The KITTI Vision Benchmark Suite

The bird's eye view benchmark consists of 7481 training images and 7518 test images as well as the corresponding point clouds, comprising a total of 80.256 labeled objects. For evaluation, we compute precision-recall curves. To rank the methods we compute average precision. 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 thank David Stutz and Bo Li for developing the 3D object detection benchmark.

We evaluate bird's eye view detection performance using the PASCAL criteria also used for 2D object detection. Far objects are thus filtered based on their bounding box height in the image plane. As only objects also appearing on the image plane are labeled, objects in don't car areas do not count as false positives. We note that the evaluation does not take care of ignoring detections that are not visible on the image plane — these detections might give rise to false positives. For cars we require a bounding box overlap of 70% in bird's eye view, while for pedestrians and cyclists we require an overlap of 50%. 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.

Car

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	GraR-Po			92.12 %	95.79 %	87.11 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
2	VPFNet			91.86 %	93.02 %	86.94 %	0.06 s	2 cores @ 2.5 Ghz (Python)
H. Zhu, J. Deng, Y. Zhang, J. Ji, Q. Mao, H. Li and Y. Zhang: VPFNet: Improving 3D Object Detection with Virtual Point based LiDAR and Stereo Data Fusion. 2021.
3	SFD			91.85 %	95.64 %	86.83 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
4	SE-SSD		code	91.84 %	95.68 %	86.72 %	0.03 s	1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, L. Jiang and C. Fu: SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud. CVPR 2021.
5	GraR-Vo			91.72 %	95.27 %	86.51 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
6	CityBrainLab			91.60 %	94.75 %	86.67 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
7	SPANet			91.59 %	95.59 %	86.53 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
Y. Ye: SPANet: Spatial and Part-Aware Aggregation Network for 3D Object Detection. Pacific Rim International Conference on Artificial Intelligence 2021.
8	CasA			91.54 %	95.19 %	86.82 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
9	Anonymous			91.53 %	95.04 %	86.69 %	0.1 s	GPU @ 2.5 Ghz (Python)
10	GraR-Pi			91.52 %	95.06 %	86.42 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
11	Anonymous			91.38 %	95.23 %	86.71 %	n/a s	1 core @ 2.5 Ghz (C/C++)
12	DGDNH			91.36 %	95.03 %	88.79 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
13	BADet		code	91.32 %	95.23 %	86.48 %	0.11 s	1 core @ 2.5 Ghz (C/C++)
R. Qian, X. Lai and X. Li: BADet: Boundary-Aware 3D Object Detection from Point Clouds. Pattern Recognition (PR) 2022.
14	Anonymous			91.04 %	94.31 %	86.31 %	0.1s	1 core @ 2.5 Ghz (C/C++)
15	Anonymous			91.04 %	94.76 %	86.31 %	n/a s	1 core @ 2.5 Ghz (Python + C/C++)
16	SA-SSD		code	91.03 %	95.03 %	85.96 %	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.
17	anonymous			90.90 %	92.96 %	86.34 %	0.09 s	GPU @ 2.5 Ghz (Python)
18	MMLab PV-RCNN		code	90.65 %	94.98 %	86.14 %	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.
19	PTA-RCNN			90.61 %	92.51 %	86.18 %	0.08 s	1 core @ 2.5 Ghz (Python)
20	VueronNet		code	90.56 %	94.67 %	85.31 %	0.06 s	1 core @ 2.0 Ghz (Python)
21	ST-RCNN			90.53 %	94.58 %	86.08 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
22	ST-RCNN (SNLW-RCNN)		code	90.53 %	94.58 %	86.08 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
23	VPFNet		code	90.52 %	93.94 %	86.25 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
C. Wang, H. Chen and L. Fu: VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection. 2021.
24	PDV			90.48 %	94.56 %	86.23 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
25	VCRCNN			90.42 %	94.55 %	86.11 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
26	M3DeTR		code	90.37 %	94.41 %	85.98 %	n/a s	GPU @ 1.0 Ghz (Python)
T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis and D. Manocha: M3DeTR: Multi-representation, Multi- scale, Mutual-relation 3D Object Detection with Transformers. 2021.
27	TBD			90.37 %	93.82 %	87.72 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
28	HyBrid Feature Det			90.35 %	92.87 %	85.87 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
29	DDet			90.34 %	94.16 %	86.01 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
30	VoTr-TSD		code	90.34 %	94.03 %	86.14 %	0.07 s	1 core @ 2.5 Ghz (C/C++)
J. Mao, Y. Xue, M. Niu, H. Bai, J. Feng, X. Liang, H. Xu and C. Xu: Voxel Transformer for 3D Object Detection. ICCV 2021.
31	TransCyclistNet			90.33 %	92.68 %	85.90 %	0.08 s	1 core @ 2.5 Ghz (Python)
32	Fast VP-RCNN		code	90.32 %	95.09 %	85.84 %	0.05 s	1 core @ 3.5 Ghz (C/C++)
33	WHUT-iou_ssd		code	90.31 %	94.22 %	85.83 %	0.045s	1 core @ 2.5 Ghz (C/C++)
34	LZY_RCNN			90.29 %	92.88 %	85.84 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
35	anonymous		code	90.22 %	94.86 %	85.73 %	0.05s	1 core @ >3.5 Ghz (python)
36	MSG-PGNN			90.20 %	92.89 %	85.80 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
37	DSA-PV-RCNN		code	90.13 %	92.42 %	85.93 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
38	XView			90.12 %	92.27 %	85.94 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
39	GraR-VoI			90.10 %	95.69 %	86.85 %	0.07 s	1 core @ 2.5 Ghz (Python + C/C++)
40	Generalized-SIENet			90.09 %	92.12 %	85.88 %	0.08 s	1 core @ 2.5 Ghz (Python)
41	CAT-Det			90.07 %	92.59 %	85.82 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)
42	SCIR-Net			90.04 %	92.11 %	85.63 %	0.03 s	GPU @ 2.5 Ghz (Python + C/C++)
43	FPC-RCNN			90.03 %	92.74 %	85.67 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
44	TPCG			90.02 %	92.22 %	85.88 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
45	Associate-3Ddet_v2			90.00 %	95.55 %	84.72 %	0.04 s	1 core @ 2.5 Ghz (Python)
46	TransDet3D			89.98 %	92.44 %	85.71 %	0.08 s	1 core @ 2.5 Ghz (Python)
47	FPV-SSD			89.93 %	91.45 %	85.21 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
48	SAA-PV-RCNN			89.88 %	91.54 %	86.93 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
49	SVGA-Net			89.88 %	92.07 %	85.59 %	0.03s	1 core @ 2.5 Ghz (Python + C/C++)
Q. He, Z. Wang, H. Zeng, Y. Zeng and Y. Liu: SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds. AAAI 2022.
50	EBM3DOD		code	89.86 %	95.64 %	84.56 %	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.
51	CIA-SSD		code	89.84 %	93.74 %	82.39 %	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.
52	CLOCs_PVCas		code	89.80 %	93.05 %	86.57 %	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.
53	PE-RCVN			89.79 %	95.55 %	84.78 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
54	Anonymous			89.76 %	95.41 %	86.42 %	0.03 s	1 core @ 2.5 Ghz (Python + C/C++)
55	EA-M-RCNN(BorderAtt)			89.76 %	94.67 %	86.73 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
56	sa-voxel-centernet		code	89.74 %	92.02 %	85.69 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
57	AM-SSD			89.74 %	95.56 %	84.65 %	0.04 s	1 core @ 2.5 Ghz (Python)
58	EBM3DOD baseline		code	89.63 %	95.44 %	84.34 %	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.
59	DSASNet			89.59 %	93.41 %	84.81 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
60	CAD			89.57 %	93.03 %	84.71 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
61	3D-CVF at SPA			89.56 %	93.52 %	82.45 %	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.
62	Struc info fusion II			89.54 %	95.26 %	82.31 %	0.05 s	GPU @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.
63	KpNet			89.53 %	93.34 %	81.95 %	0.42 s	1 core @ 2.5 Ghz (C/C++)
64	Fast-CLOCs			89.49 %	93.03 %	86.40 %	0.1 s	GPU @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: Fast-CLOCs: Fast Camera-LiDAR Object Candidates Fusion for 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022.
65	KpNet			89.49 %	93.29 %	81.92 %	42 s	1 core @ 2.5 Ghz (C/C++)
66	IA-SSD (single)			89.48 %	93.14 %	84.42 %	0.013 s	1 core @ 2.5 Ghz (C/C++)
67	CLOCs		code	89.48 %	92.91 %	86.42 %	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.
68	SA3DNet			89.46 %	93.11 %	84.60 %	0.05 s	GPU @ 2.5 Ghz (Python)
69	DVF-V			89.42 %	93.12 %	86.50 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
70	Struc info fusion I			89.38 %	94.91 %	84.29 %	0.05 s	1 core @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.
71	JPVNet			89.36 %	92.78 %	84.37 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
72	ASCNet			89.36 %	92.85 %	86.45 %	0.09 s	1 core @ 2.5 Ghz (C/C++)
73	BtcDet		code	89.34 %	92.81 %	84.55 %	0.09 s	GPU @ 2.5 Ghz (Python + C/C++)
Q. Xu, Y. Zhong and U. Neumann: Behind the Curtain: Learning Occluded Shapes for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2022.
74	IA-SSD (multi)			89.33 %	92.79 %	84.35 %	0.014 s	1 core @ 2.5 Ghz (C/C++)
75	TBD			89.29 %	92.94 %	84.46 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
76	TBD			89.24 %	92.59 %	85.99 %	0.1 s	1 core @ 2.5 Ghz (Python)
77	TBD			89.21 %	92.74 %	84.23 %	TBD	GPU @ 2.5 Ghz (Python + C/C++)
78	DVF-PV			89.20 %	93.08 %	86.28 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
79	TF3D			89.19 %	93.10 %	84.41 %	0.1 s	2 cores @ 3.0 Ghz (Python)
80	STD		code	89.19 %	94.74 %	86.42 %	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.
81	FS-Net			89.18 %	92.88 %	84.39 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
82	MBDF-Net			89.18 %	95.36 %	84.24 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
83	Point-GNN		code	89.17 %	93.11 %	83.90 %	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.
84	Anonymous			89.15 %	92.47 %	85.97 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
85	SGNet			89.14 %	93.04 %	86.54 %	0.09 s	GPU @ 2.5 Ghz (Python)
86	USVLab BSAODet (MM)			89.13 %	92.92 %	86.41 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
87	SPG_mini		code	89.12 %	92.80 %	86.27 %	0.09 s	GPU @ 2.5 Ghz (Python)
Q. Xu, Y. Zhou, W. Wang, C. Qi and D. Anguelov: SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation. Proceedings of the IEEE conference on computer vision and pattern recognition (ICCV) 2021.
88	TBD			89.11 %	92.42 %	84.26 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
89	EQ-PVRCNN			89.09 %	94.55 %	86.42 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
90	MSADet			89.08 %	92.76 %	85.66 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
91	VoxSeT			89.07 %	92.70 %	86.29 %	0.09 s	1 core @ 2.5 Ghz (C/C++)
92	3DSSD		code	89.02 %	92.66 %	85.86 %	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.
93	EPNet++			89.00 %	95.41 %	85.73 %	0.1 s	GPU @ 2.5 Ghz (Python)
94	Anonymous		code	89.00 %	92.67 %	86.33 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
95	SECOND			88.98 %	92.01 %	83.67 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
96	LGNet			88.98 %	92.83 %	86.26 %	0.07 s	1 core @ 2.5 Ghz (C/C++)
97	ISE-RCNN			88.97 %	92.86 %	86.28 %	0.09 s	1 core @ 2.5 Ghz (Python + C/C++)
98	TBD			88.94 %	92.03 %	86.35 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
99	Sem-Aug v1		code	88.92 %	92.59 %	84.29 %	0.04 s	GPU @ 3.5 Ghz (Python)
100	MBDF-Net-1			88.90 %	94.68 %	83.98 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
101	VCT			88.90 %	93.01 %	84.23 %	0.2 s	1 core @ 2.5 Ghz (Python)
102	H^23D R-CNN		code	88.87 %	92.85 %	86.07 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
J. Deng, W. Zhou, Y. Zhang and H. Li: From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2021.
103	MVOD			88.85 %	92.50 %	86.19 %	0.16 s	1 core @ 2.5 Ghz (C/C++)
104	Pyramid R-CNN			88.84 %	92.19 %	86.21 %	0.07 s	1 core @ 2.5 Ghz (C/C++)
J. Mao, M. Niu, H. Bai, X. Liang, H. Xu and C. Xu: Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection. ICCV 2021.
105	CityBrainLab-CT3D		code	88.83 %	92.36 %	84.07 %	0.07 s	1 core @ 2.5 Ghz (Python + C/C++)
H. Sheng, S. Cai, Y. Liu, B. Deng, J. Huang, X. Hua and M. Zhao: Improving 3D Object Detection with Channel- wise Transformer. ICCV 2021.
106	Voxel R-CNN		code	88.83 %	94.85 %	86.13 %	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 . AAAI 2021.
107	HVNet			88.82 %	92.83 %	83.38 %	0.03 s	GPU @ 2.0 Ghz (Python)
M. Ye, S. Xu and T. Cao: HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection. CVPR 2020.
108	SRIF-RCNN			88.77 %	92.10 %	86.06 %	0.0947 s	1 core @ 2.5 Ghz (C/C++)
109	TBD			88.75 %	92.30 %	84.01 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
110	PV-RCNN++			88.74 %	92.66 %	85.97 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
111	SPG		code	88.70 %	94.33 %	85.98 %	0.09 s	1 core @ 2.5 Ghz (Python + C/C++)
Q. Xu, Y. Zhou, W. Wang, C. Qi and D. Anguelov: SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation. Proceedings of the IEEE conference on computer vision and pattern recognition (ICCV) 2021.
112	ISE-RCNN-PV			88.69 %	92.31 %	86.10 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
113	SIENet		code	88.65 %	92.38 %	86.03 %	0.08 s	1 core @ 2.5 Ghz (Python)
Z. Li, Y. Yao, Z. Quan, W. Yang and J. Xie: SIENet: Spatial Information Enhancement Network for 3D Object Detection from Point Cloud. 2021.
114	P2V-RCNN			88.63 %	92.72 %	86.14 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.
115	demo			88.62 %	92.35 %	83.56 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
116	FromVoxelToPoint		code	88.61 %	92.23 %	86.11 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to- Point Decoder. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
117	RangeIoUDet			88.59 %	92.28 %	85.83 %	0.02 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, Z. Zhang, M. Zhang, X. Zhao and S. Pu: RangeIoUDet: Range Image Based Real-Time 3D Object Detector Optimized by Intersection Over Union. CVPR 2021.
118	DCAnet			88.55 %	92.29 %	85.85 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
119	SqueezeRCNN			88.52 %	92.65 %	85.82 %	0.08 s	1 core @ 2.5 Ghz (Python)
120	FusionDetv2-v3			88.47 %	92.55 %	85.63 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
121	EPNet		code	88.47 %	94.22 %	83.69 %	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.
122	CenterNet3D			88.46 %	91.80 %	83.62 %	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.
123	GVNet		code	88.43 %	92.19 %	85.63 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
124	USVLab BSAODet (SM)			88.42 %	92.19 %	85.55 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
125	NV-RCNN			88.41 %	92.03 %	85.66 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
126	RangeRCNN			88.40 %	92.15 %	85.74 %	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.
127	Patches			88.39 %	92.72 %	83.19 %	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.
128	Point Image Fusion			88.39 %	92.14 %	85.78 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
129	3D IoU-Net			88.38 %	94.76 %	81.93 %	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.
130	StructuralIF			88.38 %	91.78 %	85.67 %	0.02 s	8 cores @ 2.5 Ghz (Python)
J. Pei An: Deep structural information fusion for 3D object detection on LiDAR-camera system. Accepted in CVIU 2021.
131	CSVoxel-RCNN			88.38 %	92.09 %	85.59 %	0.03 s	GPU @ 1.0 Ghz (Python)
132	SA-voxel-centernet		code	88.28 %	91.80 %	85.73 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
133	SARFE			88.28 %	92.35 %	85.50 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
134	FusionDetv2-v4			88.27 %	92.05 %	85.38 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
135	TBD			88.26 %	91.44 %	85.44 %	0.06 s	GPU @ 2.5 Ghz (Python)
136	CLOCs_SecCas			88.23 %	91.16 %	82.63 %	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.
137	SPVB-SSD			88.23 %	91.82 %	85.46 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
138	UberATG-MMF			88.21 %	93.67 %	81.99 %	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.
139	Patches - EMP			88.17 %	94.49 %	84.75 %	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.
140	SRDL			88.17 %	92.01 %	85.43 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
141	FPC3D			88.15 %	91.92 %	85.32 %	33 s	1 core @ 2.5 Ghz (C/C++)
142	3D-VDNet			88.15 %	91.72 %	84.65 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
143	SAA-SECOND			88.14 %	91.32 %	85.23 %	38m s	1 core @ 2.5 Ghz (C/C++)
144	FusionDetv1			88.13 %	91.91 %	85.40 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
145	FPCR-CNN			88.12 %	92.62 %	85.18 %	0.05 s	1 core @ 2.5 Ghz (Python)
146	PointPainting			88.11 %	92.45 %	83.36 %	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.
147	SERCNN			88.10 %	94.11 %	83.43 %	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.
148	Associate-3Ddet		code	88.09 %	91.40 %	82.96 %	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.
149	HotSpotNet			88.09 %	94.06 %	83.24 %	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.
150	Faraway-Frustum		code	88.08 %	91.90 %	85.35 %	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.
151	NV2P-RCNN			88.08 %	93.44 %	85.32 %	0.1 s	GPU @ 2.5 Ghz (Python)
152	VPN			88.06 %	90.94 %	83.24 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
153	TBD			88.04 %	91.31 %	84.79 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
154	FusionDetv2-v2			88.04 %	91.77 %	85.29 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
155	UberATG-HDNET			87.98 %	93.13 %	81.23 %	0.05 s	GPU @ 2.5 Ghz (Python)
B. Yang, M. Liang and R. Urtasun: HDNET: Exploiting HD Maps for 3D Object Detection. 2nd Conference on Robot Learning (CoRL) 2018.
156	TCDVF			87.94 %	91.21 %	84.66 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
157	AIMC-RUC			87.91 %	93.92 %	82.70 %	0.11 s	1 core @ 2.5 Ghz (Python)
158	DGT-Det3D			87.88 %	91.70 %	85.14 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
159	CVFNet			87.87 %	93.65 %	82.29 %	28.1ms	1 core @ 2.5 Ghz (Python)
160	FusionDetv2-v5			87.86 %	91.92 %	83.07 %	0.05 s	1 core @ 2.5 Ghz (Java + C/C++)
161	Fast Point R-CNN			87.84 %	90.87 %	80.52 %	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.
162	CSNet			87.84 %	92.23 %	82.93 %	0.1 s	1 core @ 2.5 Ghz (Python)
163	YF			87.81 %	92.11 %	83.07 %	0.04 s	GPU @ 2.5 Ghz (C/C++)
164	MMLab-PartA^2		code	87.79 %	91.70 %	84.61 %	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.
165	SIF			87.76 %	91.44 %	85.15 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
166	MVAF-Net		code	87.73 %	91.95 %	85.00 %	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.
167	DKAnet			87.68 %	91.07 %	84.03 %	0.05 s	1 core @ 2.0 Ghz (Python)
168	DVFENet			87.68 %	90.93 %	84.60 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection. Neurocomputing 2021.
169	S-AT GCN			87.68 %	90.85 %	84.20 %	0.02 s	GPU @ 2.0 Ghz (Python)
L. Wang, C. Wang, X. Zhang, T. Lan and J. Li: S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection. CoRR 2021.
170	TBD			87.67 %	91.02 %	82.42 %	0.1 s	1 core @ 2.5 Ghz (Python)
171	TBD			87.62 %	90.86 %	82.29 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
172	AutoAlign			87.60 %	91.72 %	84.44 %	0.1 s	1 core @ 2.5 Ghz (Python)
173	MODet			87.56 %	90.80 %	82.69 %	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.
174	AB3DMOT		code	87.53 %	91.99 %	81.03 %	0.0047s	1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
175	TBD			87.51 %	90.76 %	80.15 %	0.1 s	1 core @ 2.5 Ghz (Python)
176	PointRGCN			87.49 %	91.63 %	80.73 %	0.26 s	GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
177	MGAF-3DSSD		code	87.47 %	92.70 %	82.19 %	0.1 s	1 core @ 2.5 Ghz (Python)
J. Li, H. Dai, L. Shao and Y. Ding: Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
178	PC-CNN-V2			87.40 %	91.19 %	79.35 %	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.
179	HS3D		code	87.40 %	91.97 %	82.85 %	0.12 s	1 core @ 2.5 Ghz (Python + C/C++)
180	MMLab-PointRCNN		code	87.39 %	92.13 %	82.72 %	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.
181	MAFF-Net(DAF-Pillar)			87.34 %	90.79 %	77.66 %	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.
182	HRI-VoxelFPN			87.21 %	92.75 %	79.82 %	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.
183	epBRM		code	87.13 %	90.70 %	81.92 %	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.
184	3D_att			87.09 %	93.14 %	81.92 %	0.17 s	GPU @ 2.5 Ghz (Python)
185	Contrastive PP		code	87.06 %	92.99 %	81.96 %	0.01 s	1 core @ 2.5 Ghz (Python)
186	DVF			87.05 %	92.76 %	84.13 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
187	SARPNET			86.92 %	92.21 %	81.68 %	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.
188	sscl-20p			86.82 %	91.43 %	82.06 %	0.02 s	1 core @ 2.5 Ghz (Python)
189	ARPNET			86.81 %	90.06 %	79.41 %	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	C-GCN			86.78 %	91.11 %	80.09 %	0.147 s	GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
191	HNet-3DSSD		code	86.69 %	91.65 %	81.05 %	0.05 s	GPU @ 2.5 Ghz (Python)
192	PointPillars		code	86.56 %	90.07 %	82.81 %	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.
193	TANet		code	86.54 %	91.58 %	81.19 %	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.
194	SCNet			86.48 %	90.07 %	81.30 %	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.
195	SegVoxelNet			86.37 %	91.62 %	83.04 %	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.
196	Dune-DCF-e09			86.36 %	89.33 %	81.77 %	1 s	1 core @ 2.5 Ghz (C/C++)
197	Dune-DCF-e11			86.32 %	89.32 %	81.78 %	1 s	1 core @ 2.5 Ghz (C/C++)
198	PP-PCdet		code	86.32 %	89.86 %	81.62 %	0.01 s	1 core @ 2.5 Ghz (Python)
199	3D IoU Loss			86.22 %	91.36 %	81.20 %	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.
200	Dune-DCF-e15			86.21 %	88.99 %	81.62 %	1 s	1 core @ 2.5 Ghz (C/C++)
201	APL-Second			86.16 %	91.45 %	81.08 %	0.05 s	1 core @ 2.5 Ghz (Python)
202	TBD_BD		code	86.12 %	91.00 %	81.66 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
203	CrazyTensor-CF			86.10 %	89.13 %	81.61 %	1 s	1 core @ 2.5 Ghz (C/C++)
204	City-CF-fixed			86.09 %	89.94 %	81.73 %	1 s	1 core @ 2.5 Ghz (C/C++)
205	R-GCN			86.05 %	91.91 %	81.05 %	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.
206	UberATG-PIXOR++			86.01 %	93.28 %	80.11 %	0.035 s	GPU @ 2.5 Ghz (Python)
B. Yang, M. Liang and R. Urtasun: HDNET: Exploiting HD Maps for 3D Object Detection. 2nd Conference on Robot Learning (CoRL) 2018.
207	DASS			85.85 %	91.74 %	80.97 %	0.09 s	1 core @ 2.0 Ghz (Python)
O. Unal, L. Van Gool and D. Dai: Improving Point Cloud Semantic Segmentation by Learning 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2021.
208	F-ConvNet		code	85.84 %	91.51 %	76.11 %	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.
209	City-CF			85.83 %	89.20 %	81.61 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
210	PI-RCNN			85.81 %	91.44 %	81.00 %	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.
211	LazyTorch-CP-Infer-O			85.74 %	89.19 %	81.35 %	1 s	1 core @ 2.5 Ghz (C/C++)
212	PointRGBNet			85.73 %	91.39 %	80.68 %	0.08 s	4 cores @ 2.5 Ghz (Python + C/C++)
213	LazyTorch-CP-Small-P			85.63 %	89.10 %	81.27 %	1 s	1 core @ 2.5 Ghz (C/C++)
214	CrazyTensor-CP			85.55 %	87.94 %	82.63 %	1 s	1 core @ 2.5 Ghz (Python)
215	Sem-Aug-PointRCNN		code	85.50 %	89.75 %	83.13 %	0.1 s	GPU @ 3.5 Ghz (C/C++)
216	UberATG-ContFuse			85.35 %	94.07 %	75.88 %	0.06 s	GPU @ 2.5 Ghz (Python)
M. Liang, B. Yang, S. Wang and R. Urtasun: Deep Continuous Fusion for Multi-Sensor 3D Object Detection. ECCV 2018.
217	PFF3D		code	85.08 %	89.61 %	80.42 %	0.05 s	GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
218	RangeDet		code	85.06 %	89.88 %	80.23 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
219	LazyTorch-CP			85.05 %	88.47 %	81.19 %	1 s	1 core @ 2.5 Ghz (C/C++)
220	AVOD		code	84.95 %	89.75 %	78.32 %	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.
221	WS3D			84.93 %	90.96 %	77.96 %	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.
222	FPC3D_all			84.85 %	91.05 %	80.23 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
223	AVOD-FPN		code	84.82 %	90.99 %	79.62 %	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.
224	MF			84.72 %	88.58 %	78.17 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
225	F-PointNet		code	84.67 %	91.17 %	74.77 %	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.
226	FusionDetv2-v1			84.45 %	89.64 %	79.73 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
227	FusionDetv2-baseline			84.31 %	90.38 %	79.23 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
228	3DBN			83.94 %	89.66 %	76.50 %	0.13s	1080Ti (Python+C/C++)
X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.
229	KMC		code	83.90 %	88.87 %	76.87 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
230	MLOD		code	82.68 %	90.25 %	77.97 %	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.
231	BirdNet+		code	81.85 %	87.43 %	75.36 %	0.11 s	Titan Xp (PyTorch)
A. Barrera, J. Beltrán, C. Guindel, J. Iglesias and F. García: BirdNet+: Two-Stage 3D Object Detection in LiDAR through a Sparsity-Invariant Bird’s Eye View. IEEE Access 2021.
232	TBD			81.53 %	87.90 %	74.26 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
233	AEC3D			80.37 %	86.81 %	74.26 %	18 ms	GPU @ 2.5 Ghz (Python)
234	UberATG-PIXOR			80.01 %	83.97 %	74.31 %	0.035 s	TITAN Xp (Python)
B. Yang, W. Luo and R. Urtasun: PIXOR: Real-time 3D Object Detection from Point Clouds. CVPR 2018.
235	MV3D (LIDAR)			78.98 %	86.49 %	72.23 %	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.
236	DSGN++			78.94 %	88.55 %	69.74 %	0.4 s	NVIDIA Tesla V100
237	MV3D			78.93 %	86.62 %	69.80 %	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.
238	VN3D			77.45 %	86.35 %	71.59 %	0.02 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
239	SD3DOD			76.96 %	86.82 %	70.05 %	0.04 s	GPU @ 2.5 Ghz (Python)
240	MMLAB LIGA-Stereo		code	76.78 %	88.15 %	67.40 %	0.4 s	1 core @ 2.5 Ghz (Python + C/C++)
X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
241	R-AGNO-Net			76.24 %	80.10 %	70.38 %	0.15 s	1 core @ 2.5 Ghz (C/C++)
242	RCD			75.83 %	82.26 %	69.61 %	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.
243	LIGA-Stereo-old			74.76 %	88.33 %	65.31 %	0.375 s	Titan Xp
244	LaserNet			74.52 %	79.19 %	68.45 %	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.
245	PL++ (SDN+GDC)		code	73.80 %	84.61 %	65.59 %	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.
246	A3DODWTDA		code	73.26 %	79.58 %	62.77 %	0.08 s	GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
247	ppt			70.21 %	72.17 %	65.26 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
248	Complexer-YOLO			68.96 %	77.24 %	64.95 %	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.
249	TopNet-Retina			68.16 %	80.16 %	63.43 %	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.
250	CG-Stereo			66.44 %	85.29 %	58.95 %	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.
251	PLUME			66.27 %	82.97 %	56.70 %	0.15 s	GPU @ 2.5 Ghz (Python)
Y. Wang, B. Yang, R. Hu, M. Liang and R. Urtasun: PLUME: Efficient 3D Object Detection from Stereo Images. IROS 2021.
252	CDN		code	66.24 %	83.32 %	57.65 %	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	DSGN		code	65.05 %	82.90 %	56.60 %	0.67 s	NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
254	TopNet-DecayRate			64.60 %	79.74 %	58.04 %	92 ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
255	UPF_3D			63.58 %	85.53 %	56.56 %	0.29 s	1 core @ 2.5 Ghz (Python)
256	BirdNet+ (legacy)		code	63.33 %	84.80 %	61.23 %	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. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
257	BEVC			61.89 %	69.00 %	56.32 %	35ms	GPU @ 1.5 Ghz (Python)
258	3D FCN			61.67 %	70.62 %	55.61 %	>5 s	1 core @ 2.5 Ghz (C/C++)
B. Li: 3D Fully Convolutional Network for Vehicle Detection in Point Cloud. IROS 2017.
259	CDN-PL++			61.04 %	81.27 %	52.84 %	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.
260	BirdNet			59.83 %	84.17 %	57.35 %	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.
261	TopNet-UncEst			59.67 %	72.05 %	51.67 %	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.
262	RT3D-GMP			59.00 %	69.14 %	45.49 %	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.
263	OSE+			58.65 %	79.80 %	50.52 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
264	Disp R-CNN (velo)		code	58.62 %	79.76 %	47.73 %	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.
265	SOD			58.50 %	81.25 %	49.08 %	0.1 s	1 core @ 2.5 Ghz (Python)
266	EGFN			58.12 %	78.10 %	49.28 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
267	Pseudo-LiDAR++		code	58.01 %	78.31 %	51.25 %	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.
268	Disp R-CNN		code	57.98 %	79.61 %	47.09 %	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.
269	ZoomNet		code	54.91 %	72.94 %	44.14 %	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.
270	VoxelJones		code	53.96 %	66.21 %	47.66 %	.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.
271	TopNet-HighRes			53.05 %	67.84 %	46.99 %	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.
272	OC Stereo		code	51.47 %	68.89 %	42.97 %	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.
273	YOLOStereo3D		code	50.28 %	76.10 %	36.86 %	0.1 s	GPU 1080Ti
Y. Liu, L. Wang and M. Liu: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
274	RT3DStereo			46.82 %	58.81 %	38.38 %	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.
275	Pseudo-Lidar		code	45.00 %	67.30 %	38.40 %	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.
276	RT3D			44.00 %	56.44 %	42.34 %	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.
277	Stereo CenterNet			42.12 %	62.97 %	35.37 %	0.04 s	GPU @ 2.5 Ghz (Python)
Y. Shi, Y. Guo, Z. Mi and X. Li: Stereo CenterNet-based 3D object detection for autonomous driving. Neurocomputing 2022.
278	Stereo R-CNN		code	41.31 %	61.92 %	33.42 %	0.3 s	GPU @ 2.5 Ghz (Python)
P. Li, X. Chen and S. Shen: Stereo R-CNN based 3D Object Detection for Autonomous Driving. CVPR 2019.
279	GCDR			37.34 %	50.85 %	30.51 %	0.28 s	1 core @ 2.5 Ghz (Python)
280	StereoFENet			32.96 %	49.29 %	25.90 %	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.
281	Digging_M3D			28.84 %	39.74 %	26.08 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
282	Mobile Stereo R-CNN			28.78 %	44.51 %	22.30 %	1.8 s	NVIDIA Jetson TX2
M. Hussein, M. Khalil and B. Abdullah: 3D Object Detection using Mobile Stereo R- CNN on Nvidia Jetson TX2. International Conference on Advanced Engineering, Technology and Applications (ICAETA) 2021.
283	LPCG-Monoflex			24.81 %	35.96 %	21.86 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
284	Mix-Teaching-M3D			24.23 %	35.74 %	20.80 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
285	CMKD			23.92 %	36.92 %	21.68 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
286	PS-fld			23.76 %	32.64 %	20.64 %	0.25 s	1 core @ 2.5 Ghz (C/C++)
287	SCSTSV-MonoFlex			23.71 %	34.59 %	20.41 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
288	CMKD			23.61 %	36.80 %	21.31 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
289	MonoDDE			23.46 %	33.58 %	20.37 %	0.04 s	1 core @ 2.5 Ghz (Python)
290	DD3D		code	23.41 %	32.35 %	20.42 %	n/a s	1 core @ 2.5 Ghz (C/C++)
D. Park, R. Ambrus, V. Guizilini, J. Li and A. Gaidon: Is Pseudo-Lidar needed for Monocular 3D Object detection?. IEEE/CVF International Conference on Computer Vision (ICCV) .
291	MonoDistill			22.59 %	31.87 %	19.72 %	0.04 s	1 core @ 2.5 Ghz (Python)
292	MonoCon		code	22.10 %	31.12 %	19.00 %	0.02 s	GPU @ 2.5 Ghz (Python)
T. Xianpeng Liu: Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection. AAAI 2022.
293	gupnet_se			21.98 %	32.82 %	18.70 %	0.03s	1 core @ 2.5 Ghz (C/C++)
294	ZongmuMono3d		code	21.78 %	33.18 %	18.71 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
295	MDNet			21.71 %	33.31 %	18.49 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
296	Lite-FPN-GUPNet			21.53 %	31.68 %	18.38 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
297	mono3d		code	21.39 %	32.17 %	18.47 %	TBD	TBD
298	SGM3D			21.37 %	31.49 %	18.43 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
Z. Zhou, L. Du, X. Ye, Z. Zou, X. Tan, E. Ding, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection. 2021.
299	GUPNet		code	21.19 %	30.29 %	18.20 %	NA s	1 core @ 2.5 Ghz (Python + C/C++)
Y. Lu, X. Ma, L. Yang, T. Zhang, Y. Liu, Q. Chu, J. Yan and W. Ouyang: Geometry Uncertainty Projection Network for Monocular 3D Object Detection. arXiv preprint arXiv:2107.13774 2021.
300	HBD			20.91 %	29.87 %	18.22 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
301	CA3D			20.77 %	29.57 %	17.88 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
302	mono3d			20.75 %	31.58 %	17.66 %	0.03 s	GPU @ 2.5 Ghz (Python)
303	LT-M3OD			20.74 %	29.40 %	17.83 %	0.03 s	1 core @ 2.5 Ghz (Python)
304	vadin-TBD			20.68 %	29.60 %	17.81 %	0.04 s	1 core @ 2.5 Ghz (Python)
305	MonoFlex			20.67 %	30.95 %	17.72 %	0.03 s	1 core @ 2.5 Ghz (Python)
306	MonoGround			20.47 %	30.07 %	17.74 %	0.03 s	1 core @ 2.5 Ghz (Python)
307	MonoDTR			20.38 %	28.59 %	17.14 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
308	MonoEdge			20.35 %	28.80 %	17.57 %	0.05 s	GPU @ 2.5 Ghz (Python)
309	SAIC_ADC_Mono3D		code	20.20 %	27.09 %	18.78 %	50 s	GPU @ 2.5 Ghz (Python)
310	EW		code	20.19 %	31.65 %	16.67 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
311	LPCG-M3D			20.17 %	30.72 %	16.76 %	0.11 s	1 core @ 2.5 Ghz (C/C++)
312	MonoEdge-Rotate			20.16 %	31.19 %	17.35 %	0.05 s	GPU @ 2.5 Ghz (Python)
313	AutoShape		code	20.08 %	30.66 %	15.95 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
Z. Liu, D. Zhou, F. Lu, J. Fang and L. Zhang: AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.
314	M3DSSD++		code	20.03 %	32.18 %	16.47 %	0.16s	1 core @ 2.5 Ghz (C/C++)
315	MAOLoss		code	19.95 %	28.29 %	16.94 %	0.05 s	1 core @ 2.5 Ghz (Python)
316	ANM			19.82 %	29.89 %	16.77 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
317	EM		code	19.80 %	30.61 %	16.55 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
318	KAIST-VDCLab			19.75 %	27.98 %	17.32 %	0.04 s	1 core @ 2.5 Ghz (Python)
319	MonoFlex			19.75 %	28.23 %	16.89 %	0.03 s	GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.
320	MonoEF		code	19.70 %	29.03 %	17.26 %	0.03 s	1 core @ 2.5 Ghz (Python)
Y. Zhou, Y. He, H. Zhu, C. Wang, H. Li and Q. Jiang: Monocular 3D Object Detection: An Extrinsic Parameter Free Approach. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
321	K3D			19.60 %	28.31 %	17.62 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
322	ITS-MDPL			19.54 %	33.02 %	17.56 %	0.16 s	GPU @ 2.5 Ghz (Python)
323	vadin-TBD2		code	19.25 %	29.18 %	16.21 %	0.20 s	1 core @ 2.5 Ghz (Python)
324	DFR-Net			19.17 %	28.17 %	14.84 %	0.18 s	1080 Ti (Pytorch)
Z. Zou, X. Ye, L. Du, X. Cheng, X. Tan, L. Zhang, J. Feng, X. Xue and E. Ding: The devil is in the task: Exploiting reciprocal appearance-localization features for monocular 3d object detection . ICCV 2021.
325	SwinMono3D			19.15 %	29.65 %	14.09 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
326	GAC3D++			19.05 %	26.94 %	16.48 %	0.25 s	1 core @ 2.5 Ghz (C/C++)
327	DLE		code	19.05 %	31.09 %	14.13 %	0.06 s	NVIDIA Tesla V100
C. Liu, S. Gu, L. Gool and R. Timofte: Deep Line Encoding for Monocular 3D Object Detection and Depth Prediction. Proceedings of the British Machine Vision Conference (BMVC) 2021.
328	PCT		code	19.03 %	29.65 %	15.92 %	0.045 s	1 core @ 2.5 Ghz (C/C++)
L. Wang, L. Zhang, Y. Zhu, Z. Zhang, T. He, M. Li and X. Xue: Progressive Coordinate Transforms for Monocular 3D Object Detection. NeurIPS 2021.
329	E2E-DA			19.03 %	27.41 %	16.46 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
330	MonoGeo			18.99 %	25.86 %	16.19 %	0.05 s	1 core @ 2.5 Ghz (Python)
331	Anonymous		code	18.96 %	26.54 %	16.01 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
332	CaDDN		code	18.91 %	27.94 %	17.19 %	0.63 s	GPU @ 2.5 Ghz (Python)
C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.
333	monodle		code	18.89 %	24.79 %	16.00 %	0.04 s	GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for Monocular 3D Object Detection. CVPR 2021 .
334	MonoLCD			18.68 %	25.89 %	16.30 %	0.04 s	1 core @ 2.5 Ghz (Python)
335	none			18.66 %	26.19 %	15.79 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
336	Neighbor-Vote			18.65 %	27.39 %	16.54 %	0.1 s	GPU @ 2.5 Ghz (Python)
X. Chu, J. Deng, Y. Li, Z. Yuan, Y. Zhang, J. Ji and Y. Zhang: Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance Voting. ACM MM 2021.
337	MDSNet			18.65 %	30.92 %	14.53 %	0.07 s	1 core @ 2.5 Ghz (Python)
338	GrooMeD-NMS		code	18.27 %	26.19 %	14.05 %	0.12 s	1 core @ 2.5 Ghz (Python)
A. Kumar, G. Brazil and X. Liu: GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection. CVPR 2021.
339	AutoShape			18.12 %	28.25 %	14.84 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
340	PPTrans			18.12 %	28.05 %	15.41 %	0.2 s	GPU @ 2.5 Ghz (Python)
341	MonoRCNN		code	18.11 %	25.48 %	14.10 %	0.07 s	GPU @ 2.5 Ghz (Python)
X. Shi, Q. Ye, X. Chen, C. Chen, Z. Chen and T. Kim: Geometry-based Distance Decomposition for Monocular 3D Object Detection. ICCV 2021.
342	Ground-Aware		code	17.98 %	29.81 %	13.08 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
Y. Liu, Y. Yuan and M. Liu: Ground-aware Monocular 3D Object Detection for Autonomous Driving. IEEE Robotics and Automation Letters 2021.
343	MP-Mono			17.96 %	25.36 %	13.84 %	0.16 s	GPU @ 2.5 Ghz (Python)
344	Aug3D-RPN			17.89 %	26.00 %	14.18 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth. 2021.
345	DDMP-3D			17.89 %	28.08 %	13.44 %	0.18 s	1 core @ 2.5 Ghz (Python)
L. Wang, L. Du, X. Ye, Y. Fu, G. Guo, X. Xue, J. Feng and L. Zhang: Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection. CVPR 2020.
346	IAFA			17.88 %	25.88 %	15.35 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
D. Zhou, X. Song, Y. Dai, J. Yin, F. Lu, M. Liao, J. Fang and L. Zhang: IAFA: Instance-Aware Feature Aggregation for 3D Object Detection from a Single Image. Proceedings of the Asian Conference on Computer Vision 2020.
347	RelationNet3D_dla34		code	17.74 %	24.27 %	15.38 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
348	TBD			17.70 %	29.97 %	15.04 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
349	RelationNet3D			17.66 %	25.56 %	15.52 %	0.04 s	GPU @ 2.5 Ghz (Python)
350	MonoHMOO			17.60 %	27.39 %	13.25 %	0.2 s	1 core @ 2.5 Ghz (Python + C/C++)
351	RefinedMPL			17.60 %	28.08 %	13.95 %	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.
352	Lite-FPN			17.58 %	26.67 %	14.61 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
353	Kinematic3D		code	17.52 %	26.69 %	13.10 %	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 .
354	MonoRUn		code	17.34 %	27.94 %	15.24 %	0.07 s	GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
355	RetinaMono			17.33 %	26.12 %	15.26 %	0.02 s	1 core @ 2.5 Ghz (Python)
356	AM3D			17.32 %	25.03 %	14.91 %	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.
357	YoloMono3D		code	17.15 %	26.79 %	12.56 %	0.05 s	GPU @ 2.5 Ghz (Python)
Y. Liu, L. Wang and L. Ming: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
358	GAC3D			16.93 %	25.80 %	12.50 %	0.25 s	1 core @ 2.5 Ghz (Python)
M. Bui, D. Ngo, H. Pham and D. Nguyen: GAC3D: improving monocular 3D object detection with ground-guide model and adaptive convolution. 2021.
359	PatchNet		code	16.86 %	22.97 %	14.97 %	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.
360	PGD-FCOS3D		code	16.51 %	26.89 %	13.49 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
T. Wang, X. Zhu, J. Pang and D. Lin: Probabilistic and Geometric Depth: Detecting Objects in Perspective. Conference on Robot Learning (CoRL) 2021.
361	ImVoxelNet		code	16.37 %	25.19 %	13.58 %	0.2 s	GPU @ 2.5 Ghz (Python)
D. Rukhovich, A. Vorontsova and A. Konushin: ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection. arXiv preprint arXiv:2106.01178 2021.
362	TBD			16.22 %	24.21 %	14.11 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
363	KM3D		code	16.20 %	23.44 %	14.47 %	0.03 s	1 core @ 2.5 Ghz (Python)
P. Li: Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training. 2020.
364	MM			16.09 %	24.65 %	13.99 %	1 s	1 core @ 2.5 Ghz (C/C++)
365	E2E-DA-Lite (Res18)			16.06 %	23.49 %	13.55 %	0.01 s	GPU @ 2.5 Ghz (Python)
366	D4LCN		code	16.02 %	22.51 %	12.55 %	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.
367	MK3D			15.99 %	23.00 %	13.28 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
368	Keypoint-3D			15.54 %	23.16 %	11.83 %	14 s	1 core @ 2.5 Ghz (C/C++)
369	COF3D			15.39 %	25.36 %	11.34 %	200 s	1 core @ 2.5 Ghz (Python + C/C++)
370	MonoPair			14.83 %	19.28 %	12.89 %	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.
371	Decoupled-3D			14.82 %	23.16 %	11.25 %	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.
372	QD-3DT		code	14.71 %	20.16 %	12.76 %	0.03 s	GPU @ 2.5 Ghz (Python)
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.
373	RelationNet3D_res18		code	14.59 %	20.54 %	12.51 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
374	SMOKE		code	14.49 %	20.83 %	12.75 %	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.
375	ICCV			14.30 %	19.93 %	12.37 %	0.04 s	GPU @ 2.5 Ghz (Python)
376	RTM3D		code	14.20 %	19.17 %	11.99 %	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.
377	Mono3D_PLiDAR		code	13.92 %	21.27 %	11.25 %	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.
378	M3D-RPN		code	13.67 %	21.02 %	10.23 %	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 .
379	CSoR			13.07 %	18.67 %	10.34 %	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.
380	MonoPSR		code	12.58 %	18.33 %	9.91 %	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.
381	Geo3D			11.86 %	16.31 %	10.26 %	0.04 s	GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
382	MonoCInIS			11.64 %	22.28 %	9.95 %	0,13 s	GPU @ 2.5 Ghz (C/C++)
J. Heylen, M. De Wolf, B. Dawagne, M. Proesmans, L. Van Gool, W. Abbeloos, H. Abdelkawy and D. Reino: MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.
383	SS3D			11.52 %	16.33 %	9.93 %	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.
384	MonoGRNet		code	11.17 %	18.19 %	8.73 %	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.
385	MonoFENet			11.03 %	17.03 %	9.05 %	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.
386	MonoCInIS			10.96 %	20.42 %	9.23 %	0,14 s	GPU @ 2.5 Ghz (Python)
J. Heylen, M. De Wolf, B. Dawagne, M. Proesmans, L. Van Gool, W. Abbeloos, H. Abdelkawy and D. Reino: MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.
387	A3DODWTDA (image)		code	8.66 %	10.37 %	7.06 %	0.8 s	GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
388	TLNet (Stereo)		code	7.69 %	13.71 %	6.73 %	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.
389	Shift R-CNN (mono)		code	6.82 %	11.84 %	5.27 %	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.
390	SparVox3D			6.39 %	10.20 %	5.06 %	0.05 s	GPU @ 2.0 Ghz (Python)
E. Balatkan and F. Kıraç: Improving Regression Performance on Monocular 3D Object Detection Using Bin-Mixing and Sparse Voxel Data. 2021 6th International Conference on Computer Science and Engineering (UBMK) 2021.
391	GS3D			6.08 %	8.41 %	4.94 %	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.
392	MVRA + I-FRCNN+			5.84 %	9.05 %	4.50 %	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.
393	WeakM3D			5.66 %	11.82 %	4.08 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
394	ROI-10D			4.91 %	9.78 %	3.74 %	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.
395	3D-GCK			4.57 %	5.79 %	3.64 %	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.
396	CDTrack3D		code	4.06 %	6.58 %	3.26 %	0.0106 s	NVIDIA RTX 3090 GPU, i9 10850k CPU
397	FQNet			3.23 %	5.40 %	2.46 %	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.
398	3D-SSMFCNN		code	2.63 %	3.20 %	2.40 %	0.1 s	GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.
399	VeloFCN			0.14 %	0.02 %	0.21 %	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 .
400	test		code	0.09 %	0.04 %	0.11 %	50 s	1 core @ 2.5 Ghz (Python)
401	multi-task CNN			0.00 %	0.00 %	0.00 %	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.
402	GA-Aug			0.00 %	0.00 %	0.00 %	0.04 s	GPU @ 2.5 Ghz (Python)
403	mBoW			0.00 %	0.00 %	0.00 %	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.

Pedestrian

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	PiFeNet			54.58 %	63.53 %	50.98 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
D. Le, H. Shi, H. Rezatofighi and J. Cai: PiFeNet: Pillar-Feature Network for Real-Time 3D Pedestrian Detection from Point Cloud. 2021.
2	EQ-PVRCNN			52.81 %	61.73 %	49.87 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
3	ADLAB			52.58 %	58.39 %	49.13 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
4	PV-RCNN++			52.43 %	59.73 %	48.73 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
5	VPFNet		code	52.41 %	60.07 %	50.28 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
C. Wang, H. Chen and L. Fu: VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection. 2021.
6	Frustum-PointPillars		code	52.23 %	60.98 %	48.30 %	0.06 s	4 cores @ 3.0 Ghz (Python)
A. Paigwar, D. Sierra-Gonzalez, \. Erkent and C. Laugier: Frustum-PointPillars: A Multi-Stage Approach for 3D Object Detection using RGB Camera and LiDAR. International Conference on Computer Vision, ICCV, Workshop on Autonomous Vehicle Vision 2021.
7	CAD			52.20 %	60.23 %	49.54 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
8	TANet		code	51.38 %	60.85 %	47.54 %	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.
9	CasA			51.37 %	57.95 %	49.08 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
10	ISE-RCNN			51.06 %	55.64 %	47.76 %	0.09 s	1 core @ 2.5 Ghz (Python + C/C++)
11	MMLab PV-RCNN		code	50.57 %	59.86 %	46.74 %	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.
12	HotSpotNet			50.53 %	57.39 %	46.65 %	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.
13	H^23D R-CNN			50.43 %	58.14 %	46.72 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
14	VMVS			50.34 %	60.34 %	46.45 %	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.
15	AVOD-FPN		code	50.32 %	58.49 %	46.98 %	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.
16	3DSSD		code	49.94 %	60.54 %	45.73 %	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	PointPainting			49.93 %	58.70 %	46.29 %	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.
18	SemanticVoxels			49.93 %	58.91 %	47.31 %	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.
19	MMLab-PartA^2		code	49.81 %	59.04 %	45.92 %	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.
20	TBD			49.59 %	58.17 %	47.08 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
21	SAA-PV-RCNN			49.58 %	57.07 %	46.49 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
22	F-PointNet		code	49.57 %	57.13 %	45.48 %	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.
23	TBD			49.56 %	58.10 %	47.05 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
24	AutoAlign			49.27 %	59.28 %	46.14 %	0.1 s	1 core @ 2.5 Ghz (Python)
25	VPN			49.19 %	57.98 %	45.26 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
26	F-ConvNet		code	48.96 %	57.04 %	44.33 %	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.
27	HVNet			48.86 %	54.84 %	46.33 %	0.03 s	GPU @ 2.0 Ghz (Python)
M. Ye, S. Xu and T. Cao: HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection. CVPR 2020.
28	CAT-Det			48.78 %	57.13 %	45.56 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)
29	PE-RCVN			48.72 %	54.09 %	46.71 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
30	STD		code	48.72 %	60.02 %	44.55 %	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.
31	EA-M-RCNN(BorderAtt)			48.68 %	57.06 %	45.01 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
32	VCT			48.67 %	54.64 %	46.62 %	0.2 s	1 core @ 2.5 Ghz (Python)
33	PointPillars		code	48.64 %	57.60 %	45.78 %	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.
34	USVLab BSAODet (MM)			48.61 %	55.76 %	46.08 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
35	FS-Net			48.50 %	54.91 %	46.29 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
36	EPNet++			48.47 %	56.24 %	45.73 %	0.1 s	GPU @ 2.5 Ghz (Python)
37	MGAF-3DSSD		code	48.46 %	56.09 %	44.90 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
38	Fast-CLOCs			48.27 %	57.19 %	44.55 %	0.1 s	GPU @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: Fast-CLOCs: Fast Camera-LiDAR Object Candidates Fusion for 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022.
39	FromVoxelToPoint		code	48.15 %	56.54 %	45.63 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to- Point Decoder. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
40	USVLab BSAODet (SM)			48.10 %	54.96 %	45.65 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
41	TBD			47.95 %	53.09 %	45.90 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
42	ISE-RCNN-PV			47.85 %	55.63 %	45.80 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
43	tbd			47.84 %	57.69 %	43.99 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
44	TBD			47.77 %	55.61 %	45.17 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
45	P2V-RCNN			47.36 %	54.15 %	45.10 %	0.1 s	2 cores @ 2.5 Ghz (Python)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.
46	SGNet			47.29 %	53.84 %	44.10 %	0.09 s	GPU @ 2.5 Ghz (Python)
47	TCDVF			47.11 %	55.26 %	44.53 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
48	Point-GNN		code	47.07 %	55.36 %	44.61 %	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.
49	SCIR-Net			46.76 %	53.47 %	43.72 %	0.03 s	GPU @ 2.5 Ghz (Python + C/C++)
50	TBD			46.74 %	53.44 %	43.76 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
51	SCNet			46.73 %	56.87 %	42.74 %	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.
52	TBD_IOU1			46.59 %	53.92 %	44.29 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
53	MSADet			46.27 %	55.91 %	43.83 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
54	DGT-Det3D			46.22 %	53.98 %	43.85 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
55	MMLab-PointRCNN		code	46.13 %	54.77 %	42.84 %	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.
56	TBD_IOU			46.08 %	53.25 %	43.81 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
57	ARPNET			45.92 %	55.48 %	42.54 %	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.
58	DSA-PV-RCNN		code	45.82 %	52.03 %	43.81 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
59	SVGA-Net			45.68 %	53.09 %	43.30 %	0.03s	1 core @ 2.5 Ghz (Python + C/C++)
Q. He, Z. Wang, H. Zeng, Y. Zeng and Y. Liu: SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds. AAAI 2022.
60	SARFE			45.60 %	51.45 %	43.49 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
61	epBRM		code	45.49 %	52.48 %	42.75 %	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.
62	SAA-SECOND			45.47 %	53.95 %	42.77 %	38m s	1 core @ 2.5 Ghz (C/C++)
63	PDV			45.45 %	51.95 %	43.33 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
64	FusionDetv2-v3			45.41 %	51.16 %	42.72 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
65	MLOD		code	45.40 %	55.09 %	41.42 %	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.
66	Generalized-SIENet			45.39 %	51.66 %	43.51 %	0.08 s	1 core @ 2.5 Ghz (Python)
67	SA-voxel-centernet		code	45.35 %	51.16 %	43.33 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
68	WHUT-iou_ssd		code	45.24 %	50.30 %	43.28 %	0.045s	1 core @ 2.5 Ghz (C/C++)
69	sa-voxel-centernet		code	45.20 %	51.01 %	43.25 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
70	FPCR-CNN			45.18 %	52.79 %	42.70 %	0.05 s	1 core @ 2.5 Ghz (Python)
71	TPCG			45.17 %	51.44 %	43.03 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
72	IA-SSD (single)			45.07 %	52.73 %	42.75 %	0.013 s	1 core @ 2.5 Ghz (C/C++)
73	Point Image Fusion			45.07 %	50.56 %	42.92 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
74	TBD			44.99 %	50.41 %	42.85 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
75	FPC-RCNN			44.96 %	51.54 %	42.88 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
76	NV-RCNN			44.90 %	52.65 %	41.79 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
77	FusionDetv1			44.85 %	52.42 %	42.56 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
78	Fast VP-RCNN		code	44.84 %	51.19 %	42.63 %	0.05 s	1 core @ 3.5 Ghz (C/C++)
79	SRDL			44.84 %	52.42 %	42.56 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
80	FusionDetv2-v2			44.80 %	50.61 %	42.91 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
81	M3DeTR		code	44.78 %	50.63 %	42.57 %	n/a s	GPU @ 1.0 Ghz (Python)
T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis and D. Manocha: M3DeTR: Multi-representation, Multi- scale, Mutual-relation 3D Object Detection with Transformers. 2021.
82	FusionDetv2-v5			44.64 %	51.44 %	42.32 %	0.05 s	1 core @ 2.5 Ghz (Java + C/C++)
83	Dune-DCF-e11			44.58 %	52.44 %	41.75 %	1 s	1 core @ 2.5 Ghz (C/C++)
84	Dune-DCF-e09			44.50 %	52.64 %	41.86 %	1 s	1 core @ 2.5 Ghz (C/C++)
85	anonymous		code	44.50 %	50.60 %	42.26 %	0.05s	1 core @ >3.5 Ghz (python)
86	FusionDetv2-v4			44.47 %	50.88 %	42.18 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
87	P2V_PCV1			44.33 %	49.29 %	41.71 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
88	MVOD			44.32 %	50.38 %	42.37 %	0.16 s	1 core @ 2.5 Ghz (C/C++)
89	SIF			44.28 %	52.05 %	42.03 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
90	LazyTorch-CP-Infer-O			44.27 %	51.92 %	41.99 %	1 s	1 core @ 2.5 Ghz (C/C++)
91	LazyTorch-CP-Small-P			44.25 %	51.84 %	41.97 %	1 s	1 core @ 2.5 Ghz (C/C++)
92	DDet			44.24 %	50.01 %	42.38 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
93	ST-RCNN			44.14 %	49.78 %	41.95 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
94	DVFENet			44.12 %	50.98 %	41.62 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection. Neurocomputing 2021.
95	VCRCNN			44.09 %	48.82 %	42.19 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
96	LazyTorch-CP			44.08 %	51.76 %	41.80 %	1 s	1 core @ 2.5 Ghz (C/C++)
97	CrazyTensor-CP			44.06 %	51.25 %	41.50 %	1 s	1 core @ 2.5 Ghz (Python)
98	DSASNet			43.98 %	50.55 %	40.63 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
99	City-CF-fixed			43.86 %	51.92 %	41.33 %	1 s	1 core @ 2.5 Ghz (C/C++)
100	Faraway-Frustum		code	43.85 %	52.15 %	41.68 %	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.
101	Dune-DCF-e15			43.63 %	51.18 %	41.11 %	1 s	1 core @ 2.5 Ghz (C/C++)
102	S-AT GCN			43.43 %	50.63 %	41.58 %	0.02 s	GPU @ 2.0 Ghz (Python)
L. Wang, C. Wang, X. Zhang, T. Lan and J. Li: S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection. CoRR 2021.
103	FPC3D_all			43.41 %	50.05 %	41.06 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
104	demo			43.29 %	51.78 %	40.79 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
105	FPV-SSD			43.19 %	50.37 %	40.95 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
106	City-CF			42.95 %	49.91 %	40.61 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
107	BirdNet+		code	42.87 %	48.90 %	40.59 %	0.11 s	Titan Xp (PyTorch)
A. Barrera, J. Beltrán, C. Guindel, J. Iglesias and F. García: BirdNet+: Two-Stage 3D Object Detection in LiDAR through a Sparsity-Invariant Bird’s Eye View. IEEE Access 2021.
108	IA-SSD (multi)			42.61 %	51.76 %	40.51 %	0.014 s	1 core @ 2.5 Ghz (C/C++)
109	HS3D		code	42.60 %	51.58 %	39.27 %	0.12 s	1 core @ 2.5 Ghz (Python + C/C++)
110	FusionDetv2-baseline			42.53 %	47.08 %	40.71 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
111	YF			42.43 %	50.18 %	39.99 %	0.04 s	GPU @ 2.5 Ghz (C/C++)
112	XView			42.42 %	47.24 %	39.96 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
113	TBD			41.70 %	49.81 %	39.43 %	TBD	GPU @ 2.5 Ghz (Python + C/C++)
114	ASCNet			41.46 %	47.25 %	38.83 %	0.09 s	1 core @ 2.5 Ghz (C/C++)
115	PFF3D		code	40.94 %	48.74 %	38.54 %	0.05 s	GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
116	CrazyTensor-CF			40.78 %	48.79 %	38.16 %	1 s	1 core @ 2.5 Ghz (C/C++)
117	NV2P-RCNN			40.71 %	46.83 %	38.86 %	0.1 s	GPU @ 2.5 Ghz (Python)
118	TBD_BD		code	40.41 %	48.27 %	38.45 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
119	DSGN++			38.92 %	50.26 %	35.12 %	0.4 s	NVIDIA Tesla V100
120	AB3DMOT		code	38.79 %	47.51 %	35.85 %	0.0047s	1 core @ 2.5 Ghz (python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
121	BirdNet+ (legacy)		code	38.28 %	45.53 %	35.37 %	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. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
122	PP-PCdet		code	38.21 %	45.14 %	36.04 %	0.01 s	1 core @ 2.5 Ghz (Python)
123	CSW3D			37.96 %	49.27 %	33.83 %	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.
124	Contrastive PP		code	37.68 %	44.10 %	35.84 %	0.01 s	1 core @ 2.5 Ghz (Python)
125	SparsePool		code	34.15 %	43.33 %	31.78 %	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.
126	MMLAB LIGA-Stereo		code	34.13 %	44.71 %	30.42 %	0.4 s	1 core @ 2.5 Ghz (Python + C/C++)
X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
127	AVOD		code	33.57 %	42.58 %	30.14 %	0.08 s	Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
128	SparsePool		code	33.22 %	41.55 %	29.66 %	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.
129	FusionDetv2-v1			32.24 %	37.46 %	31.61 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
130	CG-Stereo			29.56 %	39.24 %	25.87 %	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.
131	PointRGBNet			29.32 %	38.07 %	26.94 %	0.08 s	4 cores @ 2.5 Ghz (Python + C/C++)
132	Disp R-CNN		code	29.12 %	42.72 %	25.09 %	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.
133	LIGA-Stereo-old			28.84 %	36.99 %	25.78 %	0.375 s	Titan Xp
134	Disp R-CNN (velo)		code	28.34 %	40.21 %	24.46 %	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.
135	OSE+			26.02 %	36.60 %	22.89 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
136	BirdNet			23.06 %	28.20 %	21.65 %	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.
137	AEC3D			22.40 %	28.59 %	20.67 %	18 ms	GPU @ 2.5 Ghz (Python)
138	OC Stereo		code	20.80 %	29.79 %	18.62 %	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.
139	YOLOStereo3D		code	20.76 %	31.01 %	18.41 %	0.1 s	GPU 1080Ti
Y. Liu, L. Wang and M. Liu: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
140	DSGN		code	20.75 %	26.61 %	18.86 %	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.
141	BEVC			20.50 %	26.84 %	18.71 %	35ms	GPU @ 1.5 Ghz (Python)
142	VN3D			19.12 %	23.51 %	16.26 %	0.02 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
143	Complexer-YOLO			18.26 %	21.42 %	17.06 %	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.
144	SOD			15.49 %	23.56 %	13.38 %	0.1 s	1 core @ 2.5 Ghz (Python)
145	TopNet-Retina			14.57 %	18.04 %	12.48 %	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.
146	RT3D-GMP			14.22 %	19.92 %	12.83 %	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.
147	TopNet-HighRes			13.50 %	19.43 %	11.93 %	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.
148	EGFN			13.03 %	17.94 %	11.54 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
149	DD3D		code	12.51 %	18.58 %	10.65 %	n/a s	1 core @ 2.5 Ghz (C/C++)
D. Park, R. Ambrus, V. Guizilini, J. Li and A. Gaidon: Is Pseudo-Lidar needed for Monocular 3D Object detection?. IEEE/CVF International Conference on Computer Vision (ICCV) .
150	PS-fld			12.23 %	19.03 %	10.53 %	0.25 s	1 core @ 2.5 Ghz (C/C++)
151	CMKD			12.00 %	18.98 %	10.21 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
152	GCDR			10.92 %	15.65 %	9.86 %	0.28 s	1 core @ 2.5 Ghz (Python)
153	LT-M3OD			10.89 %	16.63 %	9.20 %	0.03 s	1 core @ 2.5 Ghz (Python)
154	ZongmuMono3d		code	10.65 %	16.19 %	9.04 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
155	MonoDTR			10.59 %	16.66 %	9.00 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
156	mono3d		code	10.41 %	16.66 %	9.22 %	TBD	TBD
157	GUPNet		code	10.37 %	15.62 %	8.79 %	NA s	1 core @ 2.5 Ghz (Python + C/C++)
Y. Lu, X. Ma, L. Yang, T. Zhang, Y. Liu, Q. Chu, J. Yan and W. Ouyang: Geometry Uncertainty Projection Network for Monocular 3D Object Detection. arXiv preprint arXiv:2107.13774 2021.
158	Lite-FPN-GUPNet			10.08 %	15.73 %	8.52 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
159	gupnet_se			9.85 %	14.65 %	8.32 %	0.03s	1 core @ 2.5 Ghz (C/C++)
160	SwinMono3D			9.82 %	14.55 %	8.31 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
161	HBD			9.66 %	15.26 %	8.17 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
162	SCSTSV-MonoFlex			9.62 %	14.45 %	8.14 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
163	CaDDN		code	9.41 %	14.72 %	8.17 %	0.63 s	GPU @ 2.5 Ghz (Python)
C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.
164	SGM3D			9.39 %	15.39 %	8.61 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
Z. Zhou, L. Du, X. Ye, Z. Zou, X. Tan, E. Ding, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection. 2021.
165	MonoGround			9.11 %	13.67 %	7.68 %	0.03 s	1 core @ 2.5 Ghz (Python)
166	K3D			9.06 %	14.56 %	7.59 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
167	MonoFlex			8.91 %	13.26 %	7.38 %	0.03 s	1 core @ 2.5 Ghz (Python)
168	MonoLCD			8.89 %	12.73 %	7.72 %	0.04 s	1 core @ 2.5 Ghz (Python)
169	SAIC_ADC_Mono3D		code	8.87 %	13.92 %	7.55 %	50 s	GPU @ 2.5 Ghz (Python)
170	MonoEdge			8.87 %	13.33 %	7.50 %	0.05 s	GPU @ 2.5 Ghz (Python)
171	vadin-TBD			8.81 %	13.26 %	7.41 %	0.04 s	1 core @ 2.5 Ghz (Python)
172	MonoCon		code	8.73 %	13.55 %	7.83 %	0.02 s	GPU @ 2.5 Ghz (Python)
T. Xianpeng Liu: Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection. AAAI 2022.
173	MonoDDE			8.41 %	12.38 %	7.16 %	0.04 s	1 core @ 2.5 Ghz (Python)
174	Mix-Teaching-M3D			8.40 %	12.34 %	7.06 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
175	ANM			8.28 %	12.83 %	7.59 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
176	mono3d			7.95 %	11.89 %	6.75 %	0.03 s	GPU @ 2.5 Ghz (Python)
177	LPCG-Monoflex			7.92 %	12.11 %	6.61 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
178	RefinedMPL			7.92 %	13.09 %	7.25 %	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.
179	MonoRUn		code	7.59 %	11.70 %	6.34 %	0.07 s	GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
180	MonoEdge-Rotate			7.53 %	11.62 %	6.79 %	0.05 s	GPU @ 2.5 Ghz (Python)
181	MonoFlex			7.36 %	10.36 %	6.29 %	0.03 s	GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.
182	MonoPair			7.04 %	10.99 %	6.29 %	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.
183	monodle		code	6.96 %	10.73 %	6.20 %	0.04 s	GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for Monocular 3D Object Detection. CVPR 2021 .
184	GAC3D++			6.92 %	10.56 %	5.70 %	0.25 s	1 core @ 2.5 Ghz (C/C++)
185	MK3D			6.78 %	10.15 %	5.71 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
186	MonoGeo			6.77 %	9.54 %	5.83 %	0.05 s	1 core @ 2.5 Ghz (Python)
187	TopNet-DecayRate			6.59 %	8.78 %	6.25 %	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.
188	RelationNet3D_dla34		code	6.54 %	10.17 %	5.51 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
189	ICCV			6.29 %	9.28 %	5.29 %	0.04 s	GPU @ 2.5 Ghz (Python)
190	M3DSSD++		code	6.19 %	9.24 %	5.54 %	0.16s	1 core @ 2.5 Ghz (C/C++)
191	MDNet			6.18 %	9.48 %	5.63 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
192	E2E-DA			6.15 %	10.33 %	5.99 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
193	Shift R-CNN (mono)		code	5.66 %	8.58 %	4.49 %	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.
194	MM			5.63 %	9.20 %	4.78 %	1 s	1 core @ 2.5 Ghz (C/C++)
195	MonoHMOO			5.62 %	8.69 %	5.25 %	0.2 s	1 core @ 2.5 Ghz (Python + C/C++)
196	RelationNet3D_res18		code	5.50 %	8.86 %	5.04 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
197	Aug3D-RPN			5.22 %	7.14 %	4.21 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth. 2021.
198	Lite-FPN			4.79 %	7.13 %	4.26 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
199	COF3D			4.78 %	7.20 %	4.55 %	200 s	1 core @ 2.5 Ghz (Python + C/C++)
200	MAOLoss		code	4.74 %	6.63 %	4.19 %	0.05 s	1 core @ 2.5 Ghz (Python)
201	TopNet-UncEst			4.60 %	6.88 %	3.79 %	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.
202	E2E-DA-Lite (Res18)			4.59 %	5.98 %	3.53 %	0.01 s	GPU @ 2.5 Ghz (Python)
203	MP-Mono			4.59 %	6.04 %	3.96 %	0.16 s	GPU @ 2.5 Ghz (Python)
204	MonoPSR		code	4.56 %	7.24 %	4.11 %	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.
205	DFR-Net			4.52 %	6.66 %	3.71 %	0.18 s	1080 Ti (Pytorch)
Z. Zou, X. Ye, L. Du, X. Cheng, X. Tan, L. Zhang, J. Feng, X. Xue and E. Ding: The devil is in the task: Exploiting reciprocal appearance-localization features for monocular 3d object detection . ICCV 2021.
206	QD-3DT		code	4.23 %	6.62 %	3.39 %	0.03 s	GPU @ 2.5 Ghz (Python)
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.
207	M3D-RPN		code	4.05 %	5.65 %	3.29 %	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 .
208	DDMP-3D			4.02 %	5.53 %	3.36 %	0.18 s	1 core @ 2.5 Ghz (Python)
L. Wang, L. Du, X. Ye, Y. Fu, G. Guo, X. Xue, J. Feng and L. Zhang: Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection. CVPR 2020.
209	Geo3D			3.95 %	6.18 %	3.66 %	0.04 s	GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
210	D4LCN		code	3.86 %	5.06 %	3.59 %	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.
211	RT3DStereo			3.65 %	4.72 %	3.00 %	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.
212	KAIST-VDCLab			3.26 %	4.19 %	2.72 %	0.04 s	1 core @ 2.5 Ghz (Python)
213	MonoEF		code	3.05 %	4.61 %	2.85 %	0.03 s	1 core @ 2.5 Ghz (Python)
Y. Zhou, Y. He, H. Zhu, C. Wang, H. Li and Q. Jiang: Monocular 3D Object Detection: An Extrinsic Parameter Free Approach. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
214	TBD			2.58 %	3.89 %	2.25 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
215	PPTrans			2.58 %	4.01 %	2.37 %	0.2 s	GPU @ 2.5 Ghz (Python)
216	SS3D			2.09 %	2.48 %	1.61 %	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.
217	SparVox3D			2.05 %	2.90 %	1.69 %	0.05 s	GPU @ 2.0 Ghz (Python)
E. Balatkan and F. Kıraç: Improving Regression Performance on Monocular 3D Object Detection Using Bin-Mixing and Sparse Voxel Data. 2021 6th International Conference on Computer Science and Engineering (UBMK) 2021.
218	PGD-FCOS3D		code	1.88 %	2.82 %	1.54 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
T. Wang, X. Zhu, J. Pang and D. Lin: Probabilistic and Geometric Depth: Detecting Objects in Perspective. Conference on Robot Learning (CoRL) 2021.
219	CDTrack3D		code	1.49 %	2.49 %	1.46 %	0.0106 s	NVIDIA RTX 3090 GPU, i9 10850k CPU
220	EM		code	1.25 %	1.18 %	0.89 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
221	EW		code	0.81 %	0.79 %	0.74 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
222	mBoW			0.00 %	0.00 %	0.00 %	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.