Andreas Geiger (MPI Tübingen) | Philip Lenz (KIT) | Christoph Stiller (KIT) | Raquel Urtasun (University of Toronto)

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 THU CV-AI 95.04 % 95.29 % 87.73 % 0.38 s GPU @ 2.5 Ghz (Python)
6 MVRA + I-FRCNN+ 94.98 % 95.87 % 82.52 % 0.18 s GPU @ 2.5 Ghz (Python)
7 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++)
8 BM-NET 94.49 % 95.09 % 85.06 % 0.5 s GPU @ 2.5 Ghz (Python + C/C++)
9 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.
10 EPNet 94.44 % 96.15 % 89.99 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
11 CPRCCNN 94.42 % 96.33 % 89.96 % 0.1 s 1 core @ 2.5 Ghz (Python)
12 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.
13 VCTNet 93.97 % 94.48 % 84.23 % 0.02 s GPU @ 1.5 Ghz (C/C++)
14 DGIST-CellBox 93.90 % 95.86 % 88.26 % 0.1 s GPU @ 2.5 Ghz (Java + C/C++)
15 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.
16 EM-FPS 93.70 % 94.73 % 86.38 % 0.15 s GPU @ 1.5 Ghz (Python + C/C++)
17 MDC
This method makes use of Velodyne laser scans.
 93.68 % 96.72 % 83.76 % 0.17 s GPU @ 2.5 Ghz (Python)
18 THICV-YDM 93.60 % 96.26 % 81.08 % 0.06 s GPU @ 2.5 Ghz (Python)
19 HRI-SH 93.57 % 96.23 % 86.33 % 3.6 s GPU @ >3.5 Ghz (Python + C/C++)
20 MLF_PointCas 93.55 % 96.69 % 86.16 % 0.1 s GPU @ 2.5 Ghz (Python)
21 MonoPair 93.55 % 96.61 % 83.55 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
22 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.
23 Point-GNN
This method makes use of Velodyne laser scans.
 93.50 % 96.58 % 88.35 % 0.6 s GPU @ 2.5 Ghz (Python)
24 Noah CV Lab - SSL 93.49 % 94.11 % 85.93 % 0.1 s GPU @ 2.5 Ghz (Python)
25 FichaDL 93.46 % 96.00 % 84.39 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
26 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.
27 Alibaba-AILabsX
This method makes use of Velodyne laser scans.
 93.35 % 96.38 % 85.92 % 0.2 s GPU @ >3.5 Ghz (Python)
28 ORP 93.27 % 96.76 % 85.86 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
29 CFENet 93.26 % 93.91 % 86.99 % 4 s GPU @ 2.5 Ghz (Python + C/C++)
30 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.
31 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.
32 Fast Point R-CNN v1
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.
33 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.
34 ELE 93.14 % 98.44 % 90.32 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
35 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.
36 RGB3D
This method makes use of Velodyne laser scans.
 93.07 % 96.54 % 88.04 % 0.39 s GPU @ 2.5 Ghz (Python)
37 DH-ARI 93.01 % 94.55 % 87.84 % 0.2 s 1 core @ >3.5 Ghz (Python + C/C++)
38 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++)
39 Fast Point R-CNN
This method makes use of Velodyne laser scans.
 92.93 % 96.02 % 87.41 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
40 SerialR-FCN+SG-NMS 92.93 % 95.72 % 82.92 % 0.2 s 1 core @ 2.5 Ghz (Python)
41 DH-ARI 92.83 % 92.75 % 82.92 % 4s GPU @ 2.5 Ghz (C/C++)
42 SegVoxelNet 92.73 % 96.00 % 87.60 % 0.04 s 1 core @ 2.5 Ghz (Python)
43 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.
44 PPFNet code 92.68 % 96.32 % 87.66 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
45 MVX-Net
This method makes use of Velodyne laser scans.
 92.66 % 96.06 % 85.33 % 0.06 s GPU @ 3.0 Ghz (Python + C/C++)
46 PI-RCNN 92.66 % 96.17 % 87.68 % 0.1 s 1 core @ 2.5 Ghz (Python)
47 OHS + Occ 92.65 % 96.09 % 89.72 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
48 NU-optim 92.63 % 95.67 % 87.37 % 0.04 s GPU @ >3.5 Ghz (Python)
49 3D-CVF
This method makes use of Velodyne laser scans.
 92.60 % 96.20 % 89.60 % 0.05 s GPU @ >3.5 Ghz (Python)
50 ART-Det 92.59 % 97.82 % 81.89 % 0.067s GPU @ 2.5 Ghz (Python + C/C++)
51 deprecated 92.59 % 96.21 % 89.58 % 0.05 s GPU @ >3.5 Ghz (Python)
52 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.
53 SPA 92.56 % 95.96 % 87.60 % 0.1 s 1 core @ 2.5 Ghz (Python)
54 DEFT 92.55 % 96.17 % 89.51 % 1 s GPU @ 2.5 Ghz (Python)
55 CONV-BOX
This method makes use of Velodyne laser scans.
 92.53 % 95.76 % 87.60 % 0.2 s Tesla V100
56 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++)
57 IPOD 92.44 % 95.54 % 87.55 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: IPOD: Intensive Point-based Object Detector for Point Cloud. CoRR 2018.
58 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++)
59 YOLOv3.5 92.42 % 95.22 % 82.32 % 0.05 s GPU @ 2.5 Ghz (Python)
60 OHS 92.39 % 95.84 % 89.51 % 0.03 s 1 core @ 2.5 Ghz (Python/C++)
61 PointRGCN 92.33 % 97.51 % 87.07 % 0.26 s GPU @ V100 (Python)
62 F-ConvNet
This method makes use of Velodyne laser scans.
 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.
63 IE-PointRCNN 92.08 % 96.01 % 87.05 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
64 ECV-NET 92.07 % 94.49 % 84.25 % 0.4 s GPU @ 2.5 Ghz (C/C++)
65 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.
66 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.
67 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.
68 MMLab-PartA^2
This method makes use of Velodyne laser scans.
 91.86 % 95.03 % 89.06 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, X. Wang and H. Li: Part-A^2 Net: 3D Part-Aware and Aggregation Neural Network for Object Detection from Point Cloud. arXiv preprint arXiv:1907.03670 2019.
69 MBR-SSD 91.83 % 93.46 % 84.97 % 4.0 s GPU @ 2.5 Ghz (Python + C/C++)
70 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.
71 MLF_SecCas 91.76 % 96.53 % 83.90 % 0.05 s 1 core @ 2.5 Ghz (Python)
72 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.
73 HRI-FusionRCNN 91.70 % 94.61 % 84.10 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
74 PiP 91.67 % 94.35 % 88.35 % 0.05 s 1 core @ 2.5 Ghz (Python)
75 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.
76 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.
77 deprecated 91.59 % 94.34 % 79.14 % 0.05 s GPU @ 2.0 Ghz (Python)
78 Det-RGBD
This method uses stereo information.
 91.49 % 94.30 % 79.41 % 0.58 s GPU @ 2.5 Ghz (Python + C/C++)
79 HRI-VoxelFPN 91.44 % 96.65 % 86.18 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
B. Wang, J. An and J. Cao: Voxel-FPN: multi-scale voxel feature aggregation in 3D object detection from point clouds. arXiv preprint arXiv:1907.05286v2 2019.
80 TBA 91.43 % 93.99 % 88.51 % 0.07 s 1 core @ 2.5 Ghz (Python)
81 RUC 91.40 % 95.02 % 88.41 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
82 FNV2 91.39 % 96.20 % 81.33 % 0.18 s GPU @ 2.5 Ghz (Python)
83 Faster RCNN + G 91.28 % 94.34 % 81.02 % 0.19 s GPU @ 2.5 Ghz (Python)
84 PFPN 91.25 % 94.33 % 81.41 % 0.02 s 4 cores @ >3.5 Ghz (Python)
85 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++)
86 OACV 91.21 % 94.23 % 83.07 % 0.23 s GPU @ 2.5 Ghz (Python)
87 CentrNet-v1
This method makes use of Velodyne laser scans.
 91.21 % 94.22 % 88.36 % 0.03 s GPU @ 2.5 Ghz (Python)
88 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.
89 Faster RCNN 91.19 % 94.43 % 80.99 % 0.2 s GPU @ 2.5 Ghz (Python)
90 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.
91 DDB
This method makes use of Velodyne laser scans.
 91.12 % 93.71 % 87.34 % 0.05 s GPU @ 2.5 Ghz (Python)
92 AILabs3D
This method makes use of Velodyne laser scans.
 91.11 % 96.38 % 85.77 % 0.6 s GPU @ >3.5 Ghz (Python)
93 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.
94 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.
95 PCSC-Net 90.97 % 94.20 % 87.38 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
96 MMV 90.91 % 94.16 % 83.36 % 0.4 s GPU @ 2.5 Ghz (C/C++)
97 A-VoxelNet 90.86 % 93.84 % 83.27 % 0.029 s GPU @ 2.5 Ghz (Python)
98 VAT-Net 90.83 % 96.07 % 80.56 % 1 s GPU @ 2.5 Ghz (Python + C/C++)
99 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.
100 MVSLN 90.81 % 96.12 % 83.39 % 0.1s s 1 core @ 2.5 Ghz (C/C++)
101 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++)
102 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.
103 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.
104 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++)
105 SRF 90.69 % 95.88 % 85.52 % 0.05 s GPU @ 2.5 Ghz (Python + C/C++)
106 HR-SECOND code 90.68 % 93.72 % 85.63 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
107 GA2500 90.68 % 95.86 % 80.29 % 0.2 s 1 core @ 2.5 Ghz (Python)
108 GA_rpn500 90.68 % 95.86 % 80.29 % 1 s 1 core @ 2.5 Ghz (Python)
109 TANet 90.67 % 93.67 % 85.31 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
110 SFB-SECOND 90.67 % 96.17 % 85.43 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
111 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++)
112 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++)
113 VOXEL_FPN_HR 90.55 % 93.76 % 85.42 % 0.12 s 8 cores @ 2.5 Ghz (Python)
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114 FOFNet
This method makes use of Velodyne laser scans.
 90.52 % 94.00 % 85.20 % 0.04 s GPU @ 2.5 Ghz (Python)
115 MP 90.50 % 93.86 % 85.17 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
116 Sogo_MM 90.46 % 94.31 % 80.62 % 1.5 s GPU @ 2.5 Ghz (C/C++)
117 bigger_ga 90.38 % 95.76 % 77.92 % 1 s 1 core @ 2.5 Ghz (Python)
118 RCN-resnet101 90.35 % 92.36 % 80.58 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
119 AtrousDet 90.35 % 95.94 % 77.94 % 0.05 s TITAN X
120 InNet 90.30 % 95.42 % 82.05 % 0.16 s GPU @ 3.5 Ghz (Python + C/C++)
121 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.
122 SECOND code 90.21 % 93.79 % 82.94 % 38 ms 1080Ti
Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.
123 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.
124 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.
125 BVVF 90.15 % 95.65 % 84.95 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
126 SAANet 90.14 % 95.93 % 82.95 % 0.10 s 1 core @ 2.5 Ghz (Python)
127 SAG-Net 90.08 % 94.77 % 80.27 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
128 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.
129 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.
130 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.
131 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.
132 ZRNet(ResNet-50) 89.92 % 95.24 % 79.69 % 0.04 s GPU @ 2.5 Ghz (Python)
133 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.
134 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.
135 DFD 89.72 % 93.37 % 82.41 % 0.05 s GPU @ 2.0 Ghz (Python + C/C++)
136 ZRNet 89.72 % 93.97 % 79.47 % 0.04 s GPU @ 2.5 Ghz (Python)
137 PP_v1.0 code 89.71 % 93.42 % 86.12 % 0.02s 1 core @ 2.5 Ghz (C/C++)
138 SeoulRobotics-HFD
This method makes use of Velodyne laser scans.
 89.68 % 93.30 % 84.52 % 0.035 s GPU (C++)
139 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.
140 PAD 89.49 % 93.43 % 85.85 % 0.15 s 1 core @ 2.5 Ghz (Python)
141 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.
142 4D-MSCNN+CRL
This method uses stereo information.
 89.37 % 92.40 % 77.00 % 0.2 s GPU @ 2.5 Ghz (Matlab + C/C++)
143 MonoDIS 89.15 % 94.61 % 78.37 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
144 cas+res+soft 89.14 % 94.54 % 78.37 % 0.2 s 4 cores @ 2.5 Ghz (Python)
145 merge12-12 88.96 % 94.58 % 78.22 % 0.2 s 4 cores @ 2.5 Ghz (Python)
146 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.
147 CLA 88.86 % 94.16 % 76.53 % 0.3 s GPU @ 2.5 Ghz (Matlab + C/C++)
C. Zhang and J. Kim: Object Detection With Location-Aware Deformable Convolution and Backward Attention Filtering. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
148 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.
149 SS3D_HW 88.68 % 94.49 % 68.79 % 0.4 s GPU @ 2.5 Ghz (Python)
150 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.
151 DA 88.63 % 94.59 % 76.00 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
152 CRCNNA 88.59 % 94.82 % 76.74 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
153 3DNN 88.56 % 94.52 % 81.51 % 0.09 s GPU @ 2.5 Ghz (Python)
154 CSFADet 88.54 % 93.75 % 78.62 % 0.05 s GPU @ 2.5 Ghz (Python)
155 ODES code 88.53 % 90.28 % 77.00 % 0.02 s GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
156 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.
157 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.
158 CFR
This method makes use of Velodyne laser scans.
 88.48 % 94.12 % 80.89 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
159 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.
160 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++)
161 TridentNet 88.37 % 90.33 % 80.57 % 0.2 s GPU @ 2.5 Ghz (Python)
162 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.
163 NLK 87.89 % 91.65 % 83.32 % 0.02 s 1 core @ 2.5 Ghz (Python)
164 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++)
165 ELLIOT
This method makes use of Velodyne laser scans.
 87.83 % 93.18 % 84.24 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
166 ga50 87.65 % 95.76 % 75.14 % 1 s 1 core @ 2.5 Ghz (Python)
167 cas_retina 87.64 % 93.87 % 75.30 % 0.2 s 4 cores @ 2.5 Ghz (Python)
168 SMOKE 87.51 % 93.21 % 77.66 % 0.03 s GPU @ 2.5 Ghz (Python)
169 MonoSS 87.46 % 93.15 % 77.58 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
170 cascadercnn 87.36 % 89.37 % 73.42 % 0.36 s 4 cores @ 2.5 Ghz (Python)
171 SCANet 87.28 % 92.91 % 81.99 % 0.17 s >8 cores @ 2.5 Ghz (Python)
172 SECA 87.16 % 94.95 % 80.01 % 0.09 s GPU @ 2.5 Ghz (Python)
173 SCANet 86.94 % 92.69 % 79.95 % 0.09s GPU @ 2.5 Ghz (Python)
174 anm 86.52 % 94.88 % 76.46 % 3 s 1 core @ 2.5 Ghz (C/C++)
175 DSGN
This method uses stereo information.
 86.43 % 95.53 % 78.75 % 0.67 s NVIDIA Tesla V100
176 ReSqueeze 86.12 % 90.35 % 76.53 % 0.03 s GPU @ >3.5 Ghz (Python)
177 IoU_DCRCNN 86.07 % 90.04 % 78.14 % 0.66 s GPU @ 2.5 Ghz (Python)
178 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.
179 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.
180 ResNet-RRC w/RGBD 85.58 % 91.32 % 74.80 % 0.057 s GPU @ 1.5 Ghz (Python + C/C++)
181 X_MD 85.52 % 93.31 % 78.25 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
182 cas_retina_1_13 85.48 % 91.54 % 74.60 % 0.03 s 4 cores @ 2.5 Ghz (Python)
183 NEUAV 85.42 % 89.67 % 77.28 % 0.06 s GPU @ 2.5 Ghz (Python)
184 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.
185 Cmerge 85.32 % 93.40 % 70.57 % 0.2 s 4 cores @ 2.5 Ghz (Python)
186 PL V2 (SDN+GDC)
This method uses stereo information.
This method makes use of Velodyne laser scans.
 85.15 % 94.95 % 77.78 % 0.6 s GPU @ 2.5 Ghz (C/C++)
187 RAR-Net 85.08 % 89.04 % 69.26 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
188 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 .
189 FNV1_RPN 85.07 % 94.59 % 79.91 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
190 FNV1_Fusion 85.02 % 92.64 % 79.77 % 0.11 s GPU @ 2.5 Ghz (Python)
191 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.
192 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.
193 LPN 84.77 % 89.19 % 74.08 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
194 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.
195 SECA 84.60 % 92.51 % 79.53 % 1 s GPU @ 2.5 Ghz (Python)
196 VSE 84.60 % 92.51 % 79.53 % 0.15 s GPU @ 2.5 Ghz (Python)
197 PG-MonoNet 84.42 % 88.61 % 68.59 % 0.19 s GPU @ 2.5 Ghz (Python)
198 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.
199 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.
200 YOLOv3+d 84.09 % 86.66 % 75.08 % 0.04 s GPU @ 1.5 Ghz (C/C++)
201 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.
202 D^4LCN 83.67 % 90.34 % 65.33 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
203 softretina 83.30 % 93.55 % 70.59 % 0.16 s 4 cores @ 2.5 Ghz (Python)
204 FNV1 83.20 % 91.34 % 75.93 % 0.11 s GPU @ 2.5 Ghz (Python)
205 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.
206 ZKNet 82.96 % 92.17 % 72.43 % 0.01 s GPU @ 2.0 Ghz (Python)
207 Pseudo-LiDAR V2
This method uses stereo information.
 code 82.90 % 94.46 % 75.45 % 0.4 s GPU @ 2.5 Ghz (Python)
208 Retinanet100 82.73 % 93.97 % 68.37 % 0.2 s 4 cores @ 2.5 Ghz (Python)
209 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.
210 Pseudo-LiDAR E2E
This method uses stereo information.
 82.54 % 94.00 % 75.31 % 0.4 s GPU @ 2.5 Ghz (Python)
211 Disp R-CNN
This method uses stereo information.
 82.47 % 93.15 % 70.35 % 0.42 s GPU @ 2.5 Ghz (Python + C/C++)
212 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++)
213 cascade_gw 82.35 % 85.98 % 71.60 % 0.2 s 4 cores @ 2.5 Ghz (Python)
214 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.
215 detectron code 81.51 % 91.43 % 69.50 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
216 Resnet101Faster rcnn 81.44 % 91.08 % 71.52 % 1 s 1 core @ 2.5 Ghz (Python)
217 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.
218 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.
219 MTDP 80.97 % 89.03 % 66.91 % 0.15 s GPU @ 2.0 Ghz (Python)
220 RFCN_RFB 80.89 % 88.07 % 69.66 % 0.2 s 4 cores @ 2.5 Ghz (Python)
221 Manhnet 80.85 % 89.06 % 64.29 % 26 ms 1 core @ 2.5 Ghz (C/C++)
222 centernet 80.78 % 90.29 % 70.53 % 0.01 s GPU @ 2.5 Ghz (Python)
223 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)
224 NM code 79.98 % 90.71 % 68.98 % 0.01 s GPU @ 2.5 Ghz (Python)
225 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)
226 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)
227 SceneNet 79.26 % 90.70 % 67.98 % 0.03 s GPU @ 2.5 Ghz (C/C++)
228 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.
229 CLF3D
This method makes use of Velodyne laser scans.
 79.05 % 87.57 % 67.58 % 0.13 s GPU @ 2.5 Ghz (Python)
230 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.
231 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.
232 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.
233 yolov3_warp 77.61 % 92.24 % 65.70 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
234 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.
235 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.
236 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.
237 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.
238 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)
239 RTL3D 76.74 % 79.68 % 72.56 % 0.02 s GPU @ 2.5 Ghz (Python)
240 avodC 76.58 % 87.30 % 71.65 % 0.1 s GPU @ 2.5 Ghz (Python)
241 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.
242 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.
243 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)
244 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.
245 bin 76.16 % 78.73 % 63.39 % 15ms s GPU @ >3.5 Ghz (Python)
246 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.
247 VoxelNet(Unofficial) 75.22 % 81.37 % 68.74 % 0.5 s GPU @ 2.0 Ghz (Python)
248 RFCN 75.14 % 83.04 % 61.55 % 0.2 s 4 cores @ 2.5 Ghz (Python)
249 myfaster-rcnn-v1.5 74.93 % 89.85 % 62.56 % 0.1 s 1 core @ 2.5 Ghz (Python)
250 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.
251 OC Stereo
This method uses stereo information.
 74.60 % 87.39 % 62.56 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
252 yolo800 74.31 % 78.93 % 63.83 % 0.13 s 4 cores @ 2.5 Ghz (Python)
253 ResNet-RRC (Noised) 74.30 % 79.15 % 64.80 % .057 s GPU @ 1.5 Ghz (Python + C/C++)
254 3DVSSD 74.11 % 86.99 % 63.57 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
255 Multi-task DG 74.07 % 91.06 % 64.48 % 0.06 s GPU @ 2.5 Ghz (Python)
256 FD2 73.93 % 88.65 % 64.62 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
257 BdCost+DA+BB+MS 73.72 % 85.18 % 57.79 % TBD s 4 cores @ 2.5 Ghz (Matlab + C/C++)
258 m-prcnn
This method uses stereo information.
 73.64 % 87.64 % 57.03 % 0.43 s 1 core @ 2.5 Ghz (Python)
259 BdCost+DA+MS 73.62 % 85.03 % 58.94 % TBD s 4 cores @ 2.5 Ghz (Matlab/C++)
260 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.
261 stereo_sa
This method uses stereo information.
 72.99 % 87.88 % 63.49 % 0.3 s GPU @ 2.5 Ghz (Python)
262 RuiRUC 72.08 % 87.48 % 55.28 % 0.12 s 1 core @ 2.5 Ghz (Python)
263 ANM 71.97 % 87.17 % 55.19 % 0.12 s 1 core @ 2.5 Ghz (Python)
264 MF3D 71.85 % 91.50 % 57.46 % 0.03 s GPU @ 2.5 Ghz (C/C++)
265 RFBnet 71.66 % 87.25 % 63.00 % 0.2 s 4 cores @ 2.5 Ghz (Python)
266 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.
267 GPVL 71.06 % 81.67 % 54.96 % 10 s 1 core @ 2.5 Ghz (C/C++)
268 BdCost+DA+BB 70.86 % 85.52 % 56.19 % TBD s 4 cores @ 2.5 Ghz (Matlab + C/C++)
269 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.
270 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.
271 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.
272 fasterrcnn 69.45 % 74.76 % 60.20 % 0.2 s 4 cores @ 2.5 Ghz (Python)
273 myfaster-rcnn 68.38 % 90.54 % 55.97 % 0.01 s 1 core @ 2.5 Ghz (Python)
274 Decoupled-3D v2 68.17 % 88.64 % 54.74 % 0.08 s GPU @ 2.5 Ghz (C/C++)
275 Decoupled-3D 67.92 % 87.78 % 54.53 % 0.08 s GPU @ 2.5 Ghz (C/C++)
276 Pseudo-LiDAR
This method uses stereo information.
 code 67.79 % 85.40 % 58.50 % 0.4 s GPU @ 2.5 Ghz (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. CVPR 2019.
277 SA_3D 67.50 % 88.90 % 53.04 % 0.3 s GPU @ 2.5 Ghz (Python)
278 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.
279 mymask-rcnn 66.82 % 88.60 % 52.18 % 0.3 s 1 core @ 2.5 Ghz (Python)
280 Fast-SSD 66.79 % 85.19 % 57.89 % 0.06 s GTX650Ti
281 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.
282 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.
283 E-VoxelNet 65.33 % 68.00 % 57.84 % 0.1 s GPU @ 2.5 Ghz (Python)
284 RefinedMPL 65.24 % 88.29 % 53.20 % 0.1 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.
285 BdCost48-25C 64.63 % 81.42 % 52.22 % 4 s 1 core @ 2.5 Ghz (C/C++)
286 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.
287 64.06 % 81.75 % 54.83 %
288 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.
289 MODet
This method makes use of Velodyne laser scans.
 62.54 % 66.06 % 60.04 % 0.05 s GTX1080Ti
290 yl_net 61.78 % 66.00 % 60.36 % 0.03 s GPU @ 2.5 Ghz (Python)
291 Lidar_ROI+Yolo(UJS) 61.71 % 73.32 % 53.65 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
292 GNN 61.48 % 79.09 % 51.06 % 0.2 s 1 core @ 2.5 Ghz (Python)
293 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.
294 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.
295 tiny_rfdet code 59.94 % 65.51 % 57.20 % 0.01 s GPU @ 2.5 Ghz (Python)
296 RADNet-Mono 59.85 % 67.47 % 54.14 % 0.1 s 1 core @ 2.5 Ghz (Python)
297 monoref3d 58.97 % 78.11 % 47.72 % 0.1 s 1 core @ 2.5 Ghz (Python)
298 ref3D 58.97 % 78.11 % 47.72 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
299 100Frcnn 58.92 % 82.09 % 49.04 % 2 s 4 cores @ 2.5 Ghz (Python + C/C++)
300 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.
301 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.
302 ref3D 57.16 % 77.96 % 45.99 % 0.1 s 1 core @ 2.5 Ghz (Python)
303 BirdNet
This method makes use of Velodyne laser scans.
 57.02 % 78.91 % 55.08 % 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.
304 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.
305 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.
306 mylsi-faster-rcnn 55.81 % 80.45 % 47.38 % 0.3 s 1 core @ 2.5 Ghz (Python)
307 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). .
308 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.
309 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 .
310 FailNet-Mono 47.95 % 59.59 % 41.33 % 0.1 s 1 core @ 2.5 Ghz (Python)
311 DLMB
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
 46.50 % 60.92 % 41.59 % 0.03 s 8 cores @ 3.5 Ghz (C/C++)
312 softyolo 45.97 % 66.08 % 38.02 % 0.16 s 4 cores @ 2.5 Ghz (Python)
313 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.
314 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.
315 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.
316 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.
317 rpn 40.80 % 67.42 % 32.16 % 0.01 s 1 core @ 2.5 Ghz (Python)
318 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.
319 FCY
This method makes use of Velodyne laser scans.
 36.35 % 42.46 % 36.09 % 0.02 s GPU @ 2.5 Ghz (Python)
320 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.
321 Licar
This method makes use of Velodyne laser scans.
 35.19 % 42.34 % 33.97 % 0.09 s GPU @ 2.0 Ghz (Python)
322 KD53-20 34.76 % 51.76 % 29.39 % 0.19 s 4 cores @ 2.5 Ghz (Python)
323 DT3D 34.07 % 50.81 % 31.46 % 0,21s GPU @ 2.5 Ghz (Python)
324 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)
325 FCN-Depth code 25.05 % 52.32 % 18.07 % 1 s GPU @ 1.5 Ghz (Matlab + C/C++)
326 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.
327 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.
328 R-CNN_VGG 21.36 % 29.38 % 16.61 % 10 s GPU @ 2.5 Ghz (Matlab + C/C++)
329 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.
330 DLnet 15.90 % 21.22 % 13.78 % 0.3 s 4 cores @ 2.5 Ghz (C/C++)
331 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.
332 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.
333 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.
334 FCPP 0.07 % 0.01 % 0.07 % 0.02 s 1 core @ 2.0 Ghz (Python + C/C++)
335 ANM 0.01 % 0.01 % 0.02 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
336 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.
337 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.
338 SN-net 0.00 % 0.00 % 0.00 % 0.8 s GPU @ 2.5 Ghz (Python + C/C++)
339 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 DH-ARI 78.79 % 88.87 % 71.76 % 3.6 s GPU @ 2.5 Ghz (Python + C/C++)
7 EM-FPS 78.50 % 85.58 % 73.68 % 0.15 s GPU @ 1.5 Ghz (Python + C/C++)
8 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.
9 Argus_detection_v1 77.01 % 84.86 % 72.15 % 0.25 s GPU @ 1.5 Ghz (C/C++)
10 VCTNet 76.68 % 86.56 % 71.43 % 0.02 s GPU @ 1.5 Ghz (C/C++)
11 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.
12 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.
13 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.
14 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.
15 FFNet code 75.81 % 87.17 % 69.86 % 1.07 s GPU @ 1.5 Ghz (Python)
16 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.
17 THICV-YDM 75.65 % 89.04 % 68.72 % 0.06 s GPU @ 2.5 Ghz (Python)
18 Noah CV Lab - SSL 75.41 % 86.50 % 70.33 % 0.1 s GPU @ 2.5 Ghz (Python)
19 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++)
20 CLA 75.16 % 86.02 % 69.24 % 0.3 s GPU @ 2.5 Ghz (Matlab + C/C++)
C. Zhang and J. Kim: Object Detection With Location-Aware Deformable Convolution and Backward Attention Filtering. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
21 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.
22 BOE_IOT_AIBD 74.74 % 86.06 % 69.44 % 0.8 s GPU @ 2.5 Ghz (Python)
23 Faster RCNN + G 73.75 % 85.51 % 68.54 % 0.19 s GPU @ 2.5 Ghz (Python)
24 SAITv1 73.47 % 86.68 % 68.40 % 0.15 s GPU @ 2.5 Ghz (C/C++)
25 F-ConvNet
This method makes use of Velodyne laser scans.
 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.
26 Sogo_MM 72.82 % 84.99 % 67.42 % 1.5 s GPU @ 2.5 Ghz (C/C++)
27 ECV-NET 72.73 % 86.52 % 66.15 % 0.4 s GPU @ 2.5 Ghz (C/C++)
28 Faster RCNN 72.67 % 86.21 % 67.55 % 0.2 s GPU @ 2.5 Ghz (Python)
29 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.
30 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.
31 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.
32 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.
33 MDC
This method makes use of Velodyne laser scans.
 71.13 % 86.16 % 66.15 % 0.17 s GPU @ 2.5 Ghz (Python)
34 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.
35 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.
36 TridentNet 70.07 % 83.42 % 65.28 % 0.2 s GPU @ 2.5 Ghz (Python)
37 CSFADet 70.07 % 84.72 % 64.81 % 0.05 s GPU @ 2.5 Ghz (Python)
38 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.
39 DA 69.22 % 82.75 % 63.96 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
40 HBA-RCNN 68.68 % 79.07 % 63.65 % 0.4 s 1 core @ 2.5 Ghz (C/C++)
41 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.
42 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.
43 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.
44 ODES code 68.10 % 79.98 % 61.69 % 0.02 s GPU @ 2.5 Ghz (Python)
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45 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.
46 YOLOv3.5 66.54 % 83.87 % 61.45 % 0.05 s GPU @ 2.5 Ghz (Python)
47 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.
48 AtrousDet 64.97 % 80.79 % 58.36 % 0.05 s TITAN X
49 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.
50 CRCNNA 63.69 % 78.10 % 58.41 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
51 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.
52 PCN 63.41 % 80.08 % 58.55 % 0.6 s
53 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.
54 merge12-12 62.84 % 80.27 % 56.08 % 0.2 s 4 cores @ 2.5 Ghz (Python)
55 cas+res+soft 62.71 % 80.11 % 55.99 % 0.2 s 4 cores @ 2.5 Ghz (Python)
56 cas_retina 62.37 % 79.82 % 57.15 % 0.2 s 4 cores @ 2.5 Ghz (Python)
57 cas_retina_1_13 61.87 % 79.09 % 56.70 % 0.03 s 4 cores @ 2.5 Ghz (Python)
58 MonoPair 61.57 % 78.81 % 56.51 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
59 ReSqueeze 61.33 % 73.69 % 56.65 % 0.03 s GPU @ >3.5 Ghz (Python)
60 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.
61 IPOD 61.14 % 74.79 % 56.54 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: IPOD: Intensive Point-based Object Detector for Point Cloud. CoRR 2018.
62 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.
63 bin 60.73 % 71.43 % 55.78 % 15ms s GPU @ >3.5 Ghz (Python)
64 OHS + Occ 60.63 % 69.37 % 57.64 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
65 anm 60.35 % 76.02 % 55.46 % 3 s 1 core @ 2.5 Ghz (C/C++)
66 PiP 59.94 % 70.52 % 56.51 % 0.05 s 1 core @ 2.5 Ghz (Python)
67 cascadercnn 59.50 % 78.79 % 54.44 % 0.36 s 4 cores @ 2.5 Ghz (Python)
68 ALV303 59.49 % 71.70 % 55.66 % 0.2 s GPU @ 2.5 Ghz (Matlab + C/C++)
69 A-VoxelNet 59.07 % 69.90 % 56.49 % 0.029 s GPU @ 2.5 Ghz (Python)
70 TANet 59.07 % 69.90 % 56.44 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
71 mymask-rcnn 58.89 % 72.55 % 52.58 % 0.3 s 1 core @ 2.5 Ghz (Python)
72 OHS 58.70 % 68.18 % 54.68 % 0.03 s 1 core @ 2.5 Ghz (Python/C++)
73 DDB
This method makes use of Velodyne laser scans.
 58.53 % 69.03 % 55.90 % 0.05 s GPU @ 2.5 Ghz (Python)
74 Point-GNN
This method makes use of Velodyne laser scans.
 58.20 % 71.59 % 54.06 % 0.6 s GPU @ 2.5 Ghz (Python)
75 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.
76 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.
77 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.
78 LPN 57.69 % 71.87 % 53.21 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
79 LDAM 56.68 % 64.73 % 54.21 % 0.05 s GPU @ 2.5 Ghz (C/C++)
80 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.
81 yolo800 56.67 % 71.26 % 50.91 % 0.13 s 4 cores @ 2.5 Ghz (Python)
82 ZKNet 56.58 % 71.15 % 51.87 % 0.01 s GPU @ 2.0 Ghz (Python)
83 CentrNet-v1
This method makes use of Velodyne laser scans.
 56.57 % 66.27 % 54.19 % 0.03 s GPU @ 2.5 Ghz (Python)
84 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.
85 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++)
86 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.
87 FD2 56.35 % 71.37 % 51.08 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
88 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.
89 RFCN 55.96 % 72.32 % 49.66 % 0.2 s 4 cores @ 2.5 Ghz (Python)
90 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.
91 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.
92 CONV-BOX
This method makes use of Velodyne laser scans.
 55.06 % 64.43 % 51.06 % 0.2 s Tesla V100
93 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.
94 RFCN_RFB 54.98 % 70.61 % 48.75 % 0.2 s 4 cores @ 2.5 Ghz (Python)
95 SA_3D 54.89 % 69.83 % 48.26 % 0.3 s GPU @ 2.5 Ghz (Python)
96 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.
97 CHTTL MMF 54.28 % 72.79 % 49.31 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
98 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.
99 NM code 54.05 % 69.81 % 49.32 % 0.01 s GPU @ 2.5 Ghz (Python)
100 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.
101 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.
102 fasterrcnn 53.42 % 69.29 % 48.76 % 0.2 s 4 cores @ 2.5 Ghz (Python)
103 PP_v1.0 code 53.32 % 63.06 % 50.86 % 0.02s 1 core @ 2.5 Ghz (C/C++)
104 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.
105 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.
106 Multi-task DG 52.87 % 71.27 % 48.10 % 0.06 s GPU @ 2.5 Ghz (Python)
107 SECOND code 52.81 % 66.53 % 48.47 % 38 ms 1080Ti
Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.
108 NEUAV 52.53 % 68.50 % 47.99 % 0.06 s GPU @ 2.5 Ghz (Python)
109 detectron code 52.36 % 71.51 % 47.51 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
110 deprecated 52.32 % 67.93 % 47.77 % 0.05 s GPU @ 2.0 Ghz (Python)
111 YOLOv3+d 51.94 % 68.35 % 45.60 % 0.04 s GPU @ 1.5 Ghz (C/C++)
112 MTDP 51.81 % 68.12 % 46.95 % 0.15 s GPU @ 2.0 Ghz (Python)
113 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.
114 CLF3D
This method makes use of Velodyne laser scans.
 51.24 % 67.07 % 44.98 % 0.13 s GPU @ 2.5 Ghz (Python)
115 SCANet 51.23 % 65.06 % 47.06 % 0.17 s >8 cores @ 2.5 Ghz (Python)
116 centernet 51.09 % 69.27 % 45.40 % 0.01 s GPU @ 2.5 Ghz (Python)
117 myfaster-rcnn-v1.5 50.95 % 67.68 % 46.29 % 0.1 s 1 core @ 2.5 Ghz (Python)
118 PPFNet code 50.52 % 57.82 % 47.44 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
119 FOFNet
This method makes use of Velodyne laser scans.
 50.08 % 62.64 % 46.27 % 0.04 s GPU @ 2.5 Ghz (Python)
120 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.
121 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.
122 Resnet101Faster rcnn 49.12 % 64.72 % 44.60 % 1 s 1 core @ 2.5 Ghz (Python)
123 VOXEL_FPN_HR 49.09 % 60.28 % 45.47 % 0.12 s 8 cores @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
124 SS3D_HW 49.01 % 64.67 % 42.86 % 0.4 s GPU @ 2.5 Ghz (Python)
125 cascade_gw 48.99 % 67.35 % 44.25 % 0.2 s 4 cores @ 2.5 Ghz (Python)
126 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.
127 MP 48.73 % 60.26 % 45.05 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
128 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.
129 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.
130 mylsi-faster-rcnn 47.99 % 65.03 % 43.43 % 0.3 s 1 core @ 2.5 Ghz (Python)
131 ELLIOT
This method makes use of Velodyne laser scans.
 46.84 % 58.72 % 43.91 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
132 HR-SECOND code 46.69 % 58.68 % 42.93 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
133 Cmerge 46.51 % 63.68 % 41.60 % 0.2 s 4 cores @ 2.5 Ghz (Python)
134 CFR
This method makes use of Velodyne laser scans.
 45.85 % 60.88 % 43.37 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
135 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.
136 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.
137 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.
138 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.
139 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.
140 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.
141 D^4LCN 43.50 % 59.55 % 37.12 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
142 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.
143 PG-MonoNet 43.12 % 58.51 % 38.92 % 0.19 s GPU @ 2.5 Ghz (Python)
144 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.
145 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.
146 GNN 42.28 % 58.09 % 37.81 % 0.2 s 1 core @ 2.5 Ghz (Python)
147 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)
148 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.
149 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 .
150 myfaster-rcnn 41.22 % 56.87 % 36.99 % 0.01 s 1 core @ 2.5 Ghz (Python)
151 yolov3_warp 40.64 % 55.04 % 36.33 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
152 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.
153 SAANet 40.43 % 51.16 % 38.38 % 0.10 s 1 core @ 2.5 Ghz (Python)
154 Retinanet100 40.03 % 54.30 % 35.33 % 0.2 s 4 cores @ 2.5 Ghz (Python)
155 DSGN
This method uses stereo information.
 39.93 % 49.28 % 38.13 % 0.67 s NVIDIA Tesla V100
156 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.
157 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.
158 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.
159 softyolo 39.30 % 54.49 % 36.66 % 0.16 s 4 cores @ 2.5 Ghz (Python)
160 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). .
161 pedestrian_cnn 37.90 % 52.07 % 33.58 % 1 s 1 core @ 2.5 Ghz (C/C++)
162 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.
163 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.
164 X_MD 36.61 % 48.20 % 33.09 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
165 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.
166 KD53-20 36.03 % 45.78 % 32.79 % 0.19 s 4 cores @ 2.5 Ghz (Python)
167 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.
168 Lidar_ROI+Yolo(UJS) 35.58 % 47.74 % 31.48 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
169 34.81 % 44.38 % 32.10 %
170 anonymous
This method makes use of Velodyne laser scans.
 34.58 % 45.41 % 32.53 % 0.75 s GPU @ 3.5 Ghz (C/C++)
171 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.
172 rpn 31.92 % 43.34 % 27.84 % 0.01 s 1 core @ 2.5 Ghz (Python)
173 OC Stereo
This method uses stereo information.
 30.79 % 43.50 % 28.40 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
174 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.
175 BirdNet
This method makes use of Velodyne laser scans.
 29.58 % 36.62 % 28.25 % 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.
176 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.
177 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.
178 100Frcnn 21.92 % 34.07 % 19.48 % 2 s 4 cores @ 2.5 Ghz (Python + C/C++)
179 RefinedMPL 20.81 % 30.41 % 18.72 % 0.1 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.
180 R-CNN_VGG 19.97 % 26.62 % 17.96 % 10 s GPU @ 2.5 Ghz (Matlab + C/C++)
181 FCY
This method makes use of Velodyne laser scans.
 18.48 % 26.09 % 17.22 % 0.02 s GPU @ 2.5 Ghz (Python)
182 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.
183 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.
184 DT3D 14.89 % 20.46 % 13.61 % 0,21s GPU @ 2.5 Ghz (Python)
185 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.
186 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.
187 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.
188 softretina 0.26 % 0.19 % 0.26 % 0.16 s 4 cores @ 2.5 Ghz (Python)
189 JSyolo 0.12 % 0.19 % 0.12 % 0.16 s 4 cores @ 2.5 Ghz (Python)
190 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.
191 SN-net 0.00 % 0.00 % 0.00 % 0.8 s GPU @ 2.5 Ghz (Python + C/C++)
Table as LaTeX | Only published Methods

Cyclist


Method Setting Code Moderate Easy Hard Runtime Environment
1 EM-FPS 81.22 % 85.45 % 72.28 % 0.15 s GPU @ 1.5 Ghz (Python + C/C++)
2 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++)
3 FichaDL 80.38 % 88.41 % 69.72 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
4 OHS 78.42 % 85.79 % 71.80 % 0.03 s 1 core @ 2.5 Ghz (Python/C++)
5 Noah CV Lab - SSL 78.32 % 86.31 % 69.00 % 0.1 s GPU @ 2.5 Ghz (Python)
6 F-ConvNet
This method makes use of Velodyne laser scans.
 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 VCTNet 77.28 % 84.58 % 68.22 % 0.02 s GPU @ 1.5 Ghz (C/C++)
9 SAITv1 77.21 % 86.39 % 66.75 % 0.15 s GPU @ 2.5 Ghz (C/C++)
10 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.
11 OHS + Occ 75.98 % 83.71 % 68.80 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
12 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++)
13 CLA 75.48 % 84.80 % 65.48 % 0.3 s GPU @ 2.5 Ghz (Matlab + C/C++)
C. Zhang and J. Kim: Object Detection With Location-Aware Deformable Convolution and Backward Attention Filtering. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
14 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.
15 VOXEL_FPN_HR 75.24 % 87.73 % 68.60 % 0.12 s 8 cores @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
16 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.
17 Point-GNN
This method makes use of Velodyne laser scans.
 75.08 % 85.75 % 68.69 % 0.6 s GPU @ 2.5 Ghz (Python)
18 ExtAtt 75.08 % 86.09 % 65.30 % 1.2 s GPU @ 2.5 Ghz (Python + C/C++)
19 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.
20 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.
21 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.
22 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.
23 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.
24 FOFNet
This method makes use of Velodyne laser scans.
 72.96 % 87.12 % 66.37 % 0.04 s GPU @ 2.5 Ghz (Python)
25 ECV-NET 72.93 % 84.19 % 63.24 % 0.4 s GPU @ 2.5 Ghz (C/C++)
26 HR-SECOND code 72.77 % 84.21 % 66.25 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
27 BOE_IOT_AIBD 72.34 % 84.64 % 63.52 % 0.8 s GPU @ 2.5 Ghz (Python)
28 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.
29 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.
30 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.
31 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.
32 Sogo_MM 71.57 % 79.35 % 62.22 % 1.5 s GPU @ 2.5 Ghz (C/C++)
33 PiP 71.52 % 82.97 % 65.52 % 0.05 s 1 core @ 2.5 Ghz (Python)
34 MDC
This method makes use of Velodyne laser scans.
 70.22 % 84.28 % 60.95 % 0.17 s GPU @ 2.5 Ghz (Python)
35 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.
36 ODES code 69.82 % 80.14 % 60.37 % 0.02 s GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
37 TridentNet 69.63 % 81.97 % 59.52 % 0.2 s GPU @ 2.5 Ghz (Python)
38 MP 69.52 % 85.05 % 63.17 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
39 LDAM 69.31 % 80.20 % 63.85 % 0.05 s GPU @ 2.5 Ghz (C/C++)
40 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.
41 DGIST-CellBox 68.92 % 83.72 % 61.32 % 0.1 s GPU @ 2.5 Ghz (Java + C/C++)
42 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.
43 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.
44 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.
45 TANet 68.20 % 82.24 % 62.13 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
46 Faster RCNN + G 68.09 % 83.51 % 60.60 % 0.19 s GPU @ 2.5 Ghz (Python)
47 Faster RCNN 67.84 % 82.06 % 60.52 % 0.2 s GPU @ 2.5 Ghz (Python)
48 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++)
49 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.
50 A-VoxelNet 67.37 % 81.32 % 60.27 % 0.029 s GPU @ 2.5 Ghz (Python)
51 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.
52 SAANet 66.58 % 83.07 % 59.88 % 0.10 s 1 core @ 2.5 Ghz (Python)
53 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.
54 IPOD 65.25 % 83.72 % 57.81 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: IPOD: Intensive Point-based Object Detector for Point Cloud. CoRR 2018.
55 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.
56 CONV-BOX
This method makes use of Velodyne laser scans.
 64.53 % 72.81 % 58.13 % 0.2 s Tesla V100
57 DA 63.60 % 80.72 % 56.15 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
58 deprecated 63.34 % 83.91 % 53.78 % 0.05 s GPU @ 2.0 Ghz (Python)
59 CentrNet-v1
This method makes use of Velodyne laser scans.
 62.99 % 78.90 % 56.46 % 0.03 s GPU @ 2.5 Ghz (Python)
60 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.
61 AtrousDet 62.50 % 79.02 % 53.87 % 0.05 s TITAN X
62 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.
63 SCANet 62.31 % 76.50 % 56.06 % 0.17 s >8 cores @ 2.5 Ghz (Python)
64 DDB
This method makes use of Velodyne laser scans.
 61.41 % 78.04 % 55.37 % 0.05 s GPU @ 2.5 Ghz (Python)
65 SECOND code 60.96 % 81.73 % 54.13 % 38 ms 1080Ti
Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.
66 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.
67 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.
68 CFR
This method makes use of Velodyne laser scans.
 60.04 % 76.63 % 53.40 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
69 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.
70 ELLIOT
This method makes use of Velodyne laser scans.
 59.78 % 78.40 % 54.46 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
71 PP_v1.0 code 59.48 % 77.50 % 52.86 % 0.02s 1 core @ 2.5 Ghz (C/C++)
72 merge12-12 59.48 % 77.66 % 51.41 % 0.2 s 4 cores @ 2.5 Ghz (Python)
73 cas+res+soft 59.43 % 77.85 % 51.34 % 0.2 s 4 cores @ 2.5 Ghz (Python)
74 YOLOv3.5 58.57 % 79.16 % 51.74 % 0.05 s GPU @ 2.5 Ghz (Python)
75 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.
76 cascadercnn 58.08 % 77.24 % 51.13 % 0.36 s 4 cores @ 2.5 Ghz (Python)
77 GA_rpn500 57.82 % 76.06 % 49.00 % 1 s 1 core @ 2.5 Ghz (Python)
78 GA2500 57.82 % 76.06 % 48.99 % 0.2 s 1 core @ 2.5 Ghz (Python)
79 bin 57.62 % 64.36 % 50.70 % 15ms s GPU @ >3.5 Ghz (Python)
80 cas_retina 57.14 % 73.97 % 50.32 % 0.2 s 4 cores @ 2.5 Ghz (Python)
81 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.
82 CSFADet 56.88 % 73.82 % 50.22 % 0.05 s GPU @ 2.5 Ghz (Python)
83 cas_retina_1_13 56.39 % 72.80 % 49.71 % 0.03 s 4 cores @ 2.5 Ghz (Python)
84 MonoPair 56.37 % 74.77 % 48.37 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
85 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.
86 bigger_ga 55.66 % 73.05 % 47.31 % 1 s 1 core @ 2.5 Ghz (Python)
87 Multi-task DG 55.30 % 75.48 % 48.22 % 0.06 s GPU @ 2.5 Ghz (Python)
88 ReSqueeze 54.50 % 69.64 % 48.24 % 0.03 s GPU @ >3.5 Ghz (Python)
89 CRCNNA 53.41 % 69.81 % 46.29 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
90 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.
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 % 64.97 % 44.66 % 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 detectron code 47.26 % 65.49 % 40.82 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
101 X_MD 46.97 % 62.50 % 40.84 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
102 RFCN 46.70 % 62.09 % 40.71 % 0.2 s 4 cores @ 2.5 Ghz (Python)
103 CLF3D
This method makes use of Velodyne laser scans.
 46.46 % 64.85 % 40.10 % 0.13 s GPU @ 2.5 Ghz (Python)
104 NM code 45.82 % 60.69 % 40.83 % 0.01 s GPU @ 2.5 Ghz (Python)
105 SS3D_HW 45.53 % 61.79 % 39.03 % 0.4 s GPU @ 2.5 Ghz (Python)
106 PG-MonoNet 45.40 % 63.75 % 37.14 % 0.19 s GPU @ 2.5 Ghz (Python)
107 RFCN_RFB 45.28 % 60.06 % 39.66 % 0.2 s 4 cores @ 2.5 Ghz (Python)
108 Cmerge 44.87 % 64.38 % 37.80 % 0.2 s 4 cores @ 2.5 Ghz (Python)
109 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.
110 YOLOv3+d 43.00 % 60.88 % 38.15 % 0.04 s GPU @ 1.5 Ghz (C/C++)
111 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.
112 myfaster-rcnn-v1.5 42.89 % 59.60 % 38.07 % 0.1 s 1 core @ 2.5 Ghz (Python)
113 D^4LCN 42.86 % 65.29 % 36.29 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
114 cascade_gw 42.84 % 63.58 % 36.94 % 0.2 s 4 cores @ 2.5 Ghz (Python)
115 FD2 42.67 % 62.54 % 38.41 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
116 centernet 42.45 % 58.95 % 37.56 % 0.01 s GPU @ 2.5 Ghz (Python)
117 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 .
118 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.
119 MTDP 40.46 % 53.83 % 35.74 % 0.15 s GPU @ 2.0 Ghz (Python)
120 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.
121 GNN 39.80 % 58.30 % 34.56 % 0.2 s 1 core @ 2.5 Ghz (Python)
122 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.
123 myfaster-rcnn 35.81 % 54.28 % 31.82 % 0.01 s 1 core @ 2.5 Ghz (Python)
124 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.
125 DSGN
This method uses stereo information.
 35.15 % 49.10 % 31.41 % 0.67 s NVIDIA Tesla V100
126 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.
127 FCY
This method makes use of Velodyne laser scans.
 34.24 % 50.50 % 31.06 % 0.02 s GPU @ 2.5 Ghz (Python)
128 Retinanet100 32.30 % 46.60 % 28.29 % 0.2 s 4 cores @ 2.5 Ghz (Python)
129 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.
130 yolov3_warp 29.48 % 44.46 % 25.84 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
131 OC Stereo
This method uses stereo information.
 28.76 % 43.18 % 24.80 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
132 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)
133 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.
134 softyolo 27.90 % 41.90 % 24.74 % 0.16 s 4 cores @ 2.5 Ghz (Python)
135 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.
136 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.
137 100Frcnn 27.69 % 43.23 % 23.91 % 2 s 4 cores @ 2.5 Ghz (Python + C/C++)
138 RefinedMPL 27.17 % 44.47 % 22.84 % 0.1 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.
139 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.
140 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.
141 R-CNN_VGG 25.14 % 34.28 % 22.17 % 10 s GPU @ 2.5 Ghz (Matlab + C/C++)
142 Lidar_ROI+Yolo(UJS) 24.42 % 36.43 % 21.78 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
143 rpn 23.44 % 36.85 % 21.25 % 0.01 s 1 core @ 2.5 Ghz (Python)
144 DT3D 18.25 % 30.12 % 17.20 % 0,21s GPU @ 2.5 Ghz (Python)
145 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.
146 SA_3D 14.38 % 19.40 % 12.50 % 0.3 s GPU @ 2.5 Ghz (Python)
147 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.
148 mymask-rcnn 13.58 % 18.03 % 12.42 % 0.3 s 1 core @ 2.5 Ghz (Python)
149 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.
150 KD53-20 12.81 % 20.05 % 11.99 % 0.19 s 4 cores @ 2.5 Ghz (Python)
151 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.
152 softretina 0.25 % 0.16 % 0.18 % 0.16 s 4 cores @ 2.5 Ghz (Python)
153 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.
154 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.
155 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 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++)
2 MVRA + I-FRCNN+ 94.46 % 95.66 % 81.74 % 0.18 s GPU @ 2.5 Ghz (Python)
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 + Occ 92.58 % 96.08 % 89.60 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
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 PPFNet code 92.52 % 96.30 % 87.44 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
17 PI-RCNN 92.52 % 96.15 % 87.47 % 0.1 s 1 core @ 2.5 Ghz (Python)
18 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.
19 OHS 92.32 % 95.83 % 89.39 % 0.03 s 1 core @ 2.5 Ghz (Python/C++)
20 SegVoxelNet 92.16 % 95.86 % 86.90 % 0.04 s 1 core @ 2.5 Ghz (Python)
21 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++)
22 PointRGCN 92.15 % 97.48 % 86.83 % 0.26 s GPU @ V100 (Python)
23 F-ConvNet
This method makes use of Velodyne laser scans.
 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.
24 IE-PointRCNN 91.94 % 96.00 % 86.84 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
25 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.
26 NU-optim 91.87 % 95.17 % 86.54 % 0.04 s GPU @ >3.5 Ghz (Python)
27 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.
28 MMLab-PartA^2
This method makes use of Velodyne laser scans.
 91.73 % 95.00 % 88.86 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, X. Wang and H. Li: Part-A^2 Net: 3D Part-Aware and Aggregation Neural Network for Object Detection from Point Cloud. arXiv preprint arXiv:1907.03670 2019.
29 RUC 91.25 % 95.01 % 88.14 % 0.12 s 1 core @ 2.5 Ghz (C/C++)
30 MLF_SecCas 91.18 % 96.19 % 83.25 % 0.05 s 1 core @ 2.5 Ghz (Python)
31 HRI-FusionRCNN 91.03 % 94.30 % 83.31 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
32 deprecated 91.02 % 94.06 % 78.56 % 0.05 s GPU @ 2.0 Ghz (Python)
33 HRI-VoxelFPN 90.76 % 96.35 % 85.37 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
B. Wang, J. An and J. Cao: Voxel-FPN: multi-scale voxel feature aggregation in 3D object detection from point clouds. arXiv preprint arXiv:1907.05286v2 2019.
34 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++)
35 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.
36 TBA 90.69 % 93.55 % 87.56 % 0.07 s 1 core @ 2.5 Ghz (Python)
37 SRF 90.54 % 95.86 % 85.30 % 0.05 s GPU @ 2.5 Ghz (Python + C/C++)
38 PFPN 90.50 % 94.07 % 80.58 % 0.02 s 4 cores @ >3.5 Ghz (Python)
39 CentrNet-v1
This method makes use of Velodyne laser scans.
 90.48 % 93.79 % 87.43 % 0.03 s GPU @ 2.5 Ghz (Python)
40 MMV 90.41 % 93.93 % 82.79 % 0.4 s GPU @ 2.5 Ghz (C/C++)
41 DDB
This method makes use of Velodyne laser scans.
 90.38 % 93.21 % 86.42 % 0.05 s GPU @ 2.5 Ghz (Python)
42 OACV 90.35 % 93.95 % 81.90 % 0.23 s GPU @ 2.5 Ghz (Python)
43 MVSLN 90.26 % 95.95 % 82.75 % 0.1s s 1 core @ 2.5 Ghz (C/C++)
44 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.
45 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.
46 TANet 90.11 % 93.52 % 84.61 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
47 FOFNet
This method makes use of Velodyne laser scans.
 90.05 % 93.87 % 84.52 % 0.04 s GPU @ 2.5 Ghz (Python)
48 SFB-SECOND 90.04 % 95.99 % 84.70 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
49 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++)
50 A-VoxelNet 90.00 % 93.24 % 82.31 % 0.029 s GPU @ 2.5 Ghz (Python)
51 Sogo_MM 89.97 % 94.15 % 79.94 % 1.5 s GPU @ 2.5 Ghz (C/C++)
52 PCSC-Net 89.90 % 93.54 % 86.10 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
53 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.
54 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++)
55 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++)
56 VOXEL_FPN_HR 89.81 % 93.52 % 84.59 % 0.12 s 8 cores @ 2.5 Ghz (Python)
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57 BVVF 89.77 % 95.55 % 84.48 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
58 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++)
59 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.
60 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.
61 SAANet 89.46 % 95.64 % 82.12 % 0.10 s 1 core @ 2.5 Ghz (Python)
62 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.
63 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.
64 SeoulRobotics-HFD
This method makes use of Velodyne laser scans.
 89.13 % 93.14 % 83.76 % 0.035 s GPU (C++)
65 DFD 88.86 % 93.04 % 81.47 % 0.05 s GPU @ 2.0 Ghz (Python + C/C++)
66 PAD 88.71 % 93.09 % 84.86 % 0.15 s 1 core @ 2.5 Ghz (Python)
67 PP_v1.0 code 88.68 % 92.92 % 84.82 % 0.02s 1 core @ 2.5 Ghz (C/C++)
68 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.
69 SS3D_HW 88.50 % 94.45 % 68.61 % 0.4 s GPU @ 2.5 Ghz (Python)
70 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++)
71 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.
72 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.
73 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.
74 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.
75 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.
76 CFR
This method makes use of Velodyne laser scans.
 87.31 % 93.79 % 79.56 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
77 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.
78 3DNN 87.08 % 93.78 % 79.72 % 0.09 s GPU @ 2.5 Ghz (Python)
79 SMOKE 87.02 % 92.94 % 77.12 % 0.03 s GPU @ 2.5 Ghz (Python)
80 MonoSS 86.95 % 92.88 % 77.04 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
81 ELLIOT
This method makes use of Velodyne laser scans.
 86.93 % 92.62 % 83.20 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
82 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.
83 MBR-SSD 86.57 % 90.97 % 78.03 % 4.0 s GPU @ 2.5 Ghz (Python + C/C++)
84 SECA 86.55 % 94.76 % 79.36 % 0.09 s GPU @ 2.5 Ghz (Python)
85 SCANet 86.51 % 92.47 % 81.09 % 0.17 s >8 cores @ 2.5 Ghz (Python)
86 SCANet 86.26 % 92.37 % 79.27 % 0.09s GPU @ 2.5 Ghz (Python)
87 MonoPair 86.11 % 91.65 % 76.45 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
88 DSGN
This method uses stereo information.
 86.03 % 95.42 % 78.27 % 0.67 s NVIDIA Tesla V100
89 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.
90 X_MD 85.06 % 93.00 % 77.77 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
91 FNV1_RPN 84.66 % 94.37 % 79.40 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
92 FNV1_Fusion 84.63 % 92.50 % 79.30 % 0.11 s GPU @ 2.5 Ghz (Python)
93 PL V2 (SDN+GDC)
This method uses stereo information.
This method makes use of Velodyne laser scans.
 84.42 % 94.83 % 76.95 % 0.6 s GPU @ 2.5 Ghz (C/C++)
94 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.
95 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.
96 SECA 83.99 % 92.34 % 78.85 % 1 s GPU @ 2.5 Ghz (Python)
97 VSE 83.99 % 92.34 % 78.85 % 0.15 s GPU @ 2.5 Ghz (Python)
98 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.
99 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.
100 FNV1 82.81 % 91.28 % 75.50 % 0.11 s GPU @ 2.5 Ghz (Python)
101 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 .
102 RAR-Net 82.58 % 88.38 % 66.86 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
103 SECOND code 82.55 % 90.93 % 73.62 % 38 ms 1080Ti
Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.
104 D^4LCN 82.08 % 90.01 % 63.98 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
105 Pseudo-LiDAR V2
This method uses stereo information.
 code 81.87 % 94.14 % 74.29 % 0.4 s GPU @ 2.5 Ghz (Python)
106 PG-MonoNet 81.77 % 87.61 % 66.06 % 0.19 s GPU @ 2.5 Ghz (Python)
107 Disp R-CNN
This method uses stereo information.
 81.61 % 92.91 % 69.20 % 0.42 s GPU @ 2.5 Ghz (Python + C/C++)
108 Pseudo-LiDAR E2E
This method uses stereo information.
 81.56 % 93.74 % 74.23 % 0.4 s GPU @ 2.5 Ghz (Python)
109 HR-SECOND code 81.23 % 88.32 % 74.89 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
110 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.
111 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.
112 Manhnet 79.97 % 88.71 % 63.47 % 26 ms 1 core @ 2.5 Ghz (C/C++)
113 CLF3D
This method makes use of Velodyne laser scans.
 78.49 % 87.39 % 66.82 % 0.13 s GPU @ 2.5 Ghz (Python)
114 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.
115 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.
116 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.
117 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.
118 avodC 75.35 % 86.76 % 70.17 % 0.1 s GPU @ 2.5 Ghz (Python)
119 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.
120 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.
121 OC Stereo
This method uses stereo information.
 73.34 % 86.86 % 61.37 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
122 BdCost+DA+BB+MS 72.87 % 84.39 % 57.07 % TBD s 4 cores @ 2.5 Ghz (Matlab + C/C++)
123 BdCost+DA+MS 72.65 % 84.06 % 58.08 % TBD s 4 cores @ 2.5 Ghz (Matlab/C++)
124 MF3D 70.62 % 90.75 % 56.26 % 0.03 s GPU @ 2.5 Ghz (C/C++)
125 BdCost+DA+BB 70.07 % 84.66 % 55.50 % TBD s 4 cores @ 2.5 Ghz (Matlab + C/C++)
126 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.
127 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.
128 Decoupled-3D v2 67.47 % 88.23 % 54.04 % 0.08 s GPU @ 2.5 Ghz (C/C++)
129 Decoupled-3D 67.23 % 87.34 % 53.84 % 0.08 s GPU @ 2.5 Ghz (C/C++)
130 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.
131 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.
132 deprecated 65.30 % 69.02 % 63.66 % 0.05 s GPU @ >3.5 Ghz (Python)
133 3DVSSD 65.28 % 79.56 % 55.73 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
134 RefinedMPL 64.02 % 87.95 % 52.06 % 0.1 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 BdCost48-25C 63.90 % 80.69 % 51.54 % 4 s 1 core @ 2.5 Ghz (C/C++)
136 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.
137 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.
138 monoref3d 58.30 % 77.65 % 46.90 % 0.1 s 1 core @ 2.5 Ghz (Python)
139 ref3D 58.30 % 77.65 % 46.90 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
140 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.
141 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.
142 3D-CVF
This method makes use of Velodyne laser scans.
 57.01 % 62.54 % 54.94 % 0.05 s GPU @ >3.5 Ghz (Python)
143 ref3D 56.49 % 77.52 % 45.17 % 0.1 s 1 core @ 2.5 Ghz (Python)
144 RCN-resnet101 54.47 % 57.97 % 48.15 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
145 SAG-Net 53.70 % 60.63 % 47.56 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
146 DEFT 51.66 % 57.41 % 50.02 % 1 s GPU @ 2.5 Ghz (Python)
147 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 .
148 VAT-Net 50.73 % 55.88 % 45.50 % 1 s GPU @ 2.5 Ghz (Python + C/C++)
149 InNet 50.36 % 55.35 % 46.25 % 0.16 s GPU @ 3.5 Ghz (Python + C/C++)
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 ODES code 48.86 % 48.07 % 41.72 % 0.02 s GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
152 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.
153 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.
154 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.
155 VCTNet 45.71 % 49.07 % 41.50 % 0.02 s GPU @ 1.5 Ghz (C/C++)
156 ReSqueeze 45.58 % 49.08 % 41.33 % 0.03 s GPU @ >3.5 Ghz (Python)
157 Resnet101Faster rcnn 44.01 % 51.21 % 39.19 % 1 s 1 core @ 2.5 Ghz (Python)
158 FD2 38.89 % 48.29 % 34.35 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
159 bin 38.58 % 43.36 % 32.42 % 15ms s GPU @ >3.5 Ghz (Python)
160 DGIST-CellBox 38.36 % 39.11 % 36.15 % 0.1 s GPU @ 2.5 Ghz (Java + C/C++)
161 SA-SSD 38.30 % 39.40 % 37.07 % 0.04 s 1 core @ 2.5 Ghz (Python)
162 IPOD 37.79 % 38.58 % 36.57 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: IPOD: Intensive Point-based Object Detector for Point Cloud. CoRR 2018.
163 Faster RCNN + G 37.49 % 39.05 % 33.40 % 0.19 s GPU @ 2.5 Ghz (Python)
164 Faster RCNN 37.35 % 38.75 % 33.38 % 0.2 s GPU @ 2.5 Ghz (Python)
165 Point-GNN
This method makes use of Velodyne laser scans.
 37.20 % 38.66 % 36.29 % 0.6 s GPU @ 2.5 Ghz (Python)
166 CRCNNA 37.04 % 40.19 % 32.03 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
167 CSFADet 36.83 % 39.76 % 32.73 % 0.05 s GPU @ 2.5 Ghz (Python)
168 cas_retina 36.63 % 39.70 % 31.52 % 0.2 s 4 cores @ 2.5 Ghz (Python)
169 GA_rpn500 36.54 % 38.33 % 32.67 % 1 s 1 core @ 2.5 Ghz (Python)
170 GA2500 36.54 % 38.33 % 32.67 % 0.2 s 1 core @ 2.5 Ghz (Python)
171 cas+res+soft 36.53 % 38.82 % 32.26 % 0.2 s 4 cores @ 2.5 Ghz (Python)
172 merge12-12 36.47 % 38.83 % 32.20 % 0.2 s 4 cores @ 2.5 Ghz (Python)
173 AtrousDet 36.36 % 38.86 % 31.79 % 0.05 s TITAN X
174 bigger_ga 36.21 % 38.41 % 31.58 % 1 s 1 core @ 2.5 Ghz (Python)
175 cas_retina_1_13 35.89 % 39.02 % 31.33 % 0.03 s 4 cores @ 2.5 Ghz (Python)
176 cascadercnn 35.61 % 36.22 % 30.16 % 0.36 s 4 cores @ 2.5 Ghz (Python)
177 Cmerge 35.02 % 38.33 % 29.06 % 0.2 s 4 cores @ 2.5 Ghz (Python)
178 BirdNet
This method makes use of Velodyne laser scans.
 35.00 % 50.34 % 33.40 % 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.
179 ga50 34.95 % 38.21 % 30.29 % 1 s 1 core @ 2.5 Ghz (Python)
180 softretina 34.57 % 39.31 % 29.27 % 0.16 s 4 cores @ 2.5 Ghz (Python)
181 Retinanet100 34.37 % 39.15 % 28.43 % 0.2 s 4 cores @ 2.5 Ghz (Python)
182 IoU_DCRCNN 34.33 % 36.40 % 31.89 % 0.66 s GPU @ 2.5 Ghz (Python)
183 ZKNet 34.27 % 38.09 % 29.93 % 0.01 s GPU @ 2.0 Ghz (Python)
184 LPN 33.61 % 34.57 % 29.72 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
185 cascade_gw 33.53 % 34.76 % 29.71 % 0.2 s 4 cores @ 2.5 Ghz (Python)
186 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)
187 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)
188 NM code 32.78 % 37.21 % 28.36 % 0.01 s GPU @ 2.5 Ghz (Python)
189 SceneNet 32.78 % 37.79 % 28.30 % 0.03 s GPU @ 2.5 Ghz (C/C++)
190 detectron code 32.77 % 36.91 % 28.13 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
191 MTDP 32.68 % 36.06 % 27.12 % 0.15 s GPU @ 2.0 Ghz (Python)
192 Fast-SSD 32.51 % 41.41 % 28.45 % 0.06 s GTX650Ti
193 centernet 32.22 % 35.79 % 28.50 % 0.01 s GPU @ 2.5 Ghz (Python)
194 RTL3D 32.16 % 33.73 % 30.58 % 0.02 s GPU @ 2.5 Ghz (Python)
195 RFCN_RFB 32.06 % 35.39 % 27.94 % 0.2 s 4 cores @ 2.5 Ghz (Python)
196 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)
197 yolo800 31.13 % 32.49 % 26.76 % 0.13 s 4 cores @ 2.5 Ghz (Python)
198 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)
199 VoxelNet(Unofficial) 31.08 % 34.54 % 28.79 % 0.5 s GPU @ 2.0 Ghz (Python)
200 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)
201 RFCN 30.93 % 34.24 % 25.27 % 0.2 s 4 cores @ 2.5 Ghz (Python)
202 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.
203 m-prcnn
This method uses stereo information.
 29.62 % 34.80 % 22.79 % 0.43 s 1 core @ 2.5 Ghz (Python)
204 Multi-task DG 29.49 % 36.06 % 26.06 % 0.06 s GPU @ 2.5 Ghz (Python)
205 fasterrcnn 28.42 % 30.28 % 24.95 % 0.2 s 4 cores @ 2.5 Ghz (Python)
206 RFBnet 27.91 % 34.44 % 25.24 % 0.2 s 4 cores @ 2.5 Ghz (Python)
207 E-VoxelNet 26.87 % 27.66 % 24.05 % 0.1 s GPU @ 2.5 Ghz (Python)
208 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.
209 Lidar_ROI+Yolo(UJS) 25.33 % 30.36 % 22.20 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
210 RADNet-Mono 24.78 % 28.55 % 22.84 % 0.1 s 1 core @ 2.5 Ghz (Python)
211 100Frcnn 23.32 % 32.81 % 19.45 % 2 s 4 cores @ 2.5 Ghz (Python + C/C++)
212 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.
213 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.
214 FailNet-Mono 19.63 % 25.13 % 17.19 % 0.1 s 1 core @ 2.5 Ghz (Python)
215 DLMB
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
 19.31 % 24.12 % 16.59 % 0.03 s 8 cores @ 3.5 Ghz (C/C++)
216 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.
217 softyolo 18.31 % 26.80 % 15.28 % 0.16 s 4 cores @ 2.5 Ghz (Python)
218 Licar
This method makes use of Velodyne laser scans.
 16.16 % 18.56 % 15.59 % 0.09 s GPU @ 2.0 Ghz (Python)
219 rpn 15.90 % 26.38 % 12.49 % 0.01 s 1 core @ 2.5 Ghz (Python)
220 FCY
This method makes use of Velodyne laser scans.
 15.64 % 18.34 % 15.61 % 0.02 s GPU @ 2.5 Ghz (Python)
221 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.
222 KD53-20 13.76 % 20.58 % 11.91 % 0.19 s 4 cores @ 2.5 Ghz (Python)
223 DLnet 6.40 % 8.54 % 5.70 % 0.3 s 4 cores @ 2.5 Ghz (C/C++)
224 MP 1.51 % 0.63 % 2.03 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
225 PiP 1.45 % 0.56 % 1.85 % 0.05 s 1 core @ 2.5 Ghz (Python)
226 SPA 1.25 % 0.59 % 1.64 % 0.1 s 1 core @ 2.5 Ghz (Python)
227 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++)
228 FCPP 0.06 % 0.00 % 0.07 % 0.02 s 1 core @ 2.0 Ghz (Python + C/C++)
229 SN-net 0.00 % 0.00 % 0.00 % 0.8 s GPU @ 2.5 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.
 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 FFNet code 58.87 % 69.24 % 53.75 % 1.07 s GPU @ 1.5 Ghz (Python)
10 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.
11 OHS + Occ 58.45 % 67.82 % 55.33 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
12 OHS 56.89 % 66.97 % 52.75 % 0.03 s 1 core @ 2.5 Ghz (Python/C++)
13 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.
14 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.
15 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.
16 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.
17 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.
18 PPFNet code 47.73 % 55.78 % 44.56 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
19 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.
20 CLF3D
This method makes use of Velodyne laser scans.
 47.17 % 62.48 % 41.29 % 0.13 s GPU @ 2.5 Ghz (Python)
21 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.
22 VOXEL_FPN_HR 45.65 % 56.17 % 42.10 % 0.12 s 8 cores @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
23 SS3D_HW 44.43 % 59.56 % 38.77 % 0.4 s GPU @ 2.5 Ghz (Python)
24 FOFNet
This method makes use of Velodyne laser scans.
 44.33 % 55.61 % 40.85 % 0.04 s GPU @ 2.5 Ghz (Python)
25 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.
26 DGIST-CellBox 43.86 % 48.68 % 41.52 % 0.1 s GPU @ 2.5 Ghz (Java + C/C++)
27 DDB
This method makes use of Velodyne laser scans.
 43.21 % 52.02 % 40.81 % 0.05 s GPU @ 2.5 Ghz (Python)
28 PiP 42.76 % 51.23 % 40.06 % 0.05 s 1 core @ 2.5 Ghz (Python)
29 MonoPair 42.38 % 55.26 % 38.53 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
30 SCANet 42.12 % 54.48 % 38.64 % 0.17 s >8 cores @ 2.5 Ghz (Python)
31 HR-SECOND code 40.81 % 51.12 % 37.48 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
32 Faster RCNN + G 40.49 % 47.16 % 37.57 % 0.19 s GPU @ 2.5 Ghz (Python)
33 CFR
This method makes use of Velodyne laser scans.
 40.29 % 53.96 % 37.87 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
34 Faster RCNN 39.95 % 47.52 % 37.08 % 0.2 s GPU @ 2.5 Ghz (Python)
35 CentrNet-v1
This method makes use of Velodyne laser scans.
 39.83 % 46.21 % 38.05 % 0.03 s GPU @ 2.5 Ghz (Python)
36 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.
37 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.
38 SECOND code 39.53 % 50.18 % 36.25 % 38 ms 1080Ti
Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.
39 VCTNet 39.36 % 44.15 % 36.79 % 0.02 s GPU @ 1.5 Ghz (C/C++)
40 CSFADet 38.41 % 46.75 % 35.44 % 0.05 s GPU @ 2.5 Ghz (Python)
41 HBA-RCNN 38.10 % 44.41 % 35.27 % 0.4 s 1 core @ 2.5 Ghz (C/C++)
42 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.
43 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++)
44 A-VoxelNet 36.24 % 42.48 % 34.36 % 0.029 s GPU @ 2.5 Ghz (Python)
45 TANet 36.21 % 42.54 % 34.39 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
46 SAANet 36.08 % 46.09 % 34.14 % 0.10 s 1 core @ 2.5 Ghz (Python)
47 AtrousDet 35.85 % 44.79 % 32.12 % 0.05 s TITAN X
48 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.
49 CRCNNA 34.88 % 43.18 % 31.90 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
50 IPOD 34.31 % 42.37 % 31.61 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: IPOD: Intensive Point-based Object Detector for Point Cloud. CoRR 2018.
51 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.
52 merge12-12 34.10 % 43.60 % 30.43 % 0.2 s 4 cores @ 2.5 Ghz (Python)
53 PP_v1.0 code 34.09 % 40.38 % 32.43 % 0.02s 1 core @ 2.5 Ghz (C/C++)
54 cas+res+soft 34.01 % 43.51 % 30.28 % 0.2 s 4 cores @ 2.5 Ghz (Python)
55 cas_retina 33.98 % 43.80 % 31.12 % 0.2 s 4 cores @ 2.5 Ghz (Python)
56 cas_retina_1_13 33.87 % 43.55 % 30.99 % 0.03 s 4 cores @ 2.5 Ghz (Python)
57 D^4LCN 33.62 % 46.73 % 28.71 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
58 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.
59 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.
60 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.
61 PG-MonoNet 32.67 % 44.75 % 29.33 % 0.19 s GPU @ 2.5 Ghz (Python)
62 cascadercnn 32.59 % 43.37 % 29.73 % 0.36 s 4 cores @ 2.5 Ghz (Python)
63 ReSqueeze 32.47 % 38.49 % 30.04 % 0.03 s GPU @ >3.5 Ghz (Python)
64 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.
65 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.
66 yolo800 32.12 % 40.53 % 28.83 % 0.13 s 4 cores @ 2.5 Ghz (Python)
67 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.
68 bin 31.94 % 36.94 % 29.50 % 15ms s GPU @ >3.5 Ghz (Python)
69 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 .
70 Point-GNN
This method makes use of Velodyne laser scans.
 31.86 % 39.16 % 29.65 % 0.6 s GPU @ 2.5 Ghz (Python)
71 ODES code 31.79 % 37.79 % 28.66 % 0.02 s GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
72 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.
73 ZKNet 31.21 % 39.55 % 28.61 % 0.01 s GPU @ 2.0 Ghz (Python)
74 RFCN 30.97 % 40.51 % 27.45 % 0.2 s 4 cores @ 2.5 Ghz (Python)
75 LPN 30.84 % 38.60 % 28.49 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
76 X_MD 30.57 % 40.97 % 27.47 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
77 CHTTL MMF 30.45 % 41.08 % 27.57 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
78 ELLIOT
This method makes use of Velodyne laser scans.
 30.21 % 38.79 % 28.00 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
79 RFCN_RFB 29.91 % 38.71 % 26.50 % 0.2 s 4 cores @ 2.5 Ghz (Python)
80 deprecated 29.74 % 37.71 % 27.25 % 0.05 s GPU @ 2.0 Ghz (Python)
81 NM code 29.60 % 38.81 % 26.99 % 0.01 s GPU @ 2.5 Ghz (Python)
82 fasterrcnn 29.48 % 38.63 % 26.89 % 0.2 s 4 cores @ 2.5 Ghz (Python)
83 Multi-task DG 28.71 % 38.97 % 26.13 % 0.06 s GPU @ 2.5 Ghz (Python)
84 detectron code 28.68 % 39.55 % 25.97 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
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.
 24.32 % 31.21 % 23.09 % 0.67 s NVIDIA Tesla V100
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++)
96 Retinanet100 21.71 % 29.72 % 19.12 % 0.2 s 4 cores @ 2.5 Ghz (Python)
97 softyolo 21.56 % 30.46 % 20.01 % 0.16 s 4 cores @ 2.5 Ghz (Python)
98 Lidar_ROI+Yolo(UJS) 19.43 % 26.83 % 17.14 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
99 KD53-20 19.36 % 25.10 % 17.54 % 0.19 s 4 cores @ 2.5 Ghz (Python)
100 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.
101 rpn 17.79 % 24.35 % 15.45 % 0.01 s 1 core @ 2.5 Ghz (Python)
102 RefinedMPL 17.26 % 25.83 % 15.41 % 0.1 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 BirdNet
This method makes use of Velodyne laser scans.
 16.45 % 21.07 % 15.65 % 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.
104 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.
105 100Frcnn 12.37 % 19.41 % 10.92 % 2 s 4 cores @ 2.5 Ghz (Python + C/C++)
106 FCY
This method makes use of Velodyne laser scans.
 10.27 % 14.69 % 9.51 % 0.02 s GPU @ 2.5 Ghz (Python)
107 MP 5.39 % 6.41 % 5.14 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
108 softretina 0.13 % 0.10 % 0.14 % 0.16 s 4 cores @ 2.5 Ghz (Python)
109 JSyolo 0.06 % 0.11 % 0.07 % 0.16 s 4 cores @ 2.5 Ghz (Python)
110 SN-net 0.00 % 0.00 % 0.00 % 0.8 s GPU @ 2.5 Ghz (Python + C/C++)
Table as LaTeX | Only published Methods


Cyclists


Method Setting Code Moderate Easy Hard Runtime Environment
1 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++)
2 OHS 78.23 % 85.65 % 71.60 % 0.03 s 1 core @ 2.5 Ghz (Python/C++)
3 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.
4 F-ConvNet
This method makes use of Velodyne laser scans.
 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.
5 OHS + Occ 75.59 % 83.45 % 68.42 % 0.03 s 1 core @ 2.5 Ghz (Python + C/C++)
6 VOXEL_FPN_HR 74.77 % 87.41 % 68.16 % 0.12 s 8 cores @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
7 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.
8 FOFNet
This method makes use of Velodyne laser scans.
 72.48 % 86.89 % 65.63 % 0.04 s GPU @ 2.5 Ghz (Python)
9 PiP 71.10 % 82.83 % 64.88 % 0.05 s 1 core @ 2.5 Ghz (Python)
10 HR-SECOND code 69.60 % 82.42 % 62.47 % 0.11 s 1 core @ 2.5 Ghz (Python + C/C++)
11 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.
12 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.
13 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.
14 TANet 66.37 % 81.15 % 60.10 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
15 A-VoxelNet 66.17 % 80.73 % 58.96 % 0.029 s GPU @ 2.5 Ghz (Python)
16 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++)
17 SAANet 65.52 % 82.29 % 58.81 % 0.10 s 1 core @ 2.5 Ghz (Python)
18 Sogo_MM 63.50 % 71.57 % 55.24 % 1.5 s GPU @ 2.5 Ghz (C/C++)
19 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.
20 deprecated 63.08 % 83.73 % 53.51 % 0.05 s GPU @ 2.0 Ghz (Python)
21 CentrNet-v1
This method makes use of Velodyne laser scans.
 62.11 % 78.10 % 55.54 % 0.03 s GPU @ 2.5 Ghz (Python)
22 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.
23 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.
24 SCANet 60.84 % 75.16 % 54.70 % 0.17 s >8 cores @ 2.5 Ghz (Python)
25 CFR
This method makes use of Velodyne laser scans.
 59.56 % 76.33 % 52.93 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
26 SECOND code 58.90 % 80.68 % 52.00 % 38 ms 1080Ti
Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.
27 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.
28 DDB
This method makes use of Velodyne laser scans.
 58.65 % 75.36 % 52.85 % 0.05 s GPU @ 2.5 Ghz (Python)
29 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.
30 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.
31 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.
32 PP_v1.0 code 57.76 % 76.02 % 51.19 % 0.02s 1 core @ 2.5 Ghz (C/C++)
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 ELLIOT
This method makes use of Velodyne laser scans.
 55.75 % 74.65 % 50.55 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
35 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.
36 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.
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 CLF3D
This method makes use of Velodyne laser scans.
 45.62 % 64.13 % 39.19 % 0.13 s GPU @ 2.5 Ghz (Python)
40 X_MD 44.47 % 60.77 % 38.80 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
41 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.
42 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.
43 MonoPair 39.47 % 53.36 % 33.95 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
44 VCTNet 38.38 % 47.05 % 33.68 % 0.02 s GPU @ 1.5 Ghz (C/C++)
45 SS3D_HW 37.68 % 52.40 % 32.33 % 0.4 s GPU @ 2.5 Ghz (Python)
46 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.
47 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.
48 PG-MonoNet 34.11 % 49.05 % 28.14 % 0.19 s GPU @ 2.5 Ghz (Python)
49 ODES code 33.78 % 38.51 % 29.84 % 0.02 s GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
50 Point-GNN
This method makes use of Velodyne laser scans.
 32.37 % 36.29 % 29.81 % 0.6 s GPU @ 2.5 Ghz (Python)
51 D^4LCN 31.70 % 48.03 % 26.99 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
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 30.81 % 36.25 % 27.51 % 0.2 s GPU @ 2.5 Ghz (Python)
54 Faster RCNN + G 30.61 % 36.19 % 27.22 % 0.19 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 BirdNet
This method makes use of Velodyne laser scans.
 29.65 % 41.68 % 27.21 % 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.
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 IPOD 28.07 % 35.60 % 24.95 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: IPOD: Intensive Point-based Object Detector for Point Cloud. CoRR 2018.
60 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.
61 ReSqueeze 27.40 % 36.26 % 24.04 % 0.03 s GPU @ >3.5 Ghz (Python)
62 cascadercnn 26.59 % 33.81 % 23.48 % 0.36 s 4 cores @ 2.5 Ghz (Python)
63 merge12-12 26.39 % 33.49 % 22.83 % 0.2 s 4 cores @ 2.5 Ghz (Python)
64 cas+res+soft 26.32 % 33.63 % 22.75 % 0.2 s 4 cores @ 2.5 Ghz (Python)
65 GA2500 26.08 % 32.91 % 22.06 % 0.2 s 1 core @ 2.5 Ghz (Python)
66 GA_rpn500 26.08 % 32.91 % 22.06 % 1 s 1 core @ 2.5 Ghz (Python)
67 CSFADet 25.77 % 32.19 % 22.78 % 0.05 s GPU @ 2.5 Ghz (Python)
68 cas_retina 25.24 % 31.74 % 22.30 % 0.2 s 4 cores @ 2.5 Ghz (Python)
69 cas_retina_1_13 25.01 % 31.17 % 22.12 % 0.03 s 4 cores @ 2.5 Ghz (Python)
70 Multi-task DG 24.72 % 33.39 % 21.63 % 0.06 s GPU @ 2.5 Ghz (Python)
71 bigger_ga 24.64 % 31.31 % 21.06 % 1 s 1 core @ 2.5 Ghz (Python)
72 CRCNNA 23.88 % 29.91 % 20.70 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
73 FD2 23.83 % 35.75 % 20.79 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
74 ga50 21.59 % 29.77 % 18.77 % 1 s 1 core @ 2.5 Ghz (Python)
75 fasterrcnn 21.52 % 28.50 % 18.86 % 0.2 s 4 cores @ 2.5 Ghz (Python)
76 ZKNet 21.51 % 28.26 % 18.83 % 0.01 s GPU @ 2.0 Ghz (Python)
77 LPN 21.11 % 27.67 % 18.82 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
78 detectron code 21.10 % 27.83 % 18.22 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
79 RFCN 20.77 % 26.80 % 18.25 % 0.2 s 4 cores @ 2.5 Ghz (Python)
80 yolo800 20.66 % 27.38 % 18.77 % 0.13 s 4 cores @ 2.5 Ghz (Python)
81 RFCN_RFB 20.40 % 26.19 % 17.91 % 0.2 s 4 cores @ 2.5 Ghz (Python)
82 DSGN
This method uses stereo information.
 20.28 % 29.76 % 19.13 % 0.67 s NVIDIA Tesla V100
83 NM code 20.02 % 26.27 % 17.87 % 0.01 s GPU @ 2.5 Ghz (Python)
84 Cmerge 19.78 % 27.75 % 16.58 % 0.2 s 4 cores @ 2.5 Ghz (Python)
85 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.
86 OC Stereo
This method uses stereo information.
 18.99 % 29.07 % 16.40 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
87 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.
88 cascade_gw 18.74 % 27.00 % 16.35 % 0.2 s 4 cores @ 2.5 Ghz (Python)
89 MTDP 18.02 % 23.30 % 16.07 % 0.15 s GPU @ 2.0 Ghz (Python)
90 centernet 17.55 % 23.39 % 15.59 % 0.01 s GPU @ 2.5 Ghz (Python)
91 RefinedMPL 16.02 % 26.54 % 13.20 % 0.1 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.
92 FCY
This method makes use of Velodyne laser scans.
 15.78 % 23.27 % 14.13 % 0.02 s GPU @ 2.5 Ghz (Python)
93 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.
94 Retinanet100 13.34 % 19.09 % 11.79 % 0.2 s 4 cores @ 2.5 Ghz (Python)
95 softyolo 11.12 % 15.91 % 9.84 % 0.16 s 4 cores @ 2.5 Ghz (Python)
96 100Frcnn 11.07 % 16.90 % 9.63 % 2 s 4 cores @ 2.5 Ghz (Python + C/C++)
97 rpn 9.48 % 14.71 % 8.45 % 0.01 s 1 core @ 2.5 Ghz (Python)
98 Lidar_ROI+Yolo(UJS) 8.95 % 13.15 % 7.96 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
99 KD53-20 4.86 % 7.19 % 4.74 % 0.19 s 4 cores @ 2.5 Ghz (Python)
100 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.
101 MP 0.97 % 0.62 % 0.89 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
102 softretina 0.11 % 0.07 % 0.08 % 0.16 s 4 cores @ 2.5 Ghz (Python)
103 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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