne laser scans.
code 76.71 % 86.39 % 66.92 % 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.
6 OHS-Direct 75.59 % 83.45 % 68.42 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots. 2019.
7 VOXEL_FPN_HR 74.77 % 87.41 % 68.16 % 0.12 s 8 cores @ 2.5 Ghz (Python)
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8 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 72.81 % 85.94 % 65.84 % 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.
9 FOFNet
This method makes use of Velodyne laser scans.
72.48 % 86.89 % 65.63 % 0.04 s GPU @ 2.5 Ghz (Python)
10 PiP 71.10 % 82.83 % 64.88 % 0.05 s 1 core @ 2.5 Ghz (Python)
11 HR-SECOND code 69.60 % 82.42 % 62.47 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
12 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 69.54 % 82.18 % 62.98 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
13 ARPNET 68.72 % 82.61 % 62.00 % 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.
14 PointPillars
This method makes use of Velodyne laser scans.
code 68.55 % 83.79 % 61.71 % 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.
15 TANet code 66.37 % 81.15 % 60.10 % 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.
16 A-VoxelNet 66.17 % 80.73 % 58.96 % 0.029 s GPU @ 2.5 Ghz (Python)
17 Tencent_ADlab_Lidar
This method makes use of Velodyne laser scans.
65.85 % 81.05 % 59.17 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
18 SAANet 65.52 % 82.29 % 58.81 % 0.10 s 1 core @ 2.5 Ghz (Python)
19 FCY
This method makes use of Velodyne laser scans.
64.64 % 80.76 % 58.05 % 0.02 s GPU @ 2.5 Ghz (Python)
20 Sogo_MM 63.50 % 71.57 % 55.24 % 1.5 s GPU @ 2.5 Ghz (C/C++)
21 SubCNN 63.36 % 71.97 % 55.42 % 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.
22 deprecated 63.08 % 83.73 % 53.51 % 0.05 s GPU @ 2.0 Ghz (Python)
23 CentrNet-v1
This method makes use of Velodyne laser scans.
62.11 % 78.10 % 55.54 % 0.03 s GPU @ 2.5 Ghz (Python)
24 Pose-RCNN 62.02 % 75.74 % 53.99 % 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.
25 SCNet
This method makes use of Velodyne laser scans.
61.11 % 77.77 % 54.82 % 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.
26 SCANet 60.84 % 75.16 % 54.70 % 0.17 s >8 cores @ 2.5 Ghz (Python)
27 PP-3D 60.09 % 76.73 % 53.41 % 0.1 s 1 core @ 2.5 Ghz (Python)
28 AVOD-FPN
This method makes use of Velodyne laser scans.
code 58.70 % 69.21 % 53.47 % 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.
29 DDB
This method makes use of Velodyne laser scans.
58.65 % 75.36 % 52.85 % 0.05 s GPU @ 2.5 Ghz (Python)
30 Deep3DBox 58.56 % 68.31 % 50.30 % 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.
31 3DOP
This method uses stereo information.
code 58.45 % 72.24 % 51.91 % 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.
32 Complexer-YOLO
This method makes use of Velodyne laser scans.
58.28 % 65.41 % 54.27 % 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.
33 DeepStereoOP 56.55 % 69.36 % 49.37 % 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.
34 Mono3D code 53.96 % 67.33 % 47.91 % 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.
35 AVOD
This method makes use of Velodyne laser scans.
code 51.05 % 64.81 % 45.12 % 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.
36 Mono3CN 50.58 % 66.58 % 45.21 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
37 FRCNN+Or code 49.53 % 63.45 % 43.65 % 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.
38 MonoPSR code 49.32 % 58.63 % 43.05 % 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.
39 BirdNet
This method makes use of Velodyne laser scans.
45.03 % 62.69 % 41.88 % 0.11 s Titan Xp GPU
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.
40 SparsePool code 43.50 % 59.77 % 39.36 % 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.
41 sensekitti code 41.14 % 47.48 % 35.07 % 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.
42 RTM3D code 40.99 % 60.53 % 35.19 % 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.
43 MonoPair 39.47 % 53.36 % 33.95 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
44 SS3D_HW 37.68 % 52.40 % 32.33 % 0.4 s GPU @ 2.5 Ghz (Python)
45 Shift R-CNN (mono) code 34.77 % 51.95 % 31.10 % 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.
46 SparsePool code 34.56 % 43.33 % 31.09 % 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.
47 PG-MonoNet 34.11 % 49.05 % 28.14 % 0.19 s GPU @ 2.5 Ghz (Python)
48 Point-GNN
This method makes use of Velodyne laser scans.
32.37 % 36.29 % 29.81 % 0.6 s GPU @ 2.5 Ghz (Python)
49 HWFD 31.75 % 35.47 % 27.72 % 0.21 s one 1080Ti
50 D4LCN code 31.70 % 48.03 % 26.99 % 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. arXiv preprint arXiv:1912.04799 2019.
51 Faster RCNN + Gr + A 31.55 % 36.35 % 28.43 % 1.29 s GPU @ 2.5 Ghz (Python)
52 M3D-RPN code 31.09 % 48.11 % 26.10 % 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 .
53 Faster RCNN + A 30.81 % 36.25 % 27.51 % 0.19 s GPU @ 2.5 Ghz (Python)
54 Faster RCNN + G 30.61 % 36.19 % 27.22 % 1.1 s GPU @ 2.5 Ghz (Python)
55 DGIST-CellBox 30.34 % 35.69 % 27.10 % 0.1 s GPU @ 2.5 Ghz (Java + C/C++)
56 Faster RCNN + A 30.12 % 36.03 % 26.98 % 0.19 s GPU @ 2.5 Ghz (Python + C/C++)
57 bin 29.63 % 35.40 % 25.98 % 15ms s GPU @ >3.5 Ghz (Python)
58 AtrousDet 28.26 % 34.10 % 24.69 % 0.05 s TITAN X
59 SS3D 27.79 % 42.95 % 24.26 % 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.
60 ReSqueeze 27.40 % 36.26 % 24.04 % 0.03 s GPU @ >3.5 Ghz (Python)
61 cascadercnn 26.59 % 33.81 % 23.48 % 0.36 s 4 cores @ 2.5 Ghz (Python)
62 merge12-12 26.39 % 33.49 % 22.83 % 0.2 s 4 cores @ 2.5 Ghz (Python)
63 cas+res+soft 26.32 % 33.63 % 22.75 % 0.2 s 4 cores @ 2.5 Ghz (Python)
64 GA2500 26.08 % 32.91 % 22.06 % 0.2 s 1 core @ 2.5 Ghz (Python)
65 GA_rpn500 26.08 % 32.91 % 22.06 % 1 s 1 core @ 2.5 Ghz (Python)
66 dgist_multiDetNet 25.97 % 34.56 % 22.74 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
67 GA_FULLDATA 25.80 % 33.35 % 22.70 % 1 s 4 cores @ 2.5 Ghz (Python)
68 CSFADet 25.77 % 32.19 % 22.78 % 0.05 s GPU @ 2.5 Ghz (Python)
69 GA_BALANCE 25.27 % 33.79 % 22.03 % 1 s 1 core @ 2.5 Ghz (Python)
70 cas_retina 25.24 % 31.74 % 22.30 % 0.2 s 4 cores @ 2.5 Ghz (Python)
71 cas_retina_1_13 25.01 % 31.17 % 22.12 % 0.03 s 4 cores @ 2.5 Ghz (Python)
72 Multi-task DG 24.72 % 33.39 % 21.63 % 0.06 s GPU @ 2.5 Ghz (Python)
73 bigger_ga 24.64 % 31.31 % 21.06 % 1 s 1 core @ 2.5 Ghz (Python)
74 CRCNNA 23.88 % 29.91 % 20.70 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
75 FD2 23.83 % 35.75 % 20.79 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
76 GAFM 22.84 % 31.62 % 19.88 % 0.5 s 1 core @ 2.5 Ghz (Python)
77 JSU-NET 22.83 % 31.58 % 19.81 % 0.1 s 1 core @ 2.5 Ghz (Python)
78 ga50 21.59 % 29.77 % 18.77 % 1 s 1 core @ 2.5 Ghz (Python)
79 fasterrcnn 21.52 % 28.50 % 18.86 % 0.2 s 4 cores @ 2.5 Ghz (Python)
80 ZKNet 21.51 % 28.26 % 18.83 % 0.01 s GPU @ 2.0 Ghz (Python)
81 LPN 21.11 % 27.67 % 18.82 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
82 RFCN 20.77 % 26.80 % 18.25 % 0.2 s 4 cores @ 2.5 Ghz (Python)
83 yolo800 20.66 % 27.38 % 18.77 % 0.13 s 4 cores @ 2.5 Ghz (Python)
84 RFCN_RFB 20.40 % 26.19 % 17.91 % 0.2 s 4 cores @ 2.5 Ghz (Python)
85 DSGN
This method uses stereo information.
code 20.28 % 29.76 % 19.13 % 0.67 s NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. arXiv preprint arXiv:2001.03398 2020.
86 NM code 20.02 % 26.27 % 17.87 % 0.01 s GPU @ 2.5 Ghz (Python)
87 Cmerge 19.78 % 27.75 % 16.58 % 0.2 s 4 cores @ 2.5 Ghz (Python)
88 LSVM-MDPM-sv 19.15 % 26.05 % 18.02 % 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.
89 OC Stereo
This method uses stereo information.
18.99 % 29.07 % 16.40 % 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. arXiv preprint arXiv:1909.07566 2019.
90 DPM-VOC+VP 18.92 % 27.97 % 17.43 % 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.
91 cascade_gw 18.74 % 27.00 % 16.35 % 0.2 s 4 cores @ 2.5 Ghz (Python)
92 MTDP 18.02 % 23.30 % 16.07 % 0.15 s GPU @ 2.0 Ghz (Python)
93 BdCost+DA+BB+MS 17.73 % 23.48 % 14.67 % TBD s 4 cores @ 2.5 Ghz (C/C++)
94 centernet 17.55 % 23.39 % 15.59 % 0.01 s GPU @ 2.5 Ghz (Python)
95 RefinedMPL 16.02 % 26.54 % 13.20 % 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.
96 DPM-C8B1
This method uses stereo information.
14.64 % 23.93 % 13.09 % 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.
97 Retinanet100 13.34 % 19.09 % 11.79 % 0.2 s 4 cores @ 2.5 Ghz (Python)
98 BdCost+DA+BB 13.30 % 17.22 % 11.04 % TBD s 4 cores @ 2.5 Ghz (C/C++)
99 softyolo 11.12 % 15.91 % 9.84 % 0.16 s 4 cores @ 2.5 Ghz (Python)
100 100Frcnn 11.07 % 16.90 % 9.63 % 2 s 4 cores @ 2.5 Ghz (Python + C/C++)
101 Lidar_ROI+Yolo(UJS) 8.95 % 13.15 % 7.96 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
102 KD53-20 4.86 % 7.19 % 4.74 % 0.19 s 4 cores @ 2.5 Ghz (Python)
103 RT3DStereo
This method uses stereo information.
3.88 % 5.46 % 3.54 % 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.
104 MP 0.97 % 0.62 % 0.89 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
105 CBNet 0.18 % 0.11 % 0.21 % 1 s 4 cores @ 2.5 Ghz (Python)
106 softretina 0.11 % 0.07 % 0.08 % 0.16 s 4 cores @ 2.5 Ghz (Python)
107 JSyolo 0.02 % 0.01 % 0.02 % 0.16 s 4 cores @ 2.5 Ghz (Python)
Table as LaTeX | Only published Methods


Related Datasets

Citation

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



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