The KITTI Vision Benchmark Suite

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.

• Download left color images of object data set (12 GB)
• Download right color images, if you want to use stereo information (12 GB)
• Download the 3 temporally preceding frames (left color) (36 GB)
• Download the 3 temporally preceding frames (right color) (36 GB)
• Download Velodyne point clouds, if you want to use laser information (29 GB)
• Download camera calibration matrices of object data set (16 MB)
• Download training labels of object data set (5 MB)
• Download object development kit (1 MB) (including 3D object detection and bird's eye view evaluation code)
• Download pre-trained LSVM baseline models (5 MB) used in Joint 3D Estimation of Objects and Scene Layout (NIPS 2011). These models are referred to as LSVM-MDPM-sv (supervised version) and LSVM-MDPM-us (unsupervised version) in the tables below.
• Download reference detections (L-SVM) for training and test set (800 MB)
• Qianli Liao (NYU) has put together code to convert from KITTI to PASCAL VOC file format (documentation included, requires Emacs).
• Karl Rosaen (U.Mich) has released code to convert between KITTI, KITTI tracking, Pascal VOC, Udacity, CrowdAI and AUTTI formats.

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!

Car

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	PC-CNN-V2			95.20 %	96.06 %	89.37 %	0.5 s	GPU @ 2.5 Ghz (Matlab + C/C++)
X. Du, M. Ang, S. Karaman and D. Rus: A General Pipeline for 3D Detection of Vehicles. 2018 IEEE International Conference on Robotics and Automation (ICRA) 2018.
2	F-PointNet		code	95.17 %	95.85 %	85.42 %	0.17 s	GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
3	SA-SSD		code	95.16 %	97.92 %	90.15 %	0.04 s	1 core @ 2.5 Ghz (Python)
C. He, H. Zeng, J. Huang, X. Hua and L. Zhang: Structure Aware Single-stage 3D Object Detection from Point Cloud. CVPR 2020.
4	3DSSD		code	95.10 %	97.69 %	92.18 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
5	MVRA + I-FRCNN+			94.98 %	95.87 %	82.52 %	0.18 s	GPU @ 2.5 Ghz (Python)
H. Choi, H. Kang and Y. Hyun: Multi-View Reprojection Architecture for Orientation Estimation. The IEEE International Conference on Computer Vision (ICCV) Workshops 2019.
6	MMLab PV-RCNN		code	94.70 %	98.17 %	92.04 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
7	D3D			94.66 %	95.43 %	89.72 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
8	CN			94.60 %	97.86 %	89.81 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
9	BM-NET			94.49 %	95.09 %	85.06 %	0.5 s	GPU @ 2.5 Ghz (Python + C/C++)
10	TuSimple		code	94.47 %	95.12 %	86.45 %	1.6 s	GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016. K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016.
11	EPNet			94.44 %	96.15 %	89.99 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
12	CPRCCNN			94.42 %	96.33 %	89.96 %	0.1 s	1 core @ 2.5 Ghz (Python)
13	UberATG-MMF			94.25 %	97.41 %	89.87 %	0.08 s	GPU @ 2.5 Ghz (Python)
M. Liang*, B. Yang*, Y. Chen, R. Hu and R. Urtasun: Multi-Task Multi-Sensor Fusion for 3D Object Detection. CVPR 2019.
14	OAP			93.93 %	96.85 %	86.37 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
15	DGIST-CellBox			93.90 %	95.86 %	88.26 %	0.1 s	GPU @ 2.5 Ghz (Java + C/C++)
16	Associate-3Ddet_v2			93.77 %	96.83 %	88.57 %	0.04 s	1 core @ 2.5 Ghz (Python)
17	Patches - EMP			93.75 %	97.91 %	90.56 %	0.5 s	GPU @ 2.5 Ghz (Python)
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
18	Noah CV Lab - SSL			93.65 %	94.02 %	86.02 %	0.1 s	GPU @ 2.5 Ghz (Python)
19	MVX-Net++			93.58 %	96.41 %	88.51 %	0.15 s	1 core @ 2.5 Ghz (C/C++)
20	NLK-3D			93.58 %	96.62 %	90.55 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
21	CLOCs_PointCas			93.55 %	96.69 %	86.16 %	0.1 s	GPU @ 2.5 Ghz (Python)
22	MonoPair			93.55 %	96.61 %	83.55 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
23	Deep MANTA			93.50 %	98.89 %	83.21 %	0.7 s	GPU @ 2.5 Ghz (Python + C/C++)
F. Chabot, M. Chaouch, J. Rabarisoa, C. Teulière and T. Chateau: Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image. CVPR 2017.
24	Point-GNN		code	93.50 %	96.58 %	88.35 %	0.6 s	GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
25	AIMC-RUC			93.47 %	96.75 %	88.35 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
26	dgist_multiDetNet			93.46 %	94.99 %	85.46 %	0.08 s	GPU Titanx Pascal (Python)
27	FichaDL			93.46 %	96.00 %	84.39 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
28	AAMF-SSD			93.45 %	96.72 %	88.31 %	0.05 s	GPU @ 2.5 Ghz (Python)
29	PC-RGNN			93.43 %	96.81 %	88.25 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
30	Cas-SSD			93.41 %	96.73 %	88.30 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
31	RRC		code	93.40 %	95.68 %	87.37 %	3.6 s	GPU @ 2.5 Ghz (C/C++)
J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y. Tai and L. Xu: Accurate Single Stage Detector Using Recurrent Rolling Convolution. CVPR 2017.
32	KNN-GCNN			93.39 %	96.19 %	88.17 %	0.4 s	1 core @ 2.5 Ghz (Python + C/C++)
33	F-3DNet			93.38 %	96.51 %	88.32 %	0.5 s	GPU @ 2.5 Ghz (Python)
34	3D-CVF at SPA			93.36 %	96.78 %	86.11 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
J. Yoo, Y. Kim, J. Kim and J. Choi: 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection. ECCV 2020.
35	FLID			93.35 %	95.90 %	85.69 %	0.04 s	GPU @ 2.5 Ghz (Python)
36	CFENet			93.26 %	93.91 %	86.99 %	4 s	GPU @ 2.5 Ghz (Python + C/C++)
37	STD		code	93.22 %	96.14 %	90.53 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
38	SARPNET			93.21 %	96.07 %	88.09 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
Y. Ye, H. Chen, C. Zhang, X. Hao and Z. Zhang: SARPNET: Shape Attention Regional Proposal Network for LiDAR-based 3D Object Detection. Neurocomputing 2019.
39	Fast Point R-CNN			93.18 %	96.13 %	87.68 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, S. Liu, X. Shen and J. Jia: Fast Point R-CNN. Proceedings of the IEEE international conference on computer vision (ICCV) 2019.
40	sensekitti		code	93.17 %	94.79 %	84.38 %	4.5 s	GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.
41	ELE			93.14 %	98.44 %	90.32 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
42	RethinkDet3D			93.14 %	96.16 %	88.17 %	0.15 s	1 core @ 2.5 Ghz (Python)
43	Discrete-PointDet			93.14 %	96.36 %	87.82 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
44	SJTU-HW			93.11 %	96.30 %	82.21 %	0.85s	GPU @ 1.5 Ghz (Python + C/C++)
S. Zhang, X. Zhao, L. Fang, F. Haiping and S. Haitao: LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR. IEEE International Conference on Image Processing 2018. L. Fang, X. Zhao and S. Zhang: Small-objectness sensitive detection based on shifted single shot detector. Multimedia Tools and Applications 2018.
45	PVF-NET			93.08 %	96.03 %	88.04 %	0.1 s	1 core @ 2.5 Ghz (Python)
46	SerialR-FCN+SG-NMS			93.03 %	95.81 %	83.00 %	0.2 s	1 core @ 2.5 Ghz (Python)
47	PointRCNN-deprecated			92.96 %	96.72 %	85.81 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
48	CLOCs_SecCas			92.95 %	95.43 %	89.21 %	0.1 s	1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
49	cvMax			92.84 %	96.14 %	87.87 %	0.04 s	GPU @ >3.5 Ghz (Python)
50	OHS			92.81 %	96.21 %	89.80 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
51	deprecated			92.79 %	96.12 %	87.78 %	0.04 s	GPU @ 2.5 Ghz (Python)
52	PointCSE			92.78 %	95.99 %	87.66 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
53	IGRP			92.78 %	96.28 %	87.81 %	0.18 s	1 core @ 2.5 Ghz (Python + C/C++)
54	Mono3CN			92.76 %	95.51 %	84.80 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
55	MuRF			92.74 %	95.74 %	87.64 %	0.05 s	GPU @ 1.5 Ghz (Python + C/C++)
56	SegVoxelNet			92.73 %	96.00 %	87.60 %	0.04 s	1 core @ 2.5 Ghz (Python)
H. Yi, S. Shi, M. Ding, J. Sun, K. Xu, H. Zhou, Z. Wang, S. Li and G. Wang: SegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloud. ICRA 2020.
57	Patches			92.72 %	96.34 %	87.63 %	0.15 s	GPU @ 2.0 Ghz
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
58	Chovy			92.69 %	96.06 %	89.74 %	0.04 s	GPU @ 2.5 Ghz (Python)
59	PPFNet		code	92.68 %	96.32 %	87.66 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
60	R-GCN			92.67 %	96.19 %	87.66 %	0.16 s	GPU @ 2.5 Ghz (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
61	PI-RCNN			92.66 %	96.17 %	87.68 %	0.1 s	1 core @ 2.5 Ghz (Python)
L. Xie, C. Xiang, Z. Yu, G. Xu, Z. Yang, D. Cai and X. He: PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module. AAAI 2020 : The Thirty-Fourth AAAI Conference on Artificial Intelligence 2020.
62				92.65 %	96.09 %	89.72 %
63	deprecated			92.60 %	96.20 %	89.60 %	-	-
64	deprecated			92.59 %	96.21 %	89.58 %	0.05 s	GPU @ >3.5 Ghz (Python)
65	PointPainting			92.58 %	98.39 %	89.71 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
66	SPA			92.56 %	95.96 %	87.60 %	0.1 s	1 core @ 2.5 Ghz (Python)
67	DEFT			92.55 %	96.17 %	89.51 %	1 s	GPU @ 2.5 Ghz (Python)
68	3D IoU-Net			92.47 %	96.31 %	87.67 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, S. Krylov, Y. Ding and L. Shao: 3D IoU-Net: IoU Guided 3D Object Detector for Point Clouds. arXiv preprint arXiv:2004.04962 2020.
69	PPBA			92.46 %	95.22 %	87.53 %	NA s	GPU @ 2.5 Ghz (Python)
70	TBU			92.46 %	95.22 %	87.53 %	NA s	GPU @ 2.5 Ghz (Python)
71	VAR			92.46 %	95.11 %	89.68 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
72	Associate-3Ddet		code	92.45 %	95.61 %	87.32 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
L. Du, X. Ye, X. Tan, J. Feng, Z. Xu, E. Ding and S. Wen: Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection. The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
73	CP			92.44 %	96.14 %	87.58 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
74				92.39 %	95.84 %	89.51 %
75	OneCoLab SicNet			92.37 %	95.57 %	89.79 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
76	PointRGCN			92.33 %	97.51 %	87.07 %	0.26 s	GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
77	LZY_RCNN			92.28 %	93.58 %	89.76 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
78	F-ConvNet		code	92.19 %	95.85 %	80.09 %	0.47 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
79	MDA			92.17 %	94.88 %	89.54 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
80	yolo4			92.13 %	94.20 %	79.89 %	0.02 s	1 core @ 2.5 Ghz (Python)
81	TBD			92.12 %	93.48 %	89.56 %	0.05 s	GPU @ 2.5 Ghz (Python)
82	PVNet			92.12 %	94.84 %	89.27 %	0,1 s	1 core @ 2.5 Ghz (Python)
83	IE-PointRCNN			92.08 %	96.01 %	87.05 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
84	PBASN		code	92.07 %	95.51 %	87.04 %	NA s	GPU @ 2.5 Ghz (Python)
85	SDP+RPN			92.03 %	95.16 %	79.16 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016. S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 2015.
86	AB3DMOT		code	92.00 %	95.88 %	86.98 %	0.0047s	1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
87	MMLab-PointRCNN		code	91.90 %	95.92 %	87.11 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
88	MMLab-PartA^2		code	91.86 %	95.03 %	89.06 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
89	MBR-SSD			91.83 %	93.46 %	84.97 %	4.0 s	GPU @ 2.5 Ghz (Python + C/C++)
90	Pointpillar_TV			91.82 %	94.82 %	88.57 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
91	epBRM		code	91.77 %	94.59 %	88.45 %	0.1 s	GPU @ >3.5 Ghz (Python + C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
92	deprecated			91.76 %	96.53 %	83.90 %	0.05 s	1 core @ 2.5 Ghz (Python)
93	3DBN_2			91.75 %	95.34 %	89.12 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
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94	C-GCN			91.73 %	95.64 %	86.37 %	0.147 s	GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
95	ITVD		code	91.73 %	95.85 %	79.31 %	0.3 s	GPU @ 2.5 Ghz (C/C++)
Y. Wei Liu: Improving Tiny Vehicle Detection in Complex Scenes. IEEE International Conference on Multimedia and Expo (ICME) 2018.
96	PiP			91.67 %	94.35 %	88.35 %	0.033 s	1 core @ 2.5 Ghz (Python)
97	SINet+		code	91.67 %	94.17 %	78.60 %	0.3 s	TITAN X GPU
X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
98	Faster RCNN + A			91.60 %	94.77 %	81.43 %	0.19 s	GPU @ 2.5 Ghz (Python + C/C++)
99	Cascade MS-CNN		code	91.60 %	94.26 %	78.84 %	0.25 s	GPU @ 2.5 Ghz (C/C++)
Z. Cai and N. Vasconcelos: Cascade R-CNN: High Quality Object Detection and Instance Segmentation. arXiv preprint arXiv:1906.09756 2019. Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A unified multi-scale deep convolutional neural network for fast object detection. European conference on computer vision 2016.
100	deprecated			91.59 %	94.34 %	79.14 %	0.05 s	GPU @ 2.0 Ghz (Python)
101	yolo4_5l			91.50 %	91.26 %	81.89 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
102	HRI-VoxelFPN			91.44 %	96.65 %	86.18 %	0.02 s	GPU @ 2.5 Ghz (Python + C/C++)
H. Kuang, B. Wang, J. An, M. Zhang and Z. Zhang: Voxel-FPN:multi-scale voxel feature aggregation in 3D object detection from point clouds. sensors 2020.
103	RUC			91.40 %	95.02 %	88.41 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
104	CU-PointRCNN			91.34 %	97.25 %	86.98 %	0.1 s	GPU @ 1.5 Ghz (Python + C/C++)
105	deprecated			91.31 %	96.90 %	83.91 %	0.06 s	GPU @ >3.5 Ghz (Python)
106	Faster RCNN + G			91.28 %	94.34 %	81.02 %	1.1 s	GPU @ 2.5 Ghz (Python)
107	Faster RCNN + Gr + A			91.25 %	94.09 %	81.25 %	1.29 s	GPU @ 2.5 Ghz (Python)
108	OACV			91.21 %	94.23 %	83.07 %	0.23 s	GPU @ 2.5 Ghz (Python)
109	CentrNet-v1			91.21 %	94.22 %	88.36 %	0.03 s	GPU @ 2.5 Ghz (Python)
110	CentrNet-FG			91.21 %	94.05 %	88.45 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
111	PointPillars		code	91.19 %	94.00 %	88.17 %	16 ms	1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
112	Faster RCNN + A			91.19 %	94.43 %	80.99 %	0.19 s	GPU @ 2.5 Ghz (Python)
113	LTN			91.18 %	94.68 %	81.51 %	0.4 s	GPU @ >3.5 Ghz (Python)
T. Wang, X. He, Y. Cai and G. Xiao: Learning a Layout Transfer Network for Context Aware Object Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
114	autonet			91.17 %	93.70 %	88.10 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
115	WS3D			91.15 %	95.13 %	86.52 %	0.1 s	GPU @ 2.5 Ghz (Python)
116	PointPiallars_SECA			91.12 %	93.66 %	87.94 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
117	DDB			91.12 %	93.71 %	87.34 %	0.05 s	GPU @ 2.5 Ghz (Python)
118	EPENet			91.11 %	94.31 %	88.02 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
119	anonymous			91.08 %	96.57 %	82.86 %	1 s	1 core @ 2.5 Ghz (C/C++)
120	SSL-RTM3D			91.07 %	96.44 %	81.19 %	0.03 s	1 core @ 2.5 Ghz (Python)
121	FII-CenterNet			91.03 %	94.48 %	83.00 %	0.09 s	GPU @ 2.5 Ghz (Python)
122	Aston-EAS			91.02 %	93.91 %	77.93 %	0.24 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Wei, J. He, Y. Zhou, K. Chen, Z. Tang and Z. Xiong: Enhanced Object Detection With Deep Convolutional Neural Networks for Advanced Driving Assistance. IEEE Transactions on Intelligent Transportation Systems 2019.
123	ARPNET			90.99 %	94.00 %	83.49 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
124	Bit			90.96 %	93.84 %	87.47 %	0.11 s	1 core @ 2.5 Ghz (C/C++)
125	JSU-NET			90.90 %	96.41 %	80.67 %	0.1 s	1 core @ 2.5 Ghz (Python)
126	GAFM			90.90 %	96.46 %	80.70 %	0.5 s	1 core @ 2.5 Ghz (Python)
127	PatchNet			90.87 %	93.82 %	79.62 %	0.4 s	1 core @ 2.5 Ghz (C/C++)
128	GA_BALANCE			90.86 %	96.19 %	78.40 %	1 s	1 core @ 2.5 Ghz (Python)
129	MV3D			90.83 %	96.47 %	78.63 %	0.36 s	GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
130	MVSLN			90.81 %	96.12 %	83.39 %	0.1s s	1 core @ 2.5 Ghz (C/C++)
131	3D IoU Loss			90.79 %	95.92 %	85.65 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
D. Zhou, J. Fang, X. Song, C. Guan, J. Yin, Y. Dai and R. Yang: IoU Loss for 2D/3D Object Detection. International Conference on 3D Vision (3DV) 2019.
132	SINet_VGG		code	90.79 %	93.59 %	77.53 %	0.2 s	TITAN X GPU
X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
133	Tencent_ADlab_Lidar			90.74 %	93.80 %	86.75 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
134	GA_FULLDATA			90.73 %	96.31 %	78.22 %	1 s	4 cores @ 2.5 Ghz (Python)
135	Simple3D Net			90.70 %	93.54 %	87.81 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
136	HR-SECOND		code	90.68 %	93.72 %	85.63 %	0.11 s	1 core @ 2.5 Ghz (Python + C/C++)
137	GA2500			90.68 %	95.86 %	80.29 %	0.2 s	1 core @ 2.5 Ghz (Python)
138	GA_rpn500			90.68 %	95.86 %	80.29 %	1 s	1 core @ 2.5 Ghz (Python)
139	TANet		code	90.67 %	93.67 %	85.31 %	0.035s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
140	SFB-SECOND			90.67 %	96.17 %	85.43 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
141	SECOND-V1.5		code	90.65 %	95.96 %	85.35 %	0.04 s	GPU @ 2.0 Ghz (Python + C/C++)
142	yolo4			90.63 %	94.71 %	80.38 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
143	baseline			90.59 %	93.29 %	87.18 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
144	VOXEL_FPN_HR			90.55 %	93.76 %	85.42 %	0.12 s	8 cores @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
145	MP			90.50 %	93.86 %	85.17 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
146	Sogo_MM			90.46 %	94.31 %	80.62 %	1.5 s	GPU @ 2.5 Ghz (C/C++)
147	bigger_ga			90.38 %	95.76 %	77.92 %	1 s	1 core @ 2.5 Ghz (Python)
148	CG-Stereo			90.38 %	96.31 %	82.80 %	0.57 s	GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
149	yolo4_5l		code	90.38 %	91.79 %	80.64 %	0.02 s	1 core @ 2.5 Ghz (Python + C/C++)
150	AtrousDet			90.35 %	95.94 %	77.94 %	0.05 s	TITAN X
151	SCNet			90.30 %	95.59 %	85.09 %	0.04 s	GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
152	RUC		code	90.24 %	92.60 %	86.55 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
153	Deep3DBox			90.19 %	94.71 %	76.82 %	1.5 s	GPU @ 2.5 Ghz (C/C++)
A. Mousavian, D. Anguelov, J. Flynn and J. Kosecka: 3D Bounding Box Estimation Using Deep Learning and Geometry. CVPR 2017.
154	FQNet			90.17 %	94.72 %	76.78 %	0.5 s	1 core @ 2.5 Ghz (Python)
L. Liu, J. Lu, C. Xu, Q. Tian and J. Zhou: Deep Fitting Degree Scoring Network for Monocular 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
155	BVVF			90.15 %	95.65 %	84.95 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
156	FCY			90.15 %	93.27 %	86.60 %	0.02 s	GPU @ 2.5 Ghz (Python)
157	SAANet			90.14 %	95.93 %	82.95 %	0.10 s	1 core @ 2.5 Ghz (Python)
158	DeepStereoOP			90.06 %	95.15 %	79.91 %	3.4 s	GPU @ 3.5 Ghz (Matlab + C/C++)
C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.
159	SubCNN			89.98 %	94.26 %	79.78 %	2 s	GPU @ 3.5 Ghz (Python + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.
160	MLOD		code	89.97 %	94.88 %	84.98 %	0.12 s	GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
161	GPP		code	89.96 %	94.02 %	81.13 %	0.23 s	GPU @ 1.5 Ghz (Python + C/C++)
A. Rangesh and M. Trivedi: Ground plane polling for 6dof pose estimation of objects on the road. arXiv preprint arXiv:1811.06666 2018.
162	RUC		code	89.93 %	93.12 %	85.44 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
163	AVOD		code	89.88 %	95.17 %	82.83 %	0.08 s	Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
164	SINet_PVA		code	89.86 %	92.72 %	76.47 %	0.11 s	TITAN X GPU
X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
165	MCA			89.72 %	93.42 %	79.96 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
166	3DOP		code	89.55 %	92.96 %	79.38 %	3s	GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
167	IAFA			89.46 %	93.08 %	79.83 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
168	Mono3D		code	89.37 %	94.52 %	79.15 %	4.2 s	GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.
169	4D-MSCNN+CRL			89.37 %	92.40 %	77.00 %	0.2 s	GPU @ 2.5 Ghz (Matlab + C/C++)
170	R-FCN(FPN)			89.35 %	93.53 %	79.35 %	0.2 s	1 core @ 2.5 Ghz (Python)
171	cas+res+soft			89.14 %	94.54 %	78.37 %	0.2 s	4 cores @ 2.5 Ghz (Python)
172	merge12-12			88.96 %	94.58 %	78.22 %	0.2 s	4 cores @ 2.5 Ghz (Python)
173	Scan_YOLO			88.95 %	90.69 %	79.85 %	0.1 s	4 cores @ 3.0 Ghz (Python)
174	AVOD-FPN		code	88.92 %	94.70 %	84.13 %	0.1 s	Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
175	autoRUC			88.88 %	94.23 %	81.35 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
176	Prune			88.85 %	94.20 %	81.31 %	0.11 s	1 core @ 2.5 Ghz (C/C++)
177	AM3D			88.71 %	92.55 %	77.78 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
X. Ma, Z. Wang, H. Li, P. Zhang, W. Ouyang and X. Fan: Accurate Monocular Object Detection via Color- Embedded 3D Reconstruction for Autonomous Driving. Proceedings of the IEEE international Conference on Computer Vision (ICCV) 2019.
178	SS3D_HW			88.68 %	94.49 %	68.79 %	0.4 s	GPU @ 2.5 Ghz (Python)
179	MS-CNN		code	88.68 %	93.87 %	76.11 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection. ECCV 2016.
180	Multi-task DG			88.65 %	93.83 %	76.16 %	0.06 s	GPU @ 2.5 Ghz (Python)
181	CRCNNA			88.59 %	94.82 %	76.74 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
182	3DNN			88.56 %	94.52 %	81.51 %	0.09 s	GPU @ 2.5 Ghz (Python)
183	CSFADet			88.54 %	93.75 %	78.62 %	0.05 s	GPU @ 2.5 Ghz (Python)
184	MonoPSR		code	88.50 %	93.63 %	73.36 %	0.2 s	GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
185	Shift R-CNN (mono)		code	88.48 %	94.07 %	78.34 %	0.25 s	GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
186	PSMD			88.47 %	93.67 %	75.62 %	0.1 s	GPU @ 2.5 Ghz (Python)
187	RCD			88.46 %	92.52 %	83.73 %	0.1 s	GPU @ 2.5 Ghz (Python)
188	MM-MRFC			88.46 %	95.54 %	78.14 %	0.05 s	GPU @ 2.5 Ghz (C/C++)
A. Costea, R. Varga and S. Nedevschi: Fast Boosting based Detection using Scale Invariant Multimodal Multiresolution Filtered Features. CVPR 2017.
189	PointRes			88.41 %	95.38 %	84.22 %	0.013 s	1 core @ 2.5 Ghz (Python + C/C++)
190	AACL			88.35 %	93.56 %	73.57 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
191	PP-3D			88.35 %	93.71 %	80.84 %	0.1 s	1 core @ 2.5 Ghz (Python)
192	3DBN			88.29 %	93.74 %	80.74 %	0.13s	1080Ti (Python+C/C++)
X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.
193	anonymous			88.16 %	96.22 %	75.72 %	1 s	1 core @ 2.5 Ghz (C/C++)
194	ga50			87.65 %	95.76 %	75.14 %	1 s	1 core @ 2.5 Ghz (Python)
195	cas_retina			87.64 %	93.87 %	75.30 %	0.2 s	4 cores @ 2.5 Ghz (Python)
196	SMOKE		code	87.51 %	93.21 %	77.66 %	0.03 s	GPU @ 2.5 Ghz (Python)
Z. Liu, Z. Wu and R. Tóth: SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation. 2020.
197	MonoSS			87.46 %	93.15 %	77.58 %	0.03 s	GPU @ 2.5 Ghz (Python + C/C++)
198	cascadercnn			87.36 %	89.37 %	73.42 %	0.36 s	4 cores @ 2.5 Ghz (Python)
199	MA			87.29 %	93.21 %	79.82 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
200	CDN			87.19 %	95.85 %	79.43 %	0.6 s	GPU @ 2.5 Ghz (Python)
201	IMA			87.17 %	92.67 %	77.46 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
202	RTM3D		code	86.93 %	91.82 %	77.41 %	0.05 s	GPU @ 1.0 Ghz (Python)
P. Li, H. Zhao, P. Liu and F. Cao: RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving. 2020.
203	yolo_rgb			86.90 %	90.01 %	77.52 %	0.07 s	GPU @ 2.5 Ghz (Python)
204	NL_M3D			86.80 %	91.31 %	72.37 %	0.2 s	1 core @ 2.5 Ghz (Python)
205	voxelrcnn			86.69 %	94.60 %	79.91 %	15 s	1 core @ 2.5 Ghz (C/C++)
206	anm			86.52 %	94.88 %	76.46 %	3 s	1 core @ 2.5 Ghz (C/C++)
207	DSGN		code	86.43 %	95.53 %	78.75 %	0.67 s	NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
208	PB3D			86.21 %	95.64 %	76.83 %	0.42 s	1 core @ 2.5 Ghz (C/C++)
209	ReSqueeze			86.12 %	90.35 %	76.53 %	0.03 s	GPU @ >3.5 Ghz (Python)
210	Stereo R-CNN		code	85.98 %	93.98 %	71.25 %	0.3 s	GPU @ 2.5 Ghz (Python)
P. Li, X. Chen and S. Shen: Stereo R-CNN based 3D Object Detection for Autonomous Driving. CVPR 2019.
211	StereoFENet			85.70 %	91.48 %	77.62 %	0.15 s	1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
212	ResNet-RRC w/RGBD			85.58 %	91.32 %	74.80 %	0.057 s	GPU @ 1.5 Ghz (Python + C/C++)
213	cas_retina_1_13			85.48 %	91.54 %	74.60 %	0.03 s	4 cores @ 2.5 Ghz (Python)
214	ResNet-RRC			85.33 %	91.45 %	74.27 %	0.06 s	GPU @ 1.5 Ghz (Python + C/C++)
H. Jeon and . others: High-Speed Car Detection Using ResNet- Based Recurrent Rolling Convolution. Proceedings of the IEEE conference on systems, man, and cybernetics 2018.
215	Cmerge			85.32 %	93.40 %	70.57 %	0.2 s	4 cores @ 2.5 Ghz (Python)
216	PL++ (SDN+GDC)		code	85.15 %	94.95 %	77.78 %	0.6 s	GPU @ 2.5 Ghz (C/C++)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.
217	RAR-Net			85.08 %	89.04 %	69.26 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
218	M3D-RPN		code	85.08 %	89.04 %	69.26 %	0.16 s	GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
219	CDN-PL++			85.01 %	94.66 %	77.60 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
220	SDP+CRC (ft)			85.00 %	92.06 %	71.71 %	0.6 s	GPU @ 2.5 Ghz (C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
221	bifpn_fsrn			84.93 %	93.68 %	74.45 %	0.07 s	1 core @ 2.5 Ghz (Python + C/C++)
222	IDA-3D			84.92 %	92.79 %	74.75 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
223	SS3D			84.92 %	92.72 %	70.35 %	48 ms	Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
224	LPN			84.77 %	89.19 %	74.08 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
225	MonoFENet			84.63 %	91.68 %	76.71 %	0.15 s	1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
226	SECA			84.60 %	92.51 %	79.53 %	1 s	GPU @ 2.5 Ghz (Python)
227	MV3D (LIDAR)			84.39 %	93.08 %	79.27 %	0.24 s	GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
228	Complexer-YOLO			84.16 %	91.92 %	79.62 %	0.06 s	GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
229	HG-Mono			84.01 %	89.65 %	65.28 %	0.46 s	GPU @ 2.5 Ghz (C/C++)
230	ZoomNet		code	83.92 %	94.22 %	69.00 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
L. Z. Xu: ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2020.
231	D4LCN		code	83.67 %	90.34 %	65.33 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
232	seivl			83.60 %	90.35 %	81.76 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
233	ASOD			83.52 %	94.09 %	68.68 %	0.28 s	GPU @ 2.5 Ghz (Python)
234	softretina			83.30 %	93.55 %	70.59 %	0.16 s	4 cores @ 2.5 Ghz (Python)
235	Faster R-CNN		code	83.16 %	88.97 %	72.62 %	2 s	GPU @ 3.5 Ghz (Python + C/C++)
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards Real- Time Object Detection with Region Proposal Networks. NIPS 2015.
236	MTMono3d			83.11 %	90.55 %	75.48 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
237	SSL-RTM3D Res18			82.97 %	93.35 %	73.11 %	0.02 s	GPU @ 2.5 Ghz (Python)
238	ZKNet			82.96 %	92.17 %	72.43 %	0.01 s	GPU @ 2.0 Ghz (Python)
239	Pseudo-LiDAR++		code	82.90 %	94.46 %	75.45 %	0.4 s	GPU @ 2.5 Ghz (Python)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.
240	DP3D			82.81 %	87.85 %	66.80 %	0.05 s	GPU @ 1.5 Ghz (Python + C/C++)
241	Retinanet100			82.73 %	93.97 %	68.37 %	0.2 s	4 cores @ 2.5 Ghz (Python)
242	BS3D			82.72 %	95.35 %	70.01 %	22 ms	Titan Xp
N. Gählert, J. Wan, M. Weber, J. Zöllner, U. Franke and J. Denzler: Beyond Bounding Boxes: Using Bounding Shapes for Real-Time 3D Vehicle Detection from Monocular RGB Images. 2019 IEEE Intelligent Vehicles Symposium (IV) 2019.
243	DP3D			82.63 %	87.90 %	66.62 %	0.07 s	GPU @ 1.5 Ghz (Python + C/C++)
244	Disp R-CNN		code	82.57 %	93.26 %	68.20 %	0.42 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
245	Pseudo-LiDAR E2E			82.54 %	94.00 %	75.31 %	0.4 s	GPU @ 2.5 Ghz (Python)
246	Disp R-CNN (velo)		code	82.47 %	93.20 %	68.09 %	0.42 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
247	cascade_gw			82.35 %	85.98 %	71.60 %	0.2 s	4 cores @ 2.5 Ghz (Python)
248	deprecated			82.23 %	92.21 %	67.87 %		1 core @ 2.5 Ghz (C/C++)
249	S3D			82.18 %	91.77 %	67.82 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
250	Stereo3D			82.15 %	94.81 %	62.17 %	0.1 s	GPU 1080Ti
251	LNET			82.02 %	91.49 %	67.71 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
252	FRCNN+Or		code	82.00 %	92.91 %	68.79 %	0.09 s	Titan Xp GPU
C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine 2018. C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features. IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.
253	CBNet			81.70 %	91.47 %	72.02 %	1 s	4 cores @ 2.5 Ghz (Python)
254	Resnet101Faster rcnn			81.44 %	91.08 %	71.52 %	1 s	1 core @ 2.5 Ghz (Python)
255	yyyyolo			81.33 %	94.36 %	68.72 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
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256	LCD3D			81.25 %	91.29 %	64.55 %	0.03 s	GPU @ 2.5 Ghz (Python)
257	A3DODWTDA (image)		code	81.25 %	78.96 %	70.56 %	0.8 s	GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
258	RefineNet			81.01 %	91.91 %	65.67 %	0.20 s	GPU @ 2.5 Ghz (Matlab + C++)
R. Rajaram, E. Bar and M. Trivedi: RefineNet: Refining Object Detectors for Autonomous Driving. IEEE Transactions on Intelligent Vehicles 2016. R. Rajaram, E. Bar and M. Trivedi: RefineNet: Iterative Refinement for Accurate Object Localization. Intelligent Transportation Systems Conference 2016.
259	MTDP			80.97 %	89.03 %	66.91 %	0.15 s	GPU @ 2.0 Ghz (Python)
260	RFCN_RFB			80.89 %	88.07 %	69.66 %	0.2 s	4 cores @ 2.5 Ghz (Python)
261	centernet			80.78 %	90.29 %	70.53 %	0.01 s	GPU @ 2.5 Ghz (Python)
262	UM3D_TUM			80.36 %	92.88 %	65.95 %	0.05 s	1 core @ 2.5 Ghz (Python)
263	3D-GCK			80.19 %	89.55 %	68.08 %	24 ms	Tesla V100
N. Gählert, J. Wan, N. Jourdan, J. Finkbeiner, U. Franke and J. Denzler: Single-Shot 3D Detection of Vehicles from Monocular RGB Images via Geometrically Constrained Keypoints in Real-Time. 2020 IEEE Intelligent Vehicles Symposium (IV) 2020.
264	RADNet-Fusion			80.04 %	76.72 %	76.78 %	0.1 s	1 core @ 2.5 Ghz (Python)
265	YoloMono3D			79.63 %	92.37 %	59.69 %	0.05 s	GPU @ 2.5 Ghz (Python)
266	RADNet-LIDAR			79.59 %	75.20 %	76.03 %	0.1 s	1 core @ 2.5 Ghz (Python)
267	MMRetina			79.53 %	89.66 %	69.52 %	0.38 s	GPU @ 2.5 Ghz (Python)
268	DA-3Ddet			79.47 %	89.49 %	63.04 %	0.4 s	GPU @ 2.5 Ghz (Python)
269	SceneNet			79.26 %	90.70 %	67.98 %	0.03 s	GPU @ 2.5 Ghz (C/C++)
270	A3DODWTDA		code	79.15 %	82.98 %	68.30 %	0.08 s	GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
271	MTNAS			78.82 %	88.96 %	67.07 %	0.02 s	1 core @ 2.5 Ghz (python)
272	ITS-MDPL			78.71 %	91.41 %	73.17 %	0.16 s	GPU @ 2.5 Ghz (Python)
273	spLBP			78.66 %	81.66 %	61.69 %	1.5 s	8 cores @ 2.5 Ghz (Matlab + C/C++)
Q. Hu, S. Paisitkriangkrai, C. Shen, A. Hengel and F. Porikli: Fast Detection of Multiple Objects in Traffic Scenes With a Common Detection Framework. IEEE Trans. Intelligent Transportation Systems 2016.
274	3D-SSMFCNN		code	78.19 %	77.92 %	69.19 %	0.1 s	GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.
275	MonoGRNet		code	77.94 %	88.65 %	63.31 %	0.04s	NVIDIA P40
Z. Qin, J. Wang and Y. Lu: MonoGRNet: A Geometric Reasoning Network for 3D Object Localization. The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) 2019.
276	yolov3_warp			77.61 %	92.24 %	65.70 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
277	Reinspect		code	77.48 %	90.27 %	66.73 %	2s	1 core @ 2.5 Ghz (C/C++)
R. Stewart, M. Andriluka and A. Ng: End-to-End People Detection in Crowded Scenes. CVPR 2016.
278	multi-task CNN			77.18 %	86.12 %	68.09 %	25.1 ms	GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
279	Regionlets			76.99 %	88.75 %	60.49 %	1 s	>8 cores @ 2.5 Ghz (C/C++)
X. Wang, M. Yang, S. Zhu and Y. Lin: Regionlets for Generic Object Detection. T-PAMI 2015. W. Zou, X. Wang, M. Sun and Y. Lin: Generic Object Detection with Dense Neural Patterns and Regionlets. British Machine Vision Conference 2014. C. Long, X. Wang, G. Hua, M. Yang and Y. Lin: Accurate Object Detection with Location Relaxation and Regionlets Relocalization. Asian Conference on Computer Vision 2014.
280	3DVP		code	76.98 %	84.95 %	65.78 %	40 s	8 cores @ 3.5 Ghz (Matlab + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Data-Driven 3D Voxel Patterns for Object Category Recognition. IEEE Conference on Computer Vision and Pattern Recognition 2015.
281	FailNet-Fusion			76.90 %	74.55 %	71.94 %	0.1 s	1 core @ 2.5 Ghz (Python)
282	avodC			76.58 %	87.30 %	71.65 %	0.1 s	GPU @ 2.5 Ghz (Python)
283	SubCat		code	76.36 %	84.10 %	60.56 %	0.7 s	6 cores @ 3.5 Ghz (Matlab + C/C++)
E. Ohn-Bar and M. Trivedi: Learning to Detect Vehicles by Clustering Appearance Patterns. T-ITS 2015.
284	GS3D			76.35 %	86.23 %	62.67 %	2 s	1 core @ 2.5 Ghz (C/C++)
B. Li, W. Ouyang, L. Sheng, X. Zeng and X. Wang: GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
285	FailNet-LIDAR			76.26 %	74.16 %	71.24 %	0.1 s	1 core @ 2.5 Ghz (Python)
286	AOG		code	76.24 %	86.08 %	61.51 %	3 s	4 cores @ 2.5 Ghz (Matlab)
T. Wu, B. Li and S. Zhu: Learning And-Or Models to Represent Context and Occlusion for Car Detection and Viewpoint Estimation. TPAMI 2016. B. Li, T. Wu and S. Zhu: Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model. ECCV 2014.
287	bin			76.16 %	78.73 %	63.39 %	15ms s	GPU @ >3.5 Ghz (Python)
288	Pose-RCNN			75.83 %	89.59 %	64.06 %	2 s	>8 cores @ 2.5 Ghz (Python)
M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on 2016.
289	VoxelNet(Unofficial)			75.22 %	81.37 %	68.74 %	0.5 s	GPU @ 2.0 Ghz (Python)
290	RFCN			75.14 %	83.04 %	61.55 %	0.2 s	4 cores @ 2.5 Ghz (Python)
291	3D FCN			74.65 %	86.74 %	67.85 %	>5 s	1 core @ 2.5 Ghz (C/C++)
B. Li: 3D Fully Convolutional Network for Vehicle Detection in Point Cloud. IROS 2017.
292	OC Stereo		code	74.60 %	87.39 %	62.56 %	0.35 s	1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
293	yolo_depth			74.40 %	88.71 %	65.58 %	0.07 s	GPU @ 2.5 Ghz (Python)
294	yolo800			74.31 %	78.93 %	63.83 %	0.13 s	4 cores @ 2.5 Ghz (Python)
295	3DVSSD			74.11 %	86.99 %	63.57 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
296	FD2			73.93 %	88.65 %	64.62 %	0.01 s	GPU @ >3.5 Ghz (Python + C/C++)
297	BdCost+DA+BB+MS			73.72 %	85.18 %	57.79 %	TBD s	4 cores @ 2.5 Ghz (Matlab + C/C++)
298	m-prcnn			73.64 %	87.64 %	57.03 %	0.43 s	1 core @ 2.5 Ghz (Python)
299	BdCost+DA+MS			73.62 %	85.03 %	58.94 %	TBD s	4 cores @ 2.5 Ghz (Matlab/C++)
300	Int-YOLO		code	73.23 %	75.81 %	63.59 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
301	stereo_sa			72.99 %	87.88 %	63.49 %	0.3 s	GPU @ 2.5 Ghz (Python)
302	RuiRUC			72.08 %	87.48 %	55.28 %	0.12 s	1 core @ 2.5 Ghz (Python)
303	ANM			71.97 %	87.17 %	55.19 %	0.12 s	1 core @ 2.5 Ghz (Python)
304	RFBnet			71.66 %	87.25 %	63.00 %	0.2 s	4 cores @ 2.5 Ghz (Python)
305	AOG-View			71.26 %	85.01 %	55.73 %	3 s	1 core @ 2.5 Ghz (Matlab, C/C++)
B. Li, T. Wu and S. Zhu: Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model. ECCV 2014.
306	GPVL			71.06 %	81.67 %	54.96 %	10 s	1 core @ 2.5 Ghz (C/C++)
307	BdCost+DA+BB			70.86 %	85.52 %	56.19 %	TBD s	4 cores @ 2.5 Ghz (Matlab + C/C++)
308	DAM			70.78 %	90.08 %	61.38 %	1 s	GPU @ 2.5 Ghz (Python)
309	MV-RGBD-RF			70.70 %	77.89 %	57.41 %	4 s	4 cores @ 2.5 Ghz (C/C++)
A. Gonzalez, D. Vazquez, A. Lopez and J. Amores: On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts.. IEEE Trans. on Cybernetics 2016. A. Gonzalez, G. Villalonga, J. Xu, D. Vazquez, J. Amores and A. Lopez: Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection. IEEE Intelligent Vehicles Symposium (IV) 2015.
310	Vote3Deep			70.30 %	78.95 %	63.12 %	1.5 s	4 cores @ 2.5 Ghz (C/C++)
M. Engelcke, D. Rao, D. Zeng Wang, C. Hay Tong and I. Posner: Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks. ArXiv e-prints 2016.
311	ROI-10D			70.16 %	76.56 %	61.15 %	0.2 s	GPU @ 3.5 Ghz (Python)
F. Manhardt, W. Kehl and A. Gaidon: ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape. Computer Vision and Pattern Recognition (CVPR) 2019.
312	fasterrcnn			69.45 %	74.76 %	60.20 %	0.2 s	4 cores @ 2.5 Ghz (Python)
313	Decoupled-3D v2			68.17 %	88.64 %	54.74 %	0.08 s	GPU @ 2.5 Ghz (C/C++)
314	BirdNet+		code	68.05 %	92.10 %	65.61 %	0.1 s	Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird's Eye View. arXiv:2003.04188 [cs.CV] 2020.
315	Decoupled-3D			67.92 %	87.78 %	54.53 %	0.08 s	GPU @ 2.5 Ghz (C/C++)
Y. Cai, B. Li, Z. Jiao, H. Li, X. Zeng and X. Wang: Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation. AAAI 2020.
316	Pseudo-Lidar		code	67.79 %	85.40 %	58.50 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Wang, W. Chao, D. Garg, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
317	OC-DPM			67.06 %	79.07 %	52.61 %	10 s	8 cores @ 2.5 Ghz (Matlab)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Occlusion Patterns for Object Class Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2013.
318	Fast-SSD			66.79 %	85.19 %	57.89 %	0.06 s	GTX650Ti
319	DPM-VOC+VP			66.72 %	82.15 %	49.01 %	8 s	1 core @ 2.5 Ghz (C/C++)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015.
320	BdCost48LDCF		code	66.63 %	81.38 %	52.20 %	0.5 s	8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
321	E-VoxelNet			65.33 %	68.00 %	57.84 %	0.1 s	GPU @ 2.5 Ghz (Python)
322	RefinedMPL			65.24 %	88.29 %	53.20 %	0.15 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
323	BdCost48-25C			64.63 %	81.42 %	52.22 %	4 s	1 core @ 2.5 Ghz (C/C++)
324	MDPM-un-BB			64.06 %	79.74 %	49.07 %	60 s	4 core @ 2.5 Ghz (MATLAB)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
325	TLNet (Stereo)		code	63.53 %	76.92 %	54.58 %	0.1 s	1 core @ 2.5 Ghz (Python)
Z. Qin, J. Wang and Y. Lu: Triangulation Learning Network: from Monocular to Stereo 3D Object Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
326	PDV-Subcat			63.24 %	78.27 %	47.67 %	7 s	1 core @ 2.5 Ghz (C/C++)
J. Shen, X. Zuo, J. Li, W. Yang and H. Ling: A novel pixel neighborhood differential statistic feature for pedestrian and face detection . Pattern Recognition 2017.
327	PG-MonoNet			62.75 %	70.87 %	54.34 %	0.19 s	GPU @ 2.5 Ghz (Python)
328	MODet			62.54 %	66.06 %	60.04 %	0.05 s	GTX1080Ti
Y. Zhang, Z. Xiang, C. Qiao and S. Chen: Accurate and Real-Time Object Detection Based on Bird's Eye View on 3D Point Clouds. 2019 International Conference on 3D Vision (3DV) 2019.
329	yl_net			61.78 %	66.00 %	60.36 %	0.03 s	GPU @ 2.5 Ghz (Python)
330	Lidar_ROI+Yolo(UJS)			61.71 %	73.32 %	53.65 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
331	GNN			61.48 %	79.09 %	51.06 %	0.2 s	1 core @ 2.5 Ghz (Python)
332	SubCat48LDCF		code	61.16 %	78.86 %	44.69 %	0.5 s	8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
333	DPM-C8B1			60.21 %	75.24 %	44.73 %	15 s	4 cores @ 2.5 Ghz (Matlab + C/C++)
J. Yebes, L. Bergasa and M. García-Garrido: Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes. Sensors 2015. J. Yebes, L. Bergasa, R. Arroyo and A. Lázaro: Supervised learning and evaluation of KITTI's cars detector with DPM. IV 2014.
334	SDP-Net-s			59.94 %	65.51 %	57.20 %	12ms	GPU @ 2.5 Ghz (Python)
335	RADNet-Mono			59.85 %	67.47 %	54.14 %	0.1 s	1 core @ 2.5 Ghz (Python)
336	monoref3d			58.97 %	78.11 %	47.72 %	0.1 s	1 core @ 2.5 Ghz (Python)
337	ref3D			58.97 %	78.11 %	47.72 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
338	100Frcnn			58.92 %	82.09 %	49.04 %	2 s	4 cores @ 2.5 Ghz (Python + C/C++)
339	SAMME48LDCF		code	58.38 %	77.47 %	44.43 %	0.5 s	8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
340	LSVM-MDPM-sv			58.36 %	71.11 %	43.22 %	10 s	4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010. A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout. NIPS 2011.
341	ref3D			57.16 %	77.96 %	45.99 %	0.1 s	1 core @ 2.5 Ghz (Python)
342	BirdNet			57.12 %	79.30 %	55.16 %	0.11 s	Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
343	ACF-SC			56.60 %	69.90 %	43.61 %	<0.3 s	1 core @ >3.5 Ghz (Matlab + C/C++)
C. Cadena, A. Dick and I. Reid: A Fast, Modular Scene Understanding System using Context-Aware Object Detection. Robotics and Automation (ICRA), 2015 IEEE International Conference on 2015.
344	LSVM-MDPM-us		code	55.95 %	68.94 %	41.45 %	10 s	4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
345	ACF			54.09 %	63.05 %	41.81 %	0.2 s	1 core @ >3.5 Ghz (Matlab + C/C++)
P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014. P. Doll\'ar: Piotr's Image and Video Matlab Toolbox (PMT). .
346	Mono3D_PLiDAR		code	53.36 %	80.85 %	44.80 %	0.1 s	NVIDIA GeForce 1080 (pytorch)
X. Weng and K. Kitani: Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud. arXiv:1903.09847 2019.
347	RT3D-GMP			51.95 %	62.41 %	39.14 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
348	VeloFCN			51.82 %	70.53 %	45.70 %	1 s	GPU @ 2.5 Ghz (Python + C/C++)
B. Li, T. Zhang and T. Xia: Vehicle Detection from 3D Lidar Using Fully Convolutional Network. RSS 2016 .
349	FailNet-Mono			47.95 %	59.59 %	41.33 %	0.1 s	1 core @ 2.5 Ghz (Python)
350	SF			46.68 %	60.62 %	38.22 %	0.5 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
351	softyolo			45.97 %	66.08 %	38.02 %	0.16 s	4 cores @ 2.5 Ghz (Python)
352	Vote3D			45.94 %	54.38 %	40.48 %	0.5 s	4 cores @ 2.8 Ghz (C/C++)
D. Wang and I. Posner: Voting for Voting in Online Point Cloud Object Detection. Proceedings of Robotics: Science and Systems 2015.
353	TopNet-HighRes			45.85 %	58.04 %	41.11 %	101ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
354	RT3DStereo			45.81 %	56.53 %	37.63 %	0.08 s	GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
355	Multimodal Detection		code	45.46 %	63.91 %	37.25 %	0.06 s	GPU @ 3.5 Ghz (Matlab + C/C++)
A. Asvadi, L. Garrote, C. Premebida, P. Peixoto and U. Nunes: Multimodal vehicle detection: fusing 3D- LIDAR and color camera data. Pattern Recognition Letters 2017.
356	RT3D			39.69 %	50.33 %	40.04 %	0.09 s	GPU @ 1.8Ghz
Y. Zeng, Y. Hu, S. Liu, J. Ye, Y. Han, X. Li and N. Sun: RT3D: Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving. IEEE Robotics and Automation Letters 2018.
357	VoxelJones		code	36.31 %	43.89 %	34.16 %	.18 s	1 core @ 2.5 Ghz (Python + C/C++)
M. Motro and J. Ghosh: Vehicular Multi-object Tracking with Persistent Detector Failures. arXiv preprint arXiv:1907.11306 2019.
358	Licar			35.19 %	42.34 %	33.97 %	0.09 s	GPU @ 2.0 Ghz (Python)
359	KD53-20			34.76 %	51.76 %	29.39 %	0.19 s	4 cores @ 2.5 Ghz (Python)
360	SAIC-SA-3D			31.16 %	41.51 %	29.83 %	0.05 s	GPU @ 2.5 Ghz (Python)
361	FCN-Depth		code	25.05 %	52.32 %	18.07 %	1 s	GPU @ 1.5 Ghz (Matlab + C/C++)
362	CSoR		code	21.66 %	31.52 %	17.99 %	3.5 s	4 cores @ >3.5 Ghz (Python + C/C++)
L. Plotkin: PyDriver: Entwicklung eines Frameworks für räumliche Detektion und Klassifikation von Objekten in Fahrzeugumgebung. 2015.
363	mBoW			21.59 %	35.22 %	16.89 %	10 s	1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
364	R-CNN_VGG			21.36 %	29.38 %	16.61 %	10 s	GPU @ 2.5 Ghz (Matlab + C/C++)
365	DepthCN		code	21.18 %	37.45 %	16.08 %	2.3 s	GPU @ 3.5 Ghz (Matlab)
A. Asvadi, L. Garrote, C. Premebida, P. Peixoto and U. Nunes: DepthCN: vehicle detection using 3D- LIDAR and convnet. IEEE ITSC 2017.
366	YOLOv2		code	14.31 %	26.74 %	10.94 %	0.02 s	GPU @ 3.5 Ghz (C/C++)
J. Redmon, S. Divvala, R. Girshick and A. Farhadi: You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016. J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
367	TopNet-UncEst			6.24 %	7.24 %	5.42 %	0.09 s	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
368	TopNet-Retina			5.00 %	6.82 %	4.52 %	52ms	GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
369	FCPP			0.07 %	0.01 %	0.07 %	0.02 s	1 core @ 2.0 Ghz (Python + C/C++)
370	ANM			0.01 %	0.01 %	0.02 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
371	TopNet-DecayRate			0.01 %	0.00 %	0.01 %	92 ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
372	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.
373	JSyolo			0.00 %	0.00 %	0.00 %	0.16 s	4 cores @ 2.5 Ghz (Python)

Pedestrian

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	HWFD			83.06 %	90.50 %	78.35 %	0.21 s	one 1080Ti
2	FichaDL			82.50 %	90.75 %	75.66 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
3	DGIST-CellBox			81.29 %	90.04 %	76.92 %	0.1 s	GPU @ 2.5 Ghz (Java + C/C++)
4	Alibaba-CityBrain			81.19 %	90.99 %	74.68 %	1.5 s	GPU @ 2.5 Ghz (Python + C/C++)
5	ExtAtt			81.05 %	90.60 %	76.08 %	1.2 s	GPU @ 2.5 Ghz (Python + C/C++)
6	dgist_multiDetNet			80.21 %	89.21 %	75.77 %	0.08 s	GPU Titanx Pascal (Python)
7	F-PointNet		code	80.13 %	89.83 %	75.05 %	0.17 s	GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
8	TuSimple		code	78.40 %	88.87 %	73.66 %	1.6 s	GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016. K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016.
9	Argus_detection_v1			77.01 %	84.86 %	72.15 %	0.25 s	GPU @ 1.5 Ghz (C/C++)
10	RRC		code	76.61 %	85.98 %	71.47 %	3.6 s	GPU @ 2.5 Ghz (C/C++)
J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y. Tai and L. Xu: Accurate Single Stage Detector Using Recurrent Rolling Convolution. CVPR 2017.
11	ECP Faster R-CNN			76.25 %	85.96 %	70.55 %	0.25 s	GPU @ 2.5 Ghz (Python)
M. Braun, S. Krebs, F. Flohr and D. Gavrila: The EuroCity Persons Dataset: A Novel Benchmark for Object Detection. CoRR 2018.
12	Aston-EAS			76.07 %	86.71 %	70.02 %	0.24 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Wei, J. He, Y. Zhou, K. Chen, Z. Tang and Z. Xiong: Enhanced Object Detection With Deep Convolutional Neural Networks for Advanced Driving Assistance. IEEE Transactions on Intelligent Transportation Systems 2019.
13	MHN			75.99 %	87.21 %	69.50 %	0.39 s	GPU @ 2.5 Ghz (Python)
J. Cao, Y. Pang, S. Zhao and X. Li: High-Level Semantic Networks for Multi- Scale Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2019.
14	FFNet		code	75.81 %	87.17 %	69.86 %	1.07 s	GPU @ 1.5 Ghz (Python)
C. Zhao, Y. Qian and M. Yang: Monocular Pedestrian Orientation Estimation Based on Deep 2D-3D Feedforward. Pattern Recognition 2019.
15	SJTU-HW			75.81 %	87.17 %	69.86 %	0.85s	GPU @ 1.5 Ghz (Python + C/C++)
S. Zhang, X. Zhao, L. Fang, F. Haiping and S. Haitao: LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR. IEEE International Conference on Image Processing 2018. L. Fang, X. Zhao and S. Zhang: Small-objectness sensitive detection based on shifted single shot detector. Multimedia Tools and Applications 2018.
16	Noah CV Lab - SSL			75.64 %	86.57 %	70.53 %	0.1 s	GPU @ 2.5 Ghz (Python)
17	Faster RCNN + Gr + A			74.95 %	86.95 %	69.50 %	1.29 s	GPU @ 2.5 Ghz (Python)
18	MS-CNN		code	74.89 %	85.71 %	68.99 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection. ECCV 2016.
19	Multi-task DG			73.99 %	85.21 %	68.06 %	0.06 s	GPU @ 2.5 Ghz (Python)
20	Faster RCNN + G			73.75 %	85.51 %	68.54 %	1.1 s	GPU @ 2.5 Ghz (Python)
21	F-ConvNet		code	72.91 %	83.63 %	67.18 %	0.47 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
22	Sogo_MM			72.82 %	84.99 %	67.42 %	1.5 s	GPU @ 2.5 Ghz (C/C++)
23	Faster RCNN + A			72.67 %	86.21 %	67.55 %	0.19 s	GPU @ 2.5 Ghz (Python)
24	GN			72.29 %	82.93 %	65.56 %	1 s	GPU @ 2.5 Ghz (Matlab + C/C++)
S. Jung and K. Hong: Deep network aided by guiding network for pedestrian detection. Pattern Recognition Letters 2017.
25	SubCNN			72.27 %	84.88 %	66.82 %	2 s	GPU @ 3.5 Ghz (Python + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.
26	FRCNN-WS			72.26 %	84.20 %	67.47 %	0.22 s	1 core @ 3.0 Ghz (Python)
27	Faster RCNN + A			72.09 %	85.35 %	66.87 %	0.19 s	GPU @ 2.5 Ghz (Python + C/C++)
28	VMVS			71.82 %	82.80 %	66.85 %	0.25 s	GPU @ 2.5 Ghz (Python)
J. Ku, A. Pon, S. Walsh and S. Waslander: Improving 3D object detection for pedestrians with virtual multi-view synthesis orientation estimation. IROS 2019.
29	IVA		code	71.37 %	84.61 %	64.90 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
Y. Zhu, J. Wang, C. Zhao, H. Guo and H. Lu: Scale-adaptive Deconvolutional Regression Network for Pedestrian Detection. ACCV 2016. S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 2015.
30	MM-MRFC			70.76 %	83.79 %	64.81 %	0.05 s	GPU @ 2.5 Ghz (C/C++)
A. Costea, R. Varga and S. Nedevschi: Fast Boosting based Detection using Scale Invariant Multimodal Multiresolution Filtered Features. CVPR 2017.
31	SDP+RPN			70.42 %	82.07 %	65.09 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016. S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 2015.
32	CSFADet			70.07 %	84.72 %	64.81 %	0.05 s	GPU @ 2.5 Ghz (Python)
33	Mono3CN			69.75 %	83.47 %	63.15 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
34	3DOP		code	69.57 %	83.17 %	63.48 %	3s	GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
35	MonoPSR		code	68.56 %	85.60 %	63.34 %	0.2 s	GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
36	DeepStereoOP			68.46 %	83.00 %	63.35 %	3.4 s	GPU @ 3.5 Ghz (Matlab + C/C++)
C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.
37	sensekitti		code	68.41 %	82.72 %	62.72 %	4.5 s	GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.
38	FII-CenterNet			67.31 %	81.32 %	61.29 %	0.09 s	GPU @ 2.5 Ghz (Python)
39	Mono3D		code	67.29 %	80.30 %	62.23 %	4.2 s	GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.
40	Faster R-CNN		code	66.24 %	79.97 %	61.09 %	2 s	GPU @ 3.5 Ghz (Python + C/C++)
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards Real- Time Object Detection with Region Proposal Networks. NIPS 2015.
41	AtrousDet			64.97 %	80.79 %	58.36 %	0.05 s	TITAN X
42	SDP+CRC (ft)			64.36 %	79.22 %	59.16 %	0.6 s	GPU @ 2.5 Ghz (C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
43	CRCNNA			63.69 %	78.10 %	58.41 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
44	Pose-RCNN			63.54 %	80.07 %	57.02 %	2 s	>8 cores @ 2.5 Ghz (Python)
M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on 2016.
45	PCN			63.41 %	80.08 %	58.55 %	0.6 s
46	CFM			62.84 %	74.76 %	56.06 %	<2 s	GPU @ 2.5 Ghz (Matlab + C/C++)
Q. Hu, P. Wang, C. Shen, A. Hengel and F. Porikli: Pushing the Limits of Deep CNNs for Pedestrian Detection. IEEE Transactions on Circuits and Systems for Video Technology 2017.
47	merge12-12			62.84 %	80.27 %	56.08 %	0.2 s	4 cores @ 2.5 Ghz (Python)
48	cas+res+soft			62.71 %	80.11 %	55.99 %	0.2 s	4 cores @ 2.5 Ghz (Python)
49	cas_retina			62.37 %	79.82 %	57.15 %	0.2 s	4 cores @ 2.5 Ghz (Python)
50	OHS			62.31 %	71.43 %	59.24 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
51	cas_retina_1_13			61.87 %	79.09 %	56.70 %	0.03 s	4 cores @ 2.5 Ghz (Python)
52	MonoPair			61.57 %	78.81 %	56.51 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
53	ReSqueeze			61.33 %	73.69 %	56.65 %	0.03 s	GPU @ >3.5 Ghz (Python)
54	RPN+BF		code	61.22 %	77.06 %	55.22 %	0.6 s	GPU @ 2.5 Ghz (Matlab + C/C++)
L. Zhang, L. Lin, X. Liang and K. He: Is Faster R-CNN Doing Well for Pedestrian Detection?. ECCV 2016.
55	JSU-NET			61.19 %	83.17 %	56.20 %	0.1 s	1 core @ 2.5 Ghz (Python)
56	RethinkDet3D			60.88 %	70.56 %	56.69 %	0.15 s	1 core @ 2.5 Ghz (Python)
57	Regionlets			60.83 %	73.79 %	54.72 %	1 s	>8 cores @ 2.5 Ghz (C/C++)
X. Wang, M. Yang, S. Zhu and Y. Lin: Regionlets for Generic Object Detection. T-PAMI 2015. W. Zou, X. Wang, M. Sun and Y. Lin: Generic Object Detection with Dense Neural Patterns and Regionlets. British Machine Vision Conference 2014. C. Long, X. Wang, G. Hua, M. Yang and Y. Lin: Accurate Object Detection with Location Relaxation and Regionlets Relocalization. Asian Conference on Computer Vision 2014.
58	bin			60.73 %	71.43 %	55.78 %	15ms s	GPU @ >3.5 Ghz (Python)
59				60.63 %	69.37 %	57.64 %
60	3DSSD		code	60.51 %	72.33 %	56.28 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
61	anm			60.35 %	76.02 %	55.46 %	3 s	1 core @ 2.5 Ghz (C/C++)
62	MVX-Net++			60.21 %	69.70 %	56.07 %	0.15 s	1 core @ 2.5 Ghz (C/C++)
63	PiP			59.94 %	70.52 %	56.51 %	0.033 s	1 core @ 2.5 Ghz (Python)
64	cascadercnn			59.50 %	78.79 %	54.44 %	0.36 s	4 cores @ 2.5 Ghz (Python)
65	TANet		code	59.07 %	69.90 %	56.44 %	0.035s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
66				58.70 %	68.18 %	54.68 %
67	DDB			58.53 %	69.03 %	55.90 %	0.05 s	GPU @ 2.5 Ghz (Python)
68	MMLab PV-RCNN		code	58.37 %	68.88 %	55.38 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
69	Point-GNN		code	58.20 %	71.59 %	54.06 %	0.6 s	GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
70	DeepParts			58.15 %	71.47 %	51.92 %	~1 s	GPU @ 2.5 Ghz (Matlab)
Y. Tian, P. Luo, X. Wang and X. Tang: Deep Learning Strong Parts for Pedestrian Detection. ICCV 2015.
71	CompACT-Deep			58.14 %	70.93 %	52.29 %	1 s	1 core @ 2.5 Ghz (Matlab + C/C++)
Z. Cai, M. Saberian and N. Vasconcelos: Learning Complexity-Aware Cascades for Deep Pedestrian Detection. ICCV 2015.
72	PPBA			58.06 %	67.73 %	55.69 %	NA s	GPU @ 2.5 Ghz (Python)
73	MMLab-PartA^2		code	57.96 %	68.78 %	54.01 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
74	AVOD-FPN		code	57.87 %	67.95 %	55.23 %	0.1 s	Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
75	LPN			57.69 %	71.87 %	53.21 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
76	TBU			57.44 %	67.29 %	54.00 %	NA s	GPU @ 2.5 Ghz (Python)
77	CentrNet-FG			57.40 %	68.27 %	54.11 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
78	Simple3D Net			57.00 %	66.89 %	54.38 %	0.02 s	GPU @ 2.5 Ghz (Python)
79	KNN-GCNN			56.80 %	69.53 %	52.86 %	0.4 s	1 core @ 2.5 Ghz (Python + C/C++)
80	LDAM			56.68 %	64.73 %	54.21 %	24 ms	GTX 1080 ti GPU
81	FRCNN+Or		code	56.68 %	71.64 %	51.53 %	0.09 s	Titan Xp GPU
C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine 2018. C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features. IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.
82	yolo800			56.67 %	71.26 %	50.91 %	0.13 s	4 cores @ 2.5 Ghz (Python)
83	ZKNet			56.58 %	71.15 %	51.87 %	0.01 s	GPU @ 2.0 Ghz (Python)
84	CentrNet-v1			56.57 %	66.27 %	54.19 %	0.03 s	GPU @ 2.5 Ghz (Python)
85	FilteredICF			56.53 %	69.79 %	50.32 %	~ 2 s	>8 cores @ 2.5 Ghz (Matlab + C/C++)
S. Zhang, R. Benenson and B. Schiele: Filtered Channel Features for Pedestrian Detection. CVPR 2015.
86	Tencent_ADlab_Lidar			56.49 %	66.91 %	54.06 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
87	ARPNET			56.42 %	69.08 %	52.69 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
88	FD2			56.35 %	71.37 %	51.08 %	0.01 s	GPU @ >3.5 Ghz (Python + C/C++)
89	MV-RGBD-RF			56.18 %	72.99 %	49.72 %	4 s	4 cores @ 2.5 Ghz (C/C++)
A. Gonzalez, D. Vazquez, A. Lopez and J. Amores: On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts.. IEEE Trans. on Cybernetics 2016. A. Gonzalez, G. Villalonga, J. Xu, D. Vazquez, J. Amores and A. Lopez: Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection. IEEE Intelligent Vehicles Symposium (IV) 2015.
90	RFCN			55.96 %	72.32 %	49.66 %	0.2 s	4 cores @ 2.5 Ghz (Python)
91	yolo4			55.78 %	72.49 %	51.11 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
92	MLOD		code	55.62 %	68.42 %	51.45 %	0.12 s	GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
93	DAM			55.60 %	74.85 %	50.63 %	1 s	GPU @ 2.5 Ghz (Python)
94	yolo4_5l			55.29 %	74.54 %	48.20 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
95	PointPillars		code	55.10 %	65.29 %	52.39 %	16 ms	1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
96	STD		code	55.04 %	68.33 %	50.85 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
97	RFCN_RFB			54.98 %	70.61 %	48.75 %	0.2 s	4 cores @ 2.5 Ghz (Python)
98	Vote3Deep			54.80 %	67.99 %	51.17 %	1.5 s	4 cores @ 2.5 Ghz (C/C++)
M. Engelcke, D. Rao, D. Zeng Wang, C. Hay Tong and I. Posner: Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks. ArXiv e-prints 2016.
99	yolo4			54.30 %	73.16 %	49.46 %	0.02 s	1 core @ 2.5 Ghz (Python)
100	CHTTL MMF			54.28 %	72.79 %	49.31 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
101	epBRM		code	54.13 %	62.90 %	51.95 %	0.10 s	1 core @ 2.5 Ghz (C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
102	PointPainting			53.76 %	61.86 %	50.61 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
103	PDV2			53.54 %	65.59 %	47.65 %	3.7 s	1 core @ 3.0 Ghz Matlab (C/C++)
J. Shen, X. Zuo, J. Li, W. Yang and H. Ling: A novel pixel neighborhood differential statistic feature for pedestrian and face detection . Pattern Recognition 2017.
104	fasterrcnn			53.42 %	69.29 %	48.76 %	0.2 s	4 cores @ 2.5 Ghz (Python)
105	3DBN_2			53.26 %	63.82 %	50.76 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
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106	TAFT			53.15 %	67.62 %	47.08 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
J. Shen, X. Zuo, W. Yang, D. Prokhorov, X. Mei and H. Ling: Differential Features for Pedestrian Detection: A Taylor Series Perspective. IEEE Transactions on Intelligent Transportation Systems 2018.
107	MTMono3d			52.96 %	69.01 %	46.18 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
108	pAUCEnsT			52.88 %	65.84 %	46.97 %	60 s	1 core @ 2.5 Ghz (Matlab + C/C++)
S. Paisitkriangkrai, C. Shen and A. Hengel: Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning. arXiv 2014.
109	yolo4_5l		code	52.74 %	71.89 %	47.90 %	0.02 s	1 core @ 2.5 Ghz (Python + C/C++)
110	deprecated			52.32 %	67.93 %	47.77 %	0.05 s	GPU @ 2.0 Ghz (Python)
111	PP-3D			52.11 %	63.07 %	49.79 %	0.1 s	1 core @ 2.5 Ghz (Python)
112	SF			51.83 %	67.73 %	47.45 %	0.5 s	1 core @ 2.5 Ghz (Python)
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113	MTDP			51.81 %	68.12 %	46.95 %	0.15 s	GPU @ 2.0 Ghz (Python)
114	Disp R-CNN		code	51.36 %	68.93 %	46.79 %	0.42 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
115	TBD			51.31 %	61.14 %	47.82 %	0.05 s	GPU @ 2.5 Ghz (Python)
116	Disp R-CNN (velo)		code	51.31 %	68.84 %	46.80 %	0.42 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
117	Shift R-CNN (mono)		code	51.30 %	70.86 %	46.37 %	0.25 s	GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
118	centernet			51.09 %	69.27 %	45.40 %	0.01 s	GPU @ 2.5 Ghz (Python)
119	FCY			50.88 %	59.73 %	48.61 %	0.02 s	GPU @ 2.5 Ghz (Python)
120	PPFNet		code	50.52 %	57.82 %	47.44 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
121	PVNet			50.50 %	60.58 %	48.48 %	0,1 s	1 core @ 2.5 Ghz (Python)
122	SCNet			49.61 %	60.95 %	46.91 %	0.04 s	GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
123	yolo_depth			49.47 %	67.23 %	44.99 %	0.07 s	GPU @ 2.5 Ghz (Python)
124	MMLab-PointRCNN		code	49.41 %	58.93 %	46.44 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
125	Resnet101Faster rcnn			49.12 %	64.72 %	44.60 %	1 s	1 core @ 2.5 Ghz (Python)
126	VOXEL_FPN_HR			49.09 %	60.28 %	45.47 %	0.12 s	8 cores @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
127	SS3D_HW			49.01 %	64.67 %	42.86 %	0.4 s	GPU @ 2.5 Ghz (Python)
128	cascade_gw			48.99 %	67.35 %	44.25 %	0.2 s	4 cores @ 2.5 Ghz (Python)
129	Int-YOLO		code	48.76 %	64.09 %	44.31 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
130	MP			48.73 %	60.26 %	45.05 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
131	ACFD		code	48.63 %	61.62 %	44.15 %	0.2 s	4 cores @ >3.5 Ghz (C/C++)
M. Dimitrievski, P. Veelaert and W. Philips: Semantically aware multilateral filter for depth upsampling in automotive LiDAR point clouds. IEEE Intelligent Vehicles Symposium, IV 2017, Los Angeles, CA, USA, June 11-14, 2017 2017.
132	R-CNN			48.57 %	62.88 %	43.05 %	4 s	GPU @ 3.3 Ghz (C/C++)
J. Hosang, M. Omran, R. Benenson and B. Schiele: Taking a Deeper Look at Pedestrians. arXiv 2015.
133	yolo_rgb			48.45 %	64.50 %	43.95 %	0.07 s	GPU @ 2.5 Ghz (Python)
134	PBASN		code	46.75 %	54.38 %	44.58 %	NA s	GPU @ 2.5 Ghz (Python)
135	HR-SECOND		code	46.69 %	58.68 %	42.93 %	0.11 s	1 core @ 2.5 Ghz (Python + C/C++)
136	Cmerge			46.51 %	63.68 %	41.60 %	0.2 s	4 cores @ 2.5 Ghz (Python)
137	SS3D			45.79 %	61.58 %	41.14 %	48 ms	Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
138	ACF			45.67 %	59.81 %	40.88 %	1 s	1 core @ 3.5 Ghz (Matlab + C/C++)
P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014.
139	NL_M3D			45.03 %	58.46 %	39.22 %	0.2 s	1 core @ 2.5 Ghz (Python)
140	Fusion-DPM		code	44.99 %	58.93 %	40.19 %	~ 30 s	1 core @ 3.5 Ghz (Matlab + C/C++)
C. Premebida, J. Carreira, J. Batista and U. Nunes: Pedestrian Detection Combining RGB and Dense LIDAR Data. IROS 2014.
141	ACF-MR			44.79 %	58.29 %	39.94 %	0.6 s	1 core @ 3.5 Ghz (C/C++)
R. Rajaram, E. Ohn-Bar and M. Trivedi: Looking at Pedestrians at Different Scales: A Multi-resolution Approach and Evaluations. T-ITS 2016.
142	yyyyolo			44.55 %	60.74 %	39.96 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
143	HA-SSVM			43.87 %	58.76 %	38.81 %	21 s	1 core @ >3.5 Ghz (Matlab + C/C++)
J. Xu, S. Ramos, D. Vázquez and A. López: Hierarchical Adaptive Structural SVM for Domain Adaptation. IJCV 2016.
144	AB3DMOT		code	43.86 %	54.55 %	40.99 %	0.0047s	1 core @ 2.5 Ghz (python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
145	D4LCN		code	43.50 %	59.55 %	37.12 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
146	Pointpillar_TV			43.29 %	53.06 %	41.14 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
147	DPM-VOC+VP			43.26 %	59.21 %	38.12 %	8 s	1 core @ 2.5 Ghz (C/C++)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015.
148	ACF-SC			42.97 %	53.30 %	38.12 %	<0.3 s	1 core @ >3.5 Ghz (Matlab + C/C++)
C. Cadena, A. Dick and I. Reid: A Fast, Modular Scene Understanding System using Context-Aware Object Detection. Robotics and Automation (ICRA), 2015 IEEE International Conference on 2015.
149	SquaresICF		code	42.61 %	57.08 %	37.85 %	1 s	GPU @ >3.5 Ghz (C/C++)
R. Benenson, M. Mathias, T. Tuytelaars and L. Gool: Seeking the strongest rigid detector. CVPR 2013.
150	CG-Stereo			42.54 %	54.64 %	38.45 %	0.57 s	GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
151	DP3D			42.33 %	57.82 %	38.11 %	0.05 s	GPU @ 1.5 Ghz (Python + C/C++)
152	GNN			42.28 %	58.09 %	37.81 %	0.2 s	1 core @ 2.5 Ghz (Python)
153	BirdNet+		code	41.97 %	51.38 %	40.15 %	0.1 s	Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird's Eye View. arXiv:2003.04188 [cs.CV] 2020.
154	DP3D			41.71 %	55.28 %	35.73 %	0.07 s	GPU @ 1.5 Ghz (Python + C/C++)
155	MMRetina			41.63 %	59.63 %	36.97 %	0.38 s	GPU @ 2.5 Ghz (Python)
156	CSW3D			41.50 %	53.76 %	37.25 %	0.03 s	4 cores @ 2.5 Ghz (C/C++)
J. Hu, T. Wu, H. Fu, Z. Wang and K. Ding: Cascaded Sliding Window Based Real-Time 3D Region Proposal for Pedestrian Detection. ROBIO 2019.
157	HG-Mono			41.48 %	56.67 %	37.33 %	0.46 s	GPU @ 2.5 Ghz (C/C++)
158	M3D-RPN		code	41.46 %	56.64 %	37.31 %	0.16 s	GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
159	Stereo3D			41.46 %	56.20 %	37.07 %	0.1 s	GPU 1080Ti
160	yolov3_warp			40.64 %	55.04 %	36.33 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
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161	SubCat			40.50 %	53.75 %	35.66 %	1.2 s	6 cores @ 2.5 Ghz (Matlab + C/C++)
E. Ohn-Bar and M. Trivedi: Fast and Robust Object Detection Using Visual Subcategories. Computer Vision and Pattern Recognition Workshops Mobile Vision 2014.
162	SAANet			40.43 %	51.16 %	38.38 %	0.10 s	1 core @ 2.5 Ghz (Python)
163	Retinanet100			40.03 %	54.30 %	35.33 %	0.2 s	4 cores @ 2.5 Ghz (Python)
164	DSGN		code	39.93 %	49.28 %	38.13 %	0.67 s	NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
165	RT3D-GMP			39.83 %	55.56 %	35.18 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
166	SparsePool		code	39.59 %	50.81 %	35.91 %	0.13 s	8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
167	SparsePool		code	39.43 %	50.94 %	35.78 %	0.13 s	8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
168	AVOD		code	39.43 %	50.90 %	35.75 %	0.08 s	Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
169	PG-MonoNet			39.38 %	48.57 %	35.43 %	0.19 s	GPU @ 2.5 Ghz (Python)
170	softyolo			39.30 %	54.49 %	36.66 %	0.16 s	4 cores @ 2.5 Ghz (Python)
171	ACF			39.12 %	48.42 %	35.03 %	0.2 s	1 core @ >3.5 Ghz (Matlab + C/C++)
P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014. P. Doll\'ar: Piotr's Image and Video Matlab Toolbox (PMT). .
172	PB3D			38.62 %	50.26 %	34.87 %	0.42 s	1 core @ 2.5 Ghz (C/C++)
173	pedestrian_cnn			37.90 %	52.07 %	33.58 %	1 s	1 core @ 2.5 Ghz (C/C++)
174	LSVM-MDPM-sv			37.26 %	50.74 %	33.13 %	10 s	4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010. A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout. NIPS 2011.
175	multi-task CNN			37.00 %	49.38 %	33.46 %	25.1 ms	GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
176	Complexer-YOLO			36.45 %	42.16 %	32.91 %	0.06 s	GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
177	KD53-20			36.03 %	45.78 %	32.79 %	0.19 s	4 cores @ 2.5 Ghz (Python)
178	LSVM-MDPM-us		code	35.92 %	48.73 %	31.70 %	10 s	4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
179	Lidar_ROI+Yolo(UJS)			35.58 %	47.74 %	31.48 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
180				34.81 %	44.38 %	32.10 %
181	Vote3D			33.04 %	42.66 %	30.59 %	0.5 s	4 cores @ 2.8 Ghz (C/C++)
D. Wang and I. Posner: Voting for Voting in Online Point Cloud Object Detection. Proceedings of Robotics: Science and Systems 2015.
182	OC Stereo		code	30.79 %	43.50 %	28.40 %	0.35 s	1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
183	mBoW			30.26 %	41.52 %	26.34 %	10 s	1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
184	BirdNet			30.07 %	36.82 %	28.40 %	0.11 s	Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
185	RT3DStereo			29.30 %	41.12 %	25.25 %	0.08 s	GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
186	DPM-C8B1			25.34 %	36.40 %	22.00 %	15 s	4 cores @ 2.5 Ghz (Matlab + C/C++)
J. Yebes, L. Bergasa and M. García-Garrido: Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes. Sensors 2015. J. Yebes, L. Bergasa, R. Arroyo and A. Lázaro: Supervised learning and evaluation of KITTI's cars detector with DPM. IV 2014.
187	100Frcnn			21.92 %	34.07 %	19.48 %	2 s	4 cores @ 2.5 Ghz (Python + C/C++)
188	RefinedMPL			20.81 %	30.41 %	18.72 %	0.15 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
189	R-CNN_VGG			19.97 %	26.62 %	17.96 %	10 s	GPU @ 2.5 Ghz (Matlab + C/C++)
190	TopNet-Retina			16.45 %	22.37 %	15.43 %	52ms	GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
191	TopNet-HighRes			15.28 %	21.22 %	13.89 %	101ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
192	YOLOv2		code	11.46 %	15.37 %	9.67 %	0.02 s	GPU @ 3.5 Ghz (C/C++)
J. Redmon, S. Divvala, R. Girshick and A. Farhadi: You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016. J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
193	TopNet-UncEst			8.58 %	13.00 %	7.38 %	0.09 s	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
194	BIP-HETERO			7.05 %	8.51 %	6.30 %	~2 s	1 core @ 2.5 Ghz (C/C++)
A. Mekonnen, F. Lerasle, A. Herbulot and C. Briand: People Detection with Heterogeneous Features and Explicit Optimization on Computation Time. Pattern Recognition (ICPR), 2014 22nd International Conference on 2014.
195	CBNet			1.33 %	1.03 %	1.41 %	1 s	4 cores @ 2.5 Ghz (Python)
196	softretina			0.26 %	0.19 %	0.26 %	0.16 s	4 cores @ 2.5 Ghz (Python)
197	JSyolo			0.12 %	0.19 %	0.12 %	0.16 s	4 cores @ 2.5 Ghz (Python)
198	TopNet-DecayRate			0.01 %	0.01 %	0.01 %	92 ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
199	UM3D_TUM			0.00 %	0.00 %	0.00 %	0.05 s	1 core @ 2.5 Ghz (Python)