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	ZEEWAIN-AI			96.14 %	95.22 %	88.94 %	0.3 s	GPU @ 2.5 Ghz (Python)
2	CLOCs_PVCas			95.96 %	96.76 %	91.08 %	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.
3	EA-M-RCNN(BorderAtt)			95.88 %	96.68 %	90.89 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
4	PVGNet			95.80 %	96.87 %	93.05 %	0.05 s	1 core @ >3.5 Ghz (C/C++)
5	ADLAB			95.69 %	96.69 %	90.81 %	0.05 s	1 core @ >3.5 Ghz (C/C++)
6	SE-SSD			95.60 %	96.69 %	90.53 %	0.03 s	1 core @ 2.5 Ghz (Python + C/C++)
7	HUAWEI Octopus			95.50 %	96.30 %	92.81 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
8	SPANet			95.46 %	96.54 %	90.47 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
9	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.
10	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.
11	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.
12	Voxel R-CNN			95.11 %	96.49 %	92.45 %	0.04 s	GPU @ 3.0 Ghz (C/C++)
13	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.
14	PV-RCNN-v2			95.05 %	96.08 %	92.42 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
15	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.
16	MSG-PGNN			94.92 %	95.98 %	92.42 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
17	Fast VP-RCNN		code	94.91 %	96.01 %	92.22 %	0.05 s	1 core @ >3.5 Ghz (python)
18	SimpleDET			94.90 %	95.98 %	92.39 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
19	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.
20	CVRS VIC-Net			94.69 %	95.79 %	91.89 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
21	PC-RGNN			94.68 %	95.80 %	92.20 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
22	D3D			94.66 %	95.43 %	89.72 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
23	DSA-PV-RCNN			94.64 %	95.86 %	92.10 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
24	FSA-PVRCNN			94.63 %	95.81 %	92.06 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
25	nonet			94.62 %	95.86 %	91.86 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
26	RangeIoUDet			94.61 %	95.74 %	91.98 %	0.02 s	1 core @ 2.5 Ghz (Python)
27	MSL3D			94.60 %	95.76 %	92.16 %	0.03 s	GPU @ 2.5 Ghz (Python)
28	Multi-Sensor3D			94.60 %	95.76 %	92.16 %	0.03 s	GPU @ 2.5 Ghz (Python)
29	CN			94.60 %	97.86 %	89.81 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
30	HyBrid Feature Det			94.59 %	95.76 %	92.18 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
31	ReFineNet			94.59 %	95.75 %	92.12 %	0.08 s	1 core @ 2.5 Ghz (Python)
32	MGACNet			94.57 %	95.35 %	91.77 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
33	RangeRCNN-LV			94.51 %	95.93 %	92.07 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
34	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.
35	PF-GAP			94.47 %	96.13 %	90.15 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
36	EPNet		code	94.44 %	96.15 %	89.99 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
T. Huang, Z. Liu, X. Chen and X. Bai: EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection. ECCV 2020.
37	SERCNN			94.42 %	96.33 %	89.96 %	0.1 s	1 core @ 2.5 Ghz (Python)
D. Zhou, J. Fang, X. Song, L. Liu, J. Yin, Y. Dai, H. Li and R. Yang: Joint 3D Instance Segmentation and Object Detection for Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020.
38	CVRS VIC-RCNN			94.38 %	95.89 %	91.90 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
39	CVRS_PF			94.37 %	95.56 %	91.43 %	0.09 s	1 core @ 2.5 Ghz (C/C++)
40	CVIS-DF3D_v2			94.33 %	95.70 %	91.72 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
41	SVGA-Net			94.28 %	95.69 %	91.73 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
42	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.
43	SRDL			94.24 %	95.86 %	91.80 %	0.15 s	GPU @ 2.5 Ghz (Python + C/C++)
44	Baseline of CA RCNN			94.23 %	95.84 %	91.80 %	0.1 s	GPU @ 2.5 Ghz (Python)
45	CVIS-DF3D			94.23 %	95.84 %	91.80 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
46	tbd		code	94.21 %	95.68 %	91.49 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
47	GAP-soft-filter			94.20 %	95.81 %	91.53 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
48	HR-faster-rcnn			94.14 %	95.41 %	86.88 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
49	RangeRCNN			94.03 %	95.48 %	91.74 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, M. Zhang, Z. Zhang, X. Zhao and S. Pu: RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representation. arXiv preprint arXiv:2009.00206 2020.
50	OAP			93.93 %	96.85 %	86.37 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
51	HRI-MSP-L			93.92 %	95.51 %	91.42 %	0.07 s	1 core @ 2.5 Ghz (C/C++)
52	AF_V1			93.87 %	94.45 %	86.37 %	0.1 s	1 core @ 2.5 Ghz (Python)
53	Associate-3Ddet_v2			93.77 %	96.83 %	88.57 %	0.04 s	1 core @ 2.5 Ghz (Python)
54	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.
55	CIA-SSD			93.72 %	96.87 %	86.20 %	0.03 s	1 core @ 2.5 Ghz (Python + C/C++)
56	VAL			93.71 %	96.92 %	83.76 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
57	XView-PartA^2			93.71 %	95.42 %	91.26 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
58	HRI-ADLab-HZ			93.69 %	96.70 %	88.66 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
59	deprecated			93.68 %	96.92 %	86.15 %	deprecated	deprecated
60	Noah CV Lab - SSL			93.65 %	94.02 %	86.02 %	0.1 s	GPU @ 2.5 Ghz (Python)
61	CBi-GNN			93.60 %	98.89 %	88.47 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
62	MVX-Net++			93.58 %	96.41 %	88.51 %	0.15 s	1 core @ 2.5 Ghz (C/C++)
63	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.
64	EBM3DOD			93.54 %	96.81 %	88.33 %	0.08 s	1 core @ 2.5 Ghz (Python)
65	CIA-SSD v2			93.52 %	96.63 %	88.21 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
66	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.
67	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.
68	PP-3D			93.50 %	96.58 %	88.35 %	0.1 s	1 core @ 2.5 Ghz (Python)
69	FCY			93.49 %	96.74 %	88.39 %	0.02 s	GPU @ 2.5 Ghz (Python)
70	LZnet			93.49 %	96.74 %	88.10 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
71	CJJ			93.48 %	96.68 %	90.63 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
72	AIMC-RUC			93.47 %	96.75 %	88.35 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
73	PointRes			93.47 %	96.69 %	90.46 %	0.013 s	1 core @ 2.5 Ghz (Python + C/C++)
74	dgist_multiDetNet			93.46 %	94.99 %	85.46 %	0.08 s	GPU Titanx Pascal (Python)
75	scssd-normal(0.3)			93.45 %	96.72 %	88.31 %	0.05 s	GPU @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: SCSSD for multi sensor fusion 3d object detection method. Submitted to Information Science 2020.
76	EBM3DOD baseline			93.45 %	96.72 %	88.25 %	0.08 s	1 core @ 2.5 Ghz (Python)
77	MVAF-Net		code	93.42 %	96.27 %	90.56 %	0.07 s	1 core @ 2.5 Ghz (Python + C/C++)
G. Wang, B. Tian, Y. Zhang, L. Chen, D. Cao and J. Wu: Multi-View Adaptive Fusion Network for 3D Object Detection. 2020.
78	Cas-SSD			93.41 %	96.73 %	88.30 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
79	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.
80	DGIST MT-CNN			93.39 %	95.16 %	85.50 %	0.09 s	GPU @ 1.0 Ghz (Python)
81	KNN-GCNN			93.39 %	96.19 %	88.17 %	0.4 s	1 core @ 2.5 Ghz (Python + C/C++)
82	F-3DNet			93.38 %	96.51 %	88.32 %	0.5 s	GPU @ 2.5 Ghz (Python)
83	HR-Cascade-RCNN			93.37 %	95.74 %	87.44 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
84	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.
85	ISF			93.36 %	96.64 %	90.52 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
86	FLID			93.35 %	95.90 %	85.69 %	0.04 s	GPU @ 2.5 Ghz (Python)
87	ISF-v2			93.34 %	96.73 %	90.54 %	0.04 s	1 core @ 2.5 Ghz (Python)
88	scssd-normal(0.4)			93.31 %	96.59 %	88.23 %	0.05 s	1 core @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: SCSSD for multi sensor fusion 3d object detection method. Submitted to Information Science 2020.
89	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.
90	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.
91	RoIFusion		code	93.19 %	96.29 %	88.14 %	0.22 s	1 core @ 3.0 Ghz (Python)
92	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.
93	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.
94	RethinkDet3D			93.14 %	96.16 %	88.17 %	0.15 s	1 core @ 2.5 Ghz (Python)
95	Discrete-PointDet			93.14 %	96.36 %	87.82 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
96	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.
97	BLPNet_V2			93.11 %	96.07 %	88.06 %	0.04 s	1 core @ 2.5 Ghz (Python)
98	PVF-NET			93.08 %	96.03 %	88.04 %	0.1 s	1 core @ 2.5 Ghz (Python)
99	HVPR			93.04 %	95.91 %	87.88 %	0.02 s	GPU @ 2.5 Ghz (Python)
100	SerialR-FCN+SG-NMS			93.03 %	95.81 %	83.00 %	0.2 s	1 core @ 2.5 Ghz (Python)
101	NLK-ALL		code	92.98 %	95.73 %	88.13 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
102	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.
103	cvMax			92.84 %	96.14 %	87.87 %	0.04 s	GPU @ >3.5 Ghz (Python)
104	TBD			92.82 %	96.06 %	88.00 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
105	HotSpotNet			92.81 %	96.21 %	89.80 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
106	deprecated			92.79 %	95.56 %	91.62 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
107	deprecated			92.79 %	96.12 %	87.78 %	0.04 s	GPU @ 2.5 Ghz (Python)
108	PointCSE			92.78 %	95.99 %	87.66 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
109	IGRP			92.78 %	96.28 %	87.81 %	0.18 s	1 core @ 2.5 Ghz (Python + C/C++)
110	Mono3CN			92.76 %	95.51 %	84.80 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
111	MuRF			92.74 %	95.74 %	87.64 %	0.05 s	GPU @ 1.5 Ghz (Python + C/C++)
112	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.
113	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.
114	CenterNet3D			92.69 %	95.76 %	89.81 %	0.04 s	GPU @ 1.5 Ghz (Python)
G. Wang, B. Tian, Y. Ai, T. Xu, L. Chen and D. Cao: CenterNet3D:An Anchor free Object Detector for Autonomous Driving. 2020.
115	Chovy			92.69 %	96.06 %	89.74 %	0.04 s	GPU @ 2.5 Ghz (Python)
116	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.
117	NLK-3D			92.67 %	95.44 %	87.72 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
118	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.
119	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.
120	VICNet			92.56 %	95.56 %	87.40 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
121	DASS			92.53 %	96.23 %	87.75 %	0.09 s	1 core @ 2.0 Ghz (Python)
O. Unal, L. Gool and D. Dai: Improving Point Cloud Semantic Segmentation by Learning 3D Object Detection. 2020.
122	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.
123	PPBA			92.46 %	95.22 %	87.53 %	NA s	GPU @ 2.5 Ghz (Python)
124	TBU			92.46 %	95.22 %	87.53 %	NA s	GPU @ 2.5 Ghz (Python)
125	VAR			92.46 %	95.11 %	89.68 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
126	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.
127	Dccnet			92.34 %	96.00 %	86.85 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
128	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.
129	LZY_RCNN			92.28 %	93.58 %	89.76 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
130	CCFNET			92.25 %	95.85 %	89.36 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
131	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.
132	MDA			92.17 %	94.88 %	89.54 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
133	PFF3D			92.15 %	95.37 %	87.54 %	0.05 s	GPU @ 3.0 Ghz (Python + C/C++)
134	yolo4			92.13 %	94.20 %	79.89 %	0.02 s	1 core @ 2.5 Ghz (Python)
135	TBD			92.12 %	93.48 %	89.56 %	0.05 s	GPU @ 2.5 Ghz (Python)
136	PVNet			92.12 %	94.84 %	89.27 %	0,1 s	1 core @ 2.5 Ghz (Python)
137	PBASN		code	92.07 %	95.51 %	87.04 %	NA s	GPU @ 2.5 Ghz (Python)
138	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.
139	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.
140	DPointNet			92.00 %	95.01 %	86.86 %	0.09 s	1 core @ 2.5 Ghz (C/C++)
141	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.
142	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.
143	Pointpillar_TV			91.82 %	94.82 %	88.57 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
144	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.
145	3DBN_2			91.75 %	95.34 %	89.12 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
146	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.
147	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.
148	yolo4_5l			91.71 %	93.35 %	79.49 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
149	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.
150	VOXEL_3D			91.61 %	94.50 %	86.37 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
151	Faster RCNN + A			91.60 %	94.77 %	81.43 %	0.19 s	GPU @ 2.5 Ghz (Python + C/C++)
152	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.
153	tt		code	91.59 %	95.15 %	88.72 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
154	V3D			91.52 %	94.46 %	86.34 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
155	GA-Aug			91.46 %	94.55 %	84.85 %	0.04 s	GPU @ 2.5 Ghz (Python)
156	MAFF-Net(DAF-Pillar)			91.46 %	94.38 %	83.89 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
Z. Zhang, Z. Liang, M. Zhang, X. Zhao, Y. Ming, T. Wenming and S. Pu: MAFF-Net: Filter False Positive for 3D Vehicle Detection with Multi-modal Adaptive Feature Fusion. arXiv preprint arXiv:2009.10945 2020.
157	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.
158	CU-PointRCNN			91.34 %	97.25 %	86.98 %	0.1 s	GPU @ 1.5 Ghz (Python + C/C++)
159	deprecated			91.31 %	96.90 %	83.91 %	0.06 s	GPU @ >3.5 Ghz (Python)
160	SC(DLA34)			91.29 %	96.63 %	81.46 %	0.05 s	GPU @ 2.5 Ghz (Python)
161	Faster RCNN + G			91.28 %	94.34 %	81.02 %	1.1 s	GPU @ 2.5 Ghz (Python)
162	Faster RCNN + Gr + A			91.25 %	94.09 %	81.25 %	1.29 s	GPU @ 2.5 Ghz (Python)
163	CentrNet-FG			91.21 %	94.05 %	88.45 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
164	GAA			91.20 %	94.50 %	82.97 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
165	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.
166	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.
167	autonet			91.17 %	93.70 %	88.10 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
168	WS3D			91.15 %	95.13 %	86.52 %	0.1 s	GPU @ 2.5 Ghz (Python)
Q. Meng, W. Wang, T. Zhou, J. Shen, L. Van Gool and D. Dai: Weakly Supervised 3D Object Detection from Lidar Point Cloud. 2020.
169	PointPiallars_SECA			91.12 %	93.66 %	87.94 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
170	EPENet			91.11 %	94.31 %	88.02 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
171	anonymous			91.08 %	96.57 %	82.86 %	1 s	1 core @ 2.5 Ghz (C/C++)
172	SSL-RTM3D			91.07 %	96.44 %	81.19 %	0.03 s	1 core @ 2.5 Ghz (Python)
173	FII-CenterNet			91.03 %	94.48 %	83.00 %	0.09 s	GPU @ 2.5 Ghz (Python)
174	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.
175	MonoFlex			91.02 %	96.01 %	83.38 %	0.03 s	GPU @ 2.5 Ghz (Python)
176	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.
177	Bit			90.96 %	93.84 %	87.47 %	0.11 s	1 core @ 2.5 Ghz (C/C++)
178	JSU-NET			90.90 %	96.41 %	80.67 %	0.1 s	1 core @ 2.5 Ghz (Python)
179	GAFM			90.90 %	96.46 %	80.70 %	0.5 s	1 core @ 2.5 Ghz (Python)
180	MonoEF		code	90.88 %	96.32 %	83.27 %	0.03 s	1 core @ 2.5 Ghz (Python)
181	PatchNet		code	90.87 %	93.82 %	79.62 %	0.4 s	1 core @ 2.5 Ghz (C/C++)
X. Ma, S. Liu, Z. Xia, H. Zhang, X. Zeng and W. Ouyang: Rethinking Pseudo-LiDAR Representation. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
182	GA_BALANCE			90.86 %	96.19 %	78.40 %	1 s	1 core @ 2.5 Ghz (Python)
183	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.
184	DLE			90.81 %	93.83 %	80.93 %	0.04 s	GPU @ 2.5 Ghz (Python)
185	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.
186	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.
187	GA_FULLDATA			90.73 %	96.31 %	78.22 %	1 s	4 cores @ 2.5 Ghz (Python)
188	OCM3D			90.70 %	94.36 %	84.56 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
189	Simple3D Net			90.70 %	93.54 %	87.81 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
190	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.
191	yolo4			90.63 %	94.71 %	80.38 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
192	baseline			90.59 %	93.29 %	87.18 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
193	Det3D			90.54 %	94.35 %	84.40 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
194	FADNet		code	90.49 %	96.15 %	80.71 %	0.04 s	GPU @ >3.5 Ghz (Python)
195	IGRP+			90.42 %	96.03 %	87.63 %	0.18 s	1 core @ 2.5 Ghz (Python + C/C++)
196	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.
197	yolo4_5l		code	90.38 %	91.79 %	80.64 %	0.02 s	1 core @ 2.5 Ghz (Python + C/C++)
198	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.
199	RUC		code	90.24 %	92.60 %	86.55 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
200	APL-Second			90.20 %	93.20 %	82.95 %	0.05 s	1 core @ 2.5 Ghz (Python)
201	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.
202	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.
203	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.
204	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.
205	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.
206	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.
207	RUC		code	89.93 %	93.12 %	85.44 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
208	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.
209	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.
210	MCA			89.72 %	93.42 %	79.96 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
211	UDI-mono3D			89.67 %	94.39 %	80.29 %	0.05 s	1 core @ 2.5 Ghz (Python)
212	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.
213	IAFA			89.46 %	93.08 %	79.83 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
214	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.
215	4d-MSCNN		code	89.37 %	92.40 %	77.00 %	0.3 min	GPU @ 3.0 Ghz (Matlab + C/C++)
P. Ferraz, B. Oliveira, F. Ferreira, C. Silva Martins and others: Three-stage RGBD architecture for vehicle and pedestrian detection using convolutional neural networks and stereo vision. IET Intelligent Transport Systems 2020.
216	R-FCN(FPN)			89.35 %	93.53 %	79.35 %	0.2 s	1 core @ 2.5 Ghz (Python)
217	Scan_YOLO			88.95 %	90.69 %	79.85 %	0.1 s	4 cores @ 3.0 Ghz (Python)
218	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.
219	autoRUC			88.88 %	94.23 %	81.35 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
220	Prune			88.85 %	94.20 %	81.31 %	0.11 s	1 core @ 2.5 Ghz (C/C++)
221	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.
222	EACV			88.70 %	94.51 %	81.15 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
223	SS3D_HW			88.68 %	94.49 %	68.79 %	0.4 s	GPU @ 2.5 Ghz (Python)
224	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.
225	PMN			88.65 %	93.64 %	77.94 %	0.2 s	1 core @ 2.5 Ghz (Python)
226	CRCNNA			88.59 %	94.82 %	76.74 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
227	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.
228	BFF			88.49 %	90.84 %	78.84 %	8.4 s	4 cores @ 1.5 Ghz (Python)
229	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.
230	PSMD			88.47 %	93.67 %	75.62 %	0.1 s	GPU @ 2.5 Ghz (Python)
231	RCD			88.46 %	92.52 %	83.73 %	0.1 s	GPU @ 2.5 Ghz (Python)
A. Bewley, P. Sun, T. Mensink, D. Anguelov and C. Sminchisescu: Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection. Conference on Robot Learning (CoRL) 2020.
232	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.
233	AACL			88.35 %	93.56 %	73.57 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
234	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.
235	UDI-mono3D			88.16 %	93.93 %	79.57 %	0.05 s	1 core @ 2.5 Ghz (Python)
236	anonymous			88.16 %	96.22 %	75.72 %	1 s	1 core @ 2.5 Ghz (C/C++)
237	tiny-stereo-v1			88.00 %	96.14 %	80.59 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)
238	CDI3D			87.97 %	91.46 %	80.14 %	0.03 s	GPU @ 2.5 Ghz (Python)
239	MonoRUn			87.91 %	95.48 %	78.10 %	0.07 s	GPU @ 2.5 Ghz (Python + C/C++)
240	Multi-task DG			87.72 %	95.50 %	75.51 %	0.06 s	GPU @ 2.5 Ghz (Python)
241	Object Transformer			87.67 %	93.33 %	79.98 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
242	MMCOM			87.58 %	95.08 %	77.48 %	0.04 s	1 core @ 2.5 Ghz (Python)
243	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.
244	tiny-stereo-v2			87.46 %	96.16 %	80.12 %	0.4 s	1 core @ 2.5 Ghz (Python + C/C++)
245	DAMNET		code	87.39 %	92.48 %	82.41 %	1 s	1 core @ 2.5 Ghz (C/C++)
246	MA			87.29 %	93.21 %	79.82 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
247	CDN			87.19 %	95.85 %	79.43 %	0.6 s	GPU @ 2.5 Ghz (Python)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems (NeurIPS) 2020.
248	IMA			87.17 %	92.67 %	77.46 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
249	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.
250	yolo_rgb			86.90 %	90.01 %	77.52 %	0.07 s	GPU @ 2.5 Ghz (Python)
251	NL_M3D			86.80 %	91.31 %	72.37 %	0.2 s	1 core @ 2.5 Ghz (Python)
252	voxelrcnn			86.69 %	94.60 %	79.91 %	15 s	1 core @ 2.5 Ghz (C/C++)
253	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.
254	OSE			86.21 %	95.64 %	76.83 %	0.1 s	GPU @ 2.5 Ghz (C/C++)
255	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.
256	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.
257	ResNet-RRC_Car			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.
258	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.
259	RAR-Net			85.08 %	89.04 %	69.26 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
260	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 .
261	Center3D			85.05 %	95.14 %	73.06 %	0.05 s	GPU @ 3.5 Ghz (Python)
262	CDN-PL++			85.01 %	94.66 %	77.60 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. arXiv preprint arXiv:2007.03085 2020.
263	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.
264	bifpn_fsrn			84.93 %	93.68 %	74.45 %	0.07 s	1 core @ 2.5 Ghz (Python + C/C++)
265	ResNet-RRC (pruned)			84.93 %	89.59 %	73.26 %	0.11 s	GPU @ 1.5 Ghz (Python + C/C++)
266	IDA-3D			84.92 %	92.79 %	74.75 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
267	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.
268	MP-Mono			84.83 %	91.58 %	65.89 %	0.16 s	GPU @ 2.5 Ghz (Python)
269	ResNet-RRC			84.81 %	89.43 %	73.18 %	0.11 s	GPU @ 1.5 Ghz (Python + C/C++)
270	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.
271	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.
272	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.
273	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.
274	LAPNet			83.85 %	90.81 %	65.37 %	0.03 s	1 core @ 2.5 Ghz (Python)
275	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.
276	seivl			83.60 %	90.35 %	81.76 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
277	ASOD			83.52 %	94.09 %	68.68 %	0.28 s	GPU @ 2.5 Ghz (Python)
278	Deprecated			83.39 %	89.00 %	64.29 %	Deprecated	Deprecated
279	DAMono3D			83.36 %	88.94 %	64.23 %	0.09s	1 core @ 2.5 Ghz (C/C++)
280	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.
281	Mag			83.15 %	94.24 %	70.63 %	0.07 s	1 core @ 2.5 Ghz (C/C++)
282	MTMono3d			83.11 %	90.55 %	75.48 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
283	SSL-RTM3D Res18			82.97 %	93.35 %	73.11 %	0.02 s	GPU @ 2.5 Ghz (Python)
284	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.
285	DP3D			82.81 %	87.85 %	66.80 %	0.05 s	GPU @ 1.5 Ghz (Python + C/C++)
286	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.
287	DP3D			82.63 %	87.90 %	66.62 %	0.07 s	GPU @ 1.5 Ghz (Python + C/C++)
288	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.
289	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.
290	deprecated			82.23 %	92.21 %	67.87 %		1 core @ 2.5 Ghz (C/C++)
291	S3D			82.18 %	91.77 %	67.82 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
292	Stereo3D			82.15 %	94.81 %	62.17 %	0.1 s	GPU 1080Ti
293	LNET			82.02 %	91.49 %	67.71 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
294	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.
295	DDMP-3D			81.70 %	91.15 %	63.12 %	0.18 s	1 core @ 2.5 Ghz (Python)
296	CBNet			81.70 %	91.47 %	72.02 %	1 s	4 cores @ 2.5 Ghz (Python)
297	yyyyolo			81.33 %	94.36 %	68.72 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
298	LCD3D			81.25 %	91.29 %	64.55 %	0.03 s	GPU @ 2.5 Ghz (Python)
299	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.
300	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.
301	CaDDN			80.73 %	93.61 %	71.09 %	0.63 s	GPU @ 2.5 Ghz (Python)
302	UM3D_TUM			80.36 %	92.88 %	65.95 %	0.05 s	1 core @ 2.5 Ghz (Python)
303	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.
304	YoloMono3D			79.63 %	92.37 %	59.69 %	0.05 s	GPU @ 2.5 Ghz (Python)
305	DA-3Ddet			79.47 %	89.49 %	63.04 %	0.4 s	GPU @ 2.5 Ghz (Python)
306	ITS-MDPL			79.30 %	92.31 %	71.94 %	0.16 s	GPU @ 2.5 Ghz (Python)
307	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.
308	MTNAS			78.82 %	88.96 %	67.07 %	0.02 s	1 core @ 2.5 Ghz (python)
309	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.
310	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.
311	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.
312	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.
313	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.
314	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.
315	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.
316	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.
317	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.
318	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.
319	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.
320	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.
321	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.
322	yolo_depth			74.40 %	88.71 %	65.58 %	0.07 s	GPU @ 2.5 Ghz (Python)
323	RTS3D			73.08 %	80.48 %	64.02 %	0.03 s	GPU @ 2.5 Ghz (Python)
324	Kinematic3D		code	71.73 %	89.67 %	54.97 %	0.12 s	1 core @ 1.5 Ghz (C/C++)
G. Brazil, G. Pons-Moll, X. Liu and B. Schiele: Kinematic 3D Object Detection in Monocular Video. ECCV 2020 .
325	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.
326	DAM			70.78 %	90.08 %	61.38 %	1 s	GPU @ 2.5 Ghz (Python)
327	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.
328	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.
329	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.
330	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.
331	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.
332	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.
333	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.
334	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.
335	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.
336	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.
337	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.
338	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.
339	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.
340	PG-MonoNet			62.75 %	70.87 %	54.34 %	0.19 s	GPU @ 2.5 Ghz (Python)
341	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.
342	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.
343	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.
344	FPIOD		code	60.04 %	78.81 %	50.13 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
345	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.
346	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.
347	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.
348	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.
349	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.
350	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). .
351	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.
352	RT3D-GMP			51.95 %	62.41 %	39.14 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
353	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 .
354	SparVox3D			50.28 %	53.51 %	40.38 %	0.05 s	GPU @ 2.0 Ghz (Python)
355	SF			46.68 %	60.62 %	38.22 %	0.5 s	1 core @ 2.5 Ghz (Python)
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356	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.
357	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.
358	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.
359	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.
360	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.
361	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.
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	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.
365	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.
366	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.
367	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.
368	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.
369	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.

Pedestrian

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	HWFD			83.06 %	90.50 %	78.35 %	0.21 s	one 1080Ti
2	dgist_multiDetNet			80.21 %	89.21 %	75.77 %	0.08 s	GPU Titanx Pascal (Python)
3	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.
4	DGIST MT-CNN			79.38 %	88.58 %	74.83 %	0.09 s	GPU @ 1.0 Ghz (Python)
5	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.
6	ZEEWAIN-AI			78.20 %	88.46 %	73.35 %	0.3 s	GPU @ 2.5 Ghz (Python)
7	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.
8	WSSN			76.42 %	84.91 %	71.86 %	0.41 s	GPU @ >3.5 Ghz (Python + C/C++)
9	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.
10	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.
11	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.
12	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.
13	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.
14	Noah CV Lab - SSL			75.64 %	86.57 %	70.53 %	0.1 s	GPU @ 2.5 Ghz (Python)
15	Faster RCNN + Gr + A			74.95 %	86.95 %	69.50 %	1.29 s	GPU @ 2.5 Ghz (Python)
16	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.
17	Faster RCNN + G			73.75 %	85.51 %	68.54 %	1.1 s	GPU @ 2.5 Ghz (Python)
18	MMCOM			73.08 %	86.01 %	68.38 %	0.04 s	1 core @ 2.5 Ghz (Python)
19	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.
20	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.
21	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.
22	FRCNN-WS			72.26 %	84.20 %	67.47 %	0.22 s	1 core @ 3.0 Ghz (Python)
23	HR-faster-rcnn			72.26 %	87.65 %	65.71 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
24	Faster RCNN + A			72.09 %	85.35 %	66.87 %	0.19 s	GPU @ 2.5 Ghz (Python + C/C++)
25	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.
26	Multi-task DG			71.64 %	85.34 %	66.76 %	0.06 s	GPU @ 2.5 Ghz (Python)
27	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.
28	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.
29	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.
30	Mono3CN			69.75 %	83.47 %	63.15 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
31	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.
32	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.
33	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.
34	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.
35	FII-CenterNet			67.31 %	81.32 %	61.29 %	0.09 s	GPU @ 2.5 Ghz (Python)
36	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.
37	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.
38	PMN			66.17 %	82.16 %	60.84 %	0.2 s	1 core @ 2.5 Ghz (Python)
39	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.
40	CRCNNA			63.69 %	78.10 %	58.41 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
41	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.
42	GA-Aug			63.53 %	78.06 %	57.80 %	0.04 s	GPU @ 2.5 Ghz (Python)
43	UDI-mono3D			63.24 %	77.94 %	57.35 %	0.05 s	1 core @ 2.5 Ghz (Python)
44	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.
45	HRI-ADLab-HZ-AFree			62.78 %	73.95 %	60.46 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
46	HotSpotNet			62.31 %	71.43 %	59.24 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
47	EACV			62.29 %	79.38 %	57.16 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
48	UDI-mono3D			62.26 %	77.16 %	56.39 %	0.05 s	1 core @ 2.5 Ghz (Python)
49	GAA			61.92 %	77.67 %	56.56 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
50	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.
51	HRI-ADLab-HZ			61.40 %	71.43 %	57.54 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
52	DLE			61.29 %	78.66 %	56.18 %	0.04 s	GPU @ 2.5 Ghz (Python)
53	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.
54	JSU-NET			61.19 %	83.17 %	56.20 %	0.1 s	1 core @ 2.5 Ghz (Python)
55	RethinkDet3D			60.88 %	70.56 %	56.69 %	0.15 s	1 core @ 2.5 Ghz (Python)
56	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.
57	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.
58	MVX-Net++			60.21 %	69.70 %	56.07 %	0.15 s	1 core @ 2.5 Ghz (C/C++)
59	VICNet			60.07 %	70.41 %	56.29 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
60	Fast VP-RCNN		code	59.69 %	69.48 %	57.86 %	0.05 s	1 core @ >3.5 Ghz (python)
61	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.
62	GAP-soft-filter			58.89 %	68.54 %	56.08 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
63	DSA-PV-RCNN			58.81 %	66.93 %	56.57 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
64	BFF			58.72 %	76.95 %	53.70 %	8.4 s	4 cores @ 1.5 Ghz (Python)
65	SRDL			58.70 %	68.45 %	56.23 %	0.15 s	GPU @ 2.5 Ghz (Python + C/C++)
66	Baseline of CA RCNN			58.68 %	68.44 %	56.22 %	0.1 s	GPU @ 2.5 Ghz (Python)
67	CVIS-DF3D			58.68 %	68.44 %	56.22 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
68	PF-GAP			58.65 %	70.40 %	55.02 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
69	MSL3D			58.57 %	69.07 %	55.86 %	0.03 s	GPU @ 2.5 Ghz (Python)
70	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.
71	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.
72	PP-3D			58.20 %	71.59 %	54.06 %	0.1 s	1 core @ 2.5 Ghz (Python)
73	XView-PartA^2			58.17 %	67.12 %	55.81 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
74	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.
75	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.
76	PPBA			58.06 %	67.73 %	55.69 %	NA s	GPU @ 2.5 Ghz (Python)
77	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.
78	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.
79	FSA-PVRCNN			57.67 %	65.80 %	55.34 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
80	SVGA-Net			57.57 %	67.48 %	55.11 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
81	TBU			57.44 %	67.29 %	54.00 %	NA s	GPU @ 2.5 Ghz (Python)
82	CentrNet-FG			57.40 %	68.27 %	54.11 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
83	SemanticVoxels			57.22 %	67.62 %	54.90 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Fei, W. Chen, P. Heidenreich, S. Wirges and C. Stiller: SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentation. MFI 2020.
84	CVRS VIC-RCNN			57.20 %	65.43 %	55.17 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
85	CVRS VIC-Net			57.20 %	65.53 %	54.92 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
86	Simple3D Net			57.00 %	66.89 %	54.38 %	0.02 s	GPU @ 2.5 Ghz (Python)
87	KNN-GCNN			56.80 %	69.53 %	52.86 %	0.4 s	1 core @ 2.5 Ghz (Python + C/C++)
88	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.
89	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.
90	yolo4_5l			56.46 %	73.14 %	49.57 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
91	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.
92	MonoRUn			56.40 %	73.05 %	51.40 %	0.07 s	GPU @ 2.5 Ghz (Python + C/C++)
93	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.
94	CVIS-DF3D_v2			56.02 %	65.82 %	53.58 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
95	yolo4			55.78 %	72.49 %	51.11 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
96	Mag			55.74 %	71.91 %	50.92 %	0.07 s	1 core @ 2.5 Ghz (C/C++)
97	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.
98	DAM			55.60 %	74.85 %	50.63 %	1 s	GPU @ 2.5 Ghz (Python)
99	CDI3D			55.16 %	67.35 %	51.17 %	0.03 s	GPU @ 2.5 Ghz (Python)
100	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.
101	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.
102	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.
103	yolo4			54.30 %	73.16 %	49.46 %	0.02 s	1 core @ 2.5 Ghz (Python)
104	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.
105	MGACNet			54.13 %	63.54 %	51.79 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
106	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.
107	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.
108	3DBN_2			53.26 %	63.82 %	50.76 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
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109	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.
110	MTMono3d			52.96 %	69.01 %	46.18 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
111	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.
112	yolo4_5l		code	52.74 %	71.89 %	47.90 %	0.02 s	1 core @ 2.5 Ghz (Python + C/C++)
113	deprecated			52.59 %	60.42 %	50.61 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
114	PFF3D			52.53 %	62.12 %	50.27 %	0.05 s	GPU @ 3.0 Ghz (Python + C/C++)
115	ResNet-RRC			52.09 %	66.44 %	47.51 %	0.11 s	GPU @ 1.5 Ghz (Python + C/C++)
116	SF			51.83 %	67.73 %	47.45 %	0.5 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
117	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.
118	TBD			51.31 %	61.14 %	47.82 %	0.05 s	GPU @ 2.5 Ghz (Python)
119	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.
120	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.
121	ResNet-RRC (pruned)			51.12 %	65.47 %	46.53 %	0.11 s	GPU @ 1.5 Ghz (Python + C/C++)
122	PVNet			50.50 %	60.58 %	48.48 %	0,1 s	1 core @ 2.5 Ghz (Python)
123	AF_MCLS			49.95 %	62.85 %	45.78 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
124	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.
125	yolo_depth			49.47 %	67.23 %	44.99 %	0.07 s	GPU @ 2.5 Ghz (Python)
126	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.
127	SS3D_HW			49.01 %	64.67 %	42.86 %	0.4 s	GPU @ 2.5 Ghz (Python)
128	Center3D			48.76 %	67.15 %	44.05 %	0.05 s	GPU @ 3.5 Ghz (Python)
129	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.
130	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.
131	IGRP+			48.46 %	59.37 %	44.82 %	0.18 s	1 core @ 2.5 Ghz (Python + C/C++)
132	yolo_rgb			48.45 %	64.50 %	43.95 %	0.07 s	GPU @ 2.5 Ghz (Python)
133	MonoFlex			47.58 %	62.64 %	43.15 %	0.03 s	GPU @ 2.5 Ghz (Python)
134	PBASN		code	46.75 %	54.38 %	44.58 %	NA s	GPU @ 2.5 Ghz (Python)
135	NLK-3D			46.33 %	59.46 %	43.88 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
136	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.
137	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.
138	NLK-ALL		code	45.27 %	56.86 %	41.08 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
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	CBi-GNN-persons			44.88 %	58.17 %	40.50 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
142	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.
143	Deprecated			44.65 %	60.33 %	38.51 %	Deprecated	Deprecated
144	yyyyolo			44.55 %	60.74 %	39.96 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
145	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.
146	FCY			43.87 %	56.43 %	39.87 %	0.02 s	GPU @ 2.5 Ghz (Python)
147	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.
148	MonoEF		code	43.73 %	58.79 %	39.45 %	0.03 s	1 core @ 2.5 Ghz (Python)
149	DAMono3D			43.57 %	59.80 %	39.23 %	0.09s	1 core @ 2.5 Ghz (C/C++)
150	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.
151	DAMNET		code	43.42 %	56.05 %	41.16 %	1 s	1 core @ 2.5 Ghz (C/C++)
152	Pointpillar_TV			43.29 %	53.06 %	41.14 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
153	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.
154	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.
155	SparVox3D			42.73 %	55.22 %	38.08 %	0.05 s	GPU @ 2.0 Ghz (Python)
156	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.
157	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.
158	DP3D			42.33 %	57.82 %	38.11 %	0.05 s	GPU @ 1.5 Ghz (Python + C/C++)
159	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.
160	DP3D			41.71 %	55.28 %	35.73 %	0.07 s	GPU @ 1.5 Ghz (Python + C/C++)
161	DDMP-3D			41.54 %	56.73 %	35.52 %	0.18 s	1 core @ 2.5 Ghz (Python)
162	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.
163	M3D-RPN(S-R)			41.46 %	56.64 %	37.31 %	0.16 s	GPU @ 1.5 Ghz (Python)
164	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 .
165	Stereo3D			41.46 %	56.20 %	37.07 %	0.1 s	GPU 1080Ti
166	MP-Mono			41.04 %	56.05 %	36.99 %	0.16 s	GPU @ 2.5 Ghz (Python)
167	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.
168	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.
169	RT3D-GMP			39.83 %	55.56 %	35.18 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
170	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.
171	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.
172	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.
173	PG-MonoNet			39.38 %	48.57 %	35.43 %	0.19 s	GPU @ 2.5 Ghz (Python)
174	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). .
175	OSE			38.62 %	50.26 %	34.87 %	0.1 s	GPU @ 2.5 Ghz (C/C++)
176	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.
177	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.
178	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.
179	LAPNet			36.22 %	48.96 %	32.27 %	0.03 s	1 core @ 2.5 Ghz (Python)
180	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.
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	FADNet		code	32.64 %	42.43 %	29.13 %	0.04 s	GPU @ >3.5 Ghz (Python)
183	CaDDN			32.42 %	46.35 %	29.98 %	0.63 s	GPU @ 2.5 Ghz (Python)
184	FPIOD		code	30.96 %	45.07 %	28.48 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
185	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.
186	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.
187	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.
188	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.
189	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.
190	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.
191	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.
192	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.
193	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.
194	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.
195	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.
196	CBNet			1.33 %	1.03 %	1.41 %	1 s	4 cores @ 2.5 Ghz (Python)
197	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.
198	UM3D_TUM			0.00 %	0.00 %	0.00 %	0.05 s	1 core @ 2.5 Ghz (Python)