The KITTI Vision Benchmark Suite

The 3D object detection 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 3D object 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 an 3D bounding box overlap of 70%, while for pedestrians and cyclists we require a 3D bounding box 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	VPFNet			83.21 %	91.02 %	78.20 %	0.06 s	2 cores @ 2.5 Ghz (Python)
2	Anonymous			82.99 %	91.64 %	78.02 %	0.1 s	GPU @ 2.5 Ghz (Python)
3	BtcDet			82.86 %	90.64 %	78.09 %	0.09 s	GPU @ 2.5 Ghz (Python + C/C++)
4	HIKVISION-ADLab-HZ			82.83 %	89.00 %	76.00 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
5	SFD			82.79 %	91.30 %	78.07 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
6	SPG_mini			82.66 %	90.64 %	77.91 %	0.09 s	GPU @ 2.5 Ghz (Python)
7	SE-SSD		code	82.54 %	91.49 %	77.15 %	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.
8	EA-M-RCNN(BorderAtt)			82.33 %	87.77 %	77.37 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
9	TBD			82.23 %	88.76 %	77.48 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
10	HUAWEI Octopus			82.13 %	88.26 %	77.41 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
11	SPG		code	82.13 %	90.50 %	78.90 %	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.
12	VoTr-2			82.09 %	89.90 %	79.14 %	0.07 s	1 core @ 2.5 Ghz (C/C++)
13	ADLAB			82.08 %	90.92 %	77.36 %	0.05 s	1 core @ >3.5 Ghz (C/C++)
14	Pyramid R-CNN			82.08 %	88.39 %	77.49 %	0.07 s	1 core @ 2.5 Ghz (C/C++)
15	SRF^2-RCNN			82.04 %	88.45 %	77.54 %	0.25 s	1 core @ 2.5 Ghz (C/C++)
16	Anonymous			81.96 %	89.90 %	77.20 %	0.1s	1 core @ 2.5 Ghz (C/C++)
17	PV-RCNN-v2			81.88 %	90.14 %	77.15 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
18	RangeRCNN-LV			81.85 %	88.76 %	77.18 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
19	L RCNN++			81.85 %	89.96 %	76.51 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
20	SNLW-RCNN		code	81.84 %	90.50 %	77.22 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
21	PVGNet			81.81 %	89.94 %	77.09 %	0.05 s	1 core @ >3.5 Ghz (C/C++)
22	SqueezeRCNN			81.80 %	88.72 %	77.10 %	0.08 s	1 core @ 2.5 Ghz (Python)
23	CityBrainLab-CT3D			81.77 %	87.83 %	77.16 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
24	SIENet		code	81.71 %	88.22 %	77.22 %	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.
25	E^2-PV-RCNN			81.70 %	88.33 %	77.20 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
26	PLNL-3DSSD			81.69 %	88.98 %	74.90 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
27	TBD			81.68 %	87.93 %	76.92 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
28	ASCNet			81.67 %	88.48 %	76.93 %	0.09 s	1 core @ 2.5 Ghz (C/C++)
29	Fast VP-RCNN		code	81.62 %	90.97 %	76.90 %	0.05 s	1 core @ 3.5 Ghz (C/C++)
30	Voxel R-CNN		code	81.62 %	90.90 %	77.06 %	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.
31	BANet		code	81.61 %	89.28 %	76.58 %	0.11 s	1 core @ 2.5 Ghz (C/C++)
R. Qian, X. Lai and X. Li: Boundary-Aware 3D Object Detection from Point Clouds. 2021.
32	HyBrid Feature Det			81.59 %	88.77 %	76.92 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
33	FromVoxelToPoint		code	81.58 %	88.53 %	77.37 %	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.
34	CVRS VIC-RCNN			81.57 %	88.60 %	77.09 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
35	H^23D R-CNN		code	81.55 %	90.43 %	77.22 %	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.
36	anonymous		code	81.55 %	90.94 %	76.74 %	0.05s	1 core @ >3.5 Ghz (python)
37	LZY_RCNN			81.52 %	88.77 %	78.59 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
38	TBD			81.51 %	88.96 %	77.27 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
39	FrustumRCNN			81.50 %	87.83 %	77.08 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
40	MSG-PGNN			81.50 %	88.70 %	76.88 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
41	TransCyclistNet			81.46 %	88.47 %	76.87 %	0.08 s	1 core @ 2.5 Ghz (Python)
42	DSA-PV-RCNN		code	81.46 %	88.25 %	76.96 %	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.
43	P2V-RCNN			81.45 %	88.34 %	77.20 %	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.
44	MMLab PV-RCNN		code	81.43 %	90.25 %	76.82 %	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.
45	TPCG			81.41 %	89.16 %	76.90 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
46	PC-RGNN			81.38 %	87.94 %	76.88 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
47	WHUT-iou_ssd		code	81.37 %	89.84 %	76.83 %	0.045s	1 core @ 2.5 Ghz (C/C++)
48	XView			81.35 %	89.21 %	76.87 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
49	RangeRCNN			81.33 %	88.47 %	77.09 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, M. Zhang, Z. Zhang, X. Zhao and S. Pu: RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representation. arXiv preprint arXiv:2009.00206 2020.
50	FSA-PVRCNN			81.31 %	88.01 %	76.75 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
51	TransDet3D			81.28 %	88.11 %	76.73 %	0.08 s	1 core @ 2.5 Ghz (Python)
52	Generalized-SIENet			81.24 %	87.70 %	76.79 %	0.08 s	1 core @ 2.5 Ghz (Python)
53	ReFineNet			81.24 %	87.70 %	76.77 %	0.08 s	1 core @ 2.5 Ghz (Python)
54	Point Image Fusion			81.23 %	89.01 %	76.77 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
55	MSL3D			81.15 %	87.27 %	76.56 %	0.03 s	GPU @ 2.5 Ghz (Python)
56	Multi-Sensor3D			81.15 %	87.27 %	76.56 %	0.03 s	GPU @ 2.5 Ghz (Python)
57	SAA-PV-RCNN			81.09 %	87.24 %	78.05 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
58	FPC-RCNN			81.08 %	88.68 %	76.46 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
59	VPFNet		code	80.97 %	88.51 %	76.74 %	0.25 s	1 core @ 2.5 Ghz (C/C++)
60	VueronNet		code	80.96 %	90.06 %	73.72 %	0.06 s	1 core @ 2.0 Ghz (Python)
61	FusionDetv2-v4			80.93 %	87.75 %	76.12 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
62	AIMC-RUC			80.83 %	90.14 %	73.59 %	0.11 s	1 core @ 2.5 Ghz (Python)
63	SVGA-Net			80.82 %	87.40 %	76.23 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
64	GNN-RCNN			80.81 %	87.94 %	76.53 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
65	sa-voxel-centernet		code	80.77 %	87.39 %	76.45 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
66	SA-voxel-centernet		code	80.77 %	87.28 %	76.51 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
67	Associate-3Ddet_v2			80.77 %	91.53 %	75.23 %	0.04 s	1 core @ 2.5 Ghz (Python)
68	CIA-SSD v2			80.71 %	89.61 %	75.06 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
69	FusionDetv2-v3			80.70 %	88.05 %	76.10 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
70	StructuralIF			80.69 %	87.15 %	76.26 %	0.02 s	8 cores @ 2.5 Ghz (Python)
71	CLOCs_PVCas		code	80.67 %	88.94 %	77.15 %	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.
72	SCIR-Net			80.62 %	87.53 %	76.00 %	0.03 s	GPU @ 2.5 Ghz (Python + C/C++)
73	CVRS VIC-Net			80.61 %	88.25 %	75.83 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
74	FusionDetv1			80.48 %	87.20 %	76.01 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
75	XView-PartA^2			80.41 %	87.72 %	76.22 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
76	SRDL			80.38 %	87.73 %	76.27 %	0.15 s	GPU @ 2.5 Ghz (Python + C/C++)
77	Fast			80.35 %	89.10 %	76.99 %	0.1 s	GPU @ 2.5 Ghz (Python)
78	AM-SSD			80.30 %	89.58 %	75.02 %	0.04 s	1 core @ 2.5 Ghz (Python)
79	CIA-SSD		code	80.28 %	89.59 %	72.87 %	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.
80	Baseline of CA RCNN			80.28 %	87.45 %	76.21 %	0.1 s	GPU @ 2.5 Ghz (Python)
81	TBD			80.24 %	87.67 %	76.27 %	0.07 s	1 core @ 2.5 Ghz (Python + C/C++)
82	VCT			80.19 %	89.12 %	77.19 %	0.2 s	1 core @ 2.5 Ghz (Python)
83	GAP-soft-filter			80.18 %	87.43 %	76.21 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
84	CBi-GNN			80.18 %	91.50 %	74.76 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
85	TBD			80.17 %	86.83 %	75.96 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
86	deprecated			80.16 %	89.48 %	72.75 %	deprecated	deprecated
87	TBD			80.12 %	88.30 %	75.29 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
88	EBM3DOD		code	80.12 %	91.05 %	72.78 %	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.
89	3D-CVF at SPA			80.05 %	89.20 %	73.11 %	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.
90	IA-SSD			80.02 %	88.45 %	74.85 %	<0.02 s	GPU @ 2.5 Ghz (Python + C/C++)
91	MVOD			80.01 %	88.53 %	77.24 %	0.16 s	1 core @ 2.5 Ghz (C/C++)
92	MBDF-Net			80.00 %	90.87 %	75.04 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
93	SIF			79.88 %	86.84 %	75.89 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
94	CM3DV			79.87 %	89.00 %	72.59 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
95	RangeIoUDet			79.80 %	88.60 %	76.76 %	0.02 s	1 core @ 2.5 Ghz (Python)
96	SA-SSD		code	79.79 %	88.75 %	74.16 %	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.
97	Seg-RCNN		code	79.73 %	89.16 %	72.28 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
98	CJJ			79.72 %	88.98 %	74.71 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
99	STD		code	79.71 %	87.95 %	75.09 %	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.
100	PSS			79.71 %	89.13 %	74.78 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
101	MGAF-3DSSD		code	79.68 %	88.16 %	72.39 %	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.
102	FCY			79.67 %	89.19 %	74.35 %	0.02 s	GPU @ 2.5 Ghz (Python)
103	MBDF-Net-1			79.65 %	90.43 %	74.72 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
104	Struc info fusion II			79.59 %	88.97 %	72.51 %	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.
105	3DSSD		code	79.57 %	88.36 %	74.55 %	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.
106	PointRes			79.55 %	88.73 %	74.17 %	0.013 s	1 core @ 2.5 Ghz (Python + C/C++)
107	EBM3DOD baseline		code	79.52 %	88.80 %	72.30 %	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.
108	Struc info fusion I			79.49 %	88.70 %	74.25 %	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.
109	Point-GNN		code	79.47 %	88.33 %	72.29 %	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.
110	PP-3D			79.47 %	88.33 %	72.29 %	0.1 s	1 core @ 2.5 Ghz (Python)
111	SECOND			79.46 %	87.44 %	73.97 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
112	RoIFusion		code	79.36 %	88.09 %	72.51 %	0.22 s	1 core @ 3.0 Ghz (Python)
113	3DIoU_v2			79.30 %	88.22 %	76.96 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
114	EPNet		code	79.28 %	89.81 %	74.59 %	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.
115	PF-GAP			79.27 %	87.65 %	76.43 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
116	FPCR-CNN			79.25 %	88.45 %	75.69 %	0.05 s	1 core @ 2.5 Ghz (Python)
117	3DIoU++			79.22 %	87.49 %	76.68 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
118	DVFENet			79.18 %	86.20 %	74.58 %	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.
119	NLK-ALL		code	79.13 %	87.23 %	74.30 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
120	Faraway-Frustum		code	79.05 %	87.45 %	76.14 %	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.
121	3D IoU-Net			79.03 %	87.96 %	72.78 %	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.
122	CCFNET			78.97 %	88.20 %	74.14 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
123	SERCNN			78.96 %	87.74 %	74.30 %	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.
124	YF			78.85 %	87.50 %	72.05 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
125	deprecated			78.83 %	87.89 %	73.52 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
126	FPC3D			78.81 %	87.61 %	75.49 %	33 s	1 core @ 2.5 Ghz (C/C++)
127	MVAF-Net		code	78.71 %	87.87 %	75.48 %	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.
128	ISF-v2			78.67 %	87.54 %	74.03 %	0.04 s	1 core @ 2.5 Ghz (Python)
129	BLPNet_V2			78.57 %	87.10 %	71.67 %	0.04 s	1 core @ 2.5 Ghz (Python)
130	MMLab-PartA^2		code	78.49 %	87.81 %	73.51 %	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.
131	CLOCs_SecCas			78.45 %	86.38 %	72.45 %	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.
132	FusionDetv2-v2			78.42 %	86.59 %	73.87 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
133	Patches - EMP			78.41 %	89.84 %	73.15 %	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.
134	MKFFNet			78.40 %	85.25 %	73.75 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
135	HotSpotNet			78.31 %	87.60 %	73.34 %	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.
136	FusionDetv2-v5			78.30 %	86.94 %	73.44 %	0.05 s	1 core @ 2.5 Ghz (Java + C/C++)
137	MKFFNet			78.30 %	87.25 %	73.66 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
138	MKFFNet			78.30 %	86.86 %	73.80 %	0.01s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
139	SAA-SECOND			78.13 %	86.13 %	73.34 %	38m s	1 core @ 2.5 Ghz (C/C++)
140	3D-VDNet			78.05 %	87.13 %	72.90 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
141	HV			77.92 %	86.38 %	73.04 %	0.02 s	GPU @ 2.5 Ghz (Python)
142	CenterNet3D			77.90 %	86.20 %	73.03 %	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.
143	V3D			77.87 %	86.58 %	72.52 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
144	CVFNet			77.70 %	88.75 %	71.95 %	28.1ms	1 core @ 2.5 Ghz (Python)
145	VOXEL_3D			77.69 %	86.45 %	72.20 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
146	VGCN			77.65 %	84.47 %	73.36 %	0.09 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
147	UberATG-MMF			77.43 %	88.40 %	70.22 %	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.
148	Associate-3Ddet		code	77.40 %	85.99 %	70.53 %	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	Fast Point R-CNN			77.40 %	85.29 %	70.24 %	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.
150	Dccnet			77.22 %	86.67 %	69.97 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
151	Patches			77.20 %	88.67 %	71.82 %	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.
152	HRI-VoxelFPN			76.70 %	85.64 %	69.44 %	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.
153	SARPNET			76.64 %	85.63 %	71.31 %	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.
154	YF			76.57 %	87.15 %	71.23 %	0.04 s	GPU @ 2.5 Ghz (C/C++)
155	IGRP+			76.54 %	86.90 %	71.77 %	0.18 s	1 core @ 2.5 Ghz (Python + C/C++)
156	3D IoU Loss			76.50 %	86.16 %	71.39 %	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.
157	HNet-3DSSD		code	76.48 %	86.06 %	69.71 %	0.05 s	GPU @ 2.5 Ghz (Python)
158	F-ConvNet		code	76.39 %	87.36 %	66.69 %	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.
159	DPointNet			76.34 %	81.67 %	70.34 %	0.07s	1 core @ 2.5 Ghz (C/C++)
160	SIEV-Net			76.18 %	85.21 %	70.60 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
161	SegVoxelNet			76.13 %	86.04 %	70.76 %	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.
162	S-AT GCN			76.04 %	83.20 %	71.17 %	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.
163	TANet		code	75.94 %	84.39 %	68.82 %	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.
164	APL-Second			75.75 %	84.26 %	70.65 %	0.05 s	1 core @ 2.5 Ghz (Python)
165	PointRGCN			75.73 %	85.97 %	70.60 %	0.26 s	GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
166	MMLab-PointRCNN		code	75.64 %	86.96 %	70.70 %	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.
167	AB3DMOT		code	75.43 %	86.10 %	68.88 %	0.0047s	1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
168	R-GCN			75.26 %	83.42 %	68.73 %	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.
169	epBRM		code	75.15 %	85.00 %	69.84 %	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.
170	MAFF-Net(DAF-Pillar)			75.04 %	85.52 %	67.61 %	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.
171	sscl-20p			74.82 %	86.06 %	69.87 %	0.02 s	1 core @ 2.5 Ghz (Python)
172	PI-RCNN			74.82 %	84.37 %	70.03 %	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.
173	FPGNN			74.77 %	83.82 %	67.93 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
174	FPC3D_all			74.55 %	85.50 %	69.91 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
175	PointPillars		code	74.31 %	82.58 %	68.99 %	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.
176	ARPNET			74.04 %	84.69 %	68.64 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
177	PC-CNN-V2			73.79 %	85.57 %	65.65 %	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.
178	C-GCN			73.62 %	83.49 %	67.01 %	0.147 s	GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
179	LSNet			73.55 %	86.13 %	68.58 %	0.09 s	GPU @ 2.5 Ghz (Python + C/C++)
180	3DBN			73.53 %	83.77 %	66.23 %	0.13s	1080Ti (Python+C/C++)
X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.
181	PointRGBNet			73.49 %	83.99 %	68.56 %	0.08 s	4 cores @ 2.5 Ghz (Python + C/C++)
182	RangeDet		code	73.44 %	80.53 %	67.28 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
183	SCNet			73.17 %	83.34 %	67.93 %	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.
184	TBD			73.02 %	82.74 %	67.97 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
185	PFF3D		code	72.93 %	81.11 %	67.24 %	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.
186	DASS			72.31 %	81.85 %	65.99 %	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.
187	AVOD-FPN		code	71.76 %	83.07 %	65.73 %	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.
188	PointPainting			71.70 %	82.11 %	67.08 %	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.
189	WS3D			70.59 %	80.99 %	64.23 %	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.
190	F-PointNet		code	69.79 %	82.19 %	60.59 %	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.
191	FusionDetv2-baseline			68.87 %	79.05 %	63.68 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
192	UberATG-ContFuse			68.78 %	83.68 %	61.67 %	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.
193	MLOD		code	67.76 %	77.24 %	62.05 %	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.
194	AVOD		code	66.47 %	76.39 %	60.23 %	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.
195	FusionDetv2-v1			65.65 %	75.21 %	60.65 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
196	DAMNET		code	65.52 %	76.25 %	59.54 %	1 s	1 core @ 2.5 Ghz (C/C++)
197	MMLAB LIGA-Stereo			64.66 %	81.39 %	57.22 %	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.
198	BirdNet+		code	64.04 %	76.15 %	59.79 %	0.11 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.
199	MV3D			63.63 %	74.97 %	54.00 %	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.
200	KMC		code	62.74 %	74.45 %	56.76 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
201	LIGA-Stereo-old			62.65 %	81.76 %	55.24 %	0.375 s	Titan Xp
202	VN3D			61.41 %	72.37 %	56.86 %	0.02 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
203	RCD			60.56 %	70.54 %	55.58 %	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.
204	AEC3D			59.47 %	71.76 %	54.72 %	18 ms	GPU @ 2.5 Ghz (Python)
205	deleted			57.11 %	76.87 %	50.05 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)
206	A3DODWTDA		code	56.82 %	62.84 %	48.12 %	0.08 s	GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
207	PL++ (SDN+GDC)		code	54.88 %	68.38 %	49.16 %	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.
208	MV3D (LIDAR)			54.54 %	68.35 %	49.16 %	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.
209	CDN		code	54.22 %	74.52 %	46.36 %	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.
210	CG-Stereo			53.58 %	74.39 %	46.50 %	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.
211	DSGN		code	52.18 %	73.50 %	45.14 %	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.
212	BirdNet+ (legacy)		code	51.85 %	70.14 %	50.03 %	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.
213	NCL		code	50.07 %	46.58 %	50.33 %	NA s	1 core @ 2.5 Ghz (Python)
214	SOD			48.69 %	70.90 %	40.12 %	0.1 s	1 core @ 2.5 Ghz (Python)
215	Complexer-YOLO			47.34 %	55.93 %	42.60 %	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.
216	Disp R-CNN (velo)		code	45.78 %	68.21 %	37.73 %	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.
217	CDN-PL++			44.86 %	64.31 %	38.11 %	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.
218	Disp R-CNN		code	43.27 %	67.02 %	36.43 %	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.
219	R-AGNO-Net			42.79 %	49.49 %	39.31 %	0.15 s	1 core @ 2.5 Ghz (C/C++)
220	Pseudo-LiDAR++		code	42.43 %	61.11 %	36.99 %	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.
221	OSE+			41.60 %	62.67 %	35.82 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
222	YOLOStereo3D		code	41.25 %	65.68 %	30.42 %	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.
223	BEVC			40.72 %	50.05 %	36.42 %	35ms	GPU @ 1.5 Ghz (Python)
224	RT3D-GMP			38.76 %	45.79 %	30.00 %	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.
225	ZoomNet		code	38.64 %	55.98 %	30.97 %	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.
226	OC Stereo		code	37.60 %	55.15 %	30.25 %	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.
227	RTS3D			37.38 %	58.51 %	31.12 %	0.03 s	GPU @ 2.5 Ghz (Python)
228	Pseudo-Lidar		code	34.05 %	54.53 %	28.25 %	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.
229	SC(DLA34)			31.30 %	49.94 %	25.62 %	0.04 s	GPU @ 2.5 Ghz (Python)
230	Stereo R-CNN		code	30.23 %	47.58 %	23.72 %	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.
231	BirdNet			27.26 %	40.99 %	25.32 %	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.
232	TBD			24.87 %	33.30 %	21.96 %	0.1 s	1 core @ 2.5 Ghz (Python)
233	RT3DStereo			23.28 %	29.90 %	18.96 %	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.
234	RT3D			19.14 %	23.74 %	18.86 %	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.
235	StereoFENet			18.41 %	29.14 %	14.20 %	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.
236	Mobile Stereo R-CNN			17.04 %	26.97 %	13.26 %	1.8 s	NVIDIA Jetson TX2
237	LPCG-M3D			14.82 %	22.73 %	12.88 %	0.11 s	1 core @ 2.5 Ghz (C/C++)
238	CA3D			14.49 %	20.89 %	12.19 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
239	ITS-MDPL			14.28 %	24.67 %	12.13 %	0.16 s	GPU @ 2.5 Ghz (Python)
240	AutoShape			14.17 %	22.47 %	11.36 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
241	E2E-DA			13.97 %	19.73 %	11.82 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
242	MM			13.92 %	20.82 %	12.29 %	1 s	1 core @ 2.5 Ghz (C/C++)
243	GAC3D++			13.90 %	19.53 %	11.77 %	0.25 s	1 core @ 2.5 Ghz (C/C++)
244	MonoFlex			13.89 %	19.94 %	12.07 %	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.
245	MonoEF		code	13.87 %	21.29 %	11.71 %	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.
246	MonoGeo			13.81 %	18.85 %	11.52 %	0.05 s	1 core @ 2.5 Ghz (Python)
247	none			13.79 %	18.84 %	11.52 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
248	DFR-Net			13.63 %	19.40 %	10.35 %	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.
249	CaDDN		code	13.41 %	19.17 %	11.46 %	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.
250	MDSNet			13.40 %	22.80 %	10.27 %	0.07 s	1 core @ 2.5 Ghz (Python)
251	PCT			13.37 %	21.00 %	11.31 %	0.045 s	1 core @ 2.5 Ghz (C/C++)
252	CenterNet-Boost			13.33 %	19.06 %	11.90 %	0.042 s	GPU @ 2.5 Ghz (Python)
253	Det3D			13.26 %	24.00 %	9.94 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
254	Ground-Aware		code	13.25 %	21.65 %	9.91 %	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.
255	MonoHMOO			13.12 %	20.28 %	9.56 %	0.2 s	1 core @ 2.5 Ghz (Python + C/C++)
256	Aug3D-RPN			12.99 %	17.82 %	9.78 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
257	RelationNet3D_dla34		code	12.88 %	17.67 %	11.01 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
258	PLDet3d			12.85 %	20.72 %	11.11 %	0.11 s	1 core @ 2.5 Ghz (C/C++)
259	DDMP-3D			12.78 %	19.71 %	9.80 %	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.
260	Kinematic3D		code	12.72 %	19.07 %	9.17 %	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 .
261	DA-Mono3D			12.66 %	16.99 %	9.97 %	0.09s	1 core @ 2.5 Ghz (C/C++)
262	MonoRCNN		code	12.65 %	18.36 %	10.03 %	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.
263	RelationNet3D			12.60 %	17.57 %	10.95 %	0.04 s	GPU @ 2.5 Ghz (Python)
264	Object Transformer			12.58 %	17.87 %	10.87 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
265	TBD			12.53 %	22.40 %	10.64 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
266	AutoShape			12.42 %	20.35 %	9.70 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
267	MP-Mono			12.37 %	17.89 %	9.58 %	0.16 s	GPU @ 2.5 Ghz (Python)
268	GrooMeD-NMS		code	12.32 %	18.10 %	9.65 %	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.
269	MonoRUn		code	12.30 %	19.65 %	10.58 %	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.
270	Deprecated			12.30 %	16.48 %	9.14 %	Deprecated	Deprecated
271	monodle		code	12.26 %	17.23 %	10.29 %	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 .
272	PPTrans			12.06 %	19.79 %	10.48 %	0.2 s	GPU @ 2.5 Ghz (Python)
273	YoloMono3D		code	12.06 %	18.28 %	8.42 %	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.
274	IAFA			12.01 %	17.81 %	10.61 %	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.
275	GAC3D			12.00 %	17.75 %	9.15 %	0.25 s	1 core @ 2.5 Ghz (Python)
276	D4LCN		code	11.72 %	16.65 %	9.51 %	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.
277	RetinaMono		code	11.61 %	16.68 %	9.57 %	0.02 s	1 core @ 2.5 Ghz (Python)
278	KM3D		code	11.45 %	16.73 %	9.92 %	0.03 s	1 core @ 2.5 Ghz (Python)
P. Li: Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training. 2020.
279	CDI3D			11.32 %	15.70 %	9.26 %	0.03 s	GPU @ 2.5 Ghz (Python)
280	LAPNet			11.29 %	18.02 %	8.50 %	0.03 s	1 core @ 2.5 Ghz (Python)
281	RefinedMPL			11.14 %	18.09 %	8.94 %	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.
282	PatchNet		code	11.12 %	15.68 %	10.17 %	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.
283	ImVoxelNet		code	10.97 %	17.15 %	9.15 %	0.2 s	GPU @ 2.5 Ghz (Python)
284	AM3D			10.74 %	16.50 %	9.52 %	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.
285	Lite-FPN			10.64 %	15.32 %	8.59 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
286	TBD			10.61 %	15.71 %	8.66 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
287	OCM3D			10.44 %	17.48 %	7.87 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
288	RTM3D		code	10.34 %	14.41 %	8.77 %	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.
289	E2E-DA-Lite (Res18)			10.32 %	15.56 %	8.89 %	0.01 s	GPU @ 2.5 Ghz (Python)
290	MonoPair			9.99 %	13.04 %	8.65 %	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.
291	RelationNet3D_res18		code	9.93 %	14.27 %	8.43 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
292	FADNet		code	9.92 %	16.37 %	8.05 %	0.04 s	GPU @ >3.5 Ghz (Python)
293	Neighbor-Vote			9.90 %	15.57 %	8.89 %	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.
294	SMOKE		code	9.76 %	14.03 %	7.84 %	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.
295	M3D-RPN		code	9.71 %	14.76 %	7.42 %	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 .
296	QD-3DT		code	9.33 %	12.81 %	7.86 %	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.
297	ICCV			9.31 %	13.37 %	8.29 %	0.04 s	GPU @ 2.5 Ghz (Python)
298	TopNet-HighRes			9.28 %	12.67 %	7.95 %	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.
299	Geo3D			7.70 %	11.52 %	6.80 %	0.04 s	GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
300	SS3D			7.68 %	10.78 %	6.51 %	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.
301	Mono3D_PLiDAR		code	7.50 %	10.76 %	6.10 %	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.
302	MonoPSR		code	7.25 %	10.76 %	5.85 %	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.
303	Decoupled-3D			7.02 %	11.08 %	5.63 %	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.
304	VoxelJones		code	6.35 %	7.39 %	5.80 %	.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.
305	MonoGRNet		code	5.74 %	9.61 %	4.25 %	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.
306	A3DODWTDA (image)		code	5.27 %	6.88 %	4.45 %	0.8 s	GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
307	MonoFENet			5.14 %	8.35 %	4.10 %	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.
308	TLNet (Stereo)		code	4.37 %	7.64 %	3.74 %	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.
309	CSoR			4.06 %	5.61 %	3.17 %	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.
310	Shift R-CNN (mono)		code	3.87 %	6.88 %	2.83 %	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.
311	MVRA + I-FRCNN+			3.27 %	5.19 %	2.49 %	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.
312	SparVox3D			3.20 %	5.27 %	2.56 %	0.05 s	GPU @ 2.0 Ghz (Python)
313	TopNet-UncEst			3.02 %	3.24 %	2.26 %	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.
314	GS3D			2.90 %	4.47 %	2.47 %	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.
315	3D-GCK			2.52 %	3.27 %	2.11 %	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.
316	weakm3d			2.32 %	3.80 %	1.66 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
317	ROI-10D			2.02 %	4.32 %	1.46 %	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.
318	FQNet			1.51 %	2.77 %	1.01 %	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.
319	3D-SSMFCNN		code	1.41 %	1.88 %	1.11 %	0.1 s	GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.
320	UDI-mono3D			0.72 %	0.62 %	0.53 %	0.05 s	1 core @ 2.5 Ghz (Python)
321	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	VPFNet		code	48.36 %	54.65 %	44.98 %	0.25 s	1 core @ 2.5 Ghz (C/C++)
2	HIKVISION-AFree			46.88 %	52.75 %	43.50 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
3	ADLAB			46.18 %	53.59 %	43.28 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
4	HotSpotNet			45.37 %	53.10 %	41.47 %	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.
5	H^23D R-CNN			45.26 %	52.75 %	41.56 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
6	SAA-PV-RCNN			45.00 %	52.55 %	41.82 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
7	SIEV-Net			44.80 %	54.00 %	41.11 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
8	HIKVISION-ADLab-HZ			44.78 %	52.09 %	42.57 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
9	TANet		code	44.34 %	53.72 %	40.49 %	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.
10	TBD			44.32 %	49.37 %	41.24 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
11	3DSSD		code	44.27 %	54.64 %	40.23 %	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.
12	Point-GNN		code	43.77 %	51.92 %	40.14 %	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.
13	PP-3D			43.77 %	51.92 %	40.14 %	0.1 s	1 core @ 2.5 Ghz (Python)
14	VCT			43.65 %	50.27 %	41.43 %	0.2 s	1 core @ 2.5 Ghz (Python)
15	EA-M-RCNN(BorderAtt)			43.44 %	51.81 %	39.85 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
16	F-ConvNet		code	43.38 %	52.16 %	38.80 %	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.
17	MMLab-PartA^2		code	43.35 %	53.10 %	40.06 %	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.
18	MMLab PV-RCNN		code	43.29 %	52.17 %	40.29 %	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	FromVoxelToPoint		code	43.28 %	51.80 %	40.71 %	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.
20	VMVS			43.27 %	53.44 %	39.51 %	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.
21	P2V-RCNN			43.19 %	50.91 %	40.81 %	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.
22	MGAF-3DSSD		code	43.09 %	50.65 %	39.65 %	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.
23	Fast			42.72 %	52.10 %	39.08 %	0.1 s	GPU @ 2.5 Ghz (Python)
24	STD		code	42.47 %	53.29 %	38.35 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
25	AVOD-FPN		code	42.27 %	50.46 %	39.04 %	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.
26	SemanticVoxels			42.19 %	50.90 %	39.52 %	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.
27	F-PointNet		code	42.15 %	50.53 %	38.08 %	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.
28	TBD			42.05 %	48.66 %	38.94 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
29	PointPillars		code	41.92 %	51.45 %	38.89 %	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.
30	TBD_IOU1			41.65 %	49.00 %	39.31 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
31	epBRM		code	41.52 %	49.17 %	39.08 %	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.
32	TBD_IOU			41.45 %	48.25 %	39.17 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
33	GNN-RCNN			41.32 %	47.48 %	38.97 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
34	Generalized-SIENet			40.97 %	47.01 %	38.88 %	0.08 s	1 core @ 2.5 Ghz (Python)
35	PointPainting			40.97 %	50.32 %	37.87 %	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.
36	SCIR-Net			40.95 %	49.23 %	38.47 %	0.03 s	GPU @ 2.5 Ghz (Python + C/C++)
37	DSA-PV-RCNN		code	40.89 %	46.97 %	38.80 %	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-PartA^2			40.71 %	47.73 %	38.47 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
39	SAA-SECOND			40.57 %	48.73 %	37.77 %	38m s	1 core @ 2.5 Ghz (C/C++)
40	WHUT-iou_ssd		code	40.53 %	46.41 %	38.48 %	0.045s	1 core @ 2.5 Ghz (C/C++)
41	E^2-PV-RCNN			40.47 %	46.61 %	38.60 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
42	SA-voxel-centernet		code	40.43 %	46.10 %	38.32 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
43	FusionDetv2-v3			40.38 %	46.86 %	37.41 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
44	FPCR-CNN			40.32 %	48.33 %	37.66 %	0.05 s	1 core @ 2.5 Ghz (Python)
45	P2V_PCV1			40.27 %	45.43 %	38.24 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
46	sa-voxel-centernet		code	40.24 %	46.08 %	38.07 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
47	FPC-RCNN			40.13 %	46.41 %	37.84 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
48	TPCG			39.97 %	46.35 %	37.66 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
49	FusionDetv2-v5			39.91 %	47.50 %	37.39 %	0.05 s	1 core @ 2.5 Ghz (Java + C/C++)
50	SVGA-Net			39.88 %	47.59 %	37.57 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
51	MVOD			39.82 %	46.22 %	37.56 %	0.16 s	1 core @ 2.5 Ghz (C/C++)
52	Point Image Fusion			39.79 %	45.04 %	37.62 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
53	anonymous		code	39.74 %	46.09 %	37.41 %	0.05s	1 core @ >3.5 Ghz (python)
54	FusionDetv2-v4			39.68 %	46.93 %	37.31 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
55	Fast VP-RCNN		code	39.65 %	45.95 %	37.29 %	0.05 s	1 core @ 3.5 Ghz (C/C++)
56	PF-GAP			39.53 %	47.63 %	36.44 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
57	TBD			39.48 %	45.46 %	37.35 %	0.07 s	1 core @ 2.5 Ghz (Python + C/C++)
58	GAP-soft-filter			39.47 %	46.93 %	36.99 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
59	CVRS VIC-RCNN			39.46 %	45.19 %	37.16 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
60	SRDL			39.43 %	47.30 %	36.99 %	0.15 s	GPU @ 2.5 Ghz (Python + C/C++)
61	Baseline of CA RCNN			39.42 %	47.30 %	36.97 %	0.1 s	GPU @ 2.5 Ghz (Python)
62	FSA-PVRCNN			39.39 %	44.14 %	37.13 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
63	YF			39.38 %	47.69 %	36.06 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
64	MMLab-PointRCNN		code	39.37 %	47.98 %	36.01 %	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.
65	FusionDetv2-v2			39.31 %	44.98 %	37.22 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
66	ARPNET			39.31 %	48.32 %	35.93 %	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.
67	TBD			39.31 %	46.85 %	36.11 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
68	tbd			38.89 %	45.98 %	35.94 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
69	SIF			38.74 %	46.23 %	36.06 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
70	SCNet			38.66 %	47.83 %	35.70 %	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.
71	Faraway-Frustum		code	38.58 %	46.33 %	35.71 %	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.
72	MSL3D			38.58 %	45.00 %	35.72 %	0.03 s	GPU @ 2.5 Ghz (Python)
73	FusionDetv1			38.31 %	45.10 %	36.15 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
74	AF_MCLS			38.29 %	47.07 %	34.67 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
75	MKFFNet			38.05 %	46.01 %	35.72 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
76	IGRP+			38.05 %	46.26 %	34.53 %	0.18 s	1 core @ 2.5 Ghz (Python + C/C++)
77	FPC3D_all			37.95 %	45.49 %	35.60 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
78	VGCN			37.60 %	45.28 %	34.96 %	0.09 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
79	DVFENet			37.50 %	43.55 %	35.33 %	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.
80	MLOD		code	37.47 %	47.58 %	35.07 %	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.
81	S-AT GCN			37.37 %	44.63 %	34.92 %	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.
82	CVRS VIC-Net			37.18 %	43.82 %	35.35 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
83	YF			36.99 %	44.43 %	34.40 %	0.04 s	GPU @ 2.5 Ghz (C/C++)
84	XView			36.79 %	42.44 %	34.96 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
85	MKFFNet			36.66 %	43.94 %	34.56 %	0.01s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
86	FusionDetv2-baseline			36.66 %	41.34 %	34.60 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
87	MKFFNet			36.65 %	44.00 %	34.59 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
88	IA-SSD			36.53 %	44.11 %	34.30 %	<0.02 s	GPU @ 2.5 Ghz (Python + C/C++)
89	PFF3D		code	36.07 %	43.93 %	32.86 %	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.
90	ASCNet			35.76 %	42.00 %	33.69 %	0.09 s	1 core @ 2.5 Ghz (C/C++)
91	deprecated			35.21 %	41.32 %	33.32 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
92	RoIFusion		code	35.14 %	42.22 %	32.92 %	0.22 s	1 core @ 3.0 Ghz (Python)
93	BirdNet+		code	35.06 %	41.55 %	32.93 %	0.11 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.
94	AB3DMOT		code	34.59 %	42.27 %	31.37 %	0.0047s	1 core @ 2.5 Ghz (python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
95	NLK-ALL		code	34.46 %	44.30 %	30.83 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
96	DAMNET		code	33.66 %	43.32 %	30.12 %	1 s	1 core @ 2.5 Ghz (C/C++)
97	CBi-GNN-persons			32.92 %	41.65 %	29.19 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
98	BirdNet+ (legacy)		code	31.46 %	37.99 %	29.46 %	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.
99	SparsePool		code	30.38 %	37.84 %	26.94 %	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.
100	MMLAB LIGA-Stereo			30.00 %	40.46 %	27.07 %	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.
101	FCY			29.38 %	37.28 %	26.19 %	0.02 s	GPU @ 2.5 Ghz (Python)
102	SparsePool		code	27.92 %	35.52 %	25.87 %	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.
103	AVOD		code	27.86 %	36.10 %	25.76 %	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.
104	CSW3D			26.64 %	33.75 %	23.34 %	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.
105	PointRGBNet			26.40 %	34.77 %	24.03 %	0.08 s	4 cores @ 2.5 Ghz (Python + C/C++)
106	Disp R-CNN (velo)		code	25.80 %	37.12 %	22.04 %	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.
107	Disp R-CNN		code	25.40 %	35.75 %	21.79 %	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.
108	deleted			25.13 %	35.02 %	22.36 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)
109	FusionDetv2-v1			24.55 %	30.58 %	23.64 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
110	CG-Stereo			24.31 %	33.22 %	20.95 %	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.
111	NCL		code	23.33 %	27.75 %	21.66 %	NA s	1 core @ 2.5 Ghz (Python)
112	LIGA-Stereo-old			23.23 %	30.14 %	20.58 %	0.375 s	Titan Xp
113	YOLOStereo3D		code	19.75 %	28.49 %	16.48 %	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.
114	OSE+			19.67 %	28.30 %	17.17 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
115	BEVC			17.65 %	23.49 %	15.92 %	35ms	GPU @ 1.5 Ghz (Python)
116	OC Stereo		code	17.58 %	24.48 %	15.60 %	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.
117	BirdNet			17.08 %	22.04 %	15.82 %	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.
118	AEC3D			16.81 %	20.98 %	15.22 %	18 ms	GPU @ 2.5 Ghz (Python)
119	VN3D			15.69 %	19.56 %	13.17 %	0.02 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
120	DSGN		code	15.55 %	20.53 %	14.15 %	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.
121	SOD			14.68 %	21.13 %	12.67 %	0.1 s	1 core @ 2.5 Ghz (Python)
122	Complexer-YOLO			13.96 %	17.60 %	12.70 %	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.
123	RT3D-GMP			11.41 %	16.23 %	10.12 %	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.
124	MM			8.38 %	13.22 %	6.99 %	1 s	1 core @ 2.5 Ghz (C/C++)
125	CaDDN		code	8.14 %	12.87 %	6.76 %	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.
126	RefinedMPL			7.18 %	11.14 %	5.84 %	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.
127	TopNet-HighRes			6.92 %	10.40 %	6.63 %	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.
128	MonoRUn		code	6.78 %	10.88 %	5.83 %	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.
129	MonoPair			6.68 %	10.02 %	5.53 %	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.
130	monodle		code	6.55 %	9.64 %	5.44 %	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 .
131	MonoFlex			6.31 %	9.43 %	5.26 %	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.
132	GAC3D++			6.29 %	9.29 %	5.20 %	0.25 s	1 core @ 2.5 Ghz (C/C++)
133	RelationNet3D_dla34		code	6.22 %	9.28 %	5.23 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
134	E2E-DA			5.95 %	8.79 %	5.72 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
135	DA-Mono3D			5.68 %	7.86 %	4.81 %	0.09s	1 core @ 2.5 Ghz (C/C++)
136	MonoGeo			5.63 %	8.00 %	4.71 %	0.05 s	1 core @ 2.5 Ghz (Python)
137	Deprecated			5.62 %	7.52 %	4.71 %	Deprecated	Deprecated
138	ICCV			5.25 %	8.34 %	4.72 %	0.04 s	GPU @ 2.5 Ghz (Python)
139	MonoHMOO			5.23 %	7.62 %	4.28 %	0.2 s	1 core @ 2.5 Ghz (Python + C/C++)
140	RelationNet3D_res18		code	5.19 %	7.95 %	4.21 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
141	Aug3D-RPN			4.71 %	6.01 %	3.87 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
142	Shift R-CNN (mono)		code	4.66 %	7.95 %	4.16 %	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.
143	Lite-FPN			4.38 %	6.57 %	3.56 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
144	PLDet3d			4.25 %	6.31 %	3.49 %	0.11 s	1 core @ 2.5 Ghz (C/C++)
145	M3D-RPN(S-R)			4.11 %	5.70 %	3.37 %	0.16 s	GPU @ 1.5 Ghz (Python)
146	CDI3D			4.03 %	5.64 %	3.29 %	0.03 s	GPU @ 2.5 Ghz (Python)
147	MonoPSR		code	4.00 %	6.12 %	3.30 %	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.
148	MP-Mono			3.75 %	5.09 %	3.50 %	0.16 s	GPU @ 2.5 Ghz (Python)
149	Geo3D			3.65 %	5.74 %	3.01 %	0.04 s	GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
150	DFR-Net			3.62 %	6.09 %	3.39 %	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.
151	DDMP-3D			3.55 %	4.93 %	3.01 %	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.
152	FADNet		code	3.53 %	5.40 %	3.31 %	0.04 s	GPU @ >3.5 Ghz (Python)
153	E2E-DA-Lite (Res18)			3.51 %	5.82 %	3.42 %	0.01 s	GPU @ 2.5 Ghz (Python)
154	M3D-RPN		code	3.48 %	4.92 %	2.94 %	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 .
155	D4LCN		code	3.42 %	4.55 %	2.83 %	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.
156	QD-3DT		code	3.37 %	5.53 %	3.02 %	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.
157	LAPNet			3.16 %	4.41 %	2.70 %	0.03 s	1 core @ 2.5 Ghz (Python)
158	MonoEF		code	2.79 %	4.27 %	2.21 %	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.
159	CenterNet-Boost			2.47 %	3.27 %	2.43 %	0.042 s	GPU @ 2.5 Ghz (Python)
160	RT3DStereo			2.45 %	3.28 %	2.35 %	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.
161	TopNet-UncEst			1.87 %	3.42 %	1.73 %	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.
162	PPTrans			1.85 %	2.68 %	1.44 %	0.2 s	GPU @ 2.5 Ghz (Python)
163	TBD			1.81 %	3.00 %	1.59 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
164	SS3D			1.78 %	2.31 %	1.48 %	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.
165	UDI-mono3D			1.45 %	2.18 %	1.12 %	0.05 s	1 core @ 2.5 Ghz (Python)
166	SparVox3D			1.35 %	1.93 %	1.04 %	0.05 s	GPU @ 2.0 Ghz (Python)
167	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.