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!

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 THU CV-AI 91.97 % 91.96 % 84.57 % 0.38 s GPU @ 2.5 Ghz (Python)
2 DH-ARI 91.48 % 90.87 % 82.25 % 4s GPU @ 2.5 Ghz (C/C++)
3 HRI-SH 90.71 % 91.34 % 84.28 % 3.6 s GPU @ >3.5 Ghz (Python + C/C++)
4 BM-NET 90.50 % 90.81 % 83.92 % 0.5 s GPU @ 2.5 Ghz (Python + C/C++)
5 DGIST-CellBox 90.43 % 90.93 % 86.24 % 0.1 s GPU @ 2.5 Ghz (Java + C/C++)
6 MVRA + I-FRCNN+ 90.36 % 90.78 % 80.48 % 0.18 s GPU @ 2.5 Ghz (Python)
7 FichaDL 90.36 % 90.88 % 80.15 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
8 TuSimple code 90.33 % 90.77 % 82.86 % 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 RRC code 90.23 % 90.61 % 87.44 % 3.6 s GPU @ 2.5 Ghz (C/C++)
J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y. Tai and L. Xu: Accurate Single Stage Detector Using Recurrent Rolling Convolution. CVPR 2017.
10 CFENet 90.22 % 90.33 % 84.85 % 4 s GPU @ 2.5 Ghz (Python + C/C++)
11 UberATG-MMF
This method makes use of Velodyne laser scans.
90.17 % 91.82 % 88.54 % 0.08 s GPU @ 2.5 Ghz (Python)
M. Liang*, B. Yang*, Y. Chen, R. Hu and R. Urtasun: Multi-Task Multi-Sensor Fusion for 3D Object Detection. CVPR 2019.
12 PC-CNN-V2
This method makes use of Velodyne laser scans.
90.15 % 90.79 % 87.58 % 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.
13 EM-FPS 90.15 % 90.61 % 84.01 % 0.15 s GPU @ 1.5 Ghz (Python + C/C++)
14 ELE 90.14 % 96.60 % 89.10 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
15 HRI-FusionRCNN 90.10 % 90.75 % 81.09 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
16 THICV-YDM 90.08 % 90.40 % 80.41 % 0.06 s GPU @ 2.5 Ghz (Python)
17 SJTU-HW 90.08 % 90.81 % 79.98 % 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.
18 MDC
This method makes use of Velodyne laser scans.
90.03 % 90.72 % 80.87 % 0.17 s GPU @ 2.5 Ghz (Python)
19 Deep MANTA 90.03 % 97.25 % 80.62 % 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.
20 sensekitti code 90.00 % 90.76 % 81.83 % 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.
21 F-PointNet
This method makes use of Velodyne laser scans.
code 90.00 % 90.78 % 80.80 % 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.
22 ATL 89.96 % 90.80 % 88.28 % 0.04 s 1 core @ 2.5 Ghz (Python)
23 Cascade MS-CNN code 89.95 % 90.68 % 78.40 % 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.
24 MLF 89.94 % 90.60 % 80.77 % 0.05 s GPU @ 2.0 Ghz (Python)
25 ECV-NET 89.93 % 90.61 % 81.81 % 0.4 s GPU @ 2.5 Ghz (C/C++)
26 HRI-VoxelFPN 89.89 % 90.66 % 80.97 % 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.
27 ART-Det 89.89 % 95.18 % 80.03 % 0.067s GPU @ 2.5 Ghz (Python + C/C++)
28 FNV2 89.88 % 90.51 % 80.66 % 0.18 s GPU @ 2.5 Ghz (Python)
29 Det-RGBD
This method uses stereo information.
89.83 % 90.45 % 80.56 % 0.58 s GPU @ 2.5 Ghz (Python + C/C++)
30 MBR-SSD 89.82 % 90.32 % 82.28 % 4.0 s GPU @ 2.5 Ghz (Python + C/C++)
31 F-ConvNet
This method makes use of Velodyne laser scans.
89.79 % 90.44 % 80.66 % 0.47 s GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
32 PointRCNN-deprecated
This method makes use of Velodyne laser scans.
89.75 % 90.77 % 80.98 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
33 SINet+ code 89.73 % 90.51 % 77.82 % 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.
34 STD 89.72 % 90.57 % 88.90 % 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.
35 RGB3D
This method makes use of Velodyne laser scans.
89.71 % 90.75 % 88.21 % 0.39 s GPU @ 2.5 Ghz (Python)
36 Fast Point R-CNNv1.1
This method makes use of Velodyne laser scans.
89.71 % 90.59 % 88.13 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, S. Liu, X. Shen and J. Jia: Fast Point R-CNN. ICCV 2019.
37 Alibaba-AILabsX
This method makes use of Velodyne laser scans.
89.69 % 90.59 % 80.83 % 0.2 s GPU @ >3.5 Ghz (Python)
38 AILabs3D
This method makes use of Velodyne laser scans.
89.68 % 90.57 % 80.67 % 0.6 s GPU @ >3.5 Ghz (Python)
39 Alibaba-AILabsX
This method makes use of Velodyne laser scans.
89.65 % 90.47 % 80.81 % 0.05 s 1 core @ >3.5 Ghz (C/C++)
40 Aston-EAS 89.64 % 90.49 % 77.95 % 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.
41 Patches
This method makes use of Velodyne laser scans.
89.61 % 90.75 % 87.42 % 0.15 s GPU @ 2.0 Ghz
42 epBRM
This method makes use of Velodyne laser scans.
89.58 % 90.50 % 87.51 % 0.1 s GPU @ >3.5 Ghz (Python + C/C++)
43 SINet_VGG code 89.56 % 90.60 % 78.19 % 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.
44 Patches - EMP
This method makes use of Velodyne laser scans.
89.55 % 94.64 % 88.02 % 0.5 s GPU @ 2.5 Ghz (Python)
45 PFPN 89.54 % 90.52 % 80.76 % 0.02 s 4 cores @ >3.5 Ghz (Python)
46 Fast Point R-CNN
This method makes use of Velodyne laser scans.
89.51 % 90.58 % 87.89 % 0.06 s GPU @ 2.5 Ghz (Python + C/C++)
47 DH-ARI 89.47 % 90.31 % 84.78 % 0.2 s 1 core @ >3.5 Ghz (Python + C/C++)
48 SDP+RPN 89.42 % 89.90 % 78.54 % 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.
49 VAT-Net 89.41 % 90.69 % 79.97 % 1 s GPU @ 2.5 Ghz (Python + C/C++)
50 SegVoxelNet 89.37 % 90.62 % 88.02 % 0.04 s 1 core @ 2.5 Ghz (Python)
51 MMLab-PartA^2
This method makes use of Velodyne laser scans.
89.34 % 90.60 % 87.57 % 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.
52 ZRNet(ResNet-50) 89.34 % 89.91 % 79.28 % 0.04 s GPU @ 2.5 Ghz (Python)
53 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 89.32 % 90.74 % 85.73 % 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.
54 ARPNET 89.30 % 90.50 % 80.64 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
55 IPOD 89.30 % 90.20 % 87.37 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
56 TBA 89.30 % 90.42 % 87.17 % 0.07 s 1 core @ 2.5 Ghz (Python)
57 PI-RCNN 89.29 % 90.67 % 86.99 % 0.1 s 1 core @ 2.5 Ghz (Python)
58 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 89.28 % 90.67 % 86.45 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
59 3D IoU Loss
This method makes use of Velodyne laser scans.
89.26 % 90.34 % 80.62 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
D. Zhou: IoU Loss for 2D/3D Object Detection. International Conference on 3D Vision (3DV) 2019.
60 MVX-Net
This method makes use of Velodyne laser scans.
89.25 % 90.43 % 80.47 % 0.06 s GPU @ 3.0 Ghz (Python + C/C++)
61 ITVD code 89.23 % 90.57 % 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.
62 PointPillars
This method makes use of Velodyne laser scans.
code 89.22 % 90.33 % 87.04 % 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.
63 MMV 89.22 % 90.59 % 80.49 % 0.4 s GPU @ 2.5 Ghz (C/C++)
64 MPNet
This method makes use of Velodyne laser scans.
89.21 % 90.60 % 86.19 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
65 CONV-BOX
This method makes use of Velodyne laser scans.
89.20 % 90.35 % 87.88 % 0.2 s Tesla V100
66 VCTNet 89.20 % 89.60 % 80.04 % 0.02 s GPU @ 1.5 Ghz (C/C++)
67 4D-MSCNN+CRL
This method uses stereo information.
89.19 % 90.32 % 76.26 % 0.2 s GPU @ 2.5 Ghz (Matlab + C/C++)
68 Tencent_ADlab_Lidar
This method makes use of Velodyne laser scans.
89.17 % 90.43 % 85.82 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
69 Sogo_MM 89.17 % 90.80 % 79.58 % 1.5 s GPU @ 2.5 Ghz (C/C++)
70 MV3D
This method makes use of Velodyne laser scans.
89.17 % 90.53 % 80.16 % 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.
71 NU-optim 89.17 % 90.24 % 87.92 % 0.04 s GPU @ >3.5 Ghz (Python)
72 A-VoxelNet 89.15 % 90.27 % 80.43 % 0.029 s GPU @ 2.5 Ghz (Python)
73 MVSLN 89.14 % 90.65 % 80.64 % 0.1s s 1 core @ 2.5 Ghz (C/C++)
74 SRF 89.14 % 90.34 % 80.52 % 0.05 s GPU @ 2.5 Ghz (Python + C/C++)
75 PTS
This method makes use of Velodyne laser scans.
code 89.12 % 90.38 % 80.46 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
76 YOLOv3.5 89.10 % 89.70 % 79.79 % 0.05 s GPU @ 2.5 Ghz (Python)
77 TANet 89.09 % 90.41 % 80.53 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
78 SINet_PVA code 89.08 % 90.44 % 75.85 % 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.
79 SECOND-V1.5
This method makes use of Velodyne laser scans.
code 89.06 % 90.52 % 80.40 % 0.04 s GPU @ 2.0 Ghz (Python + C/C++)
80 AtrousDet 89.01 % 90.25 % 78.98 % 0.05 s TITAN X
81 CLA 88.99 % 90.51 % 75.50 % 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.
82 InNet 88.95 % 90.26 % 79.46 % 0.16 s GPU @ 3.5 Ghz (Python + C/C++)
83 Shift R-CNN (mono) code 88.90 % 90.56 % 79.86 % 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.
84 SubCNN 88.86 % 90.75 % 79.24 % 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.
85 Deep3DBox 88.86 % 90.47 % 77.60 % 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.
86 LTN 88.85 % 90.12 % 79.62 % 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.
87 MonoPSR 88.84 % 90.18 % 71.44 % 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.
88 FQNet 88.83 % 90.45 % 77.55 % 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.
89 MS-CNN code 88.83 % 90.46 % 74.76 % 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.
90 RAR-Net 88.83 % 90.45 % 77.55 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
91 ZRNet 88.82 % 89.77 % 79.07 % 0.04 s GPU @ 2.5 Ghz (Python)
92 FOFNet
This method makes use of Velodyne laser scans.
88.81 % 90.58 % 80.38 % 0.04 s GPU @ 2.5 Ghz (Python)
93 CFR
This method makes use of Velodyne laser scans.
88.77 % 90.53 % 80.23 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
94 RCN-resnet101 88.75 % 89.08 % 79.97 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
95 DeepStereoOP 88.75 % 90.34 % 79.39 % 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.
96 SCNet
This method makes use of Velodyne laser scans.
88.67 % 90.44 % 80.35 % 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.
97 3DBN
This method makes use of Velodyne laser scans.
88.62 % 90.30 % 80.08 % 0.13s 1080Ti (Python+C/C++)
X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.
98 SAG-Net 88.61 % 89.25 % 79.72 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
99 PP_v1.0 code 88.57 % 90.41 % 84.23 % 0.02s 1 core @ 2.5 Ghz (C/C++)
100 PAD 88.47 % 90.36 % 83.19 % 0.15 s 1 core @ 2.5 Ghz (Python)
101 SECOND code 88.40 % 90.40 % 80.21 % 38 ms 1080Ti
Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.
102 3DOP
This method uses stereo information.
code 88.34 % 90.09 % 78.79 % 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.
103 GPP code 88.24 % 90.42 % 79.02 % 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.
104 DA 88.23 % 90.45 % 74.21 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
105 MM-MRFC
This method uses optical flow information.
This method makes use of Velodyne laser scans.
88.20 % 90.93 % 78.02 % 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.
106 AVOD
This method makes use of Velodyne laser scans.
code 88.08 % 89.73 % 80.14 % 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.
107 MLOD
This method makes use of Velodyne laser scans.
code 88.07 % 89.81 % 80.26 % 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.
108 Multi-3D
This method makes use of Velodyne laser scans.
88.04 % 90.10 % 75.31 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
109 cas+res+soft 88.00 % 89.82 % 77.66 % 0.2 s 4 cores @ 2.5 Ghz (Python)
110 SeoulRobotics-HFD
This method makes use of Velodyne laser scans.
87.86 % 90.02 % 79.95 % 0.035 s GPU (C++)
111 Mono3D code 87.86 % 90.27 % 78.09 % 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.
112 ZongMu-Mono 87.84 % 89.65 % 79.07 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
113 merge12-12 87.81 % 89.88 % 77.42 % 0.2 s 4 cores @ 2.5 Ghz (Python)
114 DFD 87.78 % 90.02 % 79.78 % 0.05 s GPU @ 2.0 Ghz (Python + C/C++)
115 MonoDIS 87.58 % 90.31 % 76.85 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
116 TridentNet 87.49 % 88.38 % 78.97 % 0.2 s GPU @ 2.5 Ghz (Python)
117 AVOD-FPN
This method makes use of Velodyne laser scans.
code 87.44 % 89.99 % 80.05 % 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.
118 SECA 87.42 % 89.57 % 79.43 % 0.09 s GPU @ 2.5 Ghz (Python)
119 SCANet 87.31 % 89.34 % 79.30 % 0.09s GPU @ 2.5 Ghz (Python)
120 SCANet 87.12 % 89.65 % 79.43 % 0.17 s >8 cores @ 2.5 Ghz (Python)
121 ODES code 87.10 % 86.82 % 78.32 % 0.02 s GPU @ 2.5 Ghz (Python)
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122 ELLIOT
This method makes use of Velodyne laser scans.
86.98 % 90.20 % 81.41 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
123 cas_retina 86.23 % 89.53 % 75.77 % 0.2 s 4 cores @ 2.5 Ghz (Python)
124 cascadercnn 85.86 % 84.21 % 69.57 % 0.36 s 4 cores @ 2.5 Ghz (Python)
125 ReSqueeze 85.74 % 87.12 % 77.02 % 0.03 s GPU @ >3.5 Ghz (Python)
126 NLK 85.56 % 89.00 % 79.34 % 0.02 s 1 core @ 2.5 Ghz (Python)
127 AM3D 85.42 % 87.33 % 77.43 % 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.
128 anm 85.33 % 90.11 % 76.55 % 3 s 1 core @ 2.5 Ghz (C/C++)
129 IoU_DCRCNN 84.48 % 87.68 % 76.70 % 0.66 s GPU @ 2.5 Ghz (Python)
130 cas_retina_1_13 84.43 % 89.22 % 75.39 % 0.03 s 4 cores @ 2.5 Ghz (Python)
131 YOLOv3+d 84.13 % 84.30 % 76.34 % 0.04 s GPU @ 1.5 Ghz (C/C++)
132 PL V2 (SDN+GDC)
This method uses stereo information.
This method makes use of Velodyne laser scans.
84.12 % 90.23 % 76.71 % 0.6 s GPU @ 2.5 Ghz (C/C++)
133 M3D-RPN code 83.78 % 84.34 % 67.85 % 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 .
134 StereoFENet
This method uses stereo information.
83.65 % 89.01 % 77.12 % 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. 2019.
135 NEUAV 83.25 % 87.75 % 76.38 % 0.06 s GPU @ 2.5 Ghz (Python)
136 MonoFENet 82.54 % 89.10 % 76.39 % 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. 2019.
137 cascade_gw 82.24 % 81.09 % 67.95 % 0.2 s 4 cores @ 2.5 Ghz (Python)
138 LPN 81.67 % 87.70 % 72.69 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
139 A3DODWTDA (image) code 81.54 % 76.21 % 66.85 % 0.8 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
140 SDP+CRC (ft) 81.33 % 90.39 % 70.33 % 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.
141 ResNet-RRC w/RGBD 81.09 % 89.91 % 71.78 % 0.057 s GPU @ 1.5 Ghz (Python + C/C++)
142 ResNet-RRC 81.00 % 89.89 % 71.56 % 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.
143 Stereo R-CNN
This method uses stereo information.
code 80.80 % 90.23 % 71.42 % 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.
144 X_MD 80.65 % 89.81 % 79.66 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
145 FNV1_Fusion 80.41 % 89.37 % 79.03 % 0.11 s GPU @ 2.5 Ghz (Python)
146 FNV1_RPN 80.41 % 89.44 % 79.14 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
147 Cmerge 80.25 % 89.83 % 70.76 % 0.2 s 4 cores @ 2.5 Ghz (Python)
148 SS3D 80.11 % 89.15 % 70.52 % 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.
149 SECA 80.05 % 89.26 % 78.80 % 1 s GPU @ 2.5 Ghz (Python)
150 VSE 80.05 % 89.26 % 78.80 % 0.15 s GPU @ 2.5 Ghz (Python)
151 BS3D 80.02 % 89.85 % 70.14 % 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.
152 centernet 79.97 % 89.78 % 70.57 % 0.01 s GPU @ 2.5 Ghz (Python)
153 MV3D (LIDAR)
This method makes use of Velodyne laser scans.
79.76 % 89.80 % 78.61 % 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.
154 ZKNet 79.62 % 89.89 % 70.03 % 0.01 s GPU @ 2.0 Ghz (Python)
155 RFCN_RFB 79.45 % 83.85 % 67.51 % 0.2 s 4 cores @ 2.5 Ghz (Python)
156 Complexer-YOLO
This method makes use of Velodyne laser scans.
79.31 % 88.11 % 79.11 % 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.
157 FNV1 79.28 % 88.45 % 77.14 % 0.11 s GPU @ 2.5 Ghz (Python)
158 RefineNet 79.21 % 90.16 % 65.71 % 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.
159 Pseudo-LiDAR V2
This method uses stereo information.
code 79.20 % 89.62 % 75.93 % 0.4 s GPU @ 2.5 Ghz (Python)
160 softretina 79.15 % 89.36 % 69.24 % 0.16 s 4 cores @ 2.5 Ghz (Python)
161 Faster R-CNN code 79.11 % 87.90 % 70.19 % 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.
162 detectron code 78.96 % 88.14 % 69.74 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
163 FRCNN+Or code 78.95 % 89.87 % 68.97 % 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.
164 Resnet101Faster rcnn 78.93 % 87.97 % 69.80 % 1 s 1 core @ 2.5 Ghz (Python)
165 Retinanet100 78.85 % 89.83 % 68.73 % 0.2 s 4 cores @ 2.5 Ghz (Python)
166 RADNet-Fusion
This method makes use of Velodyne laser scans.
78.80 % 75.37 % 72.78 % 0.1 s 1 core @ 2.5 Ghz (Python)
167 NM code 78.77 % 89.04 % 68.69 % 0.01 s GPU @ 2.5 Ghz (Python)
168 RADNet-LIDAR
This method makes use of Velodyne laser scans.
78.45 % 71.08 % 72.40 % 0.1 s 1 core @ 2.5 Ghz (Python)
169 SeRC 78.33 % 88.28 % 69.36 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
170 Manhnet 78.03 % 85.86 % 61.13 % 26 ms 1 core @ 2.5 Ghz (C/C++)
171 RTL3D 77.63 % 76.95 % 71.17 % 0.02 s GPU @ 2.5 Ghz (Python)
172 avodC 77.54 % 86.86 % 70.00 % 0.1 s GPU @ 2.5 Ghz (Python)
173 MonoGRNet code 77.46 % 87.23 % 61.12 % 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.
174 spLBP 77.39 % 80.16 % 60.59 % 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.
175 SceneNet 77.34 % 87.90 % 68.38 % 0.03 s GPU @ 2.5 Ghz (C/C++)
176 FailNet-LIDAR
This method makes use of Velodyne laser scans.
77.03 % 71.93 % 71.79 % 0.1 s 1 core @ 2.5 Ghz (Python)
177 CLF3D
This method makes use of Velodyne laser scans.
77.00 % 84.51 % 67.81 % 0.13 s GPU @ 2.5 Ghz (Python)
178 MTDP 76.91 % 84.24 % 67.91 % 0.15 s GPU @ 2.0 Ghz (Python)
179 yolov3_warp 76.73 % 89.13 % 67.70 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
180 FailNet-Fusion
This method makes use of Velodyne laser scans.
76.69 % 70.11 % 71.40 % 0.1 s 1 core @ 2.5 Ghz (Python)
181 Reinspect code 76.65 % 88.36 % 66.56 % 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.
182 Regionlets 76.56 % 86.50 % 59.82 % 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.
183 AOG code 75.97 % 85.58 % 60.96 % 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.
184 GS3D 75.84 % 83.92 % 60.24 % 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.
185 3D FCN
This method makes use of Velodyne laser scans.
75.83 % 85.54 % 68.30 % >5 s 1 core @ 2.5 Ghz (C/C++)
B. Li: 3D Fully Convolutional Network for Vehicle Detection in Point Cloud. IROS 2017.
186 3D-SSMFCNN code 75.78 % 75.51 % 67.75 % 0.1 s GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.
187 3DVP code 75.77 % 81.46 % 65.38 % 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.
188 Pose-RCNN 75.74 % 88.89 % 61.86 % 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.
189 SubCat code 75.46 % 81.45 % 59.71 % 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.
190 myfaster-rcnn-v1.5 75.29 % 88.35 % 60.91 % 0.1 s 1 core @ 2.5 Ghz (Python)
191 multi-task CNN 75.21 % 83.45 % 66.89 % 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.
192 Multi-task DG 75.21 % 87.87 % 66.76 % 0.06 s GPU @ 2.5 Ghz (Python)
193 A3DODWTDA
This method makes use of Velodyne laser scans.
code 74.71 % 78.21 % 66.70 % 0.08 s GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
194 FD2 74.68 % 87.14 % 65.70 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
195 BdCost+DA+MS 74.07 % 83.02 % 59.06 % TBD s 4 cores @ 2.5 Ghz (Matlab/C++)
196 RFCN 73.56 % 80.70 % 61.94 % 0.2 s 4 cores @ 2.5 Ghz (Python)
197 VoxelNet(Unofficial) 73.39 % 79.27 % 65.61 % 0.5 s GPU @ 2.0 Ghz (Python)
198 3DVSSD 73.39 % 84.39 % 65.64 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
199 bin 73.31 % 76.05 % 63.76 % 15ms s GPU @ >3.5 Ghz (Python)
200 yolo800 73.00 % 76.45 % 64.68 % 0.13 s 4 cores @ 2.5 Ghz (Python)
201 ResNet-RRC (Noised) 71.81 % 78.97 % 63.57 % .057 s GPU @ 1.5 Ghz (Python + C/C++)
202 OC Stereo
This method uses stereo information.
70.88 % 86.95 % 61.74 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
203 Int-YOLO code 70.65 % 74.76 % 63.70 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
204 MV-RGBD-RF
This method makes use of Velodyne laser scans.
69.92 % 76.49 % 57.47 % 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.
205 AOG-View 69.89 % 84.29 % 57.25 % 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.
206 ROI-10D 69.64 % 75.33 % 61.18 % 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.
207 fasterrcnn 68.76 % 73.64 % 59.72 % 0.2 s 4 cores @ 2.5 Ghz (Python)
208 MF3D 68.72 % 88.46 % 58.70 % 0.03 s GPU @ 2.5 Ghz (C/C++)
209 myfaster-rcnn 68.69 % 89.59 % 58.39 % 0.01 s 1 core @ 2.5 Ghz (Python)
210 Vote3Deep
This method makes use of Velodyne laser scans.
68.39 % 76.95 % 63.22 % 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.
211 Pseudo-LiDAR
This method uses stereo information.
code 67.96 % 85.08 % 59.55 % 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.
212 GPVL 67.89 % 77.76 % 58.23 % 10 s 1 core @ 2.5 Ghz (C/C++)
213 RFBnet 67.86 % 82.31 % 59.95 % 0.2 s 4 cores @ 2.5 Ghz (Python)
214 Fast-SSD 67.17 % 83.89 % 59.09 % 0.06 s GTX650Ti
215 BdCost48LDCF code 67.08 % 77.93 % 51.15 % 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.
216 SA_3D 66.69 % 86.36 % 55.18 % 0.3 s GPU @ 2.5 Ghz (Python)
217 OC-DPM 66.45 % 76.16 % 53.70 % 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.
218 mymask-rcnn 66.31 % 86.32 % 54.33 % 0.3 s 1 core @ 2.5 Ghz (Python)
219 DPM-VOC+VP 66.25 % 80.45 % 49.86 % 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.
220 BdCost48-25C 65.95 % 78.21 % 51.23 % 4 s 1 core @ 2.5 Ghz (C/C++)
221 65.93 % 78.51 % 57.77 %
222 MDPM-un-BB 64.20 % 77.32 % 50.18 % 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.
223 PDV-Subcat 63.15 % 77.33 % 49.75 % 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.
224 E-VoxelNet 63.00 % 70.24 % 55.94 % 0.1 s GPU @ 2.5 Ghz (Python)
225 Lidar_ROI+Yolo(UJS) 62.71 % 70.58 % 55.17 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
226 GNN 62.59 % 76.03 % 50.18 % 0.2 s 1 core @ 2.5 Ghz (Python)
227 MODet
This method makes use of Velodyne laser scans.
61.84 % 67.21 % 61.57 % 0.05 s GTX1080Ti
228 yl_net 61.01 % 66.08 % 61.29 % 0.03 s GPU @ 2.5 Ghz (Python)
229 DPM-C8B1
This method uses stereo information.
60.99 % 74.95 % 47.16 % 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.
230 tiny_rfdet code 60.89 % 66.76 % 57.88 % 0.01 s GPU @ 2.5 Ghz (Python)
231 RADNet-Mono 60.64 % 68.10 % 55.25 % 0.1 s 1 core @ 2.5 Ghz (Python)
232 SubCat48LDCF code 60.53 % 78.16 % 43.66 % 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.
233 SAMME48LDCF code 58.50 % 76.22 % 47.50 % 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.
234 monoref3d 58.27 % 74.15 % 49.71 % 0.1 s 1 core @ 2.5 Ghz (Python)
235 ref3D 58.27 % 74.15 % 49.71 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
236 BirdNet
This method makes use of Velodyne laser scans.
57.47 % 78.18 % 56.66 % 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.
237 100Frcnn 57.47 % 81.09 % 48.37 % 2 s 4 cores @ 2.5 Ghz (Python + C/C++)
238 LSVM-MDPM-sv 57.44 % 71.70 % 46.58 % 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.
239 ref3D 57.14 % 74.81 % 47.95 % 0.1 s 1 core @ 2.5 Ghz (Python)
240 mylsi-faster-rcnn 56.13 % 79.05 % 48.48 % 0.3 s 1 core @ 2.5 Ghz (Python)
241 LSVM-MDPM-us code 56.10 % 70.52 % 42.87 % 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.
242 ACF-SC 55.76 % 69.76 % 46.27 % <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.
243 Mono3D_PLiDAR code 54.41 % 80.29 % 46.67 % 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.
244 VeloFCN
This method makes use of Velodyne laser scans.
53.45 % 70.68 % 46.90 % 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 .
245 ACF 52.81 % 62.82 % 43.89 % 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). .
246 RT3DStereo
This method uses stereo information.
48.92 % 57.56 % 42.81 % 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.
247 FailNet-Mono 48.91 % 57.86 % 42.95 % 0.1 s 1 core @ 2.5 Ghz (Python)
248 TopNet-HighRes
This method makes use of Velodyne laser scans.
48.87 % 59.77 % 43.15 % 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.
249 DLMB
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
48.76 % 59.32 % 43.19 % 0.03 s 8 cores @ 3.5 Ghz (C/C++)
250 Vote3D
This method makes use of Velodyne laser scans.
48.05 % 56.66 % 42.64 % 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.
251 Multimodal Detection
This method makes use of Velodyne laser scans.
code 46.77 % 64.04 % 39.38 % 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.
252 softyolo 45.77 % 62.82 % 39.77 % 0.16 s 4 cores @ 2.5 Ghz (Python)
253 rpn 43.99 % 65.47 % 36.33 % 0.01 s 1 core @ 2.5 Ghz (Python)
254 RT3D
This method makes use of Velodyne laser scans.
39.71 % 49.96 % 41.47 % 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.
255 KD53-20 37.82 % 52.30 % 32.71 % 0.19 s 4 cores @ 2.5 Ghz (Python)
256 DT3D 35.98 % 49.23 % 31.78 % 0,21s GPU @ 2.5 Ghz (Python)
257 Licar
This method makes use of Velodyne laser scans.
33.89 % 41.60 % 35.17 % 0.09 s GPU @ 2.0 Ghz (Python)
258 SAIC-SA-3D
This method makes use of Velodyne laser scans.
31.82 % 42.47 % 31.20 % 0.05 s GPU @ 2.5 Ghz (Python)
259 CSoR
This method makes use of Velodyne laser scans.
code 26.13 % 35.24 % 22.69 % 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.
260 R-CNN_VGG 26.04 % 32.23 % 20.93 % 10 s GPU @ 2.5 Ghz (Matlab + C/C++)
261 FCN-Depth code 25.66 % 50.55 % 24.95 % 1 s GPU @ 1.5 Ghz (Matlab + C/C++)
262 mBoW
This method makes use of Velodyne laser scans.
23.76 % 37.63 % 18.44 % 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.
263 DepthCN
This method makes use of Velodyne laser scans.
code 23.21 % 37.59 % 18.00 % 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.
264 DLnet 20.30 % 23.46 % 17.96 % 0.3 s 4 cores @ 2.5 Ghz (C/C++)
265 YOLOv2 code 19.31 % 28.37 % 15.94 % 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.
266 TopNet-UncEst
This method makes use of Velodyne laser scans.
13.77 % 10.35 % 13.49 % 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.
267 TopNet-Retina
This method makes use of Velodyne laser scans.
6.36 % 7.79 % 6.31 % 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.
268 FCPP 0.20 % 0.02 % 0.22 % 0.02 s 1 core @ 2.0 Ghz (Python + C/C++)
269 TopNet-DecayRate
This method makes use of Velodyne laser scans.
0.04 % 0.04 % 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.
270 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.
271 SN-net 0.00 % 0.00 % 0.00 % 0.8 s GPU @ 2.5 Ghz (Python + C/C++)
272 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 81.73 % 88.27 % 75.29 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
2 Alibaba-CityBrain 80.90 % 88.13 % 74.08 % 1.5 s GPU @ 2.5 Ghz (Python + C/C++)
3 ExtAtt 79.63 % 87.95 % 74.78 % 1.2 s GPU @ 2.5 Ghz (Python + C/C++)
4 DGIST-CellBox 79.54 % 87.77 % 75.70 % 0.1 s GPU @ 2.5 Ghz (Java + C/C++)
5 DH-ARI 78.29 % 87.43 % 69.91 % 3.6 s GPU @ 2.5 Ghz (Python + C/C++)
6 EM-FPS 77.61 % 84.93 % 72.52 % 0.15 s GPU @ 1.5 Ghz (Python + C/C++)
7 F-PointNet
This method makes use of Velodyne laser scans.
code 77.25 % 87.81 % 74.46 % 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.
8 TuSimple code 77.04 % 86.78 % 72.40 % 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 THICV-YDM 76.91 % 87.27 % 69.02 % 0.06 s GPU @ 2.5 Ghz (Python)
10 Argus_detection_v1 75.51 % 83.49 % 71.24 % 0.25 s GPU @ 1.5 Ghz (C/C++)
11 RRC code 75.33 % 84.16 % 70.39 % 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 VCTNet 75.22 % 85.49 % 71.55 % 0.02 s GPU @ 1.5 Ghz (C/C++)
13 MHN 74.60 % 85.81 % 68.94 % 0.39 s GPU @ 2.5 Ghz (Python)
J. Cao, Y. Pang and X. Li: Exploring Multi-Branch and High-Level Semantic Networks for Improving Pedestrian Detection. arXiv:1804.00872 2018.
14 Aston-EAS 74.52 % 85.12 % 69.35 % 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.
15 ECP Faster R-CNN 74.27 % 84.12 % 70.06 % 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.
16 FFNet code 74.24 % 85.42 % 69.34 % 0.22 s GPU @ 1.5 Ghz (Python)
17 SJTU-HW 74.24 % 85.42 % 69.34 % 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.
18 CLA 74.03 % 84.26 % 68.45 % 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.
19 Multi-3D
This method makes use of Velodyne laser scans.
74.00 % 84.01 % 68.74 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
20 ECV-NET 73.74 % 84.58 % 66.35 % 0.4 s GPU @ 2.5 Ghz (C/C++)
21 BOE_IOT_AIBD 73.73 % 84.67 % 68.71 % 0.8 s GPU @ 2.5 Ghz (Python)
22 MS-CNN code 73.62 % 83.70 % 68.28 % 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.
23 SAITv1 72.61 % 84.79 % 67.94 % 0.15 s GPU @ 2.5 Ghz (C/C++)
24 F-ConvNet
This method makes use of Velodyne laser scans.
72.37 % 79.98 % 66.61 % 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.
25 Sogo_MM 71.84 % 83.45 % 67.00 % 1.5 s GPU @ 2.5 Ghz (C/C++)
26 GN 71.55 % 80.73 % 64.82 % 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.
27 SubCNN 71.34 % 83.17 % 66.36 % 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.
28 VMVS
This method makes use of Velodyne laser scans.
70.89 % 81.11 % 67.23 % 0.25 s GPU @ 2.5 Ghz (Python)
J. Ku, A. Pon, S. Walsh and S. Waslander: Improving 3D object detection for pedestrians with virtual multi-view synthesis orientation estimation. IROS 2019.
29 IVA code 70.63 % 83.03 % 64.68 % 0.4 s GPU @ 2.5 Ghz (C/C++)
Y. Zhu, J. Wang, C. Zhao, H. Guo and H. Lu: Scale-adaptive Deconvolutional Regression Network for Pedestrian Detection. ACCV 2016.
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 2015.
30 SDP+RPN 70.20 % 79.98 % 64.84 % 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.
31 MM-MRFC
This method uses optical flow information.
This method makes use of Velodyne laser scans.
69.96 % 82.37 % 64.76 % 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.
32 TridentNet 69.58 % 81.27 % 64.18 % 0.2 s GPU @ 2.5 Ghz (Python)
33 MDC
This method makes use of Velodyne laser scans.
69.58 % 86.37 % 68.44 % 0.17 s GPU @ 2.5 Ghz (Python)
34 MonoPSR 68.91 % 85.93 % 60.83 % 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.
35 YOLOv3.5 68.33 % 79.61 % 60.85 % 0.05 s GPU @ 2.5 Ghz (Python)
36 HBA-RCNN 68.26 % 77.76 % 62.86 % 0.4 s 1 core @ 2.5 Ghz (C/C++)
37 DA 67.89 % 79.91 % 64.83 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
38 3DOP
This method uses stereo information.
code 67.46 % 82.36 % 64.71 % 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 DeepStereoOP 67.32 % 82.50 % 65.14 % 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.
40 sensekitti code 67.28 % 80.12 % 62.25 % 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.
41 ODES code 67.25 % 77.95 % 62.28 % 0.02 s GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
42 Mono3D code 66.66 % 77.30 % 63.44 % 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.
43 Faster R-CNN code 65.91 % 78.35 % 61.19 % 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.
44 AtrousDet 65.18 % 77.19 % 58.14 % 0.05 s TITAN X
45 SDP+CRC (ft) 64.25 % 77.81 % 59.31 % 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.
46 PCN 63.48 % 77.88 % 58.59 % 0.6 s
47 Pose-RCNN 63.38 % 77.69 % 57.42 % 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.
48 CFM 63.26 % 74.21 % 56.44 % <2 s GPU @ 2.5 Ghz (Matlab + C/C++)
Q. Hu, P. Wang, C. Shen, A. Hengel and F. Porikli: Pushing the Limits of Deep CNNs for Pedestrian Detection. IEEE Transactions on Circuits and Systems for Video Technology 2017.
49 IPOD 63.07 % 73.28 % 56.71 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
50 ALV303 61.77 % 69.13 % 54.54 % 0.2 s GPU @ 2.5 Ghz (Matlab + C/C++)
51 RPN+BF code 61.29 % 75.58 % 56.08 % 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.
52 ReSqueeze 61.25 % 72.78 % 57.43 % 0.03 s GPU @ >3.5 Ghz (Python)
53 Regionlets 61.16 % 72.96 % 55.22 % 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.
54 merge12-12 60.66 % 78.15 % 58.67 % 0.2 s 4 cores @ 2.5 Ghz (Python)
55 cascadercnn 60.64 % 77.88 % 52.69 % 0.36 s 4 cores @ 2.5 Ghz (Python)
56 cas+res+soft 60.60 % 77.96 % 58.56 % 0.2 s 4 cores @ 2.5 Ghz (Python)
57 bin 60.54 % 70.13 % 56.55 % 15ms s GPU @ >3.5 Ghz (Python)
58 cas_retina 60.30 % 77.71 % 58.34 % 0.2 s 4 cores @ 2.5 Ghz (Python)
59 TANet 60.01 % 69.34 % 58.45 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
60 A-VoxelNet 59.98 % 69.26 % 58.48 % 0.029 s GPU @ 2.5 Ghz (Python)
61 cas_retina_1_13 59.87 % 77.11 % 57.81 % 0.03 s 4 cores @ 2.5 Ghz (Python)
62 anm 59.21 % 75.51 % 56.49 % 3 s 1 core @ 2.5 Ghz (C/C++)
63 CompACT-Deep 58.73 % 69.70 % 52.69 % 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.
64 DeepParts 58.68 % 70.46 % 52.73 % ~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.
65 AVOD-FPN
This method makes use of Velodyne laser scans.
code 58.42 % 67.32 % 57.44 % 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.
66 ARPNET 58.23 % 68.51 % 53.34 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
67 LPN 58.18 % 70.54 % 54.18 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
68 SA_3D 57.85 % 68.55 % 50.45 % 0.3 s GPU @ 2.5 Ghz (Python)
69 mymask-rcnn 57.79 % 73.18 % 55.11 % 0.3 s 1 core @ 2.5 Ghz (Python)
70 Tencent_ADlab_Lidar
This method makes use of Velodyne laser scans.
57.23 % 66.08 % 55.10 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
71 FilteredICF 57.12 % 69.05 % 51.46 % ~ 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.
72 ZKNet 57.11 % 70.20 % 52.15 % 0.01 s GPU @ 2.0 Ghz (Python)
73 LDAM 57.10 % 64.61 % 54.99 % 0.05 s GPU @ 2.5 Ghz (C/C++)
74 RFCN 56.91 % 71.17 % 50.06 % 0.2 s 4 cores @ 2.5 Ghz (Python)
75 FRCNN+Or code 56.78 % 71.18 % 52.86 % 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.
76 FD2 56.68 % 71.09 % 51.65 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
77 MV-RGBD-RF
This method makes use of Velodyne laser scans.
56.59 % 73.05 % 49.63 % 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.
78 CHTTL MMF 56.01 % 73.22 % 50.26 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
79 RFCN_RFB 55.86 % 69.32 % 49.18 % 0.2 s 4 cores @ 2.5 Ghz (Python)
80 SECOND code 55.74 % 65.73 % 49.08 % 38 ms 1080Ti
Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.
81 PointPillars
This method makes use of Velodyne laser scans.
code 55.68 % 64.66 % 53.93 % 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.
82 yolo800 55.49 % 71.11 % 53.92 % 0.13 s 4 cores @ 2.5 Ghz (Python)
83 STD 55.44 % 69.09 % 53.46 % 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.
84 Vote3Deep
This method makes use of Velodyne laser scans.
55.38 % 67.94 % 52.62 % 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.
85 MLOD
This method makes use of Velodyne laser scans.
code 55.36 % 69.42 % 54.23 % 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 CONV-BOX
This method makes use of Velodyne laser scans.
55.23 % 63.98 % 54.18 % 0.2 s Tesla V100
87 epBRM
This method makes use of Velodyne laser scans.
54.62 % 62.46 % 53.30 % 0.10 s 1 core @ 2.5 Ghz (C/C++)
88 TAFT 54.59 % 67.07 % 48.48 % 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.
89 pAUCEnsT 54.58 % 66.11 % 48.49 % 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.
90 SCANet 54.02 % 64.57 % 48.05 % 0.17 s >8 cores @ 2.5 Ghz (Python)
91 NM code 53.98 % 69.06 % 50.76 % 0.01 s GPU @ 2.5 Ghz (Python)
92 fasterrcnn 53.80 % 69.00 % 51.35 % 0.2 s 4 cores @ 2.5 Ghz (Python)
93 NEUAV 53.75 % 68.86 % 48.04 % 0.06 s GPU @ 2.5 Ghz (Python)
94 PDV2 53.74 % 65.71 % 49.47 % 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.
95 PP_v1.0 code 53.59 % 62.16 % 51.51 % 0.02s 1 core @ 2.5 Ghz (C/C++)
96 Shift R-CNN (mono) code 53.33 % 71.11 % 44.71 % 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.
97 MTDP 52.97 % 66.97 % 47.64 % 0.15 s GPU @ 2.0 Ghz (Python)
98 FOFNet
This method makes use of Velodyne laser scans.
52.94 % 62.34 % 47.54 % 0.04 s GPU @ 2.5 Ghz (Python)
99 detectron code 52.42 % 69.89 % 51.70 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
100 centernet 51.75 % 68.65 % 46.89 % 0.01 s GPU @ 2.5 Ghz (Python)
101 SCNet
This method makes use of Velodyne laser scans.
51.41 % 59.98 % 49.97 % 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.
102 Multi-task DG 51.34 % 68.07 % 50.39 % 0.06 s GPU @ 2.5 Ghz (Python)
103 cascade_gw 51.10 % 67.58 % 43.34 % 0.2 s 4 cores @ 2.5 Ghz (Python)
104 YOLOv3+d 51.03 % 67.23 % 48.87 % 0.04 s GPU @ 1.5 Ghz (C/C++)
105 ACFD
This method makes use of Velodyne laser scans.
code 50.91 % 61.59 % 45.51 % 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.
106 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 50.88 % 59.05 % 48.46 % 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.
107 MLF 50.74 % 66.67 % 49.91 % 0.05 s GPU @ 2.0 Ghz (Python)
108 myfaster-rcnn-v1.5 50.62 % 66.91 % 48.16 % 0.1 s 1 core @ 2.5 Ghz (Python)
109 CLF3D
This method makes use of Velodyne laser scans.
50.25 % 66.10 % 48.66 % 0.13 s GPU @ 2.5 Ghz (Python)
110 R-CNN 50.20 % 62.05 % 44.85 % 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.
111 SS3D 49.81 % 59.46 % 42.44 % 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.
112 SeRC 49.81 % 65.31 % 42.40 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
113 Resnet101Faster rcnn 49.64 % 64.97 % 48.47 % 1 s 1 core @ 2.5 Ghz (Python)
114 mylsi-faster-rcnn 49.31 % 64.64 % 45.96 % 0.3 s 1 core @ 2.5 Ghz (Python)
115 Int-YOLO code 48.93 % 64.40 % 48.02 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
116 ELLIOT
This method makes use of Velodyne laser scans.
48.26 % 59.05 % 45.52 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
117 CFR
This method makes use of Velodyne laser scans.
48.16 % 63.07 % 47.51 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
118 ACF 47.29 % 60.11 % 42.90 % 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.
119 Fusion-DPM
This method makes use of Velodyne laser scans.
code 46.67 % 59.38 % 42.05 % ~ 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.
120 ACF-MR 46.23 % 58.85 % 42.10 % 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.
121 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 46.06 % 55.63 % 42.60 % 0.0047s 1 core @ 2.5 Ghz (python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
122 M3D-RPN code 46.02 % 59.82 % 39.31 % 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 .
123 HA-SSVM 45.51 % 58.91 % 41.08 % 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.
124 DPM-VOC+VP 44.86 % 59.60 % 40.37 % 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.
125 Cmerge 44.81 % 62.62 % 44.53 % 0.2 s 4 cores @ 2.5 Ghz (Python)
126 ACF-SC 44.77 % 54.20 % 39.57 % <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.
127 SquaresICF code 44.42 % 57.47 % 40.08 % 1 s GPU @ >3.5 Ghz (C/C++)
R. Benenson, M. Mathias, T. Tuytelaars and L. Gool: Seeking the strongest rigid detector. CVPR 2013.
128 AVOD
This method makes use of Velodyne laser scans.
code 43.49 % 51.64 % 37.79 % 0.08 s Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
129 Retinanet100 42.83 % 52.43 % 35.02 % 0.2 s 4 cores @ 2.5 Ghz (Python)
130 GNN 42.56 % 58.22 % 40.53 % 0.2 s 1 core @ 2.5 Ghz (Python)
131 SubCat 42.34 % 54.06 % 37.95 % 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.
132 myfaster-rcnn 41.91 % 57.22 % 39.62 % 0.01 s 1 core @ 2.5 Ghz (Python)
133 yolov3_warp 41.07 % 56.07 % 39.08 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
134 softyolo 40.78 % 55.95 % 39.57 % 0.16 s 4 cores @ 2.5 Ghz (Python)
135 ACF 40.62 % 49.08 % 36.66 % 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). .
136 multi-task CNN 40.34 % 51.38 % 34.98 % 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.
137 KD53-20 39.90 % 47.15 % 35.32 % 0.19 s 4 cores @ 2.5 Ghz (Python)
138 LSVM-MDPM-sv 39.36 % 51.75 % 35.95 % 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.
139 pedestrian_cnn 39.07 % 53.60 % 37.91 % 1 s 1 core @ 2.5 Ghz (C/C++)
140 Lidar_ROI+Yolo(UJS) 38.76 % 47.11 % 32.33 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
141 LSVM-MDPM-us code 38.35 % 50.01 % 34.78 % 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.
142 37.45 % 45.89 % 35.08 %
143 X_MD 37.38 % 50.17 % 36.49 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
144 anonymous
This method makes use of Velodyne laser scans.
36.65 % 49.15 % 36.18 % 0.75 s GPU @ 3.5 Ghz (C/C++)
145 Complexer-YOLO
This method makes use of Velodyne laser scans.
36.10 % 42.63 % 35.57 % 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.
146 Vote3D
This method makes use of Velodyne laser scans.
35.74 % 44.47 % 33.72 % 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.
147 OC Stereo
This method uses stereo information.
33.13 % 47.80 % 32.53 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
148 rpn 32.79 % 46.95 % 31.70 % 0.01 s 1 core @ 2.5 Ghz (Python)
149 RT3DStereo
This method uses stereo information.
32.01 % 44.54 % 31.50 % 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 mBoW
This method makes use of Velodyne laser scans.
31.37 % 44.36 % 30.62 % 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.
151 BirdNet
This method makes use of Velodyne laser scans.
30.90 % 36.83 % 29.93 % 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.
152 DPM-C8B1
This method uses stereo information.
29.03 % 38.96 % 25.61 % 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 100Frcnn 26.73 % 35.65 % 26.46 % 2 s 4 cores @ 2.5 Ghz (Python + C/C++)
154 R-CNN_VGG 23.16 % 28.95 % 22.17 % 10 s GPU @ 2.5 Ghz (Matlab + C/C++)
155 TopNet-Retina
This method makes use of Velodyne laser scans.
19.67 % 25.17 % 16.33 % 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.
156 DT3D 19.19 % 27.02 % 18.98 % 0,21s GPU @ 2.5 Ghz (Python)
157 TopNet-HighRes
This method makes use of Velodyne laser scans.
17.57 % 22.98 % 17.35 % 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.
158 YOLOv2 code 16.19 % 20.80 % 15.43 % 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.
159 BIP-HETERO 13.38 % 14.85 % 13.25 % ~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.
160 TopNet-UncEst
This method makes use of Velodyne laser scans.
10.91 % 15.55 % 10.05 % 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.
161 softretina 0.93 % 0.68 % 0.95 % 0.16 s 4 cores @ 2.5 Ghz (Python)
162 JSyolo 0.44 % 0.35 % 0.45 % 0.16 s 4 cores @ 2.5 Ghz (Python)
163 TopNet-DecayRate
This method makes use of Velodyne laser scans.
0.04 % 0.02 % 0.05 % 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.
164 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 79.24 % 84.28 % 71.22 % 0.15 s GPU @ 1.5 Ghz (Python + C/C++)
2 FichaDL 78.56 % 86.23 % 68.99 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
3 MMLab-PartA^2
This method makes use of Velodyne laser scans.
77.48 % 85.54 % 70.35 % 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.
4 RRC code 76.49 % 84.96 % 65.46 % 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.
5 F-ConvNet
This method makes use of Velodyne laser scans.
76.18 % 84.75 % 67.55 % 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 VCTNet 75.91 % 83.20 % 67.81 % 0.02 s GPU @ 1.5 Ghz (C/C++)
7 SAITv1 75.83 % 83.99 % 66.45 % 0.15 s GPU @ 2.5 Ghz (C/C++)
8 Multi-3D
This method makes use of Velodyne laser scans.
74.88 % 82.13 % 65.53 % 0.15 s 1 core @ 2.5 Ghz (C/C++)
9 CLA 74.68 % 82.42 % 65.11 % 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.
10 MS-CNN code 74.45 % 82.34 % 64.91 % 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.
11 TuSimple code 74.26 % 81.38 % 64.88 % 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.
12 ExtAtt 74.25 % 84.04 % 65.03 % 1.2 s GPU @ 2.5 Ghz (Python + C/C++)
13 Deep3DBox 73.48 % 82.65 % 64.11 % 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.
14 SDP+RPN 73.08 % 81.05 % 64.88 % 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.
15 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 72.94 % 83.64 % 66.07 % 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.
16 ECV-NET 72.73 % 82.62 % 62.82 % 0.4 s GPU @ 2.5 Ghz (C/C++)
17 STD 72.63 % 82.18 % 65.16 % 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.
18 sensekitti code 72.50 % 81.76 % 64.00 % 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.
19 F-PointNet
This method makes use of Velodyne laser scans.
code 72.25 % 84.90 % 65.14 % 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.
20 BOE_IOT_AIBD 71.61 % 82.63 % 63.67 % 0.8 s GPU @ 2.5 Ghz (Python)
21 FOFNet
This method makes use of Velodyne laser scans.
71.45 % 83.39 % 64.44 % 0.04 s GPU @ 2.5 Ghz (Python)
22 ARPNET 71.22 % 82.93 % 64.81 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
23 SubCNN 70.77 % 77.82 % 62.71 % 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.
24 Sogo_MM 70.72 % 77.57 % 62.23 % 1.5 s GPU @ 2.5 Ghz (C/C++)
25 ODES code 69.80 % 78.51 % 61.32 % 0.02 s GPU @ 2.5 Ghz (Python)
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26 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 69.46 % 81.27 % 62.82 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
27 TridentNet 69.06 % 80.64 % 60.06 % 0.2 s GPU @ 2.5 Ghz (Python)
28 MonoPSR 68.99 % 79.80 % 60.19 % 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 LDAM 68.98 % 78.43 % 63.52 % 0.05 s GPU @ 2.5 Ghz (C/C++)
30 MDC
This method makes use of Velodyne laser scans.
68.84 % 79.81 % 60.24 % 0.17 s GPU @ 2.5 Ghz (Python)
31 3DOP
This method uses stereo information.
code 68.81 % 80.17 % 61.36 % 3s GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
32 PointPillars
This method makes use of Velodyne laser scans.
code 68.57 % 82.59 % 62.37 % 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.
33 DGIST-CellBox 68.23 % 80.46 % 61.37 % 0.1 s GPU @ 2.5 Ghz (Java + C/C++)
34 Pose-RCNN 68.04 % 80.19 % 59.95 % 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.
35 Vote3Deep
This method makes use of Velodyne laser scans.
67.96 % 76.49 % 62.88 % 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.
36 TANet 67.93 % 80.54 % 61.57 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
37 Tencent_ADlab_Lidar
This method makes use of Velodyne laser scans.
67.37 % 81.56 % 61.28 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
38 IVA code 67.36 % 77.63 % 59.62 % 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.
39 A-VoxelNet 67.13 % 80.77 % 60.37 % 0.029 s GPU @ 2.5 Ghz (Python)
40 epBRM
This method makes use of Velodyne laser scans.
66.04 % 77.78 % 60.39 % 0.10 s 1 core @ 2.5 Ghz (C/C++)
41 DeepStereoOP 65.72 % 77.00 % 57.74 % 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.
42 IPOD 65.28 % 82.90 % 57.63 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
43 Mono3D code 63.85 % 75.22 % 58.96 % 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.
44 CONV-BOX
This method makes use of Velodyne laser scans.
63.84 % 72.62 % 56.69 % 0.2 s Tesla V100
45 DA 63.58 % 79.36 % 56.80 % 0.08 s 1 core @ 2.5 Ghz (C/C++)
46 SCANet 63.26 % 73.72 % 56.41 % 0.17 s >8 cores @ 2.5 Ghz (Python)
47 AtrousDet 62.85 % 76.07 % 55.12 % 0.05 s TITAN X
48 Faster R-CNN code 62.81 % 71.41 % 55.44 % 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.
49 MLF 62.71 % 81.16 % 53.86 % 0.05 s GPU @ 2.0 Ghz (Python)
50 SCNet
This method makes use of Velodyne laser scans.
61.49 % 77.52 % 57.57 % 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.
51 cas+res+soft 60.88 % 75.24 % 53.58 % 0.2 s 4 cores @ 2.5 Ghz (Python)
52 SDP+CRC (ft) 60.87 % 74.31 % 53.95 % 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.
53 merge12-12 60.83 % 75.12 % 53.69 % 0.2 s 4 cores @ 2.5 Ghz (Python)
54 ELLIOT
This method makes use of Velodyne laser scans.
60.04 % 77.40 % 55.15 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
55 PP_v1.0 code 59.92 % 75.52 % 53.73 % 0.02s 1 core @ 2.5 Ghz (C/C++)
56 AVOD-FPN
This method makes use of Velodyne laser scans.
code 59.32 % 68.65 % 55.82 % 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.
57 SECOND code 58.94 % 81.96 % 57.20 % 38 ms 1080Ti
Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.
58 Regionlets 58.69 % 70.09 % 51.81 % 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.
59 YOLOv3.5 58.23 % 77.33 % 50.68 % 0.05 s GPU @ 2.5 Ghz (Python)
60 CFR
This method makes use of Velodyne laser scans.
58.19 % 74.83 % 56.15 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
61 cascadercnn 58.09 % 75.56 % 50.19 % 0.36 s 4 cores @ 2.5 Ghz (Python)
62 Complexer-YOLO
This method makes use of Velodyne laser scans.
57.53 % 65.82 % 57.47 % 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.
63 FRCNN+Or code 57.37 % 70.05 % 51.00 % 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.
64 bin 57.13 % 63.05 % 50.64 % 15ms s GPU @ >3.5 Ghz (Python)
65 cas_retina 56.46 % 72.52 % 52.63 % 0.2 s 4 cores @ 2.5 Ghz (Python)
66 AVOD
This method makes use of Velodyne laser scans.
code 56.01 % 65.72 % 48.89 % 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.
67 cas_retina_1_13 55.81 % 71.59 % 52.16 % 0.03 s 4 cores @ 2.5 Ghz (Python)
68 Multi-task DG 55.38 % 73.83 % 47.82 % 0.06 s GPU @ 2.5 Ghz (Python)
69 ReSqueeze 54.93 % 68.34 % 49.19 % 0.03 s GPU @ >3.5 Ghz (Python)
70 MLOD
This method makes use of Velodyne laser scans.
code 51.99 % 69.13 % 43.74 % 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.
71 anm 50.54 % 67.40 % 45.22 % 3 s 1 core @ 2.5 Ghz (C/C++)
72 ZKNet 50.24 % 66.44 % 44.19 % 0.01 s GPU @ 2.0 Ghz (Python)
73 LPN 50.02 % 65.33 % 44.85 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
74 NEUAV 49.75 % 68.20 % 43.77 % 0.06 s GPU @ 2.5 Ghz (Python)
75 yolo800 49.15 % 64.64 % 43.58 % 0.13 s 4 cores @ 2.5 Ghz (Python)
76 BirdNet
This method makes use of Velodyne laser scans.
49.04 % 64.88 % 46.61 % 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.
77 mylsi-faster-rcnn 48.85 % 67.93 % 43.77 % 0.3 s 1 core @ 2.5 Ghz (Python)
78 fasterrcnn 48.81 % 64.40 % 42.74 % 0.2 s 4 cores @ 2.5 Ghz (Python)
79 X_MD 48.07 % 63.46 % 40.76 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
80 detectron code 48.06 % 64.73 % 40.75 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
81 RFCN 47.61 % 62.17 % 43.74 % 0.2 s 4 cores @ 2.5 Ghz (Python)
82 CLF3D
This method makes use of Velodyne laser scans.
47.53 % 65.31 % 40.23 % 0.13 s GPU @ 2.5 Ghz (Python)
83 NM code 47.20 % 60.64 % 42.96 % 0.01 s GPU @ 2.5 Ghz (Python)
84 RFCN_RFB 45.36 % 59.49 % 41.63 % 0.2 s 4 cores @ 2.5 Ghz (Python)
85 cascade_gw 45.00 % 63.14 % 38.81 % 0.2 s 4 cores @ 2.5 Ghz (Python)
86 centernet 44.50 % 59.28 % 39.75 % 0.01 s GPU @ 2.5 Ghz (Python)
87 myfaster-rcnn-v1.5 44.32 % 59.99 % 39.88 % 0.1 s 1 core @ 2.5 Ghz (Python)
88 FD2 44.29 % 62.32 % 40.65 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
89 SeRC 44.28 % 55.81 % 38.50 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
90 Cmerge 43.85 % 61.60 % 42.60 % 0.2 s 4 cores @ 2.5 Ghz (Python)
91 Int-YOLO code 43.30 % 52.88 % 36.57 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
92 MTDP 43.08 % 54.53 % 38.79 % 0.15 s GPU @ 2.0 Ghz (Python)
93 GNN 42.65 % 59.43 % 37.72 % 0.2 s 1 core @ 2.5 Ghz (Python)
94 MV-RGBD-RF
This method makes use of Velodyne laser scans.
42.61 % 51.46 % 37.42 % 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.
95 YOLOv3+d 42.60 % 59.08 % 40.77 % 0.04 s GPU @ 1.5 Ghz (C/C++)
96 Shift R-CNN (mono) code 42.30 % 65.56 % 41.40 % 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.
97 M3D-RPN code 41.12 % 63.69 % 39.95 % 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 .
98 myfaster-rcnn 38.72 % 54.28 % 34.63 % 0.01 s 1 core @ 2.5 Ghz (Python)
99 SS3D 37.90 % 53.79 % 35.12 % 48 ms Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
100 pAUCEnsT 37.88 % 52.28 % 33.38 % 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.
101 Retinanet100 37.54 % 46.39 % 30.82 % 0.2 s 4 cores @ 2.5 Ghz (Python)
102 TopNet-Retina
This method makes use of Velodyne laser scans.
35.20 % 50.28 % 34.11 % 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.
103 yolov3_warp 34.39 % 48.21 % 29.30 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
104 softyolo 31.30 % 45.16 % 27.38 % 0.16 s 4 cores @ 2.5 Ghz (Python)
105 Vote3D
This method makes use of Velodyne laser scans.
31.24 % 41.45 % 28.60 % 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.
106 DPM-VOC+VP 31.16 % 43.65 % 28.29 % 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.
107 LSVM-MDPM-us code 30.81 % 40.31 % 28.17 % 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.
108 OC Stereo
This method uses stereo information.
30.10 % 44.38 % 29.08 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
109 100Frcnn 29.95 % 44.60 % 27.70 % 2 s 4 cores @ 2.5 Ghz (Python + C/C++)
110 LSVM-MDPM-sv 29.24 % 37.71 % 27.52 % 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.
111 DPM-C8B1
This method uses stereo information.
29.04 % 43.28 % 26.20 % 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.
112 R-CNN_VGG 28.79 % 37.71 % 25.82 % 10 s GPU @ 2.5 Ghz (Matlab + C/C++)
113 rpn 28.65 % 37.40 % 23.50 % 0.01 s 1 core @ 2.5 Ghz (Python)
114 Lidar_ROI+Yolo(UJS) 27.21 % 39.41 % 26.12 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
115 mBoW
This method makes use of Velodyne laser scans.
21.62 % 28.19 % 20.93 % 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.
116 DT3D 20.65 % 31.29 % 20.73 % 0,21s GPU @ 2.5 Ghz (Python)
117 mymask-rcnn 19.58 % 23.22 % 18.87 % 0.3 s 1 core @ 2.5 Ghz (Python)
118 TopNet-HighRes
This method makes use of Velodyne laser scans.
19.15 % 29.34 % 19.69 % 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.
119 SA_3D 18.51 % 22.79 % 15.33 % 0.3 s GPU @ 2.5 Ghz (Python)
120 KD53-20 17.71 % 23.15 % 17.30 % 0.19 s 4 cores @ 2.5 Ghz (Python)
121 RT3DStereo
This method uses stereo information.
17.66 % 23.73 % 11.94 % 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.
122 TopNet-UncEst
This method makes use of Velodyne laser scans.
16.21 % 19.18 % 15.99 % 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.
123 YOLOv2 code 4.55 % 4.55 % 4.55 % 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.
124 TopNet-DecayRate
This method makes use of Velodyne laser scans.
1.01 % 0.04 % 1.01 % 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.
125 softretina 0.44 % 0.29 % 0.22 % 0.16 s 4 cores @ 2.5 Ghz (Python)
126 JSyolo 0.22 % 0.22 % 0.22 % 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 ELE 90.08 % 96.59 % 88.95 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
2 MVRA + I-FRCNN+ 89.93 % 90.60 % 79.78 % 0.18 s GPU @ 2.5 Ghz (Python)
3 Deep MANTA 89.86 % 97.19 % 80.39 % 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.
4 THICV-YDM 89.66 % 90.22 % 79.89 % 0.06 s GPU @ 2.5 Ghz (Python)
5 F-ConvNet
This method makes use of Velodyne laser scans.
89.60 % 90.41 % 80.39 % 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 RGB3D
This method makes use of Velodyne laser scans.
89.60 % 90.74 % 87.98 % 0.39 s GPU @ 2.5 Ghz (Python)
7 PointRCNN-deprecated
This method makes use of Velodyne laser scans.
89.55 % 90.76 % 80.76 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
8 Patches
This method makes use of Velodyne laser scans.
89.48 % 90.73 % 87.18 % 0.15 s GPU @ 2.0 Ghz
9 HRI-FusionRCNN 89.48 % 90.49 % 80.40 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
10 Patches - EMP
This method makes use of Velodyne laser scans.
89.43 % 94.61 % 87.81 % 0.5 s GPU @ 2.5 Ghz (Python)
11 MLF 89.41 % 90.35 % 80.20 % 0.05 s GPU @ 2.0 Ghz (Python)
12 HRI-VoxelFPN 89.27 % 90.43 % 80.31 % 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.
13 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 89.22 % 90.73 % 85.53 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
14 Alibaba-AILabsX
This method makes use of Velodyne laser scans.
89.20 % 90.25 % 80.36 % 0.05 s 1 core @ >3.5 Ghz (C/C++)
15 PI-RCNN 89.17 % 90.66 % 86.76 % 0.1 s 1 core @ 2.5 Ghz (Python)
16 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 89.16 % 90.66 % 86.24 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
17 SRF 89.00 % 90.33 % 80.36 % 0.05 s GPU @ 2.5 Ghz (Python + C/C++)
18 MMLab-PartA^2
This method makes use of Velodyne laser scans.
88.98 % 90.41 % 87.08 % 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.
19 SegVoxelNet 88.88 % 90.50 % 87.34 % 0.04 s 1 core @ 2.5 Ghz (Python)
20 PFPN 88.83 % 90.30 % 79.99 % 0.02 s 4 cores @ >3.5 Ghz (Python)
21 PointPillars
This method makes use of Velodyne laser scans.
code 88.76 % 90.19 % 86.38 % 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.
22 MMV 88.74 % 90.38 % 79.99 % 0.4 s GPU @ 2.5 Ghz (C/C++)
23 3D IoU Loss
This method makes use of Velodyne laser scans.
88.72 % 90.08 % 80.06 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
D. Zhou: IoU Loss for 2D/3D Object Detection. International Conference on 3D Vision (3DV) 2019.
24 Sogo_MM 88.72 % 90.67 % 78.95 % 1.5 s GPU @ 2.5 Ghz (C/C++)
25 MVSLN 88.62 % 90.52 % 80.08 % 0.1s s 1 core @ 2.5 Ghz (C/C++)
26 TBA 88.62 % 90.03 % 86.29 % 0.07 s 1 core @ 2.5 Ghz (Python)
27 Deep3DBox 88.56 % 90.39 % 77.17 % 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.
28 PTS
This method makes use of Velodyne laser scans.
code 88.55 % 90.11 % 79.90 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
29 TANet 88.55 % 90.29 % 79.95 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
30 ARPNET 88.52 % 90.00 % 79.85 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
31 NU-optim 88.51 % 89.82 % 87.12 % 0.04 s GPU @ >3.5 Ghz (Python)
32 SubCNN 88.43 % 90.61 % 78.63 % 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.
33 FOFNet
This method makes use of Velodyne laser scans.
88.37 % 90.46 % 79.84 % 0.04 s GPU @ 2.5 Ghz (Python)
34 A-VoxelNet 88.36 % 89.72 % 79.61 % 0.029 s GPU @ 2.5 Ghz (Python)
35 Tencent_ADlab_Lidar
This method makes use of Velodyne laser scans.
88.33 % 90.05 % 84.80 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
36 SECOND-V1.5
This method makes use of Velodyne laser scans.
code 88.33 % 90.15 % 79.65 % 0.04 s GPU @ 2.0 Ghz (Python + C/C++)
37 MPNet
This method makes use of Velodyne laser scans.
88.27 % 90.27 % 85.06 % 0.02 s GPU @ 2.5 Ghz (Python + C/C++)
38 GPP code 87.96 % 90.35 % 78.57 % 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.
39 3DBN
This method makes use of Velodyne laser scans.
87.95 % 89.93 % 79.32 % 0.13s 1080Ti (Python+C/C++)
X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.
40 Shift R-CNN (mono) code 87.91 % 90.27 % 78.72 % 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.
41 MonoPSR 87.83 % 89.88 % 70.48 % 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 SCNet
This method makes use of Velodyne laser scans.
87.83 % 90.13 % 79.48 % 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.
43 PAD 87.73 % 90.06 % 82.30 % 0.15 s 1 core @ 2.5 Ghz (Python)
44 CFR
This method makes use of Velodyne laser scans.
87.67 % 90.26 % 79.02 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
45 PP_v1.0 code 87.61 % 89.97 % 83.02 % 0.02s 1 core @ 2.5 Ghz (C/C++)
46 AVOD
This method makes use of Velodyne laser scans.
code 87.46 % 89.59 % 79.54 % 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.
47 SeoulRobotics-HFD
This method makes use of Velodyne laser scans.
87.34 % 89.88 % 79.32 % 0.035 s GPU (C++)
48 ZongMu-Mono 87.32 % 89.41 % 78.50 % 0.03 s GPU @ 2.5 Ghz (Python + C/C++)
49 AVOD-FPN
This method makes use of Velodyne laser scans.
code 87.13 % 89.95 % 79.74 % 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.
50 DFD 87.01 % 89.72 % 78.98 % 0.05 s GPU @ 2.0 Ghz (Python + C/C++)
51 SECA 86.80 % 89.42 % 78.81 % 0.09 s GPU @ 2.5 Ghz (Python)
52 SCANet 86.65 % 89.06 % 78.67 % 0.09s GPU @ 2.5 Ghz (Python)
53 DeepStereoOP 86.57 % 89.01 % 77.13 % 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.
54 SCANet 86.39 % 89.25 % 78.65 % 0.17 s >8 cores @ 2.5 Ghz (Python)
55 FQNet 86.29 % 89.48 % 74.40 % 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.
56 RAR-Net 86.28 % 89.48 % 74.39 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
57 ELLIOT
This method makes use of Velodyne laser scans.
86.13 % 89.69 % 80.50 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
58 Mono3D code 85.83 % 89.00 % 76.00 % 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.
59 3DOP
This method uses stereo information.
code 85.81 % 88.56 % 76.21 % 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.
60 MBR-SSD 85.03 % 88.10 % 75.92 % 4.0 s GPU @ 2.5 Ghz (Python + C/C++)
61 PL V2 (SDN+GDC)
This method uses stereo information.
This method makes use of Velodyne laser scans.
83.40 % 90.13 % 75.92 % 0.6 s GPU @ 2.5 Ghz (C/C++)
62 StereoFENet
This method uses stereo information.
83.13 % 88.83 % 76.33 % 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. 2019.
63 MonoFENet 82.05 % 88.86 % 75.63 % 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. 2019.
64 M3D-RPN code 81.66 % 83.80 % 65.94 % 0.16 s GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
65 SECOND code 81.31 % 87.84 % 71.95 % 38 ms 1080Ti
Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.
66 X_MD 80.28 % 89.53 % 79.14 % 0.2 s 1 core @ 2.5 Ghz (Python + C/C++)
67 FNV1_Fusion 80.12 % 89.25 % 78.58 % 0.11 s GPU @ 2.5 Ghz (Python)
68 FNV1_RPN 80.10 % 89.27 % 78.66 % 0.12 s 1 core @ 2.5 Ghz (Python + C/C++)
69 SS3D 79.70 % 89.02 % 69.91 % 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.
70 SECA 79.56 % 89.11 % 78.14 % 1 s GPU @ 2.5 Ghz (Python)
71 VSE 79.56 % 89.11 % 78.14 % 0.15 s GPU @ 2.5 Ghz (Python)
72 Complexer-YOLO
This method makes use of Velodyne laser scans.
79.08 % 87.97 % 78.75 % 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.
73 FNV1 78.97 % 88.40 % 76.70 % 0.11 s GPU @ 2.5 Ghz (Python)
74 BS3D 78.68 % 89.28 % 68.52 % 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.
75 Pseudo-LiDAR V2
This method uses stereo information.
code 78.35 % 89.36 % 74.79 % 0.4 s GPU @ 2.5 Ghz (Python)
76 FRCNN+Or code 77.61 % 88.52 % 67.69 % 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.
77 Manhnet 77.21 % 85.58 % 60.50 % 26 ms 1 core @ 2.5 Ghz (C/C++)
78 CLF3D
This method makes use of Velodyne laser scans.
76.50 % 84.35 % 67.12 % 0.13 s GPU @ 2.5 Ghz (Python)
79 avodC 76.30 % 86.31 % 68.71 % 0.1 s GPU @ 2.5 Ghz (Python)
80 3D FCN
This method makes use of Velodyne laser scans.
75.71 % 85.46 % 68.19 % >5 s 1 core @ 2.5 Ghz (C/C++)
B. Li: 3D Fully Convolutional Network for Vehicle Detection in Point Cloud. IROS 2017.
81 3D-SSMFCNN code 75.42 % 75.44 % 67.27 % 0.1 s GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.
82 Pose-RCNN 75.35 % 88.78 % 61.47 % 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.
83 GS3D 75.16 % 83.52 % 59.59 % 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.
84 3DVP code 74.59 % 81.02 % 64.11 % 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.
85 SubCat code 74.42 % 80.74 % 58.83 % 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.
86 BdCost+DA+MS 73.15 % 82.12 % 58.29 % TBD s 4 cores @ 2.5 Ghz (Matlab/C++)
87 OC Stereo
This method uses stereo information.
69.87 % 86.40 % 60.71 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
88 ROI-10D 67.85 % 74.24 % 59.28 % 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.
89 MF3D 67.68 % 87.79 % 57.57 % 0.03 s GPU @ 2.5 Ghz (C/C++)
90 multi-task CNN 66.19 % 76.69 % 58.11 % 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.
91 BdCost48LDCF code 66.01 % 77.10 % 50.35 % 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.
92 BdCost48-25C 65.25 % 77.59 % 50.68 % 4 s 1 core @ 2.5 Ghz (C/C++)
93 OC-DPM 64.88 % 74.66 % 52.24 % 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.
94 3DVSSD 64.72 % 77.22 % 57.56 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
95 DPM-VOC+VP 63.27 % 77.51 % 47.57 % 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.
96 AOG-View 62.25 % 77.37 % 50.44 % 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.
97 monoref3d 57.65 % 73.74 % 48.91 % 0.1 s 1 core @ 2.5 Ghz (Python)
98 ref3D 57.65 % 73.74 % 48.91 % 0.1 s 1 core @ 2.5 Ghz (Python + C/C++)
99 SAMME48LDCF code 57.49 % 75.12 % 46.64 % 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.
100 LSVM-MDPM-sv 56.69 % 70.86 % 45.91 % 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.
101 ref3D 56.54 % 74.41 % 47.15 % 0.1 s 1 core @ 2.5 Ghz (Python)
102 RCN-resnet101 53.93 % 56.36 % 48.32 % 0.3 s GPU @ 2.5 Ghz (Python + C/C++)
103 SAG-Net 53.29 % 57.92 % 47.73 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
104 VeloFCN
This method makes use of Velodyne laser scans.
52.70 % 70.21 % 46.11 % 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 .
105 Mono3D_PLiDAR code 50.76 % 76.57 % 43.30 % 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.
106 DPM-C8B1
This method uses stereo information.
50.32 % 59.53 % 39.22 % 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.
107 VAT-Net 49.91 % 52.74 % 45.16 % 1 s GPU @ 2.5 Ghz (Python + C/C++)
108 InNet 49.55 % 52.32 % 44.79 % 0.16 s GPU @ 3.5 Ghz (Python + C/C++)
109 ODES code 48.06 % 46.22 % 42.43 % 0.02 s GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
110 LTN 45.56 % 46.61 % 40.80 % 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.
111 ReSqueeze 45.40 % 47.38 % 41.68 % 0.03 s GPU @ >3.5 Ghz (Python)
112 sensekitti code 44.56 % 47.06 % 41.50 % 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.
113 VCTNet 43.41 % 46.53 % 39.42 % 0.02 s GPU @ 1.5 Ghz (C/C++)
114 Resnet101Faster rcnn 42.62 % 49.41 % 38.21 % 1 s 1 core @ 2.5 Ghz (Python)
115 FD2 39.44 % 47.56 % 35.20 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
116 ATL 38.29 % 38.88 % 38.07 % 0.04 s 1 core @ 2.5 Ghz (Python)
117 bin 37.23 % 41.94 % 32.65 % 15ms s GPU @ >3.5 Ghz (Python)
118 IPOD 37.01 % 36.95 % 36.96 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
119 DGIST-CellBox 36.95 % 37.10 % 35.32 % 0.1 s GPU @ 2.5 Ghz (Java + C/C++)
120 cas+res+soft 36.06 % 36.91 % 31.97 % 0.2 s 4 cores @ 2.5 Ghz (Python)
121 cas_retina 36.05 % 38.05 % 31.81 % 0.2 s 4 cores @ 2.5 Ghz (Python)
122 merge12-12 36.02 % 36.92 % 31.88 % 0.2 s 4 cores @ 2.5 Ghz (Python)
123 BirdNet
This method makes use of Velodyne laser scans.
35.81 % 50.85 % 34.90 % 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.
124 AtrousDet 35.80 % 36.56 % 32.32 % 0.05 s TITAN X
125 cas_retina_1_13 35.68 % 38.39 % 31.89 % 0.03 s 4 cores @ 2.5 Ghz (Python)
126 cascadercnn 35.01 % 34.13 % 28.55 % 0.36 s 4 cores @ 2.5 Ghz (Python)
127 centernet 34.18 % 37.83 % 30.75 % 0.01 s GPU @ 2.5 Ghz (Python)
128 IoU_DCRCNN 33.60 % 38.00 % 31.21 % 0.66 s GPU @ 2.5 Ghz (Python)
129 cascade_gw 33.49 % 32.78 % 28.18 % 0.2 s 4 cores @ 2.5 Ghz (Python)
130 Cmerge 32.95 % 36.87 % 29.14 % 0.2 s 4 cores @ 2.5 Ghz (Python)
131 ZKNet 32.91 % 37.16 % 28.94 % 0.01 s GPU @ 2.0 Ghz (Python)
132 softretina 32.90 % 37.63 % 28.73 % 0.16 s 4 cores @ 2.5 Ghz (Python)
133 Fast-SSD 32.90 % 40.88 % 29.21 % 0.06 s GTX650Ti
134 Retinanet100 32.87 % 37.54 % 28.69 % 0.2 s 4 cores @ 2.5 Ghz (Python)
135 RADNet-Fusion
This method makes use of Velodyne laser scans.
32.80 % 31.40 % 31.01 % 0.1 s 1 core @ 2.5 Ghz (Python)
136 RADNet-LIDAR
This method makes use of Velodyne laser scans.
32.60 % 29.74 % 30.76 % 0.1 s 1 core @ 2.5 Ghz (Python)
137 LPN 32.41 % 33.97 % 29.15 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
138 RTL3D 32.26 % 32.32 % 29.71 % 0.02 s GPU @ 2.5 Ghz (Python)
139 NM code 32.25 % 36.53 % 28.20 % 0.01 s GPU @ 2.5 Ghz (Python)
140 Multi-task DG 32.16 % 37.01 % 29.18 % 0.06 s GPU @ 2.5 Ghz (Python)
141 SceneNet 32.02 % 36.62 % 28.46 % 0.03 s GPU @ 2.5 Ghz (C/C++)
142 detectron code 31.71 % 35.58 % 28.18 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
143 SAIC-SA-3D
This method makes use of Velodyne laser scans.
31.69 % 42.35 % 30.99 % 0.05 s GPU @ 2.5 Ghz (Python)
144 FailNet-Fusion
This method makes use of Velodyne laser scans.
31.58 % 29.02 % 30.32 % 0.1 s 1 core @ 2.5 Ghz (Python)
145 RFCN_RFB 31.47 % 33.70 % 27.02 % 0.2 s 4 cores @ 2.5 Ghz (Python)
146 FailNet-LIDAR
This method makes use of Velodyne laser scans.
31.05 % 29.38 % 29.66 % 0.1 s 1 core @ 2.5 Ghz (Python)
147 MTDP 31.04 % 34.12 % 27.50 % 0.15 s GPU @ 2.0 Ghz (Python)
148 AOG code 30.81 % 34.05 % 24.86 % 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.
149 yolo800 30.47 % 31.53 % 27.01 % 0.13 s 4 cores @ 2.5 Ghz (Python)
150 VoxelNet(Unofficial) 30.29 % 33.54 % 27.36 % 0.5 s GPU @ 2.0 Ghz (Python)
151 RFCN 30.29 % 33.30 % 25.44 % 0.2 s 4 cores @ 2.5 Ghz (Python)
152 fasterrcnn 28.13 % 29.83 % 24.76 % 0.2 s 4 cores @ 2.5 Ghz (Python)
153 E-VoxelNet 28.03 % 30.85 % 25.39 % 0.1 s GPU @ 2.5 Ghz (Python)
154 SubCat48LDCF code 26.78 % 34.43 % 19.46 % 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.
155 RFBnet 26.39 % 32.45 % 23.97 % 0.2 s 4 cores @ 2.5 Ghz (Python)
156 Lidar_ROI+Yolo(UJS) 25.40 % 28.93 % 22.51 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
157 CSoR
This method makes use of Velodyne laser scans.
code 25.38 % 34.43 % 21.95 % 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.
158 100Frcnn 25.26 % 34.82 % 21.73 % 2 s 4 cores @ 2.5 Ghz (Python + C/C++)
159 RADNet-Mono 24.50 % 27.89 % 22.74 % 0.1 s 1 core @ 2.5 Ghz (Python)
160 RT3DStereo
This method uses stereo information.
22.24 % 25.54 % 19.33 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
161 DLMB
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
21.69 % 25.31 % 18.75 % 0.03 s 8 cores @ 3.5 Ghz (C/C++)
162 FailNet-Mono 20.02 % 24.41 % 17.85 % 0.1 s 1 core @ 2.5 Ghz (Python)
163 RT3D
This method makes use of Velodyne laser scans.
18.98 % 24.23 % 20.56 % 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.
164 softyolo 18.22 % 25.50 % 15.97 % 0.16 s 4 cores @ 2.5 Ghz (Python)
165 rpn 17.04 % 25.68 % 13.96 % 0.01 s 1 core @ 2.5 Ghz (Python)
166 Licar
This method makes use of Velodyne laser scans.
15.58 % 18.24 % 16.15 % 0.09 s GPU @ 2.0 Ghz (Python)
167 KD53-20 14.27 % 20.79 % 12.61 % 0.19 s 4 cores @ 2.5 Ghz (Python)
168 DLnet 8.48 % 9.09 % 7.39 % 0.3 s 4 cores @ 2.5 Ghz (C/C++)
169 FCPP 0.19 % 0.02 % 0.20 % 0.02 s 1 core @ 2.0 Ghz (Python + C/C++)
170 SN-net 0.00 % 0.00 % 0.00 % 0.8 s GPU @ 2.5 Ghz (Python + C/C++)
171 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 70.57 % 81.79 % 63.31 % 0.06 s GPU @ 2.5 Ghz (Python)
2 VMVS
This method makes use of Velodyne laser scans.
67.66 % 78.57 % 63.83 % 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 66.83 % 78.89 % 62.06 % 1.5 s GPU @ 2.5 Ghz (C/C++)
4 SubCNN 66.28 % 78.33 % 61.37 % 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.
64.32 % 72.73 % 59.07 % 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 Pose-RCNN 59.89 % 74.10 % 54.21 % 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.
7 3DOP
This method uses stereo information.
code 59.79 % 73.46 % 57.04 % 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.
8 DeepStereoOP 59.28 % 73.37 % 56.87 % 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.
9 FFNet code 59.17 % 69.17 % 54.95 % 0.22 s GPU @ 1.5 Ghz (Python)
10 Mono3D code 58.12 % 68.58 % 54.94 % 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 MonoPSR 56.30 % 70.56 % 49.84 % 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.
12 FRCNN+Or code 52.62 % 66.84 % 48.72 % 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.
13 ARPNET 51.10 % 60.85 % 46.67 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
14 PointPillars
This method makes use of Velodyne laser scans.
code 49.66 % 58.05 % 47.88 % 16 ms 1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
15 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 48.98 % 57.49 % 46.48 % 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.
16 Shift R-CNN (mono) code 48.81 % 65.39 % 41.05 % 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.
17 FOFNet
This method makes use of Velodyne laser scans.
47.88 % 56.55 % 43.04 % 0.04 s GPU @ 2.5 Ghz (Python)
18 CLF3D
This method makes use of Velodyne laser scans.
46.86 % 62.19 % 44.92 % 0.13 s GPU @ 2.5 Ghz (Python)
19 SCANet 45.83 % 55.57 % 41.03 % 0.17 s >8 cores @ 2.5 Ghz (Python)
20 AVOD-FPN
This method makes use of Velodyne laser scans.
code 44.92 % 53.36 % 43.77 % 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.
21 SECOND code 43.51 % 51.56 % 38.78 % 38 ms 1080Ti
Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.
22 SS3D 43.45 % 52.70 % 37.20 % 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.
23 CFR
This method makes use of Velodyne laser scans.
43.34 % 56.83 % 42.44 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
24 DGIST-CellBox 43.07 % 47.73 % 41.00 % 0.1 s GPU @ 2.5 Ghz (Java + C/C++)
25 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 42.30 % 51.71 % 38.96 % 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 DPM-VOC+VP 39.83 % 53.66 % 35.73 % 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.
27 SCNet
This method makes use of Velodyne laser scans.
39.19 % 46.26 % 37.83 % 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.
28 VCTNet 38.60 % 43.57 % 36.82 % 0.02 s GPU @ 1.5 Ghz (C/C++)
29 HBA-RCNN 38.06 % 43.81 % 35.02 % 0.4 s 1 core @ 2.5 Ghz (C/C++)
30 sensekitti code 37.50 % 43.55 % 35.08 % 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.
31 Tencent_ADlab_Lidar
This method makes use of Velodyne laser scans.
37.41 % 43.10 % 35.88 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
32 cas_retina_1_13 36.62 % 46.29 % 35.40 % 0.03 s 4 cores @ 2.5 Ghz (Python)
33 AVOD
This method makes use of Velodyne laser scans.
code 36.38 % 44.12 % 31.81 % 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.
34 TANet 36.27 % 41.71 % 35.19 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
35 A-VoxelNet 36.27 % 41.58 % 35.14 % 0.029 s GPU @ 2.5 Ghz (Python)
36 AtrousDet 36.10 % 42.90 % 32.09 % 0.05 s TITAN X
37 LSVM-MDPM-sv 35.49 % 47.00 % 32.42 % 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.
38 IPOD 35.32 % 41.46 % 31.59 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
39 M3D-RPN code 35.06 % 46.19 % 29.90 % 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 .
40 PP_v1.0 code 34.25 % 39.84 % 32.86 % 0.02s 1 core @ 2.5 Ghz (C/C++)
41 SubCat 34.18 % 43.95 % 30.76 % 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.
42 CHTTL MMF 34.17 % 43.98 % 30.89 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
43 ELLIOT
This method makes use of Velodyne laser scans.
34.11 % 41.90 % 32.18 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
44 cascadercnn 33.27 % 43.05 % 28.88 % 0.36 s 4 cores @ 2.5 Ghz (Python)
45 cas_retina 33.02 % 42.79 % 31.91 % 0.2 s 4 cores @ 2.5 Ghz (Python)
46 merge12-12 32.94 % 42.47 % 31.87 % 0.2 s 4 cores @ 2.5 Ghz (Python)
47 cas+res+soft 32.84 % 42.36 % 31.66 % 0.2 s 4 cores @ 2.5 Ghz (Python)
48 MLF 32.64 % 40.98 % 32.33 % 0.05 s GPU @ 2.0 Ghz (Python)
49 RPN+BF code 32.55 % 40.97 % 29.52 % 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.
50 RFCN 32.48 % 40.51 % 28.66 % 0.2 s 4 cores @ 2.5 Ghz (Python)
51 X_MD 32.45 % 43.55 % 31.29 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
52 ReSqueeze 32.35 % 37.95 % 30.38 % 0.03 s GPU @ >3.5 Ghz (Python)
53 bin 31.81 % 36.25 % 29.83 % 15ms s GPU @ >3.5 Ghz (Python)
54 Complexer-YOLO
This method makes use of Velodyne laser scans.
31.80 % 37.80 % 31.26 % 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.
55 LPN 31.63 % 38.40 % 28.90 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
56 centernet 31.62 % 40.82 % 28.94 % 0.01 s GPU @ 2.5 Ghz (Python)
57 ZKNet 31.58 % 39.11 % 28.78 % 0.01 s GPU @ 2.0 Ghz (Python)
58 ODES code 31.43 % 36.84 % 29.00 % 0.02 s GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
59 yolo800 31.42 % 40.42 % 30.50 % 0.13 s 4 cores @ 2.5 Ghz (Python)
60 detectron code 31.20 % 41.08 % 30.78 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
61 RFCN_RFB 30.38 % 38.00 % 26.72 % 0.2 s 4 cores @ 2.5 Ghz (Python)
62 fasterrcnn 29.82 % 38.69 % 28.45 % 0.2 s 4 cores @ 2.5 Ghz (Python)
63 NM code 29.74 % 38.40 % 27.97 % 0.01 s GPU @ 2.5 Ghz (Python)
64 MTDP 29.04 % 36.90 % 25.96 % 0.15 s GPU @ 2.0 Ghz (Python)
65 FD2 28.59 % 35.53 % 26.02 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
66 ACF 28.46 % 35.69 % 26.18 % 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.
67 Multi-task DG 27.81 % 37.18 % 27.32 % 0.06 s GPU @ 2.5 Ghz (Python)
68 cascade_gw 27.51 % 36.55 % 23.20 % 0.2 s 4 cores @ 2.5 Ghz (Python)
69 multi-task CNN 26.98 % 33.58 % 23.07 % 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.
70 softyolo 26.04 % 34.86 % 25.28 % 0.16 s 4 cores @ 2.5 Ghz (Python)
71 Cmerge 24.19 % 34.08 % 24.04 % 0.2 s 4 cores @ 2.5 Ghz (Python)
72 Resnet101Faster rcnn 23.89 % 30.25 % 23.38 % 1 s 1 core @ 2.5 Ghz (Python)
73 OC Stereo
This method uses stereo information.
23.57 % 34.19 % 22.93 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
74 Lidar_ROI+Yolo(UJS) 23.43 % 28.50 % 19.87 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
75 DPM-C8B1
This method uses stereo information.
23.37 % 31.08 % 20.72 % 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.
76 Retinanet100 23.23 % 28.72 % 19.00 % 0.2 s 4 cores @ 2.5 Ghz (Python)
77 ACF-MR 23.18 % 29.35 % 21.00 % 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.
78 KD53-20 22.24 % 26.50 % 19.80 % 0.19 s 4 cores @ 2.5 Ghz (Python)
79 rpn 22.07 % 30.16 % 21.44 % 0.01 s 1 core @ 2.5 Ghz (Python)
80 RT3DStereo
This method uses stereo information.
19.84 % 23.20 % 19.57 % 0.08 s GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
81 100Frcnn 18.55 % 23.61 % 18.34 % 2 s 4 cores @ 2.5 Ghz (Python + C/C++)
82 BirdNet
This method makes use of Velodyne laser scans.
17.26 % 21.34 % 16.67 % 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.
83 softretina 0.49 % 0.35 % 0.50 % 0.16 s 4 cores @ 2.5 Ghz (Python)
84 JSyolo 0.23 % 0.20 % 0.25 % 0.16 s 4 cores @ 2.5 Ghz (Python)
85 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 MMLab-PartA^2
This method makes use of Velodyne laser scans.
76.74 % 85.37 % 69.63 % 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.
2 F-ConvNet
This method makes use of Velodyne laser scans.
74.96 % 84.38 % 66.45 % 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.
3 MMLab-PointRCNN
This method makes use of Velodyne laser scans.
code 72.35 % 83.40 % 65.50 % 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.
4 FOFNet
This method makes use of Velodyne laser scans.
71.04 % 83.20 % 63.86 % 0.04 s GPU @ 2.5 Ghz (Python)
5 AB3DMOT
This method makes use of Velodyne laser scans.
This is an online method (no batch processing).
code 68.91 % 80.68 % 62.30 % 0.0047s 1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
6 ARPNET 68.28 % 80.71 % 61.86 % 0.08 s GPU @ 2.5 Ghz (Python + C/C++)
7 PointPillars
This method makes use of Velodyne laser scans.
code 68.16 % 82.43 % 61.96 % 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.
8 TANet 66.26 % 79.55 % 59.77 % 0.035s GPU @ 2.5 Ghz (Python + C/C++)
9 A-VoxelNet 66.02 % 80.19 % 59.21 % 0.029 s GPU @ 2.5 Ghz (Python)
10 Tencent_ADlab_Lidar
This method makes use of Velodyne laser scans.
65.60 % 80.04 % 59.60 % 0.1 s GPU @ 2.5 Ghz (Python + C/C++)
11 Sogo_MM 63.59 % 70.70 % 56.15 % 1.5 s GPU @ 2.5 Ghz (C/C++)
12 SubCNN 63.41 % 71.39 % 56.34 % 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.
13 MLF 62.53 % 81.02 % 53.64 % 0.05 s GPU @ 2.0 Ghz (Python)
14 Pose-RCNN 62.25 % 74.85 % 55.09 % 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.
15 SCANet 61.96 % 72.59 % 55.26 % 0.17 s >8 cores @ 2.5 Ghz (Python)
16 SCNet
This method makes use of Velodyne laser scans.
60.32 % 76.87 % 56.16 % 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.
17 Deep3DBox 59.37 % 68.58 % 51.97 % 1.5 s GPU @ 2.5 Ghz (C/C++)
A. Mousavian, D. Anguelov, J. Flynn and J. Kosecka: 3D Bounding Box Estimation Using Deep Learning and Geometry. CVPR 2017.
18 3DOP
This method uses stereo information.
code 58.59 % 71.95 % 52.35 % 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.
19 PP_v1.0 code 58.34 % 74.19 % 52.29 % 0.02s 1 core @ 2.5 Ghz (C/C++)
20 CFR
This method makes use of Velodyne laser scans.
57.83 % 74.57 % 55.63 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
21 AVOD-FPN
This method makes use of Velodyne laser scans.
code 57.53 % 67.61 % 54.16 % 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.
22 SECOND code 57.20 % 80.97 % 55.14 % 38 ms 1080Ti
Y. Yan, Y. Mao and B. Li: SECOND: Sparsely Embedded Convolutional Detection. Sensors 2018.
23 ELLIOT
This method makes use of Velodyne laser scans.
56.42 % 74.07 % 51.61 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
24 Complexer-YOLO
This method makes use of Velodyne laser scans.
56.32 % 64.51 % 56.23 % 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.
25 DeepStereoOP 55.62 % 67.49 % 48.85 % 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.
26 AVOD
This method makes use of Velodyne laser scans.
code 54.43 % 64.36 % 47.67 % 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.
27 Mono3D code 53.11 % 65.74 % 48.87 % 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.
28 FRCNN+Or code 50.91 % 63.41 % 45.46 % 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.
29 MonoPSR 49.30 % 58.93 % 43.45 % 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.
30 CLF3D
This method makes use of Velodyne laser scans.
46.66 % 64.55 % 39.30 % 0.13 s GPU @ 2.5 Ghz (Python)
31 X_MD 45.90 % 61.86 % 39.14 % 0.2 s 1 core @ 2.5 Ghz (C/C++)
32 sensekitti code 42.12 % 46.65 % 36.66 % 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.
33 VCTNet 37.64 % 46.21 % 33.46 % 0.02 s GPU @ 1.5 Ghz (C/C++)
34 Shift R-CNN (mono) code 34.77 % 54.31 % 34.04 % 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.
35 ODES code 33.74 % 37.75 % 30.34 % 0.02 s GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
36 M3D-RPN code 33.07 % 51.41 % 31.46 % 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 .
37 SS3D 31.17 % 44.77 % 28.96 % 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 BirdNet
This method makes use of Velodyne laser scans.
30.76 % 41.48 % 28.66 % 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.
39 DGIST-CellBox 29.99 % 34.44 % 27.09 % 0.1 s GPU @ 2.5 Ghz (Java + C/C++)
40 bin 29.53 % 34.66 % 25.98 % 15ms s GPU @ >3.5 Ghz (Python)
41 IPOD 28.88 % 36.04 % 25.66 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
42 AtrousDet 28.47 % 33.27 % 25.62 % 0.05 s TITAN X
43 ReSqueeze 27.40 % 35.39 % 24.32 % 0.03 s GPU @ >3.5 Ghz (Python)
44 LPN 27.01 % 32.96 % 25.01 % 0.2 s GPU @ 2.5 Ghz (Python + C/C++)
45 merge12-12 26.91 % 32.25 % 23.72 % 0.2 s 4 cores @ 2.5 Ghz (Python)
46 cas+res+soft 26.83 % 32.33 % 23.63 % 0.2 s 4 cores @ 2.5 Ghz (Python)
47 cascadercnn 26.62 % 33.02 % 23.01 % 0.36 s 4 cores @ 2.5 Ghz (Python)
48 detectron code 26.36 % 27.44 % 23.20 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
49 Multi-task DG 26.19 % 34.00 % 22.94 % 0.06 s GPU @ 2.5 Ghz (Python)
50 FD2 24.65 % 35.58 % 21.97 % 0.01 s GPU @ >3.5 Ghz (Python + C/C++)
51 cas_retina 24.58 % 30.82 % 23.79 % 0.2 s 4 cores @ 2.5 Ghz (Python)
52 cas_retina_1_13 24.37 % 30.31 % 25.81 % 0.03 s 4 cores @ 2.5 Ghz (Python)
53 DPM-VOC+VP 23.22 % 31.24 % 21.62 % 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.
54 LSVM-MDPM-sv 23.14 % 28.89 % 22.28 % 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.
55 NM code 22.22 % 27.72 % 20.40 % 0.01 s GPU @ 2.5 Ghz (Python)
56 fasterrcnn 22.08 % 28.60 % 19.31 % 0.2 s 4 cores @ 2.5 Ghz (Python)
57 yolo800 21.69 % 28.20 % 19.53 % 0.13 s 4 cores @ 2.5 Ghz (Python)
58 OC Stereo
This method uses stereo information.
21.65 % 31.22 % 20.51 % 0.35 s 1 core @ 2.5 Ghz (Python + C/C++)
59 ZKNet 21.50 % 28.20 % 19.12 % 0.01 s GPU @ 2.0 Ghz (Python)
60 RFCN 20.80 % 26.55 % 19.22 % 0.2 s 4 cores @ 2.5 Ghz (Python)
61 RFCN_RFB 20.44 % 25.95 % 18.78 % 0.2 s 4 cores @ 2.5 Ghz (Python)
62 cascade_gw 19.56 % 26.76 % 17.09 % 0.2 s 4 cores @ 2.5 Ghz (Python)
63 DPM-C8B1
This method uses stereo information.
19.25 % 27.16 % 17.95 % 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.
64 Cmerge 19.19 % 26.37 % 18.56 % 0.2 s 4 cores @ 2.5 Ghz (Python)
65 MTDP 18.95 % 23.33 % 17.24 % 0.15 s GPU @ 2.0 Ghz (Python)
66 centernet 18.36 % 23.40 % 16.35 % 0.01 s GPU @ 2.5 Ghz (Python)
67 Retinanet100 15.16 % 18.64 % 12.49 % 0.2 s 4 cores @ 2.5 Ghz (Python)
68 softyolo 12.14 % 16.84 % 10.51 % 0.16 s 4 cores @ 2.5 Ghz (Python)
69 100Frcnn 11.79 % 17.33 % 10.99 % 2 s 4 cores @ 2.5 Ghz (Python + C/C++)
70 rpn 11.30 % 14.62 % 8.94 % 0.01 s 1 core @ 2.5 Ghz (Python)
71 Lidar_ROI+Yolo(UJS) 9.31 % 13.88 % 9.12 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
72 KD53-20 6.15 % 7.81 % 6.35 % 0.19 s 4 cores @ 2.5 Ghz (Python)
73 RT3DStereo
This method uses stereo information.
5.26 % 6.60 % 3.68 % 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.
74 softretina 0.20 % 0.14 % 0.10 % 0.16 s 4 cores @ 2.5 Ghz (Python)
75 JSyolo 0.15 % 0.15 % 0.15 % 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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