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		code	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	PLNL-3DSSD			95.38 %	96.37 %	90.31 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
10	CityBrainLab-TSD			95.21 %	96.18 %	90.48 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
11	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.
12	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.
13	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.
14	Voxel R-CNN		code	95.11 %	96.49 %	92.45 %	0.04 s	GPU @ 3.0 Ghz (C/C++)
J. Deng, S. Shi, P. Li, W. Zhou, Y. Zhang and H. Li: Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection . arXiv preprint arXiv:2012.15712 2020.
15	TBD			95.10 %	96.48 %	92.62 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
16	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.
17	3DIoU++			95.06 %	96.37 %	90.52 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
18	PV-RCNN-v2			95.05 %	96.08 %	92.42 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
19	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.
20	SIENet			94.90 %	95.98 %	92.39 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
21	DomainAdp+PVRCNN			94.85 %	95.99 %	92.27 %	0.09 s	GPU @ 2.5 Ghz (Python)
22	E^2-PV-RCNN			94.80 %	95.95 %	92.26 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
23	XView			94.77 %	95.89 %	92.23 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
24	MSG-PGNN			94.75 %	95.86 %	92.16 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
25	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.
26	3DIoU_v2			94.70 %	96.15 %	92.37 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
27	CVRS VIC-Net			94.69 %	95.79 %	91.89 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
28	HyBrid Feature Det			94.69 %	95.89 %	92.11 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
29	PC-RGNN			94.68 %	95.80 %	92.20 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
30	D3D			94.66 %	95.43 %	89.72 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
31	LZY_RCNN			94.65 %	95.81 %	92.08 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
32	DSA-PV-RCNN			94.64 %	95.86 %	92.10 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
33	FSA-PVRCNN			94.63 %	95.81 %	92.06 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
34	Fast VP-RCNN		code	94.62 %	98.00 %	91.91 %	0.05 s	1 core @ 3.5 Ghz (C/C++)
35	nonet			94.62 %	95.86 %	91.86 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
36	RangeIoUDet			94.61 %	95.74 %	91.98 %	0.02 s	1 core @ 2.5 Ghz (Python)
37	MSL3D			94.60 %	95.76 %	92.16 %	0.03 s	GPU @ 2.5 Ghz (Python)
38	Multi-Sensor3D			94.60 %	95.76 %	92.16 %	0.03 s	GPU @ 2.5 Ghz (Python)
39	CN			94.60 %	97.86 %	89.81 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
40	ReFineNet			94.59 %	95.75 %	92.12 %	0.08 s	1 core @ 2.5 Ghz (Python)
41	MGACNet			94.57 %	95.35 %	91.77 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
42	anonymous		code	94.53 %	97.51 %	91.80 %	0.05s	1 core @ >3.5 Ghz (python)
43	FPC3D			94.52 %	96.06 %	91.72 %	33 s	1 core @ 2.5 Ghz (C/C++)
44	FPC-RCNN			94.51 %	96.15 %	91.80 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
45	RangeRCNN-LV			94.51 %	95.93 %	92.07 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
46	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.
47	PF-GAP			94.47 %	96.13 %	90.15 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
48	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.
49	GNN-RCNN			94.44 %	95.85 %	91.96 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
50	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.
51	CVRS VIC-RCNN			94.38 %	95.89 %	91.90 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
52	CVRS_PF			94.37 %	95.56 %	91.43 %	0.09 s	1 core @ 2.5 Ghz (C/C++)
53	CVIS-DF3D_v2			94.33 %	95.70 %	91.72 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
54	SVGA-Net			94.28 %	95.69 %	91.73 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
55	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.
56	SRDL			94.24 %	95.86 %	91.80 %	0.15 s	GPU @ 2.5 Ghz (Python + C/C++)
57	Baseline of CA RCNN			94.23 %	95.84 %	91.80 %	0.1 s	GPU @ 2.5 Ghz (Python)
58	CVIS-DF3D			94.23 %	95.84 %	91.80 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
59	tbd		code	94.21 %	95.68 %	91.49 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
60	TBD			94.21 %	95.51 %	91.69 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
61	GAP-soft-filter			94.20 %	95.81 %	91.53 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
62	HR-faster-rcnn			94.14 %	95.41 %	86.88 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
63	FPCR-CNN			94.13 %	95.95 %	91.20 %	0.05 s	1 core @ 2.5 Ghz (Python)
64	SAA-PV-RCNN			94.11 %	95.01 %	92.50 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
65	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.
66	Faraway-Frustum		code	93.99 %	95.81 %	91.72 %	0.1 s	GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion. arXiv preprint arXiv:2011.01404 2020.
67	SIF			93.95 %	95.51 %	91.57 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
68	OAP			93.93 %	96.85 %	86.37 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
69	HRI-MSP-L			93.92 %	95.51 %	91.42 %	0.07 s	1 core @ 2.5 Ghz (C/C++)
70	AF_V1			93.87 %	94.45 %	86.37 %	0.1 s	1 core @ 2.5 Ghz (Python)
71	Associate-3Ddet_v2			93.77 %	96.83 %	88.57 %	0.04 s	1 core @ 2.5 Ghz (Python)
72	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.
73	CIA-SSD		code	93.72 %	96.87 %	86.20 %	0.03 s	1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, S. Chen, L. Jiang and C. Fu: CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud. AAAI 2021.
74	VAL			93.71 %	96.92 %	83.76 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
75	XView-PartA^2			93.71 %	95.42 %	91.26 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
76	HIKVISION-ADLab-HZ			93.69 %	96.70 %	88.66 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
77	deprecated			93.68 %	96.92 %	86.15 %	deprecated	deprecated
78	modat3D			93.66 %	94.26 %	83.63 %	0.03 s	GPU @ 2.5 Ghz (Python)
79	MVAF-Net		code	93.66 %	95.37 %	90.90 %	0.06 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. arXiv preprint arXiv:2011.00652 2020.
80	TBD			93.64 %	95.31 %	91.21 %	0.07 s	1 core @ 2.5 Ghz (Python + C/C++)
81	CBi-GNN			93.60 %	98.89 %	88.47 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
82	MVX-Net++			93.58 %	96.41 %	88.51 %	0.15 s	1 core @ 2.5 Ghz (C/C++)
83	AM-SSD			93.58 %	96.78 %	90.61 %	0.04 s	1 core @ 2.5 Ghz (Python)
84	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.
85	EBM3DOD		code	93.54 %	96.81 %	88.33 %	0.12 s	1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.
86	CM3DV			93.53 %	96.79 %	88.35 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
87	CIA-SSD v2			93.52 %	96.63 %	88.21 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
88	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.
89	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.
90	PP-3D			93.50 %	96.58 %	88.35 %	0.1 s	1 core @ 2.5 Ghz (Python)
91	FCY			93.49 %	96.74 %	88.39 %	0.02 s	GPU @ 2.5 Ghz (Python)
92	Seg-RCNN		code	93.49 %	96.74 %	88.10 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
93	CJJ			93.48 %	96.68 %	90.63 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
94	AIMC-RUC			93.47 %	96.75 %	88.35 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
95	PointRes			93.47 %	96.69 %	90.46 %	0.013 s	1 core @ 2.5 Ghz (Python + C/C++)
96	dgist_multiDetNet			93.46 %	94.99 %	85.46 %	0.08 s	GPU Titanx Pascal (Python)
97	CDE-Net(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: Multi sensor fusion 3d object detection method. Submitted to OSA 2021.
98	EBM3DOD baseline		code	93.45 %	96.72 %	88.25 %	0.05 s	1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.
99	Cas-SSD			93.41 %	96.73 %	88.30 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
100	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.
101	DGIST MT-CNN			93.39 %	95.16 %	85.50 %	0.09 s	GPU @ 1.0 Ghz (Python)
102	KNN-GCNN			93.39 %	96.19 %	88.17 %	0.4 s	1 core @ 2.5 Ghz (Python + C/C++)
103	F-3DNet			93.38 %	96.51 %	88.32 %	0.5 s	GPU @ 2.5 Ghz (Python)
104	HR-Cascade-RCNN			93.37 %	95.74 %	87.44 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
105	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.
106	PSS			93.36 %	96.64 %	90.52 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
107	FLID			93.35 %	95.90 %	85.69 %	0.04 s	GPU @ 2.5 Ghz (Python)
108	ISF-v2			93.34 %	96.73 %	90.54 %	0.04 s	1 core @ 2.5 Ghz (Python)
109	CDE-Net(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: Multi sensor fusion 3d object detection method. Submitted to OSA 2021.
110	RoIFusion		code	93.30 %	96.30 %	88.22 %	0.22 s	1 core @ 3.0 Ghz (Python)
111	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.
112	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.
113	H^23D R-CNN			93.20 %	96.20 %	90.55 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
114	TBD			93.18 %	95.73 %	90.88 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
115	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.
116	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.
117	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.
118	BLPNet_V2			93.11 %	96.07 %	88.06 %	0.04 s	1 core @ 2.5 Ghz (Python)
119	PVF-NET			93.08 %	96.03 %	88.04 %	0.1 s	1 core @ 2.5 Ghz (Python)
120	3DIoU+++			93.06 %	96.08 %	90.53 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
121	HV			93.04 %	95.91 %	87.88 %	0.02 s	GPU @ 2.5 Ghz (Python)
122	NLK-ALL		code	92.98 %	95.73 %	88.13 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
123	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.
124	FPGNN			92.83 %	96.26 %	87.69 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
125	TBD			92.82 %	96.06 %	88.00 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
126	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.
127	deprecated			92.79 %	95.56 %	91.62 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
128	IGRP			92.78 %	96.28 %	87.81 %	0.18 s	1 core @ 2.5 Ghz (Python + C/C++)
129	DPointNet			92.77 %	95.55 %	89.63 %	0.07s	1 core @ 2.5 Ghz (C/C++)
130	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.
131	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.
132	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.
133	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.
134	NLK-3D			92.67 %	95.44 %	87.72 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
135	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.
136	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.
137	SIEV-Net			92.56 %	95.56 %	87.40 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
138	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.
139	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.
140	VAR			92.46 %	95.11 %	89.68 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
141	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.
142	Dccnet			92.34 %	96.00 %	86.85 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
143	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.
144	CCFNET			92.25 %	95.85 %	89.36 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
145	LSNet			92.23 %	96.06 %	87.35 %	0.09 s	GPU @ 2.5 Ghz (Python + C/C++)
146	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.
147	MDA			92.17 %	94.88 %	89.54 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
148	PFF3D			92.15 %	95.37 %	87.54 %	0.05 s	GPU @ 3.0 Ghz (Python + C/C++)
149	yolo4			92.13 %	94.20 %	79.89 %	0.02 s	1 core @ 2.5 Ghz (Python)
150	TBD			92.12 %	93.48 %	89.56 %	0.05 s	GPU @ 2.5 Ghz (Python)
151	PVNet			92.12 %	94.84 %	89.27 %	0,1 s	1 core @ 2.5 Ghz (Python)
152	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.
153	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.
154	VGCN			91.97 %	94.91 %	89.34 %	0.09 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
155	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.
156	MKFFNet			91.88 %	95.29 %	89.21 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
157	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.
158	Pointpillar_TV			91.82 %	94.82 %	88.57 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
159	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.
160	3DBN_2			91.75 %	95.34 %	89.12 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
161	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.
162	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.
163	yolo4_5l			91.71 %	93.35 %	79.49 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
164	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.
165	VOXEL_3D			91.61 %	94.50 %	86.37 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
166	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.
167	tt		code	91.59 %	95.15 %	88.72 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
168	MKFFNet			91.54 %	95.32 %	89.02 %	0.01s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
169	V3D			91.52 %	94.46 %	86.34 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
170	MKFFNet			91.51 %	95.19 %	89.01 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
171	GA-Aug			91.46 %	94.55 %	84.85 %	0.04 s	GPU @ 2.5 Ghz (Python)
172	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.
173	AIMC-RUC			91.45 %	96.94 %	86.28 %	0.11 s	1 core @ 2.5 Ghz (Python)
174	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.
175	FPC3D_all			91.42 %	95.52 %	86.76 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
176	CU-PointRCNN			91.34 %	97.25 %	86.98 %	0.1 s	GPU @ 1.5 Ghz (Python + C/C++)
177	deprecated			91.31 %	96.90 %	83.91 %	0.06 s	GPU @ >3.5 Ghz (Python)
178	SC(DLA34+DCO)			91.27 %	96.61 %	83.50 %	0.07 s	GPU @ 2.5 Ghz (Python)
179	GAA			91.20 %	94.50 %	82.97 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
180	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.
181	IOU-SSD		code	91.18 %	94.25 %	87.58 %	0.045s	1 core @ 2.5 Ghz (C/C++)
182	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.
183	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.
184	anonymous			91.08 %	96.57 %	82.86 %	1 s	1 core @ 2.5 Ghz (C/C++)
185	KM3D		code	91.07 %	96.44 %	81.19 %	0.03 s	1 core @ 2.5 Ghz (Python)
P. Li: Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training. 2020.
186	FII-CenterNet			91.03 %	94.48 %	83.00 %	0.09 s	GPU @ 2.5 Ghz (Python)
187	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.
188	MonoFlex			91.02 %	96.01 %	83.38 %	0.03 s	GPU @ 2.5 Ghz (Python)
189	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.
190	MonoEF		code	90.88 %	96.32 %	83.27 %	0.03 s	1 core @ 2.5 Ghz (Python)
191	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.
192	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.
193	DLE			90.81 %	93.83 %	80.93 %	0.04 s	GPU @ 2.5 Ghz (Python)
194	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.
195	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.
196	OCM3D			90.70 %	94.36 %	84.56 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
197	Simple3D Net			90.70 %	93.54 %	87.81 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
198	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.
199	yolo4			90.63 %	94.71 %	80.38 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
200	NF2			90.62 %	94.14 %	81.30 %	0.1 s	GPU @ 2.5 Ghz (Python)
201	baseline			90.59 %	93.29 %	87.18 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
202	Det3D			90.54 %	94.35 %	84.40 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
203	FADNet		code	90.49 %	96.15 %	80.71 %	0.04 s	GPU @ >3.5 Ghz (Python)
204	IGRP+			90.42 %	96.03 %	87.63 %	0.18 s	1 core @ 2.5 Ghz (Python + C/C++)
205	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.
206	yolo4_5l		code	90.38 %	91.79 %	80.64 %	0.02 s	1 core @ 2.5 Ghz (Python + C/C++)
207	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.
208	APL-Second			90.20 %	93.20 %	82.95 %	0.05 s	1 core @ 2.5 Ghz (Python)
209	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.
210	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.
211	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.
212	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.
213	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.
214	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. IEEE Transactions on Intelligent Vehicles 2020.
215	LCA			89.94 %	93.40 %	82.76 %	0.05 s	1 core @ 2.5 Ghz (Python)
216	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.
217	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.
218	MonoGeo			89.77 %	94.68 %	80.00 %	0.05 s	1 core @ 2.5 Ghz (Python)
219	MCA			89.72 %	93.42 %	79.96 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
220	UDI-mono3D			89.67 %	94.39 %	80.29 %	0.05 s	1 core @ 2.5 Ghz (Python)
221	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.
222	IAFA			89.46 %	93.08 %	79.83 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
223	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.
224	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.
225	R-FCN(FPN)			89.35 %	93.53 %	79.35 %	0.2 s	1 core @ 2.5 Ghz (Python)
226	Scan_YOLO			88.95 %	90.69 %	79.85 %	0.1 s	4 cores @ 3.0 Ghz (Python)
227	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.
228	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.
229	EACV			88.70 %	94.51 %	81.15 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
230	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.
231	PMN			88.65 %	93.64 %	77.94 %	0.2 s	1 core @ 2.5 Ghz (Python)
232	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.
233	BFF			88.49 %	90.84 %	78.84 %	8.4 s	4 cores @ 1.5 Ghz (Python)
234	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.
235	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.
236	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.
237	tiny-stereo-v2			88.38 %	96.52 %	81.01 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)
238	AACL			88.35 %	93.56 %	73.57 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
239	stereo-tkc			88.30 %	96.49 %	80.94 %	0.4 s	GPU @ 2.0 Ghz (Python + C/C++)
240	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.
241	UDI-mono3D			88.16 %	93.93 %	79.57 %	0.05 s	1 core @ 2.5 Ghz (Python)
242	anonymous			88.16 %	96.22 %	75.72 %	1 s	1 core @ 2.5 Ghz (C/C++)
243	tiny-stereo			88.16 %	96.49 %	80.74 %	0.4 s	1 core @ 2.5 Ghz (Python + C/C++)
244	CDI3D			87.97 %	91.46 %	80.14 %	0.03 s	GPU @ 2.5 Ghz (Python)
245	MonoRUn			87.91 %	95.48 %	78.10 %	0.07 s	GPU @ 2.5 Ghz (Python + C/C++)
246	Multi-task DG			87.72 %	95.50 %	75.51 %	0.06 s	GPU @ 2.5 Ghz (Python)
247	Object Transformer			87.67 %	93.33 %	79.98 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
248	MMCOM			87.58 %	95.08 %	77.48 %	0.04 s	1 core @ 2.5 Ghz (Python)
249	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.
250	DAMNET		code	87.39 %	92.48 %	82.41 %	1 s	1 core @ 2.5 Ghz (C/C++)
251	MA			87.29 %	93.21 %	79.82 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
252	CDN		code	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.
253	IMA			87.17 %	92.67 %	77.46 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
254	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.
255	yolo_rgb			86.90 %	90.01 %	77.52 %	0.07 s	GPU @ 2.5 Ghz (Python)
256	NL_M3D			86.80 %	91.31 %	72.37 %	0.2 s	1 core @ 2.5 Ghz (Python)
257	voxelrcnn			86.69 %	94.60 %	79.91 %	15 s	1 core @ 2.5 Ghz (C/C++)
258	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.
259	OSE			86.21 %	95.64 %	76.83 %	0.1 s	GPU @ 2.5 Ghz (C/C++)
260	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.
261	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.
262	PLDet3d			85.51 %	88.65 %	77.30 %	0.11 s	1 core @ 2.5 Ghz (C/C++)
263	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.
264	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.
265	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 .
266	Center3D			85.05 %	95.14 %	73.06 %	0.05 s	GPU @ 3.5 Ghz (Python)
267	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. Advances in Neural Information Processing Systems 2020.
268	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.
269	LGDet3d			84.95 %	87.35 %	77.05 %	0.11 s	1 core @ 2.5 Ghz (C/C++)
270	bifpn_fsrn			84.93 %	93.68 %	74.45 %	0.07 s	1 core @ 2.5 Ghz (Python + C/C++)
271	ResNet-RRC (pruned)			84.93 %	89.59 %	73.26 %	0.11 s	GPU @ 1.5 Ghz (Python + C/C++)
272	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.
273	MP-Mono			84.83 %	91.58 %	65.89 %	0.16 s	GPU @ 2.5 Ghz (Python)
274	ResNet-RRC			84.81 %	89.43 %	73.18 %	0.11 s	GPU @ 1.5 Ghz (Python + C/C++)
275	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.
276	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.
277	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.
278	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.
279	OSE+			83.92 %	95.20 %	76.69 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
280	LAPNet			83.85 %	90.81 %	65.37 %	0.03 s	1 core @ 2.5 Ghz (Python)
281	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.
282	Deprecated			83.39 %	89.00 %	64.29 %	Deprecated	Deprecated
283	DAMono3D			83.36 %	88.94 %	64.23 %	0.09s	1 core @ 2.5 Ghz (C/C++)
284	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.
285	Mag			83.15 %	94.24 %	70.63 %	0.07 s	1 core @ 2.5 Ghz (C/C++)
286	MTMono3d			83.11 %	90.55 %	75.48 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
287	SSL-RTM3D Res18			82.97 %	93.35 %	73.11 %	0.02 s	GPU @ 2.5 Ghz (Python)
288	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.
289	Disp R-CNN		code	82.86 %	93.64 %	68.33 %	0.387 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	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.
291	Disp R-CNN (velo)		code	82.64 %	93.45 %	70.45 %	0.387 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.
292	DP3D			82.63 %	87.90 %	66.62 %	0.07 s	GPU @ 1.5 Ghz (Python + C/C++)
293	Stereo3D			82.15 %	94.81 %	62.17 %	0.1 s	GPU 1080Ti
294	LNET			82.02 %	91.49 %	67.71 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
295	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.
296	DDMP-3D			81.70 %	91.15 %	63.12 %	0.18 s	1 core @ 2.5 Ghz (Python)
297	LCD3D			81.25 %	91.29 %	64.55 %	0.03 s	GPU @ 2.5 Ghz (Python)
298	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.
299	SOD			81.18 %	94.24 %	66.44 %	0.1 s	1 core @ 2.5 Ghz (Python)
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	KMC		code	79.99 %	89.71 %	73.33 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
305	YoloMono3D		code	79.63 %	92.37 %	59.69 %	0.05 s	GPU @ 2.5 Ghz (Python)
306	DA-3Ddet			79.47 %	89.49 %	63.04 %	0.4 s	GPU @ 2.5 Ghz (Python)
307	ITS-MDPL			79.20 %	92.45 %	71.88 %	0.16 s	GPU @ 2.5 Ghz (Python)
308	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.
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	AEC3D			78.59 %	88.58 %	74.62 %	0.01 s	GPU @ 2.5 Ghz (Python)
311	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.
312	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.
313	VN3D			77.90 %	86.89 %	72.05 %	0.02 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
314	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.
315	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.
316	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.
317	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.
318	RelationNet3D			76.62 %	81.36 %	68.48 %	0.04 s	GPU @ 2.5 Ghz (Python)
319	RelationNet3D_res18			76.45 %	85.48 %	65.52 %	0.04 s	GPU @ 2.5 Ghz (Python)
320	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.
321	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.
322	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.
323	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.
324	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.
325	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.
326	yolo_depth			74.40 %	88.71 %	65.58 %	0.07 s	GPU @ 2.5 Ghz (Python)
327	RTS3D			73.08 %	80.48 %	64.02 %	0.03 s	GPU @ 2.5 Ghz (Python)
328	NCL		code	71.91 %	64.71 %	71.78 %	NA s	1 core @ 2.5 Ghz (Python)
329	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 .
330	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.
331	DAM			70.78 %	90.08 %	61.38 %	1 s	GPU @ 2.5 Ghz (Python)
332	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.
333	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.
334	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.
335	RetinaMono		code	69.01 %	75.18 %	58.98 %	0.02 s	1 core @ 2.5 Ghz (Python)
336	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.
337	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.
338	SparVox3D			67.88 %	83.76 %	52.56 %	0.05 s	GPU @ 2.0 Ghz (Python)
339	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.
340	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.
341	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.
342	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.
343	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.
344	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.
345	Y4		code	63.60 %	81.79 %	56.20 %	0.03 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
346	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.
347	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.
348	PG-MonoNet			62.75 %	70.87 %	54.34 %	0.19 s	GPU @ 2.5 Ghz (Python)
349	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.
350	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.
351	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.
352	FPIOD		code	60.04 %	78.81 %	50.13 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
353	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.
354	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.
355	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.
356	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.
357	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.
358	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). .
359	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.
360	RT3D-GMP			51.95 %	62.41 %	39.14 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
361	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 .
362	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.
363	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.
364	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.
365	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.
366	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.
367	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.
368	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.
369	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.
370	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.
371	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.
372	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.
373	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.
374	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.
375	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.
376	NVNet(BEV-3D)			0.00 %	0.00 %	0.00 %	0.1 s	1 core @ 2.5 Ghz (Python)
377	Neighbor-VoteNet			0.00 %	0.00 %	0.00 %	0.1 s	1 core @ 2.5 Ghz (Python)

Pedestrian

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	HWFD			83.06 %	90.50 %	78.35 %	0.21 s	one 1080Ti
2	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	NF2			79.59 %	88.28 %	75.47 %	0.1 s	GPU @ 2.5 Ghz (Python)
5	DGIST MT-CNN			79.38 %	88.58 %	74.83 %	0.09 s	GPU @ 1.0 Ghz (Python)
6	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.
7	ZEEWAIN-AI			78.20 %	88.46 %	73.35 %	0.3 s	GPU @ 2.5 Ghz (Python)
8	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.
9	WSSN			76.42 %	84.91 %	71.86 %	0.37 s	GPU @ >3.5 Ghz (Python + C/C++)
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	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.
16	MMCOM			73.08 %	86.01 %	68.38 %	0.04 s	1 core @ 2.5 Ghz (Python)
17	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.
18	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.
19	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.
20	HR-faster-rcnn			72.26 %	87.65 %	65.71 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
21	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.
22	Multi-task DG			71.64 %	85.34 %	66.76 %	0.06 s	GPU @ 2.5 Ghz (Python)
23	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.
24	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.
25	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.
26	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.
27	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.
28	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.
29	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.
30	FII-CenterNet			67.31 %	81.32 %	61.29 %	0.09 s	GPU @ 2.5 Ghz (Python)
31	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.
32	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.
33	PMN			66.17 %	82.16 %	60.84 %	0.2 s	1 core @ 2.5 Ghz (Python)
34	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.
35	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.
36	GA-Aug			63.53 %	78.06 %	57.80 %	0.04 s	GPU @ 2.5 Ghz (Python)
37	ADLAB			63.25 %	70.86 %	60.52 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
38	UDI-mono3D			63.24 %	77.94 %	57.35 %	0.05 s	1 core @ 2.5 Ghz (Python)
39	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.
40	HIKVISION-AFree			62.78 %	73.95 %	60.46 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
41	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.
42	EACV			62.29 %	79.38 %	57.16 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
43	UDI-mono3D			62.26 %	77.16 %	56.39 %	0.05 s	1 core @ 2.5 Ghz (Python)
44	GAA			61.92 %	77.67 %	56.56 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
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45	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.
46	SAA-PV-RCNN			61.41 %	70.35 %	58.18 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
47	HIKVISION-ADLab-HZ			61.40 %	71.43 %	57.54 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
48	DLE			61.29 %	78.66 %	56.18 %	0.04 s	GPU @ 2.5 Ghz (Python)
49	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.
50	TBD			61.22 %	71.33 %	57.62 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
51	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.
52	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.
53	MVX-Net++			60.21 %	69.70 %	56.07 %	0.15 s	1 core @ 2.5 Ghz (C/C++)
54	SIEV-Net			60.07 %	70.41 %	56.29 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
55	Fast VP-RCNN		code	59.32 %	69.51 %	56.66 %	0.05 s	1 core @ 3.5 Ghz (C/C++)
56	modat3D			59.26 %	78.41 %	54.37 %	0.03 s	GPU @ 2.5 Ghz (Python)
57	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.
58	anonymous		code	59.04 %	69.62 %	56.45 %	0.05s	1 core @ >3.5 Ghz (python)
59	GAP-soft-filter			58.89 %	68.54 %	56.08 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
60	DSA-PV-RCNN			58.81 %	66.93 %	56.57 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
61	BFF			58.72 %	76.95 %	53.70 %	8.4 s	4 cores @ 1.5 Ghz (Python)
62	SRDL			58.70 %	68.45 %	56.23 %	0.15 s	GPU @ 2.5 Ghz (Python + C/C++)
63	Baseline of CA RCNN			58.68 %	68.44 %	56.22 %	0.1 s	GPU @ 2.5 Ghz (Python)
64	CVIS-DF3D			58.68 %	68.44 %	56.22 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
65	PF-GAP			58.65 %	70.40 %	55.02 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
66	MSL3D			58.57 %	69.07 %	55.86 %	0.03 s	GPU @ 2.5 Ghz (Python)
67	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.
68	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.
69	PP-3D			58.20 %	71.59 %	54.06 %	0.1 s	1 core @ 2.5 Ghz (Python)
70	XView-PartA^2			58.17 %	67.12 %	55.81 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
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	E^2-PV-RCNN			58.01 %	67.39 %	55.77 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
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. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
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	FSA-PVRCNN			57.67 %	65.80 %	55.34 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
77	SVGA-Net			57.57 %	67.48 %	55.11 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
78	TBD			57.56 %	66.43 %	55.21 %	0.07 s	1 core @ 2.5 Ghz (Python + C/C++)
79	FPC-RCNN			57.46 %	66.88 %	55.09 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
80	Faraway-Frustum		code	57.35 %	67.88 %	54.42 %	0.1 s	GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion. arXiv preprint arXiv:2011.01404 2020.
81	SIF			57.32 %	67.78 %	54.86 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
82	GNN-RCNN			57.32 %	66.78 %	55.77 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
83	TBD_IOU2			57.26 %	68.26 %	54.74 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
84	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.
85	CVRS VIC-RCNN			57.20 %	65.43 %	55.17 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
86	CVRS VIC-Net			57.20 %	65.53 %	54.92 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
87	FPCR-CNN			57.14 %	66.59 %	54.43 %	0.05 s	1 core @ 2.5 Ghz (Python)
88	Simple3D Net			57.00 %	66.89 %	54.38 %	0.02 s	GPU @ 2.5 Ghz (Python)
89	KNN-GCNN			56.80 %	69.53 %	52.86 %	0.4 s	1 core @ 2.5 Ghz (Python + C/C++)
90	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.
91	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.
92	yolo4_5l			56.46 %	73.14 %	49.57 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
93	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.
94	MonoRUn			56.40 %	73.05 %	51.40 %	0.07 s	GPU @ 2.5 Ghz (Python + C/C++)
95	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.
96	TBD_IOU1			56.02 %	66.44 %	53.62 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
97	CVIS-DF3D_v2			56.02 %	65.82 %	53.58 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
98	TBD_IOU			55.90 %	64.94 %	53.60 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
99	AF			55.80 %	66.31 %	52.02 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
100	yolo4			55.78 %	72.49 %	51.11 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
101	Mag			55.74 %	71.91 %	50.92 %	0.07 s	1 core @ 2.5 Ghz (C/C++)
102	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.
103	DAM			55.60 %	74.85 %	50.63 %	1 s	GPU @ 2.5 Ghz (Python)
104	TBD			55.43 %	65.62 %	51.97 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
105	CDI3D			55.16 %	67.35 %	51.17 %	0.03 s	GPU @ 2.5 Ghz (Python)
106	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.
107	FPC3D_all			55.08 %	64.74 %	52.59 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
108	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.
109	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.
110	yolo4			54.30 %	73.16 %	49.46 %	0.02 s	1 core @ 2.5 Ghz (Python)
111	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.
112	MGACNet			54.13 %	63.54 %	51.79 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
113	OSE+			54.12 %	68.48 %	49.93 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
114	XView			53.83 %	62.27 %	51.61 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
115	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.
116	MKFFNet			53.64 %	63.25 %	51.07 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
117	MKFFNet			53.55 %	62.18 %	50.80 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
118	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.
119	3DBN_2			53.26 %	63.82 %	50.76 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
120	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.
121	Disp R-CNN		code	52.98 %	71.79 %	48.20 %	0.387 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.
122	MTMono3d			52.96 %	69.01 %	46.18 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
123	Disp R-CNN (velo)		code	52.90 %	71.63 %	48.15 %	0.387 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.
124	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.
125	SparVox3D			52.84 %	69.33 %	48.49 %	0.05 s	GPU @ 2.0 Ghz (Python)
126	VGCN			52.80 %	61.86 %	50.66 %	0.09 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
127	tbd			52.78 %	63.45 %	50.79 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
128	yolo4_5l		code	52.74 %	71.89 %	47.90 %	0.02 s	1 core @ 2.5 Ghz (Python + C/C++)
129	deprecated			52.59 %	60.42 %	50.61 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
130	PFF3D			52.53 %	62.12 %	50.27 %	0.05 s	GPU @ 3.0 Ghz (Python + C/C++)
131	ResNet-RRC			52.09 %	66.44 %	47.51 %	0.11 s	GPU @ 1.5 Ghz (Python + C/C++)
132	MKFFNet			51.96 %	60.31 %	49.70 %	0.01s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
133	TBD			51.31 %	61.14 %	47.82 %	0.05 s	GPU @ 2.5 Ghz (Python)
134	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.
135	ResNet-RRC (pruned)			51.12 %	65.47 %	46.53 %	0.11 s	GPU @ 1.5 Ghz (Python + C/C++)
136	PVNet			50.50 %	60.58 %	48.48 %	0,1 s	1 core @ 2.5 Ghz (Python)
137	AF_MCLS			49.95 %	62.85 %	45.78 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
138	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.
139	yolo_depth			49.47 %	67.23 %	44.99 %	0.07 s	GPU @ 2.5 Ghz (Python)
140	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.
141	Y4		code	49.24 %	68.07 %	44.42 %	0.03 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
142	Center3D			48.76 %	67.15 %	44.05 %	0.05 s	GPU @ 3.5 Ghz (Python)
143	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.
144	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.
145	IGRP+			48.46 %	59.37 %	44.82 %	0.18 s	1 core @ 2.5 Ghz (Python + C/C++)
146	yolo_rgb			48.45 %	64.50 %	43.95 %	0.07 s	GPU @ 2.5 Ghz (Python)
147	IOU-SSD		code	47.92 %	58.09 %	45.60 %	0.045s	1 core @ 2.5 Ghz (C/C++)
148	MonoFlex			47.58 %	62.64 %	43.15 %	0.03 s	GPU @ 2.5 Ghz (Python)
149	tiny-stereo			47.25 %	60.73 %	43.13 %	0.4 s	1 core @ 2.5 Ghz (Python + C/C++)
150	RoIFusion		code	46.81 %	56.26 %	44.58 %	0.22 s	1 core @ 3.0 Ghz (Python)
151	NLK-3D			46.33 %	59.46 %	43.88 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
152	tiny-stereo-v2			46.14 %	58.97 %	42.48 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)
153	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.
154	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.
155	MonoGeo			45.50 %	59.53 %	41.18 %	0.05 s	1 core @ 2.5 Ghz (Python)
156	NLK-ALL		code	45.27 %	56.86 %	41.08 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
157	NL_M3D			45.03 %	58.46 %	39.22 %	0.2 s	1 core @ 2.5 Ghz (Python)
158	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.
159	stereo-tkc			44.91 %	58.10 %	42.39 %	0.4 s	GPU @ 2.0 Ghz (Python + C/C++)
160	CBi-GNN-persons			44.88 %	58.17 %	40.50 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
161	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.
162	Deprecated			44.65 %	60.33 %	38.51 %	Deprecated	Deprecated
163	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.
164	FCY			43.87 %	56.43 %	39.87 %	0.02 s	GPU @ 2.5 Ghz (Python)
165	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.
166	MonoEF		code	43.73 %	58.79 %	39.45 %	0.03 s	1 core @ 2.5 Ghz (Python)
167	DAMono3D			43.57 %	59.80 %	39.23 %	0.09s	1 core @ 2.5 Ghz (C/C++)
168	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.
169	DAMNET		code	43.42 %	56.05 %	41.16 %	1 s	1 core @ 2.5 Ghz (C/C++)
170	Pointpillar_TV			43.29 %	53.06 %	41.14 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
171	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.
172	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.
173	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.
174	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.
175	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.
176	RelationNet3D_res18			41.88 %	55.14 %	37.55 %	0.04 s	GPU @ 2.5 Ghz (Python)
177	PLDet3d			41.86 %	55.94 %	37.64 %	0.11 s	1 core @ 2.5 Ghz (C/C++)
178	DP3D			41.71 %	55.28 %	35.73 %	0.07 s	GPU @ 1.5 Ghz (Python + C/C++)
179	DDMP-3D			41.54 %	56.73 %	35.52 %	0.18 s	1 core @ 2.5 Ghz (Python)
180	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.
181	M3D-RPN(S-R)			41.46 %	56.64 %	37.31 %	0.16 s	GPU @ 1.5 Ghz (Python)
182	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 .
183	Stereo3D			41.46 %	56.20 %	37.07 %	0.1 s	GPU 1080Ti
184	MP-Mono			41.04 %	56.05 %	36.99 %	0.16 s	GPU @ 2.5 Ghz (Python)
185	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.
186	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.
187	RT3D-GMP			39.83 %	55.56 %	35.18 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
188	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.
189	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.
190	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.
191	PG-MonoNet			39.38 %	48.57 %	35.43 %	0.19 s	GPU @ 2.5 Ghz (Python)
192	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). .
193	OSE			38.62 %	50.26 %	34.87 %	0.1 s	GPU @ 2.5 Ghz (C/C++)
194	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.
195	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.
196	NCL		code	36.89 %	42.81 %	34.76 %	NA s	1 core @ 2.5 Ghz (Python)
197	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.
198	LAPNet			36.22 %	48.96 %	32.27 %	0.03 s	1 core @ 2.5 Ghz (Python)
199	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.
200	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.
201	FADNet		code	32.64 %	42.43 %	29.13 %	0.04 s	GPU @ >3.5 Ghz (Python)
202	CaDDN			32.42 %	46.35 %	29.98 %	0.63 s	GPU @ 2.5 Ghz (Python)
203	VN3D			31.51 %	41.80 %	29.76 %	0.02 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
204	FPIOD		code	30.96 %	45.07 %	28.48 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
205	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.
206	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.
207	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.
208	SOD			29.47 %	46.61 %	26.97 %	0.1 s	1 core @ 2.5 Ghz (Python)
209	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.
210	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.
211	AEC3D			22.20 %	29.92 %	20.63 %	0.01 s	GPU @ 2.5 Ghz (Python)
212	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.
213	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.
214	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.
215	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.
216	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.
217	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.
218	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.
219	UM3D_TUM			0.00 %	0.00 %	0.00 %	0.05 s	1 core @ 2.5 Ghz (Python)