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			95.16 %	97.92 %	90.15 %	0.04 s	1 core @ 2.5 Ghz (Python)
4	3DSSD			95.10 %	97.69 %	92.18 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
5	MVRA + I-FRCNN+			94.98 %	95.87 %	82.52 %	0.18 s	GPU @ 2.5 Ghz (Python)
H. Choi, H. Kang and Y. Hyun: Multi-View Reprojection Architecture for Orientation Estimation. The IEEE International Conference on Computer Vision (ICCV) Workshops 2019.
6	MMLab PV-RCNN			94.70 %	98.17 %	92.04 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. arXiv preprint arXiv:1912.13192 2019.
7	BM-NET			94.49 %	95.09 %	85.06 %	0.5 s	GPU @ 2.5 Ghz (Python + C/C++)
8	TuSimple		code	94.47 %	95.12 %	86.45 %	1.6 s	GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016. K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016.
9	EPNet			94.44 %	96.15 %	89.99 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
10	CPRCCNN			94.42 %	96.33 %	89.96 %	0.1 s	1 core @ 2.5 Ghz (Python)
11	UberATG-MMF			94.25 %	97.41 %	89.87 %	0.08 s	GPU @ 2.5 Ghz (Python)
M. Liang*, B. Yang*, Y. Chen, R. Hu and R. Urtasun: Multi-Task Multi-Sensor Fusion for 3D Object Detection. CVPR 2019.
12	DGIST-CellBox			93.90 %	95.86 %	88.26 %	0.1 s	GPU @ 2.5 Ghz (Java + C/C++)
13	Patches - EMP			93.75 %	97.91 %	90.56 %	0.5 s	GPU @ 2.5 Ghz (Python)
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
14	Noah CV Lab - SSL			93.65 %	94.02 %	86.02 %	0.1 s	GPU @ 2.5 Ghz (Python)
15	THICV-YDM			93.60 %	96.26 %	81.08 %	0.06 s	GPU @ 2.5 Ghz (Python)
16	MLF_PointCas			93.55 %	96.69 %	86.16 %	0.1 s	GPU @ 2.5 Ghz (Python)
17	MonoPair			93.55 %	96.61 %	83.55 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
18	Deep MANTA			93.50 %	98.89 %	83.21 %	0.7 s	GPU @ 2.5 Ghz (Python + C/C++)
F. Chabot, M. Chaouch, J. Rabarisoa, C. Teulière and T. Chateau: Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image. CVPR 2017.
19	Point-GNN			93.50 %	96.58 %	88.35 %	0.6 s	GPU @ 2.5 Ghz (Python)
20	FichaDL			93.46 %	96.00 %	84.39 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
21	RRC		code	93.40 %	95.68 %	87.37 %	3.6 s	GPU @ 2.5 Ghz (C/C++)
J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y. Tai and L. Xu: Accurate Single Stage Detector Using Recurrent Rolling Convolution. CVPR 2017.
22	ORP			93.27 %	96.76 %	85.86 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
23	CFENet			93.26 %	93.91 %	86.99 %	4 s	GPU @ 2.5 Ghz (Python + C/C++)
24	STD			93.22 %	96.14 %	90.53 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
25	SARPNET			93.21 %	96.07 %	88.09 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
Y. Ye, H. Chen, C. Zhang, X. Hao and Z. Zhang: SARPNET: Shape Attention Regional Proposal Network for LiDAR-based 3D Object Detection. Neurocomputing 2019.
26	Fast Point R-CNN			93.18 %	96.13 %	87.68 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, S. Liu, X. Shen and J. Jia: Fast Point R-CNN. Proceedings of the IEEE international conference on computer vision (ICCV) 2019.
27	sensekitti		code	93.17 %	94.79 %	84.38 %	4.5 s	GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.
28	ELE			93.14 %	98.44 %	90.32 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
29	SJTU-HW			93.11 %	96.30 %	82.21 %	0.85s	GPU @ 1.5 Ghz (Python + C/C++)
S. Zhang, X. Zhao, L. Fang, F. Haiping and S. Haitao: LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR. IEEE International Conference on Image Processing 2018. L. Fang, X. Zhao and S. Zhang: Small-objectness sensitive detection based on shifted single shot detector. Multimedia Tools and Applications 2018.
30	RGB3D			93.07 %	96.54 %	88.04 %	0.39 s	GPU @ 2.5 Ghz (Python)
31	PointRCNN-deprecated			92.96 %	96.72 %	85.81 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
32	SerialR-FCN+SG-NMS			92.93 %	95.72 %	82.92 %	0.2 s	1 core @ 2.5 Ghz (Python)
33	PointCSE			92.78 %	95.99 %	87.66 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
34	Mono3CN			92.76 %	95.51 %	84.80 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
35	MuRF			92.74 %	95.74 %	87.64 %	0.05 s	GPU @ 1.5 Ghz (Python + C/C++)
36	SegVoxelNet			92.73 %	96.00 %	87.60 %	0.04 s	1 core @ 2.5 Ghz (Python)
H. Yi, S. Shi, M. Ding, J. Sun, K. Xu, H. Zhou, Z. Wang, S. Li and G. Wang: SegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloud. ICRA 2020.
37	Patches			92.72 %	96.34 %	87.63 %	0.15 s	GPU @ 2.0 Ghz
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
38	PPFNet		code	92.68 %	96.32 %	87.66 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
39	R-GCN			92.67 %	96.19 %	87.66 %	0.16 s	GPU @ 2.5 Ghz (Python)
40	PI-RCNN			92.66 %	96.17 %	87.68 %	0.1 s	1 core @ 2.5 Ghz (Python)
41	OHS-Direct			92.65 %	96.09 %	89.72 %	0.03 s	1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots. 2019.
42	NU-optim			92.63 %	95.67 %	87.37 %	0.04 s	GPU @ >3.5 Ghz (Python)
43	3D-CVF			92.60 %	96.20 %	89.60 %	0.05 s	GPU @ >3.5 Ghz (Python)
44	deprecated			92.59 %	96.21 %	89.58 %	0.05 s	GPU @ >3.5 Ghz (Python)
45	PointPainting			92.58 %	98.39 %	89.71 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. arXiv preprint arXiv:1911.10150 2019.
46	SPA			92.56 %	95.96 %	87.60 %	0.1 s	1 core @ 2.5 Ghz (Python)
47	DEFT			92.55 %	96.17 %	89.51 %	1 s	GPU @ 2.5 Ghz (Python)
48	CAASS-3D			92.47 %	96.31 %	87.67 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
49	PPBA			92.46 %	95.22 %	87.53 %	NA s	GPU @ 2.5 Ghz (Python)
50	TBU			92.46 %	95.22 %	87.53 %	NA s	GPU @ 2.5 Ghz (Python)
51	Associate-3Ddet			92.45 %	95.61 %	87.32 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
52	CP			92.44 %	96.14 %	87.58 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
53	YOLOv3.5			92.42 %	95.22 %	82.32 %	0.05 s	GPU @ 2.5 Ghz (Python)
54	OHS-Dense			92.39 %	95.84 %	89.51 %	0.03 s	1 core @ 2.5 Ghz (Python/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots. 2019.
55	PointRGCN			92.33 %	97.51 %	87.07 %	0.26 s	GPU @ V100 (Python)
56	F-ConvNet		code	92.19 %	95.85 %	80.09 %	0.47 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
57	IE-PointRCNN			92.08 %	96.01 %	87.05 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
58	SDP+RPN			92.03 %	95.16 %	79.16 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016. S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 2015.
59	AB3DMOT		code	92.00 %	95.88 %	86.98 %	0.0047s	1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
60	MMLab-PointRCNN		code	91.90 %	95.92 %	87.11 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
61	MMLab-PartA^2		code	91.86 %	95.03 %	89.06 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. arXiv preprint arXiv:1907.03670 2019.
62	MBR-SSD			91.83 %	93.46 %	84.97 %	4.0 s	GPU @ 2.5 Ghz (Python + C/C++)
63	epBRM		code	91.77 %	94.59 %	88.45 %	0.1 s	GPU @ >3.5 Ghz (Python + C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
64	MLF_SecCas			91.76 %	96.53 %	83.90 %	0.05 s	1 core @ 2.5 Ghz (Python)
65	C-GCN			91.73 %	95.64 %	86.37 %	0.147 s	GPU @ V100 (Python)
66	ITVD		code	91.73 %	95.85 %	79.31 %	0.3 s	GPU @ 2.5 Ghz (C/C++)
Y. Wei Liu: Improving Tiny Vehicle Detection in Complex Scenes. IEEE International Conference on Multimedia and Expo (ICME) 2018.
67	HRI-FusionRCNN			91.70 %	94.61 %	84.10 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
68	PiP			91.67 %	94.35 %	88.35 %	0.05 s	1 core @ 2.5 Ghz (Python)
69	SINet+		code	91.67 %	94.17 %	78.60 %	0.3 s	TITAN X GPU
X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
70	Faster RCNN + A			91.60 %	94.77 %	81.43 %	0.19 s	GPU @ 2.5 Ghz (Python + C/C++)
71	Cascade MS-CNN		code	91.60 %	94.26 %	78.84 %	0.25 s	GPU @ 2.5 Ghz (C/C++)
Z. Cai and N. Vasconcelos: Cascade R-CNN: High Quality Object Detection and Instance Segmentation. arXiv preprint arXiv:1906.09756 2019. Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A unified multi-scale deep convolutional neural network for fast object detection. European conference on computer vision 2016.
72	deprecated			91.59 %	94.34 %	79.14 %	0.05 s	GPU @ 2.0 Ghz (Python)
73	Det-RGBD			91.49 %	94.30 %	79.41 %	0.58 s	GPU @ 2.5 Ghz (Python + C/C++)
74	HRI-VoxelFPN			91.44 %	96.65 %	86.18 %	0.02 s	GPU @ 2.5 Ghz (Python + C/C++)
H. Kuang, B. Wang, J. An, M. Zhang and Z. Zhang: Voxel-FPN:multi-scale voxel feature aggregation in 3D object detection from point clouds. sensors 2020.
75	TBA			91.43 %	93.99 %	88.51 %	0.07 s	1 core @ 2.5 Ghz (Python)
76	RUC			91.40 %	95.02 %	88.41 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
77	Faster RCNN + G			91.28 %	94.34 %	81.02 %	1.1 s	GPU @ 2.5 Ghz (Python)
78	Faster RCNN + Gr + A			91.25 %	94.09 %	81.25 %	1.29 s	GPU @ 2.5 Ghz (Python)
79	Alibaba-AILabsX			91.23 %	96.33 %	83.75 %	0.05 s	1 core @ >3.5 Ghz (C/C++)
80	OACV			91.21 %	94.23 %	83.07 %	0.23 s	GPU @ 2.5 Ghz (Python)
81	CentrNet-v1			91.21 %	94.22 %	88.36 %	0.03 s	GPU @ 2.5 Ghz (Python)
82	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.
83	Faster RCNN + A			91.19 %	94.43 %	80.99 %	0.19 s	GPU @ 2.5 Ghz (Python)
84	LTN			91.18 %	94.68 %	81.51 %	0.4 s	GPU @ >3.5 Ghz (Python)
T. Wang, X. He, Y. Cai and G. Xiao: Learning a Layout Transfer Network for Context Aware Object Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
85	autonet			91.17 %	93.70 %	88.10 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
86	PointPiallars_SECA			91.12 %	93.66 %	87.94 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
87	DDB			91.12 %	93.71 %	87.34 %	0.05 s	GPU @ 2.5 Ghz (Python)
88	PCSC-Net			91.11 %	94.31 %	88.02 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
89	Aston-EAS			91.02 %	93.91 %	77.93 %	0.24 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Wei, J. He, Y. Zhou, K. Chen, Z. Tang and Z. Xiong: Enhanced Object Detection With Deep Convolutional Neural Networks for Advanced Driving Assistance. IEEE Transactions on Intelligent Transportation Systems 2019.
90	ARPNET			90.99 %	94.00 %	83.49 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
91	Bit			90.96 %	93.84 %	87.47 %	0.11 s	1 core @ 2.5 Ghz (C/C++)
92	MMV			90.91 %	94.16 %	83.36 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
93	JSU-NET			90.90 %	96.41 %	80.67 %	0.1 s	1 core @ 2.5 Ghz (Python)
94	GAFM			90.90 %	96.46 %	80.70 %	0.5 s	1 core @ 2.5 Ghz (Python)
95	GA_BALANCE			90.86 %	96.19 %	78.40 %	1 s	1 core @ 2.5 Ghz (Python)
96	A-VoxelNet			90.86 %	93.84 %	83.27 %	0.029 s	GPU @ 2.5 Ghz (Python)
97	MV3D			90.83 %	96.47 %	78.63 %	0.36 s	GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
98	MVSLN			90.81 %	96.12 %	83.39 %	0.1s s	1 core @ 2.5 Ghz (C/C++)
99	MPNet			90.80 %	94.68 %	87.30 %	0.02 s	GPU @ 2.5 Ghz (Python + C/C++)
100	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.
101	SINet_VGG		code	90.79 %	93.59 %	77.53 %	0.2 s	TITAN X GPU
X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
102	Tencent_ADlab_Lidar			90.74 %	93.80 %	86.75 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
103	GA_FULLDATA			90.73 %	96.31 %	78.22 %	1 s	4 cores @ 2.5 Ghz (Python)
104	SRF			90.69 %	95.88 %	85.52 %	0.05 s	GPU @ 2.5 Ghz (Python + C/C++)
105	HR-SECOND		code	90.68 %	93.72 %	85.63 %	0.11 s	1 core @ 2.5 Ghz (Python + C/C++)
106	GA2500			90.68 %	95.86 %	80.29 %	0.2 s	1 core @ 2.5 Ghz (Python)
107	GA_rpn500			90.68 %	95.86 %	80.29 %	1 s	1 core @ 2.5 Ghz (Python)
108	TANet		code	90.67 %	93.67 %	85.31 %	0.035s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
109	SFB-SECOND			90.67 %	96.17 %	85.43 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
110	SECOND-V1.5		code	90.65 %	95.96 %	85.35 %	0.04 s	GPU @ 2.0 Ghz (Python + C/C++)
111	PTS		code	90.64 %	95.74 %	85.41 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
112	VOXEL_FPN_HR			90.55 %	93.76 %	85.42 %	0.12 s	8 cores @ 2.5 Ghz (Python)
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113	FOFNet			90.52 %	94.00 %	85.20 %	0.04 s	GPU @ 2.5 Ghz (Python)
114	MP			90.50 %	93.86 %	85.17 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
115	Sogo_MM			90.46 %	94.31 %	80.62 %	1.5 s	GPU @ 2.5 Ghz (C/C++)
116	bigger_ga			90.38 %	95.76 %	77.92 %	1 s	1 core @ 2.5 Ghz (Python)
117	AtrousDet			90.35 %	95.94 %	77.94 %	0.05 s	TITAN X
118	SCNet			90.30 %	95.59 %	85.09 %	0.04 s	GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
119	RUC		code	90.24 %	92.60 %	86.55 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
120	Deep3DBox			90.19 %	94.71 %	76.82 %	1.5 s	GPU @ 2.5 Ghz (C/C++)
A. Mousavian, D. Anguelov, J. Flynn and J. Kosecka: 3D Bounding Box Estimation Using Deep Learning and Geometry. CVPR 2017.
121	FQNet			90.17 %	94.72 %	76.78 %	0.5 s	1 core @ 2.5 Ghz (Python)
L. Liu, J. Lu, C. Xu, Q. Tian and J. Zhou: Deep Fitting Degree Scoring Network for Monocular 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
122	BVVF			90.15 %	95.65 %	84.95 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
123	FCY			90.15 %	93.27 %	86.60 %	0.02 s	GPU @ 2.5 Ghz (Python)
124	SAANet			90.14 %	95.93 %	82.95 %	0.10 s	1 core @ 2.5 Ghz (Python)
125	DeepStereoOP			90.06 %	95.15 %	79.91 %	3.4 s	GPU @ 3.5 Ghz (Matlab + C/C++)
C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.
126	SubCNN			89.98 %	94.26 %	79.78 %	2 s	GPU @ 3.5 Ghz (Python + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.
127	MLOD		code	89.97 %	94.88 %	84.98 %	0.12 s	GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
128	GPP		code	89.96 %	94.02 %	81.13 %	0.23 s	GPU @ 1.5 Ghz (Python + C/C++)
A. Rangesh and M. Trivedi: Ground plane polling for 6dof pose estimation of objects on the road. arXiv preprint arXiv:1811.06666 2018.
129	RUC		code	89.93 %	93.12 %	85.44 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
130	ZRNet(ResNet-50)			89.92 %	95.24 %	79.69 %	0.04 s	GPU @ 2.5 Ghz (Python)
131	AVOD		code	89.88 %	95.17 %	82.83 %	0.08 s	Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
132	SINet_PVA		code	89.86 %	92.72 %	76.47 %	0.11 s	TITAN X GPU
X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
133	ZRNet			89.72 %	93.97 %	79.47 %	0.04 s	GPU @ 2.5 Ghz (Python)
134	3DOP		code	89.55 %	92.96 %	79.38 %	3s	GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
135	PAD			89.49 %	93.43 %	85.85 %	0.15 s	1 core @ 2.5 Ghz (Python)
136	Mono3D		code	89.37 %	94.52 %	79.15 %	4.2 s	GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.
137	4D-MSCNN+CRL			89.37 %	92.40 %	77.00 %	0.2 s	GPU @ 2.5 Ghz (Matlab + C/C++)
138	MonoDIS			89.15 %	94.61 %	78.37 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
139	cas+res+soft			89.14 %	94.54 %	78.37 %	0.2 s	4 cores @ 2.5 Ghz (Python)
140	merge12-12			88.96 %	94.58 %	78.22 %	0.2 s	4 cores @ 2.5 Ghz (Python)
141	AVOD-FPN		code	88.92 %	94.70 %	84.13 %	0.1 s	Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
142	autoRUC			88.88 %	94.23 %	81.35 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
143	Prune			88.85 %	94.20 %	81.31 %	0.11 s	1 core @ 2.5 Ghz (C/C++)
144	AM3D			88.71 %	92.55 %	77.78 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
X. Ma, Z. Wang, H. Li, P. Zhang, W. Ouyang and X. Fan: Accurate Monocular Object Detection via Color- Embedded 3D Reconstruction for Autonomous Driving. Proceedings of the IEEE international Conference on Computer Vision (ICCV) 2019.
145	SS3D_HW			88.68 %	94.49 %	68.79 %	0.4 s	GPU @ 2.5 Ghz (Python)
146	MS-CNN		code	88.68 %	93.87 %	76.11 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection. ECCV 2016.
147	CRCNNA			88.59 %	94.82 %	76.74 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
148	3DNN			88.56 %	94.52 %	81.51 %	0.09 s	GPU @ 2.5 Ghz (Python)
149	CSFADet			88.54 %	93.75 %	78.62 %	0.05 s	GPU @ 2.5 Ghz (Python)
150	MonoPSR		code	88.50 %	93.63 %	73.36 %	0.2 s	GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
151	Shift R-CNN (mono)		code	88.48 %	94.07 %	78.34 %	0.25 s	GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
152	DPSM			88.47 %	93.67 %	75.62 %	0.1 s	GPU @ 2.5 Ghz (Python)
153	MM-MRFC			88.46 %	95.54 %	78.14 %	0.05 s	GPU @ 2.5 Ghz (C/C++)
A. Costea, R. Varga and S. Nedevschi: Fast Boosting based Detection using Scale Invariant Multimodal Multiresolution Filtered Features. CVPR 2017.
154	PointRes			88.41 %	95.38 %	84.22 %	0.013 s	1 core @ 2.5 Ghz (Python + C/C++)
155	TridentNet			88.37 %	90.33 %	80.57 %	0.2 s	GPU @ 2.5 Ghz (Python)
156	PP-3D			88.35 %	93.71 %	80.84 %	0.1 s	1 core @ 2.5 Ghz (Python)
157	3DBN			88.29 %	93.74 %	80.74 %	0.13s	1080Ti (Python+C/C++)
X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.
158	NLK			87.89 %	91.65 %	83.32 %	0.02 s	1 core @ 2.5 Ghz (Python)
159	Multi-3D			87.87 %	93.70 %	76.07 %	0.15 s	1 core @ 2.5 Ghz (C/C++)
160	ga50			87.65 %	95.76 %	75.14 %	1 s	1 core @ 2.5 Ghz (Python)
161	cas_retina			87.64 %	93.87 %	75.30 %	0.2 s	4 cores @ 2.5 Ghz (Python)
162	SMOKE			87.51 %	93.21 %	77.66 %	0.03 s	GPU @ 2.5 Ghz (Python)
163	MonoSS			87.46 %	93.15 %	77.58 %	0.03 s	GPU @ 2.5 Ghz (Python + C/C++)
164	cascadercnn			87.36 %	89.37 %	73.42 %	0.36 s	4 cores @ 2.5 Ghz (Python)
165	SCANet			87.28 %	92.91 %	81.99 %	0.17 s	>8 cores @ 2.5 Ghz (Python)
166	RTM3D		code	86.92 %	91.85 %	77.41 %	0.05 s	GPU @ 1.0 Ghz (Python)
P. Li, H. Zhao, P. Liu and F. Cao: RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving. 2020.
167	anm			86.52 %	94.88 %	76.46 %	3 s	1 core @ 2.5 Ghz (C/C++)
168	DSGN		code	86.43 %	95.53 %	78.75 %	0.67 s	NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. arXiv preprint arXiv:2001.03398 2020.
169	ReSqueeze			86.12 %	90.35 %	76.53 %	0.03 s	GPU @ >3.5 Ghz (Python)
170	Stereo R-CNN		code	85.98 %	93.98 %	71.25 %	0.3 s	GPU @ 2.5 Ghz (Python)
P. Li, X. Chen and S. Shen: Stereo R-CNN based 3D Object Detection for Autonomous Driving. CVPR 2019.
171	StereoFENet			85.70 %	91.48 %	77.62 %	0.15 s	1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
172	ResNet-RRC w/RGBD			85.58 %	91.32 %	74.80 %	0.057 s	GPU @ 1.5 Ghz (Python + C/C++)
173	cas_retina_1_13			85.48 %	91.54 %	74.60 %	0.03 s	4 cores @ 2.5 Ghz (Python)
174	NEUAV			85.42 %	89.67 %	77.28 %	0.06 s	GPU @ 2.5 Ghz (Python)
175	ResNet-RRC			85.33 %	91.45 %	74.27 %	0.06 s	GPU @ 1.5 Ghz (Python + C/C++)
H. Jeon and . others: High-Speed Car Detection Using ResNet- Based Recurrent Rolling Convolution. Proceedings of the IEEE conference on systems, man, and cybernetics 2018.
176	Cmerge			85.32 %	93.40 %	70.57 %	0.2 s	4 cores @ 2.5 Ghz (Python)
177	PL++ (SDN+GDC)		code	85.15 %	94.95 %	77.78 %	0.6 s	GPU @ 2.5 Ghz (C/C++)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.
178	RAR-Net			85.08 %	89.04 %	69.26 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
179	M3D-RPN		code	85.08 %	89.04 %	69.26 %	0.16 s	GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
180	SDP+CRC (ft)			85.00 %	92.06 %	71.71 %	0.6 s	GPU @ 2.5 Ghz (C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
181	IDA-3D			84.92 %	92.79 %	74.75 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
182	SS3D			84.92 %	92.72 %	70.35 %	48 ms	Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
183	LPN			84.77 %	89.19 %	74.08 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
184	MonoFENet			84.63 %	91.68 %	76.71 %	0.15 s	1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
185	SECA			84.60 %	92.51 %	79.53 %	1 s	GPU @ 2.5 Ghz (Python)
186	PG-MonoNet			84.42 %	88.61 %	68.59 %	0.19 s	GPU @ 2.5 Ghz (Python)
187	MV3D (LIDAR)			84.39 %	93.08 %	79.27 %	0.24 s	GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
188	Complexer-YOLO			84.16 %	91.92 %	79.62 %	0.06 s	GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
189	ZoomNet		code	83.92 %	94.22 %	69.00 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
L. Z. Xu: ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2020.
190	D4LCN		code	83.67 %	90.34 %	65.33 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. arXiv preprint arXiv:1912.04799 2019.
191	ASOD			83.52 %	94.09 %	68.68 %	0.28 s	GPU @ 2.5 Ghz (Python)
192	softretina			83.30 %	93.55 %	70.59 %	0.16 s	4 cores @ 2.5 Ghz (Python)
193	Faster R-CNN		code	83.16 %	88.97 %	72.62 %	2 s	GPU @ 3.5 Ghz (Python + C/C++)
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards Real- Time Object Detection with Region Proposal Networks. NIPS 2015.
194	ZKNet			82.96 %	92.17 %	72.43 %	0.01 s	GPU @ 2.0 Ghz (Python)
195	Pseudo-LiDAR++		code	82.90 %	94.46 %	75.45 %	0.4 s	GPU @ 2.5 Ghz (Python)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.
196	Retinanet100			82.73 %	93.97 %	68.37 %	0.2 s	4 cores @ 2.5 Ghz (Python)
197	BS3D			82.72 %	95.35 %	70.01 %	22 ms	Titan Xp
N. Gählert, J. Wan, M. Weber, J. Zöllner, U. Franke and J. Denzler: Beyond Bounding Boxes: Using Bounding Shapes for Real-Time 3D Vehicle Detection from Monocular RGB Images. 2019 IEEE Intelligent Vehicles Symposium (IV) 2019.
198	Pseudo-LiDAR E2E			82.54 %	94.00 %	75.31 %	0.4 s	GPU @ 2.5 Ghz (Python)
199	Disp R-CNN			82.47 %	93.15 %	70.35 %	0.42 s	GPU @ 2.5 Ghz (Python + C/C++)
200	Disp R-CNN (velo)			82.40 %	93.11 %	70.26 %	0.42 s	GPU @ 2.5 Ghz (Python + C/C++)
201	cascade_gw			82.35 %	85.98 %	71.60 %	0.2 s	4 cores @ 2.5 Ghz (Python)
202	FRCNN+Or		code	82.00 %	92.91 %	68.79 %	0.09 s	Titan Xp GPU
C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine 2018. C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features. IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.
203	CBNet			81.70 %	91.47 %	72.02 %	1 s	4 cores @ 2.5 Ghz (Python)
204	Resnet101Faster rcnn			81.44 %	91.08 %	71.52 %	1 s	1 core @ 2.5 Ghz (Python)
205	yyyyolo			81.33 %	94.36 %	68.72 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
206	A3DODWTDA (image)		code	81.25 %	78.96 %	70.56 %	0.8 s	GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
207	RefineNet			81.01 %	91.91 %	65.67 %	0.20 s	GPU @ 2.5 Ghz (Matlab + C++)
R. Rajaram, E. Bar and M. Trivedi: RefineNet: Refining Object Detectors for Autonomous Driving. IEEE Transactions on Intelligent Vehicles 2016. R. Rajaram, E. Bar and M. Trivedi: RefineNet: Iterative Refinement for Accurate Object Localization. Intelligent Transportation Systems Conference 2016.
208	MTDP			80.97 %	89.03 %	66.91 %	0.15 s	GPU @ 2.0 Ghz (Python)
209	RFCN_RFB			80.89 %	88.07 %	69.66 %	0.2 s	4 cores @ 2.5 Ghz (Python)
210	Manhnet			80.85 %	89.06 %	64.29 %	26 ms	1 core @ 2.5 Ghz (C/C++)
211	centernet			80.78 %	90.29 %	70.53 %	0.01 s	GPU @ 2.5 Ghz (Python)
212	3D-GCK			80.19 %	89.55 %	68.08 %	24 ms	Tesla V100
213	RADNet-Fusion			80.04 %	76.72 %	76.78 %	0.1 s	1 core @ 2.5 Ghz (Python)
214	NM		code	79.98 %	90.71 %	68.98 %	0.01 s	GPU @ 2.5 Ghz (Python)
215	RADNet-LIDAR			79.59 %	75.20 %	76.03 %	0.1 s	1 core @ 2.5 Ghz (Python)
216	MMRetina			79.53 %	89.66 %	69.52 %	0.38 s	GPU @ 2.5 Ghz (Python)
217	SceneNet			79.26 %	90.70 %	67.98 %	0.03 s	GPU @ 2.5 Ghz (C/C++)
218	A3DODWTDA		code	79.15 %	82.98 %	68.30 %	0.08 s	GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
219	MTNAS			78.82 %	88.96 %	67.07 %	0.02 s	1 core @ 2.5 Ghz (python)
220	spLBP			78.66 %	81.66 %	61.69 %	1.5 s	8 cores @ 2.5 Ghz (Matlab + C/C++)
Q. Hu, S. Paisitkriangkrai, C. Shen, A. Hengel and F. Porikli: Fast Detection of Multiple Objects in Traffic Scenes With a Common Detection Framework. IEEE Trans. Intelligent Transportation Systems 2016.
221	dgist_multiDetNet			78.26 %	93.58 %	70.04 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
222	3D-SSMFCNN		code	78.19 %	77.92 %	69.19 %	0.1 s	GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.
223	MonoGRNet		code	77.94 %	88.65 %	63.31 %	0.04s	NVIDIA P40
Z. Qin, J. Wang and Y. Lu: MonoGRNet: A Geometric Reasoning Network for 3D Object Localization. The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) 2019.
224	yolov3_warp			77.61 %	92.24 %	65.70 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
225	Reinspect		code	77.48 %	90.27 %	66.73 %	2s	1 core @ 2.5 Ghz (C/C++)
R. Stewart, M. Andriluka and A. Ng: End-to-End People Detection in Crowded Scenes. CVPR 2016.
226	multi-task CNN			77.18 %	86.12 %	68.09 %	25.1 ms	GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
227	YoloMono3D			77.02 %	92.32 %	57.09 %	0.05 s	GPU @ 2.5 Ghz (Python)
228	Regionlets			76.99 %	88.75 %	60.49 %	1 s	>8 cores @ 2.5 Ghz (C/C++)
X. Wang, M. Yang, S. Zhu and Y. Lin: Regionlets for Generic Object Detection. T-PAMI 2015. W. Zou, X. Wang, M. Sun and Y. Lin: Generic Object Detection with Dense Neural Patterns and Regionlets. British Machine Vision Conference 2014. C. Long, X. Wang, G. Hua, M. Yang and Y. Lin: Accurate Object Detection with Location Relaxation and Regionlets Relocalization. Asian Conference on Computer Vision 2014.
229	3DVP		code	76.98 %	84.95 %	65.78 %	40 s	8 cores @ 3.5 Ghz (Matlab + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Data-Driven 3D Voxel Patterns for Object Category Recognition. IEEE Conference on Computer Vision and Pattern Recognition 2015.
230	FailNet-Fusion			76.90 %	74.55 %	71.94 %	0.1 s	1 core @ 2.5 Ghz (Python)
231	RTL3D			76.74 %	79.68 %	72.56 %	0.02 s	GPU @ 2.5 Ghz (Python)
232	avodC			76.58 %	87.30 %	71.65 %	0.1 s	GPU @ 2.5 Ghz (Python)
233	SubCat		code	76.36 %	84.10 %	60.56 %	0.7 s	6 cores @ 3.5 Ghz (Matlab + C/C++)
E. Ohn-Bar and M. Trivedi: Learning to Detect Vehicles by Clustering Appearance Patterns. T-ITS 2015.
234	GS3D			76.35 %	86.23 %	62.67 %	2 s	1 core @ 2.5 Ghz (C/C++)
B. Li, W. Ouyang, L. Sheng, X. Zeng and X. Wang: GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
235	FailNet-LIDAR			76.26 %	74.16 %	71.24 %	0.1 s	1 core @ 2.5 Ghz (Python)
236	AOG		code	76.24 %	86.08 %	61.51 %	3 s	4 cores @ 2.5 Ghz (Matlab)
T. Wu, B. Li and S. Zhu: Learning And-Or Models to Represent Context and Occlusion for Car Detection and Viewpoint Estimation. TPAMI 2016. B. Li, T. Wu and S. Zhu: Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model. ECCV 2014.
237	bin			76.16 %	78.73 %	63.39 %	15ms s	GPU @ >3.5 Ghz (Python)
238	Pose-RCNN			75.83 %	89.59 %	64.06 %	2 s	>8 cores @ 2.5 Ghz (Python)
M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on 2016.
239	VoxelNet(Unofficial)			75.22 %	81.37 %	68.74 %	0.5 s	GPU @ 2.0 Ghz (Python)
240	RFCN			75.14 %	83.04 %	61.55 %	0.2 s	4 cores @ 2.5 Ghz (Python)
241	myfaster-rcnn-v1.5			74.93 %	89.85 %	62.56 %	0.1 s	1 core @ 2.5 Ghz (Python)
242	3D FCN			74.65 %	86.74 %	67.85 %	>5 s	1 core @ 2.5 Ghz (C/C++)
B. Li: 3D Fully Convolutional Network for Vehicle Detection in Point Cloud. IROS 2017.
243	OC Stereo			74.60 %	87.39 %	62.56 %	0.35 s	1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. arXiv preprint arXiv:1909.07566 2019.
244	yolo800			74.31 %	78.93 %	63.83 %	0.13 s	4 cores @ 2.5 Ghz (Python)
245	3DVSSD			74.11 %	86.99 %	63.57 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
246	Multi-task DG			74.07 %	91.06 %	64.48 %	0.06 s	GPU @ 2.5 Ghz (Python)
247	FD2			73.93 %	88.65 %	64.62 %	0.01 s	GPU @ >3.5 Ghz (Python + C/C++)
248	BdCost+DA+BB+MS			73.72 %	85.18 %	57.79 %	TBD s	4 cores @ 2.5 Ghz (Matlab + C/C++)
249	m-prcnn			73.64 %	87.64 %	57.03 %	0.43 s	1 core @ 2.5 Ghz (Python)
250	BdCost+DA+MS			73.62 %	85.03 %	58.94 %	TBD s	4 cores @ 2.5 Ghz (Matlab/C++)
251	Int-YOLO		code	73.23 %	75.81 %	63.59 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
252	stereo_sa			72.99 %	87.88 %	63.49 %	0.3 s	GPU @ 2.5 Ghz (Python)
253	RuiRUC			72.08 %	87.48 %	55.28 %	0.12 s	1 core @ 2.5 Ghz (Python)
254	ANM			71.97 %	87.17 %	55.19 %	0.12 s	1 core @ 2.5 Ghz (Python)
255	RFBnet			71.66 %	87.25 %	63.00 %	0.2 s	4 cores @ 2.5 Ghz (Python)
256	AOG-View			71.26 %	85.01 %	55.73 %	3 s	1 core @ 2.5 Ghz (Matlab, C/C++)
B. Li, T. Wu and S. Zhu: Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model. ECCV 2014.
257	GPVL			71.06 %	81.67 %	54.96 %	10 s	1 core @ 2.5 Ghz (C/C++)
258	BdCost+DA+BB			70.86 %	85.52 %	56.19 %	TBD s	4 cores @ 2.5 Ghz (Matlab + C/C++)
259	MV-RGBD-RF			70.70 %	77.89 %	57.41 %	4 s	4 cores @ 2.5 Ghz (C/C++)
A. Gonzalez, D. Vazquez, A. Lopez and J. Amores: On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts.. IEEE Trans. on Cybernetics 2016. A. Gonzalez, G. Villalonga, J. Xu, D. Vazquez, J. Amores and A. Lopez: Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection. IEEE Intelligent Vehicles Symposium (IV) 2015.
260	Vote3Deep			70.30 %	78.95 %	63.12 %	1.5 s	4 cores @ 2.5 Ghz (C/C++)
M. Engelcke, D. Rao, D. Zeng Wang, C. Hay Tong and I. Posner: Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks. ArXiv e-prints 2016.
261	ROI-10D			70.16 %	76.56 %	61.15 %	0.2 s	GPU @ 3.5 Ghz (Python)
F. Manhardt, W. Kehl and A. Gaidon: ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape. Computer Vision and Pattern Recognition (CVPR) 2019.
262	fasterrcnn			69.45 %	74.76 %	60.20 %	0.2 s	4 cores @ 2.5 Ghz (Python)
263	myfaster-rcnn			68.38 %	90.54 %	55.97 %	0.01 s	1 core @ 2.5 Ghz (Python)
264	Decoupled-3D v2			68.17 %	88.64 %	54.74 %	0.08 s	GPU @ 2.5 Ghz (C/C++)
265	Decoupled-3D			67.92 %	87.78 %	54.53 %	0.08 s	GPU @ 2.5 Ghz (C/C++)
266	Pseudo-Lidar		code	67.79 %	85.40 %	58.50 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Wang, W. Chao, D. Garg, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
267	OC-DPM			67.06 %	79.07 %	52.61 %	10 s	8 cores @ 2.5 Ghz (Matlab)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Occlusion Patterns for Object Class Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2013.
268	mymask-rcnn			66.82 %	88.60 %	52.18 %	0.3 s	1 core @ 2.5 Ghz (Python)
269	Fast-SSD			66.79 %	85.19 %	57.89 %	0.06 s	GTX650Ti
270	DPM-VOC+VP			66.72 %	82.15 %	49.01 %	8 s	1 core @ 2.5 Ghz (C/C++)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015.
271	BdCost48LDCF		code	66.63 %	81.38 %	52.20 %	0.5 s	8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
272	E-VoxelNet			65.33 %	68.00 %	57.84 %	0.1 s	GPU @ 2.5 Ghz (Python)
273	RefinedMPL			65.24 %	88.29 %	53.20 %	0.15 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
274	BdCost48-25C			64.63 %	81.42 %	52.22 %	4 s	1 core @ 2.5 Ghz (C/C++)
275	MDPM-un-BB			64.06 %	79.74 %	49.07 %	60 s	4 core @ 2.5 Ghz (MATLAB)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
276	PDV-Subcat			63.24 %	78.27 %	47.67 %	7 s	1 core @ 2.5 Ghz (C/C++)
J. Shen, X. Zuo, J. Li, W. Yang and H. Ling: A novel pixel neighborhood differential statistic feature for pedestrian and face detection . Pattern Recognition 2017.
277	MODet			62.54 %	66.06 %	60.04 %	0.05 s	GTX1080Ti
Y. Zhang, Z. Xiang, C. Qiao and S. Chen: Accurate and Real-Time Object Detection Based on Bird's Eye View on 3D Point Clouds. 2019 International Conference on 3D Vision (3DV) 2019.
278	yl_net			61.78 %	66.00 %	60.36 %	0.03 s	GPU @ 2.5 Ghz (Python)
279	Lidar_ROI+Yolo(UJS)			61.71 %	73.32 %	53.65 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
280	GNN			61.48 %	79.09 %	51.06 %	0.2 s	1 core @ 2.5 Ghz (Python)
281	SubCat48LDCF		code	61.16 %	78.86 %	44.69 %	0.5 s	8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
282	DPM-C8B1			60.21 %	75.24 %	44.73 %	15 s	4 cores @ 2.5 Ghz (Matlab + C/C++)
J. Yebes, L. Bergasa and M. García-Garrido: Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes. Sensors 2015. J. Yebes, L. Bergasa, R. Arroyo and A. Lázaro: Supervised learning and evaluation of KITTI's cars detector with DPM. IV 2014.
283	SDP-Net-s			59.94 %	65.51 %	57.20 %	0.01 s	GPU @ 2.5 Ghz (Python)
284	RADNet-Mono			59.85 %	67.47 %	54.14 %	0.1 s	1 core @ 2.5 Ghz (Python)
285	monoref3d			58.97 %	78.11 %	47.72 %	0.1 s	1 core @ 2.5 Ghz (Python)
286	ref3D			58.97 %	78.11 %	47.72 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
287	100Frcnn			58.92 %	82.09 %	49.04 %	2 s	4 cores @ 2.5 Ghz (Python + C/C++)
288	SAMME48LDCF		code	58.38 %	77.47 %	44.43 %	0.5 s	8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
289	LSVM-MDPM-sv			58.36 %	71.11 %	43.22 %	10 s	4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010. A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout. NIPS 2011.
290	ref3D			57.16 %	77.96 %	45.99 %	0.1 s	1 core @ 2.5 Ghz (Python)
291	BirdNet			57.12 %	79.30 %	55.16 %	0.11 s	Titan Xp GPU
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
292	ACF-SC			56.60 %	69.90 %	43.61 %	<0.3 s	1 core @ >3.5 Ghz (Matlab + C/C++)
C. Cadena, A. Dick and I. Reid: A Fast, Modular Scene Understanding System using Context-Aware Object Detection. Robotics and Automation (ICRA), 2015 IEEE International Conference on 2015.
293	LSVM-MDPM-us		code	55.95 %	68.94 %	41.45 %	10 s	4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
294	mylsi-faster-rcnn			55.81 %	80.45 %	47.38 %	0.3 s	1 core @ 2.5 Ghz (Python)
295	ACF			54.09 %	63.05 %	41.81 %	0.2 s	1 core @ >3.5 Ghz (Matlab + C/C++)
P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014. P. Doll\'ar: Piotr's Image and Video Matlab Toolbox (PMT). .
296	Mono3D_PLiDAR		code	53.36 %	80.85 %	44.80 %	0.1 s	NVIDIA GeForce 1080 (pytorch)
X. Weng and K. Kitani: Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud. arXiv:1903.09847 2019.
297	VeloFCN			51.82 %	70.53 %	45.70 %	1 s	GPU @ 2.5 Ghz (Python + C/C++)
B. Li, T. Zhang and T. Xia: Vehicle Detection from 3D Lidar Using Fully Convolutional Network. RSS 2016 .
298	FailNet-Mono			47.95 %	59.59 %	41.33 %	0.1 s	1 core @ 2.5 Ghz (Python)
299	SF			46.68 %	60.62 %	38.22 %	0.5 s	1 core @ 2.5 Ghz (Python)
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300	softyolo			45.97 %	66.08 %	38.02 %	0.16 s	4 cores @ 2.5 Ghz (Python)
301	Vote3D			45.94 %	54.38 %	40.48 %	0.5 s	4 cores @ 2.8 Ghz (C/C++)
D. Wang and I. Posner: Voting for Voting in Online Point Cloud Object Detection. Proceedings of Robotics: Science and Systems 2015.
302	TopNet-HighRes			45.85 %	58.04 %	41.11 %	101ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
303	RT3DStereo			45.81 %	56.53 %	37.63 %	0.08 s	GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
304	Multimodal Detection		code	45.46 %	63.91 %	37.25 %	0.06 s	GPU @ 3.5 Ghz (Matlab + C/C++)
A. Asvadi, L. Garrote, C. Premebida, P. Peixoto and U. Nunes: Multimodal vehicle detection: fusing 3D- LIDAR and color camera data. Pattern Recognition Letters 2017.
305	RT3D			39.69 %	50.33 %	40.04 %	0.09 s	GPU @ 1.8Ghz
Y. Zeng, Y. Hu, S. Liu, J. Ye, Y. Han, X. Li and N. Sun: RT3D: Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving. IEEE Robotics and Automation Letters 2018.
306	VoxelJones		code	36.31 %	43.89 %	34.16 %	.18 s	1 core @ 2.5 Ghz (Python + C/C++)
M. Motro and J. Ghosh: Vehicular Multi-object Tracking with Persistent Detector Failures. arXiv preprint arXiv:1907.11306 2019.
307	Licar			35.19 %	42.34 %	33.97 %	0.09 s	GPU @ 2.0 Ghz (Python)
308	KD53-20			34.76 %	51.76 %	29.39 %	0.19 s	4 cores @ 2.5 Ghz (Python)
309	SAIC-SA-3D			31.16 %	41.51 %	29.83 %	0.05 s	GPU @ 2.5 Ghz (Python)
310	FCN-Depth		code	25.05 %	52.32 %	18.07 %	1 s	GPU @ 1.5 Ghz (Matlab + C/C++)
311	CSoR		code	21.66 %	31.52 %	17.99 %	3.5 s	4 cores @ >3.5 Ghz (Python + C/C++)
L. Plotkin: PyDriver: Entwicklung eines Frameworks für räumliche Detektion und Klassifikation von Objekten in Fahrzeugumgebung. 2015.
312	mBoW			21.59 %	35.22 %	16.89 %	10 s	1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
313	R-CNN_VGG			21.36 %	29.38 %	16.61 %	10 s	GPU @ 2.5 Ghz (Matlab + C/C++)
314	DepthCN		code	21.18 %	37.45 %	16.08 %	2.3 s	GPU @ 3.5 Ghz (Matlab)
A. Asvadi, L. Garrote, C. Premebida, P. Peixoto and U. Nunes: DepthCN: vehicle detection using 3D- LIDAR and convnet. IEEE ITSC 2017.
315	YOLOv2		code	14.31 %	26.74 %	10.94 %	0.02 s	GPU @ 3.5 Ghz (C/C++)
J. Redmon, S. Divvala, R. Girshick and A. Farhadi: You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016. J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
316	TopNet-UncEst			6.24 %	7.24 %	5.42 %	0.09 s	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
317	TopNet-Retina			5.00 %	6.82 %	4.52 %	52ms	GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
318	FCPP			0.07 %	0.01 %	0.07 %	0.02 s	1 core @ 2.0 Ghz (Python + C/C++)
319	ANM			0.01 %	0.01 %	0.02 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
320	TopNet-DecayRate			0.01 %	0.00 %	0.01 %	92 ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
321	LaserNet			0.00 %	0.00 %	0.00 %	12 ms	GPU @ 2.5 Ghz (C/C++)
G. Meyer, A. Laddha, E. Kee, C. Vallespi-Gonzalez and C. Wellington: LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
322	JSyolo			0.00 %	0.00 %	0.00 %	0.16 s	4 cores @ 2.5 Ghz (Python)

Pedestrian

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	FichaDL			82.50 %	90.75 %	75.66 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
2	DGIST-CellBox			81.29 %	90.04 %	76.92 %	0.1 s	GPU @ 2.5 Ghz (Java + C/C++)
3	Alibaba-CityBrain			81.19 %	90.99 %	74.68 %	1.5 s	GPU @ 2.5 Ghz (Python + C/C++)
4	ExtAtt			81.05 %	90.60 %	76.08 %	1.2 s	GPU @ 2.5 Ghz (Python + C/C++)
5	F-PointNet		code	80.13 %	89.83 %	75.05 %	0.17 s	GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
6	HWFD			79.06 %	88.94 %	73.98 %	0.21 s	one 1080Ti
7	TuSimple		code	78.40 %	88.87 %	73.66 %	1.6 s	GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016. K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016.
8	Argus_detection_v1			77.01 %	84.86 %	72.15 %	0.25 s	GPU @ 1.5 Ghz (C/C++)
9	RRC		code	76.61 %	85.98 %	71.47 %	3.6 s	GPU @ 2.5 Ghz (C/C++)
J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y. Tai and L. Xu: Accurate Single Stage Detector Using Recurrent Rolling Convolution. CVPR 2017.
10	ECP Faster R-CNN			76.25 %	85.96 %	70.55 %	0.25 s	GPU @ 2.5 Ghz (Python)
M. Braun, S. Krebs, F. Flohr and D. Gavrila: The EuroCity Persons Dataset: A Novel Benchmark for Object Detection. CoRR 2018.
11	Aston-EAS			76.07 %	86.71 %	70.02 %	0.24 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Wei, J. He, Y. Zhou, K. Chen, Z. Tang and Z. Xiong: Enhanced Object Detection With Deep Convolutional Neural Networks for Advanced Driving Assistance. IEEE Transactions on Intelligent Transportation Systems 2019.
12	MHN			75.99 %	87.21 %	69.50 %	0.39 s	GPU @ 2.5 Ghz (Python)
J. Cao, Y. Pang, S. Zhao and X. Li: High-Level Semantic Networks for Multi- Scale Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2019.
13	FFNet		code	75.81 %	87.17 %	69.86 %	1.07 s	GPU @ 1.5 Ghz (Python)
C. Zhao, Y. Qian and M. Yang: Monocular Pedestrian Orientation Estimation Based on Deep 2D-3D Feedforward. Pattern Recognition 2019.
14	SJTU-HW			75.81 %	87.17 %	69.86 %	0.85s	GPU @ 1.5 Ghz (Python + C/C++)
S. Zhang, X. Zhao, L. Fang, F. Haiping and S. Haitao: LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR. IEEE International Conference on Image Processing 2018. L. Fang, X. Zhao and S. Zhang: Small-objectness sensitive detection based on shifted single shot detector. Multimedia Tools and Applications 2018.
15	THICV-YDM			75.65 %	89.04 %	68.72 %	0.06 s	GPU @ 2.5 Ghz (Python)
16	Noah CV Lab - SSL			75.64 %	86.57 %	70.53 %	0.1 s	GPU @ 2.5 Ghz (Python)
17	Multi-3D			75.32 %	86.00 %	69.55 %	0.15 s	1 core @ 2.5 Ghz (C/C++)
18	Faster RCNN + Gr + A			74.95 %	86.95 %	69.50 %	1.29 s	GPU @ 2.5 Ghz (Python)
19	MS-CNN		code	74.89 %	85.71 %	68.99 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection. ECCV 2016.
20	Faster RCNN + G			73.75 %	85.51 %	68.54 %	1.1 s	GPU @ 2.5 Ghz (Python)
21	F-ConvNet		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	TridentNet			70.07 %	83.42 %	65.28 %	0.2 s	GPU @ 2.5 Ghz (Python)
33	CSFADet			70.07 %	84.72 %	64.81 %	0.05 s	GPU @ 2.5 Ghz (Python)
34	Mono3CN			69.75 %	83.47 %	63.15 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
35	3DOP		code	69.57 %	83.17 %	63.48 %	3s	GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
36	MonoPSR		code	68.56 %	85.60 %	63.34 %	0.2 s	GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
37	DeepStereoOP			68.46 %	83.00 %	63.35 %	3.4 s	GPU @ 3.5 Ghz (Matlab + C/C++)
C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.
38	sensekitti		code	68.41 %	82.72 %	62.72 %	4.5 s	GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.
39	Mono3D		code	67.29 %	80.30 %	62.23 %	4.2 s	GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.
40	dgist_multiDetNet			67.03 %	81.96 %	61.39 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
41	YOLOv3.5			66.54 %	83.87 %	61.45 %	0.05 s	GPU @ 2.5 Ghz (Python)
42	Faster R-CNN		code	66.24 %	79.97 %	61.09 %	2 s	GPU @ 3.5 Ghz (Python + C/C++)
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards Real- Time Object Detection with Region Proposal Networks. NIPS 2015.
43	AtrousDet			64.97 %	80.79 %	58.36 %	0.05 s	TITAN X
44	SDP+CRC (ft)			64.36 %	79.22 %	59.16 %	0.6 s	GPU @ 2.5 Ghz (C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
45	CRCNNA			63.69 %	78.10 %	58.41 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
46	Pose-RCNN			63.54 %	80.07 %	57.02 %	2 s	>8 cores @ 2.5 Ghz (Python)
M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on 2016.
47	PCN			63.41 %	80.08 %	58.55 %	0.6 s
48	CFM			62.84 %	74.76 %	56.06 %	<2 s	GPU @ 2.5 Ghz (Matlab + C/C++)
Q. Hu, P. Wang, C. Shen, A. Hengel and F. Porikli: Pushing the Limits of Deep CNNs for Pedestrian Detection. IEEE Transactions on Circuits and Systems for Video Technology 2017.
49	merge12-12			62.84 %	80.27 %	56.08 %	0.2 s	4 cores @ 2.5 Ghz (Python)
50	cas+res+soft			62.71 %	80.11 %	55.99 %	0.2 s	4 cores @ 2.5 Ghz (Python)
51	cas_retina			62.37 %	79.82 %	57.15 %	0.2 s	4 cores @ 2.5 Ghz (Python)
52	cas_retina_1_13			61.87 %	79.09 %	56.70 %	0.03 s	4 cores @ 2.5 Ghz (Python)
53	MonoPair			61.57 %	78.81 %	56.51 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
54	ReSqueeze			61.33 %	73.69 %	56.65 %	0.03 s	GPU @ >3.5 Ghz (Python)
55	RPN+BF		code	61.22 %	77.06 %	55.22 %	0.6 s	GPU @ 2.5 Ghz (Matlab + C/C++)
L. Zhang, L. Lin, X. Liang and K. He: Is Faster R-CNN Doing Well for Pedestrian Detection?. ECCV 2016.
56	JSU-NET			61.19 %	83.17 %	56.20 %	0.1 s	1 core @ 2.5 Ghz (Python)
57	Regionlets			60.83 %	73.79 %	54.72 %	1 s	>8 cores @ 2.5 Ghz (C/C++)
X. Wang, M. Yang, S. Zhu and Y. Lin: Regionlets for Generic Object Detection. T-PAMI 2015. W. Zou, X. Wang, M. Sun and Y. Lin: Generic Object Detection with Dense Neural Patterns and Regionlets. British Machine Vision Conference 2014. C. Long, X. Wang, G. Hua, M. Yang and Y. Lin: Accurate Object Detection with Location Relaxation and Regionlets Relocalization. Asian Conference on Computer Vision 2014.
58	bin			60.73 %	71.43 %	55.78 %	15ms s	GPU @ >3.5 Ghz (Python)
59	OHS-Direct			60.63 %	69.37 %	57.64 %	0.03 s	1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots. 2019.
60	3DSSD			60.51 %	72.33 %	56.28 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
61	anm			60.35 %	76.02 %	55.46 %	3 s	1 core @ 2.5 Ghz (C/C++)
62	PiP			59.94 %	70.52 %	56.51 %	0.05 s	1 core @ 2.5 Ghz (Python)
63	cascadercnn			59.50 %	78.79 %	54.44 %	0.36 s	4 cores @ 2.5 Ghz (Python)
64	A-VoxelNet			59.07 %	69.90 %	56.49 %	0.029 s	GPU @ 2.5 Ghz (Python)
65	TANet		code	59.07 %	69.90 %	56.44 %	0.035s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
66	mymask-rcnn			58.89 %	72.55 %	52.58 %	0.3 s	1 core @ 2.5 Ghz (Python)
67	OHS-Dense			58.70 %	68.18 %	54.68 %	0.03 s	1 core @ 2.5 Ghz (Python/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: Object as Hotspots: An Anchor-Free 3D Object Detection Approach via Firing of Hotspots. 2019.
68	DDB			58.53 %	69.03 %	55.90 %	0.05 s	GPU @ 2.5 Ghz (Python)
69	MMLab PV-RCNN			58.37 %	68.88 %	55.38 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. arXiv preprint arXiv:1912.13192 2019.
70	Point-GNN			58.20 %	71.59 %	54.06 %	0.6 s	GPU @ 2.5 Ghz (Python)
71	DeepParts			58.15 %	71.47 %	51.92 %	~1 s	GPU @ 2.5 Ghz (Matlab)
Y. Tian, P. Luo, X. Wang and X. Tang: Deep Learning Strong Parts for Pedestrian Detection. ICCV 2015.
72	CompACT-Deep			58.14 %	70.93 %	52.29 %	1 s	1 core @ 2.5 Ghz (Matlab + C/C++)
Z. Cai, M. Saberian and N. Vasconcelos: Learning Complexity-Aware Cascades for Deep Pedestrian Detection. ICCV 2015.
73	PPBA			58.06 %	67.73 %	55.69 %	NA s	GPU @ 2.5 Ghz (Python)
74	MMLab-PartA^2		code	57.96 %	68.78 %	54.01 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. arXiv preprint arXiv:1907.03670 2019.
75	AVOD-FPN		code	57.87 %	67.95 %	55.23 %	0.1 s	Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
76	LPN			57.69 %	71.87 %	53.21 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
77	TBU			57.44 %	67.29 %	54.00 %	NA s	GPU @ 2.5 Ghz (Python)
78	LDAM			56.68 %	64.73 %	54.21 %	24 ms	GTX 1080 ti GPU
79	FRCNN+Or		code	56.68 %	71.64 %	51.53 %	0.09 s	Titan Xp GPU
C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine 2018. C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features. IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.
80	yolo800			56.67 %	71.26 %	50.91 %	0.13 s	4 cores @ 2.5 Ghz (Python)
81	ZKNet			56.58 %	71.15 %	51.87 %	0.01 s	GPU @ 2.0 Ghz (Python)
82	CentrNet-v1			56.57 %	66.27 %	54.19 %	0.03 s	GPU @ 2.5 Ghz (Python)
83	FilteredICF			56.53 %	69.79 %	50.32 %	~ 2 s	>8 cores @ 2.5 Ghz (Matlab + C/C++)
S. Zhang, R. Benenson and B. Schiele: Filtered Channel Features for Pedestrian Detection. CVPR 2015.
84	Tencent_ADlab_Lidar			56.49 %	66.91 %	54.06 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
85	ARPNET			56.42 %	69.08 %	52.69 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
86	FD2			56.35 %	71.37 %	51.08 %	0.01 s	GPU @ >3.5 Ghz (Python + C/C++)
87	MV-RGBD-RF			56.18 %	72.99 %	49.72 %	4 s	4 cores @ 2.5 Ghz (C/C++)
A. Gonzalez, D. Vazquez, A. Lopez and J. Amores: On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts.. IEEE Trans. on Cybernetics 2016. A. Gonzalez, G. Villalonga, J. Xu, D. Vazquez, J. Amores and A. Lopez: Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection. IEEE Intelligent Vehicles Symposium (IV) 2015.
88	RFCN			55.96 %	72.32 %	49.66 %	0.2 s	4 cores @ 2.5 Ghz (Python)
89	MLOD		code	55.62 %	68.42 %	51.45 %	0.12 s	GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
90	PointPillars		code	55.10 %	65.29 %	52.39 %	16 ms	1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
91	STD			55.04 %	68.33 %	50.85 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
92	RFCN_RFB			54.98 %	70.61 %	48.75 %	0.2 s	4 cores @ 2.5 Ghz (Python)
93	Vote3Deep			54.80 %	67.99 %	51.17 %	1.5 s	4 cores @ 2.5 Ghz (C/C++)
M. Engelcke, D. Rao, D. Zeng Wang, C. Hay Tong and I. Posner: Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks. ArXiv e-prints 2016.
94	CHTTL MMF			54.28 %	72.79 %	49.31 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
95	epBRM		code	54.13 %	62.90 %	51.95 %	0.10 s	1 core @ 2.5 Ghz (C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
96	NM		code	54.05 %	69.81 %	49.32 %	0.01 s	GPU @ 2.5 Ghz (Python)
97	PointPainting			53.76 %	61.86 %	50.61 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. arXiv preprint arXiv:1911.10150 2019.
98	PDV2			53.54 %	65.59 %	47.65 %	3.7 s	1 core @ 3.0 Ghz Matlab (C/C++)
J. Shen, X. Zuo, J. Li, W. Yang and H. Ling: A novel pixel neighborhood differential statistic feature for pedestrian and face detection . Pattern Recognition 2017.
99	fasterrcnn			53.42 %	69.29 %	48.76 %	0.2 s	4 cores @ 2.5 Ghz (Python)
100	TAFT			53.15 %	67.62 %	47.08 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
J. Shen, X. Zuo, W. Yang, D. Prokhorov, X. Mei and H. Ling: Differential Features for Pedestrian Detection: A Taylor Series Perspective. IEEE Transactions on Intelligent Transportation Systems 2018.
101	pAUCEnsT			52.88 %	65.84 %	46.97 %	60 s	1 core @ 2.5 Ghz (Matlab + C/C++)
S. Paisitkriangkrai, C. Shen and A. Hengel: Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning. arXiv 2014.
102	Multi-task DG			52.87 %	71.27 %	48.10 %	0.06 s	GPU @ 2.5 Ghz (Python)
103	NEUAV			52.53 %	68.50 %	47.99 %	0.06 s	GPU @ 2.5 Ghz (Python)
104	deprecated			52.32 %	67.93 %	47.77 %	0.05 s	GPU @ 2.0 Ghz (Python)
105	PP-3D			52.11 %	63.07 %	49.79 %	0.1 s	1 core @ 2.5 Ghz (Python)
106	SF			51.83 %	67.73 %	47.45 %	0.5 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
107	MTDP			51.81 %	68.12 %	46.95 %	0.15 s	GPU @ 2.0 Ghz (Python)
108	Shift R-CNN (mono)		code	51.30 %	70.86 %	46.37 %	0.25 s	GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
109	SCANet			51.23 %	65.06 %	47.06 %	0.17 s	>8 cores @ 2.5 Ghz (Python)
110	centernet			51.09 %	69.27 %	45.40 %	0.01 s	GPU @ 2.5 Ghz (Python)
111	myfaster-rcnn-v1.5			50.95 %	67.68 %	46.29 %	0.1 s	1 core @ 2.5 Ghz (Python)
112	FCY			50.88 %	59.73 %	48.61 %	0.02 s	GPU @ 2.5 Ghz (Python)
113	PPFNet		code	50.52 %	57.82 %	47.44 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
114	FOFNet			50.08 %	62.64 %	46.27 %	0.04 s	GPU @ 2.5 Ghz (Python)
115	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.
116	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.
117	Resnet101Faster rcnn			49.12 %	64.72 %	44.60 %	1 s	1 core @ 2.5 Ghz (Python)
118	VOXEL_FPN_HR			49.09 %	60.28 %	45.47 %	0.12 s	8 cores @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
119	SS3D_HW			49.01 %	64.67 %	42.86 %	0.4 s	GPU @ 2.5 Ghz (Python)
120	cascade_gw			48.99 %	67.35 %	44.25 %	0.2 s	4 cores @ 2.5 Ghz (Python)
121	Int-YOLO		code	48.76 %	64.09 %	44.31 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
122	MP			48.73 %	60.26 %	45.05 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
123	ACFD		code	48.63 %	61.62 %	44.15 %	0.2 s	4 cores @ >3.5 Ghz (C/C++)
M. Dimitrievski, P. Veelaert and W. Philips: Semantically aware multilateral filter for depth upsampling in automotive LiDAR point clouds. IEEE Intelligent Vehicles Symposium, IV 2017, Los Angeles, CA, USA, June 11-14, 2017 2017.
124	R-CNN			48.57 %	62.88 %	43.05 %	4 s	GPU @ 3.3 Ghz (C/C++)
J. Hosang, M. Omran, R. Benenson and B. Schiele: Taking a Deeper Look at Pedestrians. arXiv 2015.
125	mylsi-faster-rcnn			47.99 %	65.03 %	43.43 %	0.3 s	1 core @ 2.5 Ghz (Python)
126	HR-SECOND		code	46.69 %	58.68 %	42.93 %	0.11 s	1 core @ 2.5 Ghz (Python + C/C++)
127	Cmerge			46.51 %	63.68 %	41.60 %	0.2 s	4 cores @ 2.5 Ghz (Python)
128	SS3D			45.79 %	61.58 %	41.14 %	48 ms	Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
129	ACF			45.67 %	59.81 %	40.88 %	1 s	1 core @ 3.5 Ghz (Matlab + C/C++)
P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014.
130	Fusion-DPM		code	44.99 %	58.93 %	40.19 %	~ 30 s	1 core @ 3.5 Ghz (Matlab + C/C++)
C. Premebida, J. Carreira, J. Batista and U. Nunes: Pedestrian Detection Combining RGB and Dense LIDAR Data. IROS 2014.
131	ACF-MR			44.79 %	58.29 %	39.94 %	0.6 s	1 core @ 3.5 Ghz (C/C++)
R. Rajaram, E. Ohn-Bar and M. Trivedi: Looking at Pedestrians at Different Scales: A Multi-resolution Approach and Evaluations. T-ITS 2016.
132	yyyyolo			44.55 %	60.74 %	39.96 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
133	HA-SSVM			43.87 %	58.76 %	38.81 %	21 s	1 core @ >3.5 Ghz (Matlab + C/C++)
J. Xu, S. Ramos, D. Vázquez and A. López: Hierarchical Adaptive Structural SVM for Domain Adaptation. IJCV 2016.
134	AB3DMOT		code	43.86 %	54.55 %	40.99 %	0.0047s	1 core @ 2.5 Ghz (python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
135	D4LCN		code	43.50 %	59.55 %	37.12 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. arXiv preprint arXiv:1912.04799 2019.
136	DPM-VOC+VP			43.26 %	59.21 %	38.12 %	8 s	1 core @ 2.5 Ghz (C/C++)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015.
137	PG-MonoNet			43.12 %	58.51 %	38.92 %	0.19 s	GPU @ 2.5 Ghz (Python)
138	ACF-SC			42.97 %	53.30 %	38.12 %	<0.3 s	1 core @ >3.5 Ghz (Matlab + C/C++)
C. Cadena, A. Dick and I. Reid: A Fast, Modular Scene Understanding System using Context-Aware Object Detection. Robotics and Automation (ICRA), 2015 IEEE International Conference on 2015.
139	SquaresICF		code	42.61 %	57.08 %	37.85 %	1 s	GPU @ >3.5 Ghz (C/C++)
R. Benenson, M. Mathias, T. Tuytelaars and L. Gool: Seeking the strongest rigid detector. CVPR 2013.
140	GNN			42.28 %	58.09 %	37.81 %	0.2 s	1 core @ 2.5 Ghz (Python)
141	MMRetina			41.63 %	59.63 %	36.97 %	0.38 s	GPU @ 2.5 Ghz (Python)
142	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.
143	M3D-RPN		code	41.46 %	56.64 %	37.31 %	0.16 s	GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
144	RTM3D		code	41.43 %	58.14 %	37.09 %	0.05 s	GPU @ 1.0 Ghz (Python)
P. Li, H. Zhao, P. Liu and F. Cao: RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving. 2020.
145	myfaster-rcnn			41.22 %	56.87 %	36.99 %	0.01 s	1 core @ 2.5 Ghz (Python)
146	yolov3_warp			40.64 %	55.04 %	36.33 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
147	SubCat			40.50 %	53.75 %	35.66 %	1.2 s	6 cores @ 2.5 Ghz (Matlab + C/C++)
E. Ohn-Bar and M. Trivedi: Fast and Robust Object Detection Using Visual Subcategories. Computer Vision and Pattern Recognition Workshops Mobile Vision 2014.
148	SAANet			40.43 %	51.16 %	38.38 %	0.10 s	1 core @ 2.5 Ghz (Python)
149	Retinanet100			40.03 %	54.30 %	35.33 %	0.2 s	4 cores @ 2.5 Ghz (Python)
150	DSGN		code	39.93 %	49.28 %	38.13 %	0.67 s	NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. arXiv preprint arXiv:2001.03398 2020.
151	SparsePool		code	39.59 %	50.81 %	35.91 %	0.13 s	8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
152	SparsePool		code	39.43 %	50.94 %	35.78 %	0.13 s	8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
153	AVOD		code	39.43 %	50.90 %	35.75 %	0.08 s	Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
154	softyolo			39.30 %	54.49 %	36.66 %	0.16 s	4 cores @ 2.5 Ghz (Python)
155	ACF			39.12 %	48.42 %	35.03 %	0.2 s	1 core @ >3.5 Ghz (Matlab + C/C++)
P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014. P. Doll\'ar: Piotr's Image and Video Matlab Toolbox (PMT). .
156	pedestrian_cnn			37.90 %	52.07 %	33.58 %	1 s	1 core @ 2.5 Ghz (C/C++)
157	LSVM-MDPM-sv			37.26 %	50.74 %	33.13 %	10 s	4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010. A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout. NIPS 2011.
158	multi-task CNN			37.00 %	49.38 %	33.46 %	25.1 ms	GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
159	Complexer-YOLO			36.45 %	42.16 %	32.91 %	0.06 s	GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
160	KD53-20			36.03 %	45.78 %	32.79 %	0.19 s	4 cores @ 2.5 Ghz (Python)
161	LSVM-MDPM-us		code	35.92 %	48.73 %	31.70 %	10 s	4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
162	Lidar_ROI+Yolo(UJS)			35.58 %	47.74 %	31.48 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
163				34.81 %	44.38 %	32.10 %
164	Vote3D			33.04 %	42.66 %	30.59 %	0.5 s	4 cores @ 2.8 Ghz (C/C++)
D. Wang and I. Posner: Voting for Voting in Online Point Cloud Object Detection. Proceedings of Robotics: Science and Systems 2015.
165	OC Stereo			30.79 %	43.50 %	28.40 %	0.35 s	1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. arXiv preprint arXiv:1909.07566 2019.
166	mBoW			30.26 %	41.52 %	26.34 %	10 s	1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.
167	BirdNet			30.07 %	36.82 %	28.40 %	0.11 s	Titan Xp GPU
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
168	RT3DStereo			29.30 %	41.12 %	25.25 %	0.08 s	GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
169	DPM-C8B1			25.34 %	36.40 %	22.00 %	15 s	4 cores @ 2.5 Ghz (Matlab + C/C++)
J. Yebes, L. Bergasa and M. García-Garrido: Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes. Sensors 2015. J. Yebes, L. Bergasa, R. Arroyo and A. Lázaro: Supervised learning and evaluation of KITTI's cars detector with DPM. IV 2014.
170	100Frcnn			21.92 %	34.07 %	19.48 %	2 s	4 cores @ 2.5 Ghz (Python + C/C++)
171	RefinedMPL			20.81 %	30.41 %	18.72 %	0.15 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
172	R-CNN_VGG			19.97 %	26.62 %	17.96 %	10 s	GPU @ 2.5 Ghz (Matlab + C/C++)
173	TopNet-Retina			16.45 %	22.37 %	15.43 %	52ms	GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
174	TopNet-HighRes			15.28 %	21.22 %	13.89 %	101ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
175	YOLOv2		code	11.46 %	15.37 %	9.67 %	0.02 s	GPU @ 3.5 Ghz (C/C++)
J. Redmon, S. Divvala, R. Girshick and A. Farhadi: You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016. J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
176	TopNet-UncEst			8.58 %	13.00 %	7.38 %	0.09 s	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
177	BIP-HETERO			7.05 %	8.51 %	6.30 %	~2 s	1 core @ 2.5 Ghz (C/C++)
A. Mekonnen, F. Lerasle, A. Herbulot and C. Briand: People Detection with Heterogeneous Features and Explicit Optimization on Computation Time. Pattern Recognition (ICPR), 2014 22nd International Conference on 2014.
178	CBNet			1.33 %	1.03 %	1.41 %	1 s	4 cores @ 2.5 Ghz (Python)
179	softretina			0.26 %	0.19 %	0.26 %	0.16 s	4 cores @ 2.5 Ghz (Python)
180	JSyolo			0.12 %	0.19 %	0.12 %	0.16 s	4 cores @ 2.5 Ghz (Python)
181	TopNet-DecayRate			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.