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	SFD			83.96 %	90.83 %	77.47 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
2	DGDNH			83.83 %	90.47 %	79.45 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
3	VPFNet			83.21 %	91.02 %	78.20 %	0.06 s	2 cores @ 2.5 Ghz (Python)
4	Anonymous			82.99 %	91.64 %	78.02 %	0.1 s	GPU @ 2.5 Ghz (Python)
5	NFAF3D			82.97 %	91.57 %	77.72 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
6	BtcDet			82.86 %	90.64 %	78.09 %	0.09 s	GPU @ 2.5 Ghz (Python + C/C++)
7	sfd			82.79 %	91.30 %	78.07 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
8	PE-RCVN			82.69 %	91.51 %	77.75 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
9	SPG_mini			82.66 %	90.64 %	77.91 %	0.09 s	GPU @ 2.5 Ghz (Python)
10	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.
11	DFNet-V			82.45 %	89.40 %	77.56 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
12	DFNet-PV			82.40 %	90.99 %	77.37 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
13	EA-M-RCNN(BorderAtt)			82.33 %	87.77 %	77.37 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
14	NFAF3D-light			82.30 %	90.88 %	76.89 %	0.03 s	1 core @ 2.5 Ghz (Python + C/C++)
15	CLOCs		code	82.28 %	89.16 %	77.23 %	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.
16	PPAF			82.23 %	88.76 %	77.48 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
17	CityBrainLab			82.19 %	90.51 %	77.17 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
18	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.
19	VoTr-TSD			82.09 %	89.90 %	79.14 %	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.
20	Pyramid R-CNN			82.08 %	88.39 %	77.49 %	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.
21	SRIF-RCNN			82.04 %	88.45 %	77.54 %	0.0947 s	1 core @ 2.5 Ghz (C/C++)
22	Anonymous			81.96 %	89.90 %	77.20 %	0.1s	1 core @ 2.5 Ghz (C/C++)
23	EPNet++			81.96 %	91.37 %	76.71 %	0.1 s	GPU @ 2.5 Ghz (Python)
24	PV-RCNN-v2			81.88 %	90.14 %	77.15 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
25	SGNet			81.85 %	88.83 %	77.47 %	0.09 s	GPU @ 2.5 Ghz (Python)
26	Anonymous			81.85 %	89.96 %	76.51 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
27	ST-RCNN			81.84 %	90.50 %	77.22 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
28	ST-RCNN (SNLW-RCNN)		code	81.84 %	90.50 %	77.22 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
29	ISE-RCNN			81.83 %	89.12 %	77.29 %	0.09 s	1 core @ 2.5 Ghz (Python + C/C++)
30	SqueezeRCNN			81.80 %	88.72 %	77.10 %	0.08 s	1 core @ 2.5 Ghz (Python)
31	CityBrainLab-CT3D		code	81.77 %	87.83 %	77.16 %	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.
32	JPVNet			81.73 %	88.66 %	76.94 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
33	M3DeTR		code	81.73 %	90.28 %	76.96 %	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.
34	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.
35	E^2-PV-RCNN			81.70 %	88.33 %	77.20 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
36	PLNL-3DSSD			81.69 %	88.98 %	74.90 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
37	VCRCNN			81.68 %	90.52 %	77.26 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
38	TBD			81.68 %	87.93 %	76.92 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
39	ASCNet			81.67 %	88.48 %	76.93 %	0.09 s	1 core @ 2.5 Ghz (C/C++)
40	Fast VP-RCNN		code	81.62 %	90.97 %	76.90 %	0.05 s	1 core @ 3.5 Ghz (C/C++)
41	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.
42	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.
43	SARFE			81.59 %	88.88 %	76.74 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
44	HyBrid Feature Det			81.59 %	88.77 %	76.92 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
45	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.
46	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.
47	anonymous		code	81.55 %	90.94 %	76.74 %	0.05s	1 core @ >3.5 Ghz (python)
48	LZY_RCNN			81.52 %	88.77 %	78.59 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
49	TBD			81.51 %	88.96 %	77.27 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
50	FrustumRCNN			81.50 %	87.83 %	77.08 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
51	MSG-PGNN			81.50 %	88.70 %	76.88 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
52	TransCyclistNet			81.46 %	88.47 %	76.87 %	0.08 s	1 core @ 2.5 Ghz (Python)
53	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.
54	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.
55	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.
56	TPCG			81.41 %	89.16 %	76.90 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
57	PC-RGNN			81.38 %	87.94 %	76.88 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
58	DDet			81.38 %	89.63 %	78.57 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
59	WHUT-iou_ssd		code	81.37 %	89.84 %	76.83 %	0.045s	1 core @ 2.5 Ghz (C/C++)
60	XView			81.35 %	89.21 %	76.87 %	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.
61	ISE-RCNN-PV			81.34 %	88.05 %	76.99 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
62	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.
63	FSA-PVRCNN			81.31 %	88.01 %	76.75 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
64	TransDet3D			81.28 %	88.11 %	76.73 %	0.08 s	1 core @ 2.5 Ghz (Python)
65	Generalized-SIENet			81.24 %	87.70 %	76.79 %	0.08 s	1 core @ 2.5 Ghz (Python)
66	Point Image Fusion			81.23 %	89.01 %	76.77 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
67	MSL3D			81.15 %	87.27 %	76.56 %	0.03 s	GPU @ 2.5 Ghz (Python)
68	Multi-Sensor3D			81.15 %	87.27 %	76.56 %	0.03 s	GPU @ 2.5 Ghz (Python)
69	SAA-PV-RCNN			81.09 %	87.24 %	78.05 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
70	FPC-RCNN			81.08 %	88.68 %	76.46 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
71	VPFNet		code	80.97 %	88.51 %	76.74 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
72	CSVoxel-RCNN			80.97 %	87.66 %	76.29 %	0.03 s	GPU @ 1.0 Ghz (Python)
73	VueronNet		code	80.96 %	90.06 %	73.72 %	0.06 s	1 core @ 2.0 Ghz (Python)
74	FusionDetv2-v4			80.93 %	87.75 %	76.12 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
75	AIMC-RUC			80.83 %	90.14 %	73.59 %	0.11 s	1 core @ 2.5 Ghz (Python)
76	SVGA-Net			80.82 %	87.40 %	76.23 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
77	GNN-RCNN			80.81 %	87.94 %	76.53 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
78	sa-voxel-centernet		code	80.77 %	87.39 %	76.45 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
79	SA-voxel-centernet		code	80.77 %	87.28 %	76.51 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
80	Associate-3Ddet_v2			80.77 %	91.53 %	75.23 %	0.04 s	1 core @ 2.5 Ghz (Python)
81	FusionDetv2-v3			80.70 %	88.05 %	76.10 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
82	StructuralIF			80.69 %	87.15 %	76.26 %	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.
83	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.
84	SCIR-Net			80.62 %	87.53 %	76.00 %	0.03 s	GPU @ 2.5 Ghz (Python + C/C++)
85	XView-PartA^2			80.41 %	87.72 %	76.22 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
86	Sem-Aug v1		code	80.40 %	88.92 %	77.37 %	0.04 s	GPU @ 3.5 Ghz (Python)
87	SRDL			80.38 %	87.73 %	76.27 %	0.15 s	GPU @ 2.5 Ghz (Python + C/C++)
88	Fast-CLOCs			80.35 %	89.10 %	76.99 %	0.1 s	GPU @ 2.5 Ghz (Python)
89	SPANet			80.34 %	91.05 %	74.89 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
90	IA-SSD (single)			80.32 %	88.87 %	75.10 %	0.013 s	1 core @ 2.5 Ghz (C/C++)
91	AM-SSD			80.30 %	89.58 %	75.02 %	0.04 s	1 core @ 2.5 Ghz (Python)
92	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.
93	FusionDetv1			80.28 %	87.45 %	76.21 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
94	TBD			80.24 %	87.67 %	76.27 %	0.07 s	1 core @ 2.5 Ghz (Python + C/C++)
95	VPV			80.21 %	88.97 %	75.22 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
96	VCT			80.19 %	89.12 %	77.19 %	0.2 s	1 core @ 2.5 Ghz (Python)
97	TBD			80.17 %	86.83 %	75.96 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
98	IA-SSD (multi)			80.13 %	88.34 %	75.04 %	0.014 s	1 core @ 2.5 Ghz (C/C++)
99	TBD			80.12 %	88.30 %	75.29 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
100	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.
101	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.
102	TBD			80.02 %	88.45 %	74.85 %	TBD	GPU @ 2.5 Ghz (Python + C/C++)
103	MVOD			80.01 %	88.53 %	77.24 %	0.16 s	1 core @ 2.5 Ghz (C/C++)
104	MBDF-Net			80.00 %	90.87 %	75.04 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
105	SIF			79.88 %	86.84 %	75.89 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
106	CM3DV			79.87 %	89.00 %	72.59 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
107	RangeIoUDet			79.80 %	88.60 %	76.76 %	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.
108	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.
109	KpNet			79.75 %	88.92 %	72.17 %	0.42 s	1 core @ 2.5 Ghz (C/C++)
110	KpNet			79.74 %	88.88 %	72.13 %	42 s	1 core @ 2.5 Ghz (C/C++)
111	Seg-RCNN		code	79.73 %	89.16 %	72.28 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
112	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.
113	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.
114	MBDF-Net-1			79.65 %	90.43 %	74.72 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
115	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.
116	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.
117	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.
118	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.
119	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.
120	SECOND			79.46 %	87.44 %	73.97 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
121	RoIFusion		code	79.36 %	88.09 %	72.51 %	0.22 s	1 core @ 3.0 Ghz (Python)
122	NV-RCNN			79.32 %	87.58 %	74.74 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
123	3DIoU_v2			79.30 %	88.22 %	76.96 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
124	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.
125	FPCR-CNN			79.25 %	88.45 %	75.69 %	0.05 s	1 core @ 2.5 Ghz (Python)
126	3DIoU++			79.22 %	87.49 %	76.68 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
127	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.
128	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.
129	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.
130	CCFNET			78.97 %	88.20 %	74.14 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
131	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.
132	NV2P-RCNN			78.92 %	87.36 %	74.16 %	0.1 s	GPU @ 2.5 Ghz (Python)
133	demo			78.85 %	87.50 %	72.05 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
134	FPC3D			78.81 %	87.61 %	75.49 %	33 s	1 core @ 2.5 Ghz (C/C++)
135	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.
136	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.
137	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.
138	FusionDetv2-v2			78.42 %	86.59 %	73.87 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
139	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.
140	MKFFNet			78.40 %	85.25 %	73.75 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
141	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.
142	FusionDetv2-v5			78.30 %	86.94 %	73.44 %	0.05 s	1 core @ 2.5 Ghz (Java + C/C++)
143	MKFFNet			78.30 %	87.25 %	73.66 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
144	MKFFNet			78.30 %	86.86 %	73.80 %	0.01s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
145	SAA-SECOND			78.13 %	86.13 %	73.34 %	38m s	1 core @ 2.5 Ghz (C/C++)
146	3D-VDNet			78.05 %	87.13 %	72.90 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
147	VPN			77.93 %	85.02 %	72.97 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
148	HVPR			77.92 %	86.38 %	73.04 %	0.02 s	GPU @ 2.5 Ghz (Python)
149	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.
150	CVFNet			77.70 %	88.75 %	71.95 %	28.1ms	1 core @ 2.5 Ghz (Python)
151	VOXEL_3D			77.69 %	86.45 %	72.20 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
152	VGCN			77.65 %	84.47 %	73.36 %	0.09 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
153	AutoAlign			77.58 %	86.84 %	73.23 %	0.1 s	1 core @ 2.5 Ghz (Python)
154	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.
155	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.
156	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.
157	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.
158	Sem-Aug-PointRCNN		code	77.04 %	82.75 %	73.21 %	0.1 s	GPU @ 3.5 Ghz (C/C++)
159	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.
160	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.
161	YF			76.57 %	87.15 %	71.23 %	0.04 s	GPU @ 2.5 Ghz (C/C++)
162	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.
163	HNet-3DSSD		code	76.48 %	86.06 %	69.71 %	0.05 s	GPU @ 2.5 Ghz (Python)
164	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.
165	DPointNet			76.34 %	81.67 %	70.34 %	0.07s	1 core @ 2.5 Ghz (C/C++)
166	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.
167	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.
168	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.
169	APL-Second			75.75 %	84.26 %	70.65 %	0.05 s	1 core @ 2.5 Ghz (Python)
170	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.
171	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.
172	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.
173	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.
174	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.
175	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.
176	sscl-20p			74.82 %	86.06 %	69.87 %	0.02 s	1 core @ 2.5 Ghz (Python)
177	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.
178	FPGNN			74.77 %	83.82 %	67.93 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
179	FPC3D_all			74.55 %	85.50 %	69.91 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
180	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.
181	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.
182	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.
183	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.
184	LSNet			73.55 %	86.13 %	68.58 %	0.09 s	GPU @ 2.5 Ghz (Python + C/C++)
185	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.
186	PointRGBNet			73.49 %	83.99 %	68.56 %	0.08 s	4 cores @ 2.5 Ghz (Python + C/C++)
187	RangeDet		code	73.44 %	80.53 %	67.28 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
188	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.
189	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.
190	DASS			72.31 %	81.85 %	65.99 %	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.
191	HS3D		code	72.25 %	83.57 %	67.49 %	0.12 s	1 core @ 2.5 Ghz (Python + C/C++)
192	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.
193	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.
194	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.
195	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.
196	FusionDetv2-baseline			68.87 %	79.05 %	63.68 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
197	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.
198	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.
199	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.
200	FusionDetv2-v1			65.65 %	75.21 %	60.65 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
201	MMLAB LIGA-Stereo		code	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.
202	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.
203	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.
204	KMC		code	62.74 %	74.45 %	56.76 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
205	LIGA-Stereo-old			62.65 %	81.76 %	55.24 %	0.375 s	Titan Xp
206	AEC3D			61.99 %	72.16 %	57.11 %	18 ms	GPU @ 2.5 Ghz (Python)
207	VN3D			61.41 %	72.37 %	56.86 %	0.02 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
208	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.
209	deleted			57.11 %	76.87 %	50.05 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)
210	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.
211	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.
212	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.
213	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.
214	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.
215	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.
216	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.
217	NCL		code	50.07 %	46.58 %	50.33 %	NA s	1 core @ 2.5 Ghz (Python)
218	SOD			48.69 %	70.90 %	40.12 %	0.1 s	1 core @ 2.5 Ghz (Python)
219	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.
220	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.
221	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.
222	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.
223	R-AGNO-Net			42.79 %	49.49 %	39.31 %	0.15 s	1 core @ 2.5 Ghz (C/C++)
224	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.
225	OSE+			41.60 %	62.67 %	35.82 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
226	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.
227	BEVC			40.72 %	50.05 %	36.42 %	35ms	GPU @ 1.5 Ghz (Python)
228	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.
229	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.
230	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.
231	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.
232	SC(DLA34)			31.30 %	49.94 %	25.62 %	0.04 s	GPU @ 2.5 Ghz (Python)
233	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.
234	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.
235	TBD			24.87 %	33.30 %	21.96 %	0.1 s	1 core @ 2.5 Ghz (Python)
236	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.
237	Digging_M3D			21.24 %	29.15 %	19.18 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
238	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.
239	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.
240	SCSTSV-MonoFlex			17.91 %	27.38 %	15.58 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
241	LPCG-Monoflex			17.80 %	25.56 %	15.38 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
242	MonoDDE			17.14 %	24.93 %	15.10 %	0.04 s	1 core @ 2.5 Ghz (Python)
243	Mobile Stereo R-CNN			17.04 %	26.97 %	13.26 %	1.8 s	NVIDIA Jetson TX2
244	CMKD			16.99 %	27.20 %	15.08 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
245	MonoCon			16.46 %	22.50 %	13.95 %	0.02 s	GPU @ 2.5 Ghz (Python)
246	DD3D		code	16.34 %	23.22 %	14.20 %	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) .
247	mono3d			15.26 %	23.41 %	12.80 %	0.03 s	GPU @ 2.5 Ghz (Python)
248	ZongmuMono3d		code	15.08 %	23.79 %	13.25 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
249	GUPNet		code	15.02 %	22.26 %	13.12 %	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.
250	vadin-TBD			14.94 %	21.75 %	13.07 %	0.04 s	1 core @ 2.5 Ghz (Python)
251	LPCG-M3D			14.82 %	22.73 %	12.88 %	0.11 s	1 core @ 2.5 Ghz (C/C++)
252	M3DSSD++			14.75 %	23.61 %	11.80 %	0.16s	1 core @ 2.5 Ghz (C/C++)
253	MonoFlex			14.73 %	22.29 %	12.77 %	0.03 s	1 core @ 2.5 Ghz (Python)
254	CA3D			14.49 %	20.89 %	12.19 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
255	MM			14.38 %	21.26 %	11.87 %	1 s	1 core @ 2.5 Ghz (C/C++)
256	ANM			14.33 %	20.84 %	11.61 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
257	DLE		code	14.33 %	24.23 %	10.30 %	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.
258	ITS-MDPL			14.28 %	24.67 %	12.13 %	0.16 s	GPU @ 2.5 Ghz (Python)
259	SwinMono3D			14.24 %	22.61 %	10.11 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
260	AutoShape		code	14.17 %	22.47 %	11.36 %	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.
261	MAOLoss		code	14.00 %	20.05 %	11.81 %	0.05 s	1 core @ 2.5 Ghz (Python)
262	E2E-DA			13.97 %	19.73 %	11.82 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
263	GAC3D++			13.90 %	19.53 %	11.77 %	0.25 s	1 core @ 2.5 Ghz (C/C++)
264	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.
265	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.
266	MonoGeo			13.81 %	18.85 %	11.52 %	0.05 s	1 core @ 2.5 Ghz (Python)
267	K3D			13.80 %	20.04 %	11.67 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
268	none			13.79 %	18.84 %	11.52 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
269	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.
270	MonoLCD			13.52 %	18.08 %	11.58 %	0.04 s	1 core @ 2.5 Ghz (Python)
271	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.
272	MDSNet			13.40 %	22.80 %	10.27 %	0.07 s	1 core @ 2.5 Ghz (Python)
273	PCT		code	13.37 %	21.00 %	11.31 %	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.
274	KAIST-VDCLab			13.33 %	19.06 %	11.90 %	0.04 s	1 core @ 2.5 Ghz (Python)
275	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.
276	MonoHMOO			13.12 %	20.28 %	9.56 %	0.2 s	1 core @ 2.5 Ghz (Python + C/C++)
277	Aug3D-RPN			12.99 %	17.82 %	9.78 %	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.
278	vadin-TBD2		code	12.99 %	20.10 %	10.50 %	0.20 s	1 core @ 2.5 Ghz (Python)
279	RelationNet3D_dla34		code	12.88 %	17.67 %	11.01 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
280	PLDet3d			12.85 %	20.72 %	11.11 %	0.11 s	1 core @ 2.5 Ghz (C/C++)
281	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.
282	RetinaMono			12.73 %	19.41 %	10.45 %	0.02 s	1 core @ 2.5 Ghz (Python)
283	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 .
284	DA-Mono3D			12.66 %	16.99 %	9.97 %	0.09s	1 core @ 2.5 Ghz (C/C++)
285	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.
286	RelationNet3D			12.60 %	17.57 %	10.95 %	0.04 s	GPU @ 2.5 Ghz (Python)
287	Object Transformer			12.58 %	17.87 %	10.87 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
288	TBD			12.53 %	22.40 %	10.64 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
289	AutoShape			12.42 %	20.35 %	9.70 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
290	MP-Mono			12.37 %	17.89 %	9.58 %	0.16 s	GPU @ 2.5 Ghz (Python)
291	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.
292	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.
293	Deprecated			12.30 %	16.48 %	9.14 %	Deprecated	Deprecated
294	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 .
295	PPTrans			12.06 %	19.79 %	10.48 %	0.2 s	GPU @ 2.5 Ghz (Python)
296	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.
297	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.
298	GAC3D			12.00 %	17.75 %	9.15 %	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.
299	PGD-FCOS3D		code	11.76 %	19.05 %	9.39 %	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 2021.
300	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.
301	RetinaMono		code	11.61 %	16.68 %	9.57 %	0.02 s	1 core @ 2.5 Ghz (Python)
302	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.
303	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.
304	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.
305	ImVoxelNet		code	10.97 %	17.15 %	9.15 %	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.
306	COF3D			10.91 %	17.86 %	8.20 %	200 s	1 core @ 2.5 Ghz (Python + C/C++)
307	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.
308	Lite-FPN			10.64 %	15.32 %	8.59 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
309	TBD			10.61 %	15.71 %	8.66 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
310	Keypoint-3D			10.42 %	15.97 %	7.91 %	14 s	1 core @ 2.5 Ghz (C/C++)
311	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.
312	E2E-DA-Lite (Res18)			10.32 %	15.56 %	8.89 %	0.01 s	GPU @ 2.5 Ghz (Python)
313	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.
314	RelationNet3D_res18		code	9.93 %	14.27 %	8.43 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
315	FADNet		code	9.92 %	16.37 %	8.05 %	0.04 s	GPU @ >3.5 Ghz (Python)
316	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.
317	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.
318	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 .
319	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.
320	ICCV			9.31 %	13.37 %	8.29 %	0.04 s	GPU @ 2.5 Ghz (Python)
321	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.
322	MonoCInIS			7.94 %	15.82 %	6.68 %	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.
323	Geo3D			7.70 %	11.52 %	6.80 %	0.04 s	GPU @ 2.5 Ghz (Python)
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324	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.
325	MonoCInIS			7.66 %	15.21 %	6.24 %	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.
326	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.
327	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.
328	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.
329	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.
330	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.
331	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.
332	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.
333	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.
334	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.
335	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.
336	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.
337	SparVox3D			3.20 %	5.27 %	2.56 %	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.
338	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.
339	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.
340	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.
341	weakm3d			2.26 %	5.03 %	1.63 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
342	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.
343	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.
344	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.
345	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 %	0.2 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	PiFeNet			45.89 %	54.84 %	42.02 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
5	ISE-RCNN			45.66 %	51.44 %	42.43 %	0.09 s	1 core @ 2.5 Ghz (Python + C/C++)
6	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.
7	H^23D R-CNN			45.26 %	52.75 %	41.56 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
8	PE-RCVN			45.01 %	50.29 %	41.85 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
9	SAA-PV-RCNN			45.00 %	52.55 %	41.82 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
10	VPN			44.56 %	54.13 %	41.73 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
11	EPNet++			44.38 %	52.79 %	41.29 %	0.1 s	GPU @ 2.5 Ghz (Python)
12	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.
13	TBD			44.32 %	49.37 %	41.24 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
14	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.
15	AutoAlign			44.08 %	53.99 %	40.82 %	0.1 s	1 core @ 2.5 Ghz (Python)
16	ISE-RCNN-PV			43.78 %	50.03 %	40.50 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
17	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.
18	VCT			43.65 %	50.27 %	41.43 %	0.2 s	1 core @ 2.5 Ghz (Python)
19	EA-M-RCNN(BorderAtt)			43.44 %	51.81 %	39.85 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
20	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.
21	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.
22	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.
23	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.
24	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.
25	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.
26	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.
27	SGNet			43.00 %	49.68 %	40.45 %	0.09 s	GPU @ 2.5 Ghz (Python)
28	Frustum-PointPillars			42.89 %	51.22 %	39.28 %	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.
29	Fast-CLOCs			42.72 %	52.10 %	39.08 %	0.1 s	GPU @ 2.5 Ghz (Python)
30	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.
31	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.
32	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.
33	TBD			42.19 %	49.89 %	39.34 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
34	TBD			42.19 %	49.89 %	39.34 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
35	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.
36	PPAF			42.05 %	48.66 %	38.94 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
37	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.
38	TBD_IOU1			41.65 %	49.00 %	39.31 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
39	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.
40	TBD_IOU			41.45 %	48.25 %	39.17 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
41	GNN-RCNN			41.32 %	47.48 %	38.97 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
42	tbd			41.10 %	50.56 %	37.49 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
43	IA-SSD (single)			41.03 %	47.90 %	37.98 %	0.013 s	1 core @ 2.5 Ghz (C/C++)
44	Generalized-SIENet			40.97 %	47.01 %	38.88 %	0.08 s	1 core @ 2.5 Ghz (Python)
45	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.
46	SCIR-Net			40.95 %	49.23 %	38.47 %	0.03 s	GPU @ 2.5 Ghz (Python + C/C++)
47	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.
48	SARFE			40.79 %	47.29 %	38.25 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
49	XView-PartA^2			40.71 %	47.73 %	38.47 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
50	SAA-SECOND			40.57 %	48.73 %	37.77 %	38m s	1 core @ 2.5 Ghz (C/C++)
51	WHUT-iou_ssd		code	40.53 %	46.41 %	38.48 %	0.045s	1 core @ 2.5 Ghz (C/C++)
52	E^2-PV-RCNN			40.47 %	46.61 %	38.60 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
53	SA-voxel-centernet		code	40.43 %	46.10 %	38.32 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
54	FusionDetv2-v3			40.38 %	46.86 %	37.41 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
55	FPCR-CNN			40.32 %	48.33 %	37.66 %	0.05 s	1 core @ 2.5 Ghz (Python)
56	P2V_PCV1			40.27 %	45.43 %	38.24 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
57	sa-voxel-centernet		code	40.24 %	46.08 %	38.07 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
58	FPC-RCNN			40.13 %	46.41 %	37.84 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
59	TPCG			39.97 %	46.35 %	37.66 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
60	M3DeTR		code	39.94 %	45.70 %	37.66 %	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.
61	FusionDetv2-v5			39.91 %	47.50 %	37.39 %	0.05 s	1 core @ 2.5 Ghz (Java + C/C++)
62	SVGA-Net			39.88 %	47.59 %	37.57 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
63	DDet			39.87 %	45.82 %	38.00 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
64	MVOD			39.82 %	46.22 %	37.56 %	0.16 s	1 core @ 2.5 Ghz (C/C++)
65	Point Image Fusion			39.79 %	45.04 %	37.62 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
66	anonymous		code	39.74 %	46.09 %	37.41 %	0.05s	1 core @ >3.5 Ghz (python)
67	FusionDetv2-v4			39.68 %	46.93 %	37.31 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
68	Fast VP-RCNN		code	39.65 %	45.95 %	37.29 %	0.05 s	1 core @ 3.5 Ghz (C/C++)
69	VCRCNN			39.64 %	45.19 %	37.55 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
70	TBD			39.48 %	45.46 %	37.35 %	0.07 s	1 core @ 2.5 Ghz (Python + C/C++)
71	SRDL			39.43 %	47.30 %	36.99 %	0.15 s	GPU @ 2.5 Ghz (Python + C/C++)
72	FusionDetv1			39.42 %	47.30 %	36.97 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
73	FSA-PVRCNN			39.39 %	44.14 %	37.13 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
74	demo			39.38 %	47.69 %	36.06 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
75	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.
76	ST-RCNN			39.36 %	44.96 %	37.09 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
77	FusionDetv2-v2			39.31 %	44.98 %	37.22 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
78	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.
79	TBD			39.31 %	46.85 %	36.11 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
80	IA-SSD (multi)			39.03 %	46.51 %	35.61 %	0.014 s	1 core @ 2.5 Ghz (C/C++)
81	tbd			38.89 %	45.98 %	35.94 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
82	NV-RCNN			38.75 %	47.05 %	36.52 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
83	SIF			38.74 %	46.23 %	36.06 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
84	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.
85	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.
86	MSL3D			38.58 %	45.00 %	35.72 %	0.03 s	GPU @ 2.5 Ghz (Python)
87	AF_MCLS			38.29 %	47.07 %	34.67 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
88	MKFFNet			38.05 %	46.01 %	35.72 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
89	FPC3D_all			37.95 %	45.49 %	35.60 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
90	VGCN			37.60 %	45.28 %	34.96 %	0.09 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
91	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.
92	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.
93	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.
94	YF			36.99 %	44.43 %	34.40 %	0.04 s	GPU @ 2.5 Ghz (C/C++)
95	HS3D		code	36.86 %	45.62 %	33.67 %	0.12 s	1 core @ 2.5 Ghz (Python + C/C++)
96	XView			36.79 %	42.44 %	34.96 %	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.
97	MKFFNet			36.66 %	43.94 %	34.56 %	0.01s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
98	FusionDetv2-baseline			36.66 %	41.34 %	34.60 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
99	MKFFNet			36.65 %	44.00 %	34.59 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
100	TBD			36.53 %	44.11 %	34.30 %	TBD	GPU @ 2.5 Ghz (Python + C/C++)
101	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.
102	NV2P-RCNN			35.98 %	43.18 %	33.88 %	0.1 s	GPU @ 2.5 Ghz (Python)
103	ASCNet			35.76 %	42.00 %	33.69 %	0.09 s	1 core @ 2.5 Ghz (C/C++)
104	RoIFusion		code	35.14 %	42.22 %	32.92 %	0.22 s	1 core @ 3.0 Ghz (Python)
105	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.
106	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.
107	CBi-GNN-persons			32.92 %	41.65 %	29.19 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
108	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.
109	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.
110	MMLAB LIGA-Stereo		code	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.
111	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.
112	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.
113	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.
114	PointRGBNet			26.40 %	34.77 %	24.03 %	0.08 s	4 cores @ 2.5 Ghz (Python + C/C++)
115	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.
116	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.
117	deleted			25.13 %	35.02 %	22.36 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)
118	FusionDetv2-v1			24.55 %	30.58 %	23.64 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
119	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.
120	NCL		code	23.33 %	27.75 %	21.66 %	NA s	1 core @ 2.5 Ghz (Python)
121	LIGA-Stereo-old			23.23 %	30.14 %	20.58 %	0.375 s	Titan Xp
122	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.
123	OSE+			19.67 %	28.30 %	17.17 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
124	AEC3D			19.00 %	24.39 %	17.43 %	18 ms	GPU @ 2.5 Ghz (Python)
125	BEVC			17.65 %	23.49 %	15.92 %	35ms	GPU @ 1.5 Ghz (Python)
126	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.
127	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.
128	VN3D			15.69 %	19.56 %	13.17 %	0.02 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
129	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.
130	SOD			14.68 %	21.13 %	12.67 %	0.1 s	1 core @ 2.5 Ghz (Python)
131	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.
132	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.
133	CMKD			10.39 %	16.89 %	9.29 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
134	GUPNet		code	9.76 %	14.95 %	8.41 %	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.
135	DD3D		code	9.30 %	13.91 %	8.05 %	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) .
136	ZongmuMono3d		code	9.18 %	14.23 %	7.82 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
137	MM			8.81 %	13.99 %	7.26 %	1 s	1 core @ 2.5 Ghz (C/C++)
138	SCSTSV-MonoFlex			8.75 %	13.10 %	7.38 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
139	SwinMono3D			8.54 %	12.96 %	7.19 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
140	MonoCon			8.41 %	13.10 %	6.94 %	0.02 s	GPU @ 2.5 Ghz (Python)
141	MonoFlex			8.16 %	11.89 %	6.81 %	0.03 s	1 core @ 2.5 Ghz (Python)
142	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.
143	vadin-TBD			7.66 %	11.87 %	6.82 %	0.04 s	1 core @ 2.5 Ghz (Python)
144	MonoLCD			7.62 %	11.21 %	6.47 %	0.04 s	1 core @ 2.5 Ghz (Python)
145	K3D			7.60 %	12.58 %	6.73 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
146	ANM			7.54 %	11.92 %	6.37 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
147	LPCG-Monoflex			7.33 %	10.82 %	6.18 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
148	MonoDDE			7.32 %	11.13 %	6.67 %	0.04 s	1 core @ 2.5 Ghz (Python)
149	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.
150	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.
151	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.
152	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.
153	mono3d			6.62 %	10.10 %	5.46 %	0.03 s	GPU @ 2.5 Ghz (Python)
154	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 .
155	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.
156	GAC3D++			6.29 %	9.29 %	5.20 %	0.25 s	1 core @ 2.5 Ghz (C/C++)
157	RelationNet3D_dla34		code	6.22 %	9.28 %	5.23 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
158	E2E-DA			5.95 %	8.79 %	5.72 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
159	DA-Mono3D			5.68 %	7.86 %	4.81 %	0.09s	1 core @ 2.5 Ghz (C/C++)
160	M3DSSD++			5.65 %	8.10 %	4.72 %	0.16s	1 core @ 2.5 Ghz (C/C++)
161	MonoGeo			5.63 %	8.00 %	4.71 %	0.05 s	1 core @ 2.5 Ghz (Python)
162	Deprecated			5.62 %	7.52 %	4.71 %	Deprecated	Deprecated
163	ICCV			5.25 %	8.34 %	4.72 %	0.04 s	GPU @ 2.5 Ghz (Python)
164	MonoHMOO			5.23 %	7.62 %	4.28 %	0.2 s	1 core @ 2.5 Ghz (Python + C/C++)
165	RelationNet3D_res18		code	5.19 %	7.95 %	4.21 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
166	Aug3D-RPN			4.71 %	6.01 %	3.87 %	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.
167	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.
168	Lite-FPN			4.38 %	6.57 %	3.56 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
169	COF3D			4.37 %	6.02 %	3.55 %	200 s	1 core @ 2.5 Ghz (Python + C/C++)
170	PLDet3d			4.25 %	6.31 %	3.49 %	0.11 s	1 core @ 2.5 Ghz (C/C++)
171	MAOLoss		code	4.18 %	5.81 %	3.67 %	0.05 s	1 core @ 2.5 Ghz (Python)
172	M3D-RPN(S-R)			4.11 %	5.70 %	3.37 %	0.16 s	GPU @ 1.5 Ghz (Python)
173	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.
174	MP-Mono			3.75 %	5.09 %	3.50 %	0.16 s	GPU @ 2.5 Ghz (Python)
175	Geo3D			3.65 %	5.74 %	3.01 %	0.04 s	GPU @ 2.5 Ghz (Python)
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176	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.
177	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.
178	FADNet		code	3.53 %	5.40 %	3.31 %	0.04 s	GPU @ >3.5 Ghz (Python)
179	E2E-DA-Lite (Res18)			3.51 %	5.82 %	3.42 %	0.01 s	GPU @ 2.5 Ghz (Python)
180	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 .
181	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.
182	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.
183	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.
184	KAIST-VDCLab			2.47 %	3.27 %	2.43 %	0.04 s	1 core @ 2.5 Ghz (Python)
185	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.
186	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.
187	PPTrans			1.85 %	2.68 %	1.44 %	0.2 s	GPU @ 2.5 Ghz (Python)
188	TBD			1.81 %	3.00 %	1.59 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
189	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.
190	PGD-FCOS3D		code	1.49 %	2.28 %	1.38 %	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 2021.
191	SparVox3D			1.35 %	1.93 %	1.04 %	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.
192	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.