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			84.76 %	91.73 %	77.92 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
2	Anonymous			83.96 %	90.83 %	77.47 %	n/a s	1 core @ 2.5 Ghz (Python + C/C++)
3	DGDNH			83.88 %	90.69 %	79.50 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
4	GraR-VoI			83.27 %	91.89 %	77.78 %	0.07 s	1 core @ 2.5 Ghz (Python + C/C++)
5	VPFNet			83.21 %	91.02 %	78.20 %	0.06 s	2 cores @ 2.5 Ghz (Python)
H. Zhu, J. Deng, Y. Zhang, J. Ji, Q. Mao, H. Li and Y. Zhang: VPFNet: Improving 3D Object Detection with Virtual Point based LiDAR and Stereo Data Fusion. 2021.
6	GraR-Po			83.18 %	91.79 %	77.98 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
7	CasA			83.06 %	91.58 %	80.08 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
8	Anonymous			82.99 %	91.64 %	78.02 %	0.1 s	GPU @ 2.5 Ghz (Python)
9	BtcDet		code	82.86 %	90.64 %	78.09 %	0.09 s	GPU @ 2.5 Ghz (Python + C/C++)
Q. Xu, Y. Zhong and U. Neumann: Behind the Curtain: Learning Occluded Shapes for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2022.
10	Anonymous			82.79 %	91.30 %	78.07 %	n/a s	1 core @ 2.5 Ghz (C/C++)
11	GraR-Vo			82.77 %	91.29 %	77.20 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
12	PE-RCVN			82.69 %	91.51 %	77.75 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
13	CAD			82.68 %	88.96 %	77.91 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
14	SPG_mini		code	82.66 %	90.64 %	77.91 %	0.09 s	GPU @ 2.5 Ghz (Python)
Q. Xu, Y. Zhou, W. Wang, C. Qi and D. Anguelov: SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation. Proceedings of the IEEE conference on computer vision and pattern recognition (ICCV) 2021.
15	DSASNet			82.63 %	89.48 %	77.94 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
16	SA3DNet			82.57 %	90.49 %	77.88 %	0.05 s	GPU @ 2.5 Ghz (Python)
17	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.
18	TF3D			82.46 %	89.10 %	77.78 %	0.1 s	2 cores @ 3.0 Ghz (Python)
19	DVF-V			82.45 %	89.40 %	77.56 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
20	GraR-Pi			82.42 %	90.94 %	77.00 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
21	DVF-PV			82.40 %	90.99 %	77.37 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
22	EA-M-RCNN(BorderAtt)			82.33 %	87.77 %	77.37 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
23	Anonymous			82.30 %	90.88 %	76.89 %	0.03 s	1 core @ 2.5 Ghz (Python + C/C++)
24	Anonymous		code	82.28 %	90.55 %	77.59 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
25	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.
26	TBD			82.23 %	88.76 %	77.48 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
27	CityBrainLab			82.19 %	90.51 %	77.17 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
28	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.
29	VoTr-TSD		code	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.
30	TBD			82.09 %	89.50 %	79.29 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
31	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.
32	FS-Net			82.07 %	88.68 %	77.42 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
33	VoxSeT			82.06 %	88.53 %	77.46 %	0.09 s	1 core @ 2.5 Ghz (C/C++)
34	SRIF-RCNN			82.04 %	88.45 %	77.54 %	0.0947 s	1 core @ 2.5 Ghz (C/C++)
35	LGNet			82.02 %	90.65 %	77.34 %	0.07 s	1 core @ 2.5 Ghz (C/C++)
36	EQ-PVRCNN			82.01 %	90.13 %	77.53 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
37	anonymous			81.99 %	88.82 %	77.26 %	0.09 s	GPU @ 2.5 Ghz (Python)
38	Anonymous			81.96 %	89.90 %	77.20 %	0.1s	1 core @ 2.5 Ghz (C/C++)
39	EPNet++			81.96 %	91.37 %	76.71 %	0.1 s	GPU @ 2.5 Ghz (Python)
Z. Liu, H. tengteng, B. Li, X. Chen, X. Wang and X. Bai: EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object Detection. arXiv preprint arXiv:2112.11088 2021.
40	PV-RCNN++			81.88 %	90.14 %	77.15 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
41	PDV			81.86 %	90.43 %	77.36 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
42	SGNet			81.85 %	88.83 %	77.47 %	0.09 s	GPU @ 2.5 Ghz (Python)
43	Anonymous			81.85 %	89.96 %	76.51 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
44	ST-RCNN			81.84 %	90.50 %	77.22 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
45	ST-RCNN (SNLW-RCNN)		code	81.84 %	90.50 %	77.22 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
46	ISE-RCNN			81.83 %	89.12 %	77.29 %	0.09 s	1 core @ 2.5 Ghz (Python + C/C++)
47	SqueezeRCNN			81.80 %	88.72 %	77.10 %	0.08 s	1 core @ 2.5 Ghz (Python)
48	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.
49	USVLab BSAODet (MM)			81.74 %	88.89 %	77.14 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
50	JPVNet			81.73 %	88.66 %	76.94 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
51	TBD			81.73 %	89.48 %	79.05 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
52	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.
53	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.
54	TBD			81.71 %	88.46 %	76.63 %	0.1 s	1 core @ 2.5 Ghz (Python)
55	DCAnet			81.70 %	88.42 %	77.18 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
56	VCRCNN			81.68 %	90.52 %	77.26 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
57	TBD			81.68 %	87.93 %	76.92 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
58	ASCNet			81.67 %	88.48 %	76.93 %	0.09 s	1 core @ 2.5 Ghz (C/C++)
59	Fast VP-RCNN		code	81.62 %	90.97 %	76.90 %	0.05 s	1 core @ 3.5 Ghz (C/C++)
60	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.
61	BADet		code	81.61 %	89.28 %	76.58 %	0.11 s	1 core @ 2.5 Ghz (C/C++)
R. Qian, X. Lai and X. Li: BADet: Boundary-Aware 3D Object Detection from Point Clouds. Pattern Recognition (PR) 2022.
62	SARFE			81.59 %	88.88 %	76.74 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
63	HyBrid Feature Det			81.59 %	88.77 %	76.92 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
64	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.
65	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.
66	anonymous		code	81.55 %	90.94 %	76.74 %	0.05s	1 core @ >3.5 Ghz (python)
67	LZY_RCNN			81.52 %	88.77 %	78.59 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
68	MSG-PGNN			81.50 %	88.70 %	76.88 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
69	PTA-RCNN			81.48 %	88.18 %	76.98 %	0.08 s	1 core @ 2.5 Ghz (Python)
70	TransCyclistNet			81.46 %	88.47 %	76.87 %	0.08 s	1 core @ 2.5 Ghz (Python)
71	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.
72	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.
73	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.
74	TPCG			81.41 %	89.16 %	76.90 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
75	DDet			81.38 %	89.63 %	78.57 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
76	WHUT-iou_ssd		code	81.37 %	89.84 %	76.83 %	0.045s	1 core @ 2.5 Ghz (C/C++)
77	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.
78	ISE-RCNN-PV			81.34 %	88.05 %	76.99 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
79	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.
80	CAT-Det			81.32 %	89.87 %	76.68 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)
81	TransDet3D			81.28 %	88.11 %	76.73 %	0.08 s	1 core @ 2.5 Ghz (Python)
82	Generalized-SIENet			81.24 %	87.70 %	76.79 %	0.08 s	1 core @ 2.5 Ghz (Python)
83	Point Image Fusion			81.23 %	89.01 %	76.77 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
84	SAA-PV-RCNN			81.09 %	87.24 %	78.05 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
85	FPC-RCNN			81.08 %	88.68 %	76.46 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
86	VPFNet		code	80.97 %	88.51 %	76.74 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
C. Wang, H. Chen and L. Fu: VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection. 2021.
87	CSVoxel-RCNN			80.97 %	87.66 %	76.29 %	0.03 s	GPU @ 1.0 Ghz (Python)
88	GVNet-V2			80.96 %	87.57 %	76.32 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
89	VueronNet		code	80.96 %	90.06 %	73.72 %	0.06 s	1 core @ 2.0 Ghz (Python)
90	FusionDetv2-v4			80.93 %	87.75 %	76.12 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
91	AIMC-RUC			80.83 %	90.14 %	73.59 %	0.11 s	1 core @ 2.5 Ghz (Python)
92	sa-voxel-centernet		code	80.77 %	87.39 %	76.45 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
93	SA-voxel-centernet		code	80.77 %	87.28 %	76.51 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
94	Associate-3Ddet_v2			80.77 %	91.53 %	75.23 %	0.04 s	1 core @ 2.5 Ghz (Python)
95	FusionDetv2-v3			80.70 %	88.05 %	76.10 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
96	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.
97	SPVB-SSD			80.68 %	86.99 %	76.23 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
98	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.
99	SCIR-Net			80.62 %	87.53 %	76.00 %	0.03 s	GPU @ 2.5 Ghz (Python + C/C++)
100	GVNet		code	80.52 %	87.63 %	75.99 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
101	SVGA-Net			80.47 %	87.33 %	75.91 %	0.03s	1 core @ 2.5 Ghz (Python + C/C++)
Q. He, Z. Wang, H. Zeng, Y. Zeng and Y. Liu: SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds. AAAI 2022.
102	TBD			80.44 %	88.83 %	73.18 %	0.1 s	1 core @ 2.5 Ghz (Python)
103	Sem-Aug v1		code	80.40 %	88.92 %	77.37 %	0.04 s	GPU @ 3.5 Ghz (Python)
104	SRDL			80.38 %	87.73 %	76.27 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
105	Fast-CLOCs			80.35 %	89.10 %	76.99 %	0.1 s	GPU @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: Fast-CLOCs: Fast Camera-LiDAR Object Candidates Fusion for 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022.
106	FPV-SSD			80.34 %	87.72 %	75.40 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
107	SPANet			80.34 %	91.05 %	74.89 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
Y. Ye: SPANet: Spatial and Part-Aware Aggregation Network for 3D Object Detection. Pacific Rim International Conference on Artificial Intelligence 2021.
108	IA-SSD (single)			80.32 %	88.87 %	75.10 %	0.013 s	1 core @ 2.5 Ghz (C/C++)
109	AM-SSD			80.30 %	89.58 %	75.02 %	0.04 s	1 core @ 2.5 Ghz (Python)
110	TBD			80.29 %	87.37 %	73.05 %	0.1 s	1 core @ 2.5 Ghz (Python)
111	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.
112	FusionDetv1			80.28 %	87.45 %	76.21 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
113	DVF			80.21 %	88.97 %	75.22 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
114	VCT			80.19 %	89.12 %	77.19 %	0.2 s	1 core @ 2.5 Ghz (Python)
115	IA-SSD (multi)			80.13 %	88.34 %	75.04 %	0.014 s	1 core @ 2.5 Ghz (C/C++)
116	TBD			80.12 %	88.30 %	75.29 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
117	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.
118	TBD			80.12 %	86.50 %	75.72 %	0.06 s	GPU @ 2.5 Ghz (Python)
119	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.
120	TBD			80.02 %	88.45 %	74.85 %	TBD	GPU @ 2.5 Ghz (Python + C/C++)
121	MVOD			80.01 %	88.53 %	77.24 %	0.16 s	1 core @ 2.5 Ghz (C/C++)
122	MBDF-Net			80.00 %	90.87 %	75.04 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
123	SIF			79.88 %	86.84 %	75.89 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
124	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.
125	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.
126	DGT-Det3D			79.78 %	86.76 %	75.73 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
127	KpNet			79.75 %	88.92 %	72.17 %	0.42 s	1 core @ 2.5 Ghz (C/C++)
128	KpNet			79.74 %	88.88 %	72.13 %	42 s	1 core @ 2.5 Ghz (C/C++)
129	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.
130	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.
131	MBDF-Net-1			79.65 %	90.43 %	74.72 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
132	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.
133	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.
134	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.
135	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.
136	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.
137	SECOND			79.46 %	87.44 %	73.97 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
138	NV-RCNN			79.32 %	87.58 %	74.74 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
139	USVLab BSAODet (SM)			79.30 %	88.02 %	76.05 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
140	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.
141	FPCR-CNN			79.25 %	88.45 %	75.69 %	0.05 s	1 core @ 2.5 Ghz (Python)
142	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.
143	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.
144	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.
145	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.
146	NV2P-RCNN			78.92 %	87.36 %	74.16 %	0.1 s	GPU @ 2.5 Ghz (Python)
147	demo			78.85 %	87.50 %	72.05 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
148	MSADet			78.81 %	88.31 %	73.82 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
149	FPC3D			78.81 %	87.61 %	75.49 %	33 s	1 core @ 2.5 Ghz (C/C++)
150	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.
151	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.
152	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.
153	CSNet			78.42 %	87.39 %	71.75 %	0.1 s	1 core @ 2.5 Ghz (Python)
154	FusionDetv2-v2			78.42 %	86.59 %	73.87 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
155	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.
156	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.
157	FusionDetv2-v5			78.30 %	86.94 %	73.44 %	0.05 s	1 core @ 2.5 Ghz (Java + C/C++)
158	SAA-SECOND			78.13 %	86.13 %	73.34 %	38m s	1 core @ 2.5 Ghz (C/C++)
159	3D-VDNet			78.05 %	87.13 %	72.90 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
160	VPN			77.93 %	85.02 %	72.97 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
161	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.
162	TBD			77.85 %	86.46 %	72.39 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
163	CVFNet			77.70 %	88.75 %	71.95 %	28.1ms	1 core @ 2.5 Ghz (Python)
164	AutoAlign			77.58 %	86.84 %	73.23 %	0.1 s	1 core @ 2.5 Ghz (Python)
165	TBD			77.56 %	85.38 %	72.32 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
166	TCDVF			77.49 %	85.55 %	72.29 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
167	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.
168	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.
169	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.
170	3D_att			77.27 %	88.46 %	70.11 %	0.17 s	GPU @ 2.5 Ghz (Python)
171	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.
172	Sem-Aug-PointRCNN		code	77.04 %	82.75 %	73.21 %	0.1 s	GPU @ 3.5 Ghz (C/C++)
173	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.
174	DKAnet			76.70 %	84.57 %	71.54 %	0.05 s	1 core @ 2.0 Ghz (Python)
175	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.
176	YF			76.57 %	87.15 %	71.23 %	0.04 s	GPU @ 2.5 Ghz (C/C++)
177	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.
178	HNet-3DSSD		code	76.48 %	86.06 %	69.71 %	0.05 s	GPU @ 2.5 Ghz (Python)
179	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.
180	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.
181	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.
182	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.
183	APL-Second			75.75 %	84.26 %	70.65 %	0.05 s	1 core @ 2.5 Ghz (Python)
184	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.
185	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.
186	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.
187	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.
188	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.
189	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.
190	sscl-20p			74.82 %	86.06 %	69.87 %	0.02 s	1 core @ 2.5 Ghz (Python)
191	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.
192	MF			74.70 %	83.42 %	66.51 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
193	LazyTorch-CP-Infer-O			74.57 %	81.82 %	70.24 %	1 s	1 core @ 2.5 Ghz (C/C++)
194	FPC3D_all			74.55 %	85.50 %	69.91 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
195	LazyTorch-CP-Small-P			74.44 %	81.73 %	70.14 %	1 s	1 core @ 2.5 Ghz (C/C++)
196	City-CF-fixed			74.37 %	83.23 %	69.65 %	1 s	1 core @ 2.5 Ghz (C/C++)
197	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.
198	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.
199	LazyTorch-CP			73.96 %	81.17 %	69.48 %	1 s	1 core @ 2.5 Ghz (C/C++)
200	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.
201	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.
202	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.
203	Dune-DCF-e11			73.51 %	80.89 %	68.89 %	1 s	1 core @ 2.5 Ghz (C/C++)
204	CrazyTensor-CP			73.50 %	81.04 %	69.87 %	1 s	1 core @ 2.5 Ghz (Python)
205	PointRGBNet			73.49 %	83.99 %	68.56 %	0.08 s	4 cores @ 2.5 Ghz (Python + C/C++)
206	City-CF			73.48 %	80.85 %	69.07 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
207	RangeDet		code	73.44 %	80.53 %	67.28 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
208	Dune-DCF-e15			73.29 %	80.34 %	68.54 %	1 s	1 core @ 2.5 Ghz (C/C++)
209	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.
210	Dune-DCF-e09			73.15 %	80.40 %	68.57 %	1 s	1 core @ 2.5 Ghz (C/C++)
211	PP-PCdet		code	73.07 %	83.32 %	68.18 %	0.01 s	1 core @ 2.5 Ghz (Python)
212	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.
213	CrazyTensor-CF			72.92 %	79.87 %	68.41 %	1 s	1 core @ 2.5 Ghz (C/C++)
214	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.
215	HS3D		code	72.25 %	83.57 %	67.49 %	0.12 s	1 core @ 2.5 Ghz (Python + C/C++)
216	TBD_BD		code	72.16 %	83.36 %	66.87 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
217	TBD			71.94 %	83.20 %	66.83 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
218	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.
219	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.
220	Contrastive PP		code	71.64 %	84.80 %	66.49 %	0.01 s	1 core @ 2.5 Ghz (Python)
221	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.
222	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.
223	FusionDetv2-baseline			68.87 %	79.05 %	63.68 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
224	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.
225	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.
226	DSGN++			67.37 %	83.21 %	59.91 %	0.4 s	NVIDIA Tesla V100
227	DisposalNet			67.33 %	77.55 %	62.44 %	0.2 s	1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
228	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.
229	FusionDetv2-v1			65.65 %	75.21 %	60.65 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
230	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.
231	BirdNet+		code	64.04 %	76.15 %	59.79 %	0.11 s	Titan Xp (PyTorch)
A. Barrera, J. Beltrán, C. Guindel, J. Iglesias and F. García: BirdNet+: Two-Stage 3D Object Detection in LiDAR through a Sparsity-Invariant Bird’s Eye View. IEEE Access 2021.
232	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.
233	KMC		code	62.74 %	74.45 %	56.76 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
234	LIGA-Stereo-old			62.65 %	81.76 %	55.24 %	0.375 s	Titan Xp
235	SD3DOD			62.00 %	76.09 %	55.46 %	0.04 s	GPU @ 2.5 Ghz (Python)
236	AEC3D			61.99 %	72.16 %	57.11 %	18 ms	GPU @ 2.5 Ghz (Python)
237	VN3D			61.41 %	72.37 %	56.86 %	0.02 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
238	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.
239	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.
240	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.
241	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.
242	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.
243	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.
244	UPF_3D			52.83 %	78.24 %	46.12 %	0.29 s	1 core @ 2.5 Ghz (Python)
245	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.
246	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.
247	ppt			50.41 %	54.19 %	45.14 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
248	SOD			48.69 %	70.90 %	40.12 %	0.1 s	1 core @ 2.5 Ghz (Python)
249	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.
250	EGFN			46.39 %	65.80 %	38.42 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
251	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.
252	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.
253	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.
254	R-AGNO-Net			42.79 %	49.49 %	39.31 %	0.15 s	1 core @ 2.5 Ghz (C/C++)
255	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.
256	OSE+			41.60 %	62.67 %	35.82 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
257	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.
258	BEVC			40.72 %	50.05 %	36.42 %	35ms	GPU @ 1.5 Ghz (Python)
259	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.
260	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.
261	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.
262	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.
263	Stereo CenterNet			31.30 %	49.94 %	25.62 %	0.04 s	GPU @ 2.5 Ghz (Python)
Y. Shi, Y. Guo, Z. Mi and X. Li: Stereo CenterNet-based 3D object detection for autonomous driving. Neurocomputing 2022.
264	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.
265	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.
266	GCDR			23.92 %	34.89 %	19.59 %	0.28 s	1 core @ 2.5 Ghz (Python)
267	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.
268	Digging_M3D			21.24 %	29.15 %	19.18 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
269	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.
270	Mix-Teaching-M3D			18.54 %	26.89 %	15.79 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
271	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.
272	CMKD			18.00 %	28.10 %	15.60 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
273	SCSTSV-MonoFlex			17.91 %	27.38 %	15.58 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
274	LPCG-Monoflex			17.80 %	25.56 %	15.38 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
275	PS-fld			17.74 %	23.74 %	15.14 %	0.25 s	1 core @ 2.5 Ghz (C/C++)
276	MonoDDE			17.14 %	24.93 %	15.10 %	0.04 s	1 core @ 2.5 Ghz (Python)
277	Mobile Stereo R-CNN			17.04 %	26.97 %	13.26 %	1.8 s	NVIDIA Jetson TX2
M. Hussein, M. Khalil and B. Abdullah: 3D Object Detection using Mobile Stereo R- CNN on Nvidia Jetson TX2. International Conference on Advanced Engineering, Technology and Applications (ICAETA) 2021.
278	CMKD			16.99 %	27.20 %	15.08 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
279	DD3D		code	16.87 %	23.19 %	14.36 %	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) .
280	MonoCon		code	16.46 %	22.50 %	13.95 %	0.02 s	GPU @ 2.5 Ghz (Python)
T. Xianpeng Liu: Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection. AAAI 2022.
281	Lite-FPN-GUPNet			16.20 %	23.58 %	13.56 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
282	gupnet_se			16.10 %	23.62 %	13.41 %	0.03s	1 core @ 2.5 Ghz (C/C++)
283	MonoDistill			16.03 %	22.97 %	13.60 %	0.04 s	1 core @ 2.5 Ghz (Python)
284	MDNet			16.01 %	24.59 %	13.49 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
285	mono3d		code	15.73 %	23.96 %	13.35 %	TBD	TBD
286	MonoDTR			15.39 %	21.99 %	12.73 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
287	mono3d			15.26 %	23.41 %	12.80 %	0.03 s	GPU @ 2.5 Ghz (Python)
288	HBD			15.17 %	21.71 %	13.06 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
289	ZongmuMono3d		code	15.08 %	23.79 %	13.25 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
290	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.
291	vadin-TBD			14.94 %	21.75 %	13.07 %	0.04 s	1 core @ 2.5 Ghz (Python)
292	LPCG-M3D			14.82 %	22.73 %	12.88 %	0.11 s	1 core @ 2.5 Ghz (C/C++)
293	M3DSSD++		code	14.75 %	23.61 %	11.80 %	0.16s	1 core @ 2.5 Ghz (C/C++)
294	MonoFlex			14.73 %	22.29 %	12.77 %	0.03 s	1 core @ 2.5 Ghz (Python)
295	SGM3D			14.65 %	22.46 %	12.97 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
Z. Zhou, L. Du, X. Ye, Z. Zou, X. Tan, E. Ding, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection. 2021.
296	Anonymous		code	14.56 %	20.65 %	11.92 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
297	SAIC_ADC_Mono3D		code	14.54 %	18.98 %	12.86 %	50 s	GPU @ 2.5 Ghz (Python)
298	EW		code	14.50 %	23.37 %	11.88 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
299	CA3D			14.49 %	20.89 %	12.19 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
300	MonoEdge			14.47 %	21.08 %	12.73 %	0.05 s	GPU @ 2.5 Ghz (Python)
301	MonoGround			14.36 %	21.37 %	12.62 %	0.03 s	1 core @ 2.5 Ghz (Python)
302	ANM			14.33 %	20.84 %	11.61 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
303	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.
304	ITS-MDPL			14.28 %	24.67 %	12.13 %	0.16 s	GPU @ 2.5 Ghz (Python)
305	SwinMono3D			14.24 %	22.61 %	10.11 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
306	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.
307	MonoEdge-Rotate			14.13 %	21.60 %	12.27 %	0.05 s	GPU @ 2.5 Ghz (Python)
308	EM		code	14.00 %	22.93 %	11.26 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
309	MAOLoss		code	14.00 %	20.05 %	11.81 %	0.05 s	1 core @ 2.5 Ghz (Python)
310	E2E-DA			13.97 %	19.73 %	11.82 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
311	GAC3D++			13.90 %	19.53 %	11.77 %	0.25 s	1 core @ 2.5 Ghz (C/C++)
312	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.
313	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.
314	MonoGeo			13.81 %	18.85 %	11.52 %	0.05 s	1 core @ 2.5 Ghz (Python)
315	K3D			13.80 %	20.04 %	11.67 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
316	none			13.79 %	18.84 %	11.52 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
317	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.
318	MonoLCD			13.52 %	18.08 %	11.58 %	0.04 s	1 core @ 2.5 Ghz (Python)
319	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.
320	MDSNet			13.40 %	22.80 %	10.27 %	0.07 s	1 core @ 2.5 Ghz (Python)
321	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.
322	KAIST-VDCLab			13.33 %	19.06 %	11.90 %	0.04 s	1 core @ 2.5 Ghz (Python)
323	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.
324	MonoHMOO			13.12 %	20.28 %	9.56 %	0.2 s	1 core @ 2.5 Ghz (Python + C/C++)
325	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.
326	vadin-TBD2		code	12.99 %	20.10 %	10.50 %	0.20 s	1 core @ 2.5 Ghz (Python)
327	RelationNet3D_dla34		code	12.88 %	17.67 %	11.01 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
328	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.
329	RetinaMono			12.73 %	19.41 %	10.45 %	0.02 s	1 core @ 2.5 Ghz (Python)
330	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 .
331	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.
332	RelationNet3D			12.60 %	17.57 %	10.95 %	0.04 s	GPU @ 2.5 Ghz (Python)
333	TBD			12.53 %	22.40 %	10.64 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
334	AutoShape			12.42 %	20.35 %	9.70 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
335	MP-Mono			12.37 %	17.89 %	9.58 %	0.16 s	GPU @ 2.5 Ghz (Python)
336	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.
337	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.
338	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 .
339	LT-M3OD			12.26 %	18.15 %	10.05 %	0.03 s	1 core @ 2.5 Ghz (Python)
340	PPTrans			12.06 %	19.79 %	10.48 %	0.2 s	GPU @ 2.5 Ghz (Python)
341	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.
342	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.
343	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.
344	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 (CoRL) 2021.
345	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.
346	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.
347	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.
348	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.
349	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.
350	COF3D			10.91 %	17.86 %	8.20 %	200 s	1 core @ 2.5 Ghz (Python + C/C++)
351	MM			10.74 %	15.80 %	8.64 %	1 s	1 core @ 2.5 Ghz (C/C++)
352	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.
353	Lite-FPN			10.64 %	15.32 %	8.59 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
354	TBD			10.61 %	15.71 %	8.66 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
355	Keypoint-3D			10.42 %	15.97 %	7.91 %	14 s	1 core @ 2.5 Ghz (C/C++)
356	MK3D			10.37 %	15.39 %	8.82 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
357	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.
358	E2E-DA-Lite (Res18)			10.32 %	15.56 %	8.89 %	0.01 s	GPU @ 2.5 Ghz (Python)
359	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.
360	RelationNet3D_res18		code	9.93 %	14.27 %	8.43 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
361	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.
362	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.
363	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 .
364	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.
365	ICCV			9.31 %	13.37 %	8.29 %	0.04 s	GPU @ 2.5 Ghz (Python)
366	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.
367	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.
368	Geo3D			7.70 %	11.52 %	6.80 %	0.04 s	GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
369	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.
370	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.
371	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.
372	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.
373	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.
374	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.
375	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.
376	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.
377	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.
378	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.
379	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.
380	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.
381	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.
382	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.
383	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.
384	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.
385	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.
386	WeakM3D			2.26 %	5.03 %	1.63 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
387	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.
388	CDTrack3D		code	1.65 %	3.01 %	1.40 %	0.0106 s	NVIDIA RTX 3090 GPU, i9 10850k CPU
389	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.
390	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.
391	test		code	0.03 %	0.01 %	0.03 %	50 s	1 core @ 2.5 Ghz (Python)
392	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++)
C. Wang, H. Chen and L. Fu: VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection. 2021.
2	CAD			47.91 %	55.98 %	44.63 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
3	PV-RCNN++			47.19 %	54.29 %	43.49 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
4	CasA			47.09 %	54.04 %	44.56 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
5	EQ-PVRCNN			47.02 %	55.84 %	42.94 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
6	ADLAB			46.18 %	53.59 %	43.28 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
7	PiFeNet			45.89 %	54.84 %	42.02 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
D. Le, H. Shi, H. Rezatofighi and J. Cai: PiFeNet: Pillar-Feature Network for Real-Time 3D Pedestrian Detection from Point Cloud. 2021.
8	ISE-RCNN			45.66 %	51.44 %	42.43 %	0.09 s	1 core @ 2.5 Ghz (Python + C/C++)
9	CAT-Det			45.44 %	54.26 %	41.94 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)
10	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.
11	H^23D R-CNN			45.26 %	52.75 %	41.56 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
12	PE-RCVN			45.01 %	50.29 %	41.85 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
13	SAA-PV-RCNN			45.00 %	52.55 %	41.82 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
14	VPN			44.56 %	54.13 %	41.73 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
15	EPNet++			44.38 %	52.79 %	41.29 %	0.1 s	GPU @ 2.5 Ghz (Python)
Z. Liu, H. tengteng, B. Li, X. Chen, X. Wang and X. Bai: EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object Detection. arXiv preprint arXiv:2112.11088 2021.
16	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.
17	TBD			44.32 %	49.37 %	41.24 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
18	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.
19	AutoAlign			44.08 %	53.99 %	40.82 %	0.1 s	1 core @ 2.5 Ghz (Python)
20	ISE-RCNN-PV			43.78 %	50.03 %	40.50 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
21	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.
22	VCT			43.65 %	50.27 %	41.43 %	0.2 s	1 core @ 2.5 Ghz (Python)
23	USVLab BSAODet (MM)			43.63 %	51.71 %	41.09 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
24	EA-M-RCNN(BorderAtt)			43.44 %	51.81 %	39.85 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
25	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.
26	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.
27	FS-Net			43.31 %	49.82 %	40.89 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
28	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.
29	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.
30	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.
31	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.
32	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.
33	SGNet			43.00 %	49.68 %	40.45 %	0.09 s	GPU @ 2.5 Ghz (Python)
34	Frustum-PointPillars		code	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.
35	Fast-CLOCs			42.72 %	52.10 %	39.08 %	0.1 s	GPU @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: Fast-CLOCs: Fast Camera-LiDAR Object Candidates Fusion for 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022.
36	TBD			42.65 %	50.88 %	39.78 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
37	USVLab BSAODet (SM)			42.62 %	49.52 %	39.12 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
38	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.
39	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.
40	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.
41	TBD			42.19 %	49.89 %	39.34 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
42	TBD			42.19 %	49.89 %	39.34 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
43	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.
44	TBD			42.05 %	48.66 %	38.94 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
45	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.
46	TBD_IOU1			41.65 %	49.00 %	39.31 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
47	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.
48	TCDVF			41.47 %	49.44 %	38.41 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
49	TBD_IOU			41.45 %	48.25 %	39.17 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
50	DGT-Det3D			41.40 %	49.06 %	38.43 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
51	tbd			41.10 %	50.56 %	37.49 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
52	IA-SSD (single)			41.03 %	47.90 %	37.98 %	0.013 s	1 core @ 2.5 Ghz (C/C++)
53	Generalized-SIENet			40.97 %	47.01 %	38.88 %	0.08 s	1 core @ 2.5 Ghz (Python)
54	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.
55	SCIR-Net			40.95 %	49.23 %	38.47 %	0.03 s	GPU @ 2.5 Ghz (Python + C/C++)
56	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.
57	SARFE			40.79 %	47.29 %	38.25 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
58	MSADet			40.58 %	49.54 %	38.19 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
59	SAA-SECOND			40.57 %	48.73 %	37.77 %	38m s	1 core @ 2.5 Ghz (C/C++)
60	PDV			40.56 %	47.80 %	38.46 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
61	WHUT-iou_ssd		code	40.53 %	46.41 %	38.48 %	0.045s	1 core @ 2.5 Ghz (C/C++)
62	SA-voxel-centernet		code	40.43 %	46.10 %	38.32 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
63	SVGA-Net			40.39 %	48.48 %	37.92 %	0.03s	1 core @ 2.5 Ghz (Python + C/C++)
Q. He, Z. Wang, H. Zeng, Y. Zeng and Y. Liu: SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds. AAAI 2022.
64	FusionDetv2-v3			40.38 %	46.86 %	37.41 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
65	FPCR-CNN			40.32 %	48.33 %	37.66 %	0.05 s	1 core @ 2.5 Ghz (Python)
66	P2V_PCV1			40.27 %	45.43 %	38.24 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
67	sa-voxel-centernet		code	40.24 %	46.08 %	38.07 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
68	FPC-RCNN			40.13 %	46.41 %	37.84 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
69	TBD			40.07 %	46.11 %	37.87 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
70	TPCG			39.97 %	46.35 %	37.66 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
71	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.
72	FusionDetv2-v5			39.91 %	47.50 %	37.39 %	0.05 s	1 core @ 2.5 Ghz (Java + C/C++)
73	DDet			39.87 %	45.82 %	38.00 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
74	MVOD			39.82 %	46.22 %	37.56 %	0.16 s	1 core @ 2.5 Ghz (C/C++)
75	Point Image Fusion			39.79 %	45.04 %	37.62 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
76	anonymous		code	39.74 %	46.09 %	37.41 %	0.05s	1 core @ >3.5 Ghz (python)
77	FusionDetv2-v4			39.68 %	46.93 %	37.31 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
78	DSASNet			39.65 %	47.14 %	37.05 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
79	Fast VP-RCNN		code	39.65 %	45.95 %	37.29 %	0.05 s	1 core @ 3.5 Ghz (C/C++)
80	VCRCNN			39.64 %	45.19 %	37.55 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
81	Dune-DCF-e09			39.43 %	47.29 %	36.74 %	1 s	1 core @ 2.5 Ghz (C/C++)
82	LazyTorch-CP-Infer-O			39.43 %	47.38 %	36.96 %	1 s	1 core @ 2.5 Ghz (C/C++)
83	SRDL			39.43 %	47.30 %	36.99 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
84	FusionDetv1			39.42 %	47.30 %	36.97 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
85	demo			39.38 %	47.69 %	36.06 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
86	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.
87	ST-RCNN			39.36 %	44.96 %	37.09 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
88	LazyTorch-CP-Small-P			39.33 %	47.27 %	36.89 %	1 s	1 core @ 2.5 Ghz (C/C++)
89	FusionDetv2-v2			39.31 %	44.98 %	37.22 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
90	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.
91	LazyTorch-CP			39.28 %	47.25 %	36.78 %	1 s	1 core @ 2.5 Ghz (C/C++)
92	Dune-DCF-e11			39.26 %	47.32 %	36.63 %	1 s	1 core @ 2.5 Ghz (C/C++)
93	City-CF-fixed			39.22 %	47.68 %	36.55 %	1 s	1 core @ 2.5 Ghz (C/C++)
94	IA-SSD (multi)			39.03 %	46.51 %	35.61 %	0.014 s	1 core @ 2.5 Ghz (C/C++)
95	NV-RCNN			38.75 %	47.05 %	36.52 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
96	SIF			38.74 %	46.23 %	36.06 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
97	CrazyTensor-CP			38.67 %	46.58 %	36.15 %	1 s	1 core @ 2.5 Ghz (Python)
98	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.
99	Dune-DCF-e15			38.61 %	46.41 %	36.02 %	1 s	1 core @ 2.5 Ghz (C/C++)
100	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.
101	FPV-SSD			38.45 %	45.83 %	36.03 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
102	TBD			38.27 %	46.35 %	36.39 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
103	City-CF			38.04 %	45.42 %	35.40 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
104	FPC3D_all			37.95 %	45.49 %	35.60 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
105	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.
106	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.
107	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.
108	YF			36.99 %	44.43 %	34.40 %	0.04 s	GPU @ 2.5 Ghz (C/C++)
109	HS3D		code	36.86 %	45.62 %	33.67 %	0.12 s	1 core @ 2.5 Ghz (Python + C/C++)
110	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.
111	FusionDetv2-baseline			36.66 %	41.34 %	34.60 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
112	TBD			36.53 %	44.11 %	34.30 %	TBD	GPU @ 2.5 Ghz (Python + C/C++)
113	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.
114	NV2P-RCNN			35.98 %	43.18 %	33.88 %	0.1 s	GPU @ 2.5 Ghz (Python)
115	CrazyTensor-CF			35.83 %	43.50 %	33.23 %	1 s	1 core @ 2.5 Ghz (C/C++)
116	ASCNet			35.76 %	42.00 %	33.69 %	0.09 s	1 core @ 2.5 Ghz (C/C++)
117	BirdNet+		code	35.06 %	41.55 %	32.93 %	0.11 s	Titan Xp (PyTorch)
A. Barrera, J. Beltrán, C. Guindel, J. Iglesias and F. García: BirdNet+: Two-Stage 3D Object Detection in LiDAR through a Sparsity-Invariant Bird’s Eye View. IEEE Access 2021.
118	TBD_BD		code	34.86 %	42.56 %	32.49 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
119	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.
120	DSGN++			32.74 %	43.05 %	29.54 %	0.4 s	NVIDIA Tesla V100
121	PP-PCdet		code	32.04 %	39.23 %	29.79 %	0.01 s	1 core @ 2.5 Ghz (Python)
122	Contrastive PP		code	31.64 %	38.47 %	29.30 %	0.01 s	1 core @ 2.5 Ghz (Python)
123	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.
124	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.
125	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.
126	DisposalNet			29.77 %	37.21 %	27.62 %	0.2 s	1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
127	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.
128	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.
129	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.
130	PointRGBNet			26.40 %	34.77 %	24.03 %	0.08 s	4 cores @ 2.5 Ghz (Python + C/C++)
131	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.
132	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.
133	FusionDetv2-v1			24.55 %	30.58 %	23.64 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
134	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.
135	LIGA-Stereo-old			23.23 %	30.14 %	20.58 %	0.375 s	Titan Xp
136	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.
137	OSE+			19.67 %	28.30 %	17.17 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
138	AEC3D			19.00 %	24.39 %	17.43 %	18 ms	GPU @ 2.5 Ghz (Python)
139	BEVC			17.65 %	23.49 %	15.92 %	35ms	GPU @ 1.5 Ghz (Python)
140	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.
141	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.
142	VN3D			15.69 %	19.56 %	13.17 %	0.02 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
143	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.
144	SOD			14.68 %	21.13 %	12.67 %	0.1 s	1 core @ 2.5 Ghz (Python)
145	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.
146	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.
147	DD3D		code	11.04 %	16.64 %	9.38 %	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) .
148	PS-fld			10.82 %	16.95 %	9.26 %	0.25 s	1 core @ 2.5 Ghz (C/C++)
149	CMKD			10.39 %	16.89 %	9.29 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
150	EGFN			10.27 %	14.05 %	9.02 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
151	MonoDTR			10.18 %	15.33 %	8.61 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
152	LT-M3OD			9.99 %	14.85 %	8.52 %	0.03 s	1 core @ 2.5 Ghz (Python)
153	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.
154	Lite-FPN-GUPNet			9.32 %	14.13 %	7.93 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
155	mono3d		code	9.20 %	14.53 %	7.82 %	TBD	TBD
156	ZongmuMono3d		code	9.18 %	14.23 %	7.82 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
157	SGM3D			8.81 %	13.99 %	7.26 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
Z. Zhou, L. Du, X. Ye, Z. Zou, X. Tan, E. Ding, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection. 2021.
158	SCSTSV-MonoFlex			8.75 %	13.10 %	7.38 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
159	gupnet_se			8.65 %	13.40 %	7.78 %	0.03s	1 core @ 2.5 Ghz (C/C++)
160	SwinMono3D			8.54 %	12.96 %	7.19 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
161	MonoCon		code	8.41 %	13.10 %	6.94 %	0.02 s	GPU @ 2.5 Ghz (Python)
T. Xianpeng Liu: Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection. AAAI 2022.
162	HBD			8.33 %	13.47 %	6.99 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
163	MonoEdge			8.33 %	12.11 %	7.03 %	0.05 s	GPU @ 2.5 Ghz (Python)
164	GCDR			8.27 %	11.50 %	7.37 %	0.28 s	1 core @ 2.5 Ghz (Python)
165	MonoFlex			8.16 %	11.89 %	6.81 %	0.03 s	1 core @ 2.5 Ghz (Python)
166	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.
167	MonoGround			7.89 %	12.37 %	7.13 %	0.03 s	1 core @ 2.5 Ghz (Python)
168	vadin-TBD			7.66 %	11.87 %	6.82 %	0.04 s	1 core @ 2.5 Ghz (Python)
169	MonoLCD			7.62 %	11.21 %	6.47 %	0.04 s	1 core @ 2.5 Ghz (Python)
170	K3D			7.60 %	12.58 %	6.73 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
171	ANM			7.54 %	11.92 %	6.37 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
172	SAIC_ADC_Mono3D		code	7.54 %	12.06 %	6.41 %	50 s	GPU @ 2.5 Ghz (Python)
173	Mix-Teaching-M3D			7.47 %	11.67 %	6.61 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
174	LPCG-Monoflex			7.33 %	10.82 %	6.18 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
175	MonoDDE			7.32 %	11.13 %	6.67 %	0.04 s	1 core @ 2.5 Ghz (Python)
176	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.
177	MonoEdge-Rotate			7.02 %	10.47 %	5.84 %	0.05 s	GPU @ 2.5 Ghz (Python)
178	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.
179	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.
180	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.
181	mono3d			6.62 %	10.10 %	5.46 %	0.03 s	GPU @ 2.5 Ghz (Python)
182	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 .
183	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.
184	GAC3D++			6.29 %	9.29 %	5.20 %	0.25 s	1 core @ 2.5 Ghz (C/C++)
185	RelationNet3D_dla34		code	6.22 %	9.28 %	5.23 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
186	E2E-DA			5.95 %	8.79 %	5.72 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
187	MK3D			5.72 %	9.19 %	4.54 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
188	MDNet			5.66 %	8.24 %	4.74 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
189	M3DSSD++		code	5.65 %	8.10 %	4.72 %	0.16s	1 core @ 2.5 Ghz (C/C++)
190	MonoGeo			5.63 %	8.00 %	4.71 %	0.05 s	1 core @ 2.5 Ghz (Python)
191	ICCV			5.25 %	8.34 %	4.72 %	0.04 s	GPU @ 2.5 Ghz (Python)
192	MonoHMOO			5.23 %	7.62 %	4.28 %	0.2 s	1 core @ 2.5 Ghz (Python + C/C++)
193	RelationNet3D_res18		code	5.19 %	7.95 %	4.21 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
194	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.
195	MM			4.70 %	7.81 %	4.01 %	1 s	1 core @ 2.5 Ghz (C/C++)
196	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.
197	Lite-FPN			4.38 %	6.57 %	3.56 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
198	COF3D			4.37 %	6.02 %	3.55 %	200 s	1 core @ 2.5 Ghz (Python + C/C++)
199	MAOLoss		code	4.18 %	5.81 %	3.67 %	0.05 s	1 core @ 2.5 Ghz (Python)
200	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.
201	MP-Mono			3.75 %	5.09 %	3.50 %	0.16 s	GPU @ 2.5 Ghz (Python)
202	Geo3D			3.65 %	5.74 %	3.01 %	0.04 s	GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
203	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.
204	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.
205	E2E-DA-Lite (Res18)			3.51 %	5.82 %	3.42 %	0.01 s	GPU @ 2.5 Ghz (Python)
206	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 .
207	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.
208	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.
209	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.
210	KAIST-VDCLab			2.47 %	3.27 %	2.43 %	0.04 s	1 core @ 2.5 Ghz (Python)
211	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.
212	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.
213	PPTrans			1.85 %	2.68 %	1.44 %	0.2 s	GPU @ 2.5 Ghz (Python)
214	TBD			1.81 %	3.00 %	1.59 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
215	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.
216	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 (CoRL) 2021.
217	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.
218	EM		code	1.18 %	1.09 %	0.80 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
219	CDTrack3D		code	1.07 %	1.49 %	0.71 %	0.0106 s	NVIDIA RTX 3090 GPU, i9 10850k CPU
220	EW		code	0.81 %	0.79 %	0.74 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
221	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.