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.
• Jonas Heylen (TRACE vzw) has released pixel accurate instance segmentations for all 7481 training images.
• 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	TED			85.28 %	91.61 %	80.68 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
2	LIVOX_Det			84.94 %	91.72 %	80.10 %	n/a s	1 core @ 2.5 Ghz (Python + C/C++)
3	SFD		code	84.76 %	91.73 %	77.92 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
X. Wu, L. Peng, H. Yang, L. Xie, C. Huang, C. Deng, H. Liu and D. Cai: Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completion. CVPR 2022.
4	Anonymous			84.76 %	91.99 %	79.81 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
5	CasA++			84.04 %	90.68 %	79.69 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
6	Anonymous			83.96 %	90.83 %	77.47 %	n/a s	1 core @ 2.5 Ghz (Python + C/C++)
7	DGDNH			83.88 %	90.69 %	79.50 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
8	Anonymous			83.51 %	89.08 %	78.94 %	n/a s	1 core @ 2.5 Ghz (C/C++)
9	GraR-VoI			83.27 %	91.89 %	77.78 %	0.07 s	1 core @ 2.5 Ghz (Python + C/C++)
10	GLENet-VR			83.23 %	91.67 %	78.43 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
11	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.
12	GraR-Po			83.18 %	91.79 %	77.98 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
13	CasA			83.06 %	91.58 %	80.08 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
14	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.
15	GraR-Vo			82.77 %	91.29 %	77.20 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
16	3SNet			82.70 %	89.41 %	78.03 %	0.07 s	GPU @ 2.5 Ghz (Python)
17	PE-RCVN			82.69 %	91.51 %	77.75 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
18	CAD			82.68 %	88.96 %	77.91 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
19	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.
20	SGFusion			82.64 %	91.13 %	77.53 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
21	HCPVF			82.63 %	89.34 %	77.72 %	0.07 s	1 core @ 2.5 Ghz (Python + C/C++)
22	DSASNet			82.63 %	89.48 %	77.94 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
23	SA3DNet			82.57 %	90.49 %	77.88 %	0.05 s	GPU @ 2.5 Ghz (Python)
24	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.
25	TF3D			82.46 %	89.10 %	77.78 %	0.1 s	2 cores @ 3.0 Ghz (Python)
26	DVF-V			82.45 %	89.40 %	77.56 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
A. Mahmoud, J. Hu and S. Waslander: Dense Voxel Fusion for 3D Object Detection. 2022.
27	GraR-Pi			82.42 %	90.94 %	77.00 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
28	DVF-PV			82.40 %	90.99 %	77.37 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
A. Mahmoud, J. Hu and S. Waslander: Dense Voxel Fusion for 3D Object Detection. 2022.
29	Anonymous			82.30 %	90.88 %	76.89 %	0.03 s	1 core @ 2.5 Ghz (Python + C/C++)
30	PVT-SSD			82.29 %	90.65 %	76.85 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
31	Focals Conv		code	82.28 %	90.55 %	77.59 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
Y. Chen, Y. Li, X. Zhang, J. Sun and J. Jia: Focal Sparse Convolutional Networks for 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.
32	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.
33	CityBrainLab			82.22 %	90.54 %	77.19 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
34	SPT			82.18 %	90.52 %	77.62 %	0.1 s	GPU @ 2.5 Ghz (Python)
35	SASA		code	82.16 %	88.76 %	77.16 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
C. Chen, Z. Chen, J. Zhang and D. Tao: SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object Detection. arXiv preprint arXiv:2201.01976 2022.
36	ImpDet			82.14 %	88.39 %	76.98 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
37	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.
38	SPNet		code	82.11 %	88.53 %	77.41 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
39	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.
40	TBD			82.09 %	89.50 %	79.29 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
41	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.
42	FS-Net			82.07 %	88.68 %	77.42 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
43	VoxSeT		code	82.06 %	88.53 %	77.46 %	33 ms	1 core @ 2.5 Ghz (C/C++)
C. He, R. Li, S. Li and L. Zhang: Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds. CVPR 2022.
44	SRIF-RCNN			82.04 %	88.45 %	77.54 %	0.0947 s	1 core @ 2.5 Ghz (C/C++)
X. Li and D. Kong: SRIF-RCNN: Sparsely Represented Inputs Fusion of Different Sensors for 3D Object Detection. Applied Intelligence 2022.
45	LGNet			82.02 %	90.65 %	77.34 %	0.07 s	1 core @ 2.5 Ghz (C/C++)
46	EQ-PVRCNN		code	82.01 %	90.13 %	77.53 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, L. Jiang, Y. Sun, B. Schiele and J. Jia: A Unified Query-based Paradigm for Point Cloud Understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.
47	anonymous			81.99 %	88.82 %	77.26 %	0.09 s	GPU @ 2.5 Ghz (Python)
48	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.
49	HMFI		code	81.93 %	88.90 %	77.30 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
50	PV-RCNN++		code	81.88 %	90.14 %	77.15 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
51	GLENet			81.86 %	89.87 %	77.32 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
52	PDV		code	81.86 %	90.43 %	77.36 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
J. Hu, T. Kuai and S. Waslander: Point Density-Aware Voxels for LiDAR 3D Object Detection. CVPR 2022.
53	SGNet			81.85 %	88.83 %	77.47 %	0.09 s	GPU @ 2.5 Ghz (Python)
54	Anonymous			81.85 %	89.96 %	76.51 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
55	ST-RCNN			81.84 %	90.50 %	77.22 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
56	ISE-RCNN			81.83 %	89.12 %	77.29 %	0.09 s	1 core @ 2.5 Ghz (Python + C/C++)
57	FV2P v2			81.81 %	88.17 %	77.43 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
58	Anonymous			81.80 %	89.86 %	77.26 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
59	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.
60	GV-RCNN		code	81.75 %	90.31 %	77.17 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
61	USVLab BSAODet			81.74 %	88.89 %	77.14 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
62	JPVNet			81.73 %	88.66 %	76.94 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
63	TBD			81.73 %	89.48 %	79.05 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
64	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.
65	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.
66	TBD			81.71 %	88.46 %	76.63 %	0.1 s	1 core @ 2.5 Ghz (Python)
67	DCCA			81.70 %	88.42 %	77.18 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
68	VCRCNN			81.68 %	90.52 %	77.26 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
69	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.
70	BADet		code	81.61 %	89.28 %	76.58 %	0.14 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 2022.
71	SARFE			81.59 %	88.88 %	76.74 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
72	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.
73	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.
74	Anonymous			81.55 %	87.90 %	77.03 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
75	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.
76	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.
77	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.
78	DDet			81.38 %	89.63 %	78.57 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
79	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.
80	ISE-RCNN-PV			81.34 %	88.05 %	76.99 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
81	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.
82	CAT-Det			81.32 %	89.87 %	76.68 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Zhang, J. Chen and D. Huang: CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection. CVPR 2022.
83	GT3D			81.24 %	87.61 %	74.96 %	0.1 s	1 core @ 2.5 Ghz (Python)
84	IKT3D			80.97 %	87.84 %	76.43 %	0.05 s	1 core @ 2.5 Ghz (Python)
85	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.
86	GVNet-V2			80.96 %	87.57 %	76.32 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
87	VueronNet		code	80.96 %	90.06 %	73.72 %	0.06 s	1 core @ 2.0 Ghz (Python)
88	DKDet			80.94 %	87.66 %	76.23 %	0.03 s	GPU @ 2.5 Ghz (Python + C/C++)
89	FusionDetv2-v4			80.93 %	87.75 %	76.12 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
90	NV-RCNN			80.78 %	86.35 %	76.39 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
91	Sem-Aug			80.77 %	89.41 %	75.90 %	0.08 s	GPU @ 2.5 Ghz (Python)
92	CSVoxel-RCNN			80.73 %	87.44 %	76.18 %	0.03 s	GPU @ 1.0 Ghz (Python)
93	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.
94	SPVB-SSD			80.68 %	86.99 %	76.23 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
95	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.
96	GVNet		code	80.52 %	87.63 %	75.99 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
97	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.
98	SC-Voxel-RCNN			80.46 %	86.94 %	75.85 %	0.12 s	GPU @ 1.0 Ghz (Python)
99	TBD			80.44 %	88.83 %	73.18 %	0.1 s	1 core @ 2.5 Ghz (Python)
100	Sem-Aug v1		code	80.40 %	88.92 %	77.37 %	0.04 s	GPU @ 3.5 Ghz (Python)
101	SRDL			80.38 %	87.73 %	76.27 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
102	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.
103	FPV-SSD			80.34 %	87.72 %	75.40 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
104	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.
105	IA-SSD (single)		code	80.32 %	88.87 %	75.10 %	0.013 s	1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
106	TBD			80.29 %	87.37 %	73.05 %	0.1 s	1 core @ 2.5 Ghz (Python)
107	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.
108	FusionDetv1			80.28 %	87.45 %	76.21 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
109	DVF			80.21 %	88.97 %	75.22 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
110	PVTr			80.16 %	86.90 %	75.98 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
111	IA-SSD (multi)		code	80.13 %	88.34 %	75.04 %	0.014 s	1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
112	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.
113	TBD			80.12 %	86.50 %	75.72 %	0.06 s	GPU @ 2.5 Ghz (Python)
114	ATT_SSD			80.11 %	88.94 %	74.91 %	0.01 s	1 core @ 2.5 Ghz (Python)
115	TBD		code	80.06 %	88.75 %	74.84 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
116	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.
117	SIF			79.88 %	86.84 %	75.89 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
118	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.
119	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.
120	DGT-Det3D			79.78 %	86.76 %	75.73 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
121	KpNet			79.75 %	88.92 %	72.17 %	0.42 s	1 core @ 2.5 Ghz (C/C++)
122	KpNet			79.74 %	88.88 %	72.13 %	42 s	1 core @ 2.5 Ghz (C/C++)
123	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.
124	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.
125	DGCN			79.67 %	88.33 %	74.30 %	0.1 s	GPU @ 2.5 Ghz (Python)
126	mbdf-netv1		code	79.66 %	90.19 %	74.76 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
127	PTA-RCNN			79.61 %	87.84 %	74.43 %	0.08 s	1 core @ 2.5 Ghz (Python)
128	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.
129	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.
130	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.
131	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.
132	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.
133	SECOND			79.46 %	87.44 %	73.97 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
134	DCAN-Second		code	79.40 %	88.57 %	75.05 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
135	AGS-SSD[la]			79.39 %	88.13 %	74.11 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
136	SSL-PointGNN		code	79.36 %	87.78 %	74.15 %	0.56 s	GPU @ 1.5 Ghz (Python)
E. Erçelik, E. Yurtsever, M. Liu, Z. Yang, H. Zhang, P. Topçam, M. Listl, Y. Çaylı and A. Knoll: 3D Object Detection with a Self-supervised Lidar Scene Flow Backbone. arXiv preprint arXiv:2205.00705 2022.
137	USVLab BSAODet (S)			79.30 %	88.02 %	76.05 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
138	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.
139	WGVRF			79.25 %	88.47 %	74.60 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
140	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.
141	ITCA-SSD		code	79.11 %	88.66 %	72.12 %	0.05 s	1 core @ 2.5 Ghz (Python)
142	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. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) 2021.
143	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.
144	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.
145	NV2P-RCNN			78.92 %	87.36 %	74.16 %	0.1 s	GPU @ 2.5 Ghz (Python)
146	MVMM		code	78.87 %	87.59 %	73.78 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
147	MSADet			78.81 %	88.31 %	73.82 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
148	PSA-Det3D			78.80 %	87.46 %	74.47 %	0.1 s	GPU @ 2.5 Ghz (Python)
149	CSNet8306		code	78.74 %	89.57 %	72.09 %	0.1 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	CenterFuse			78.70 %	86.92 %	73.87 %	0.059 sec/frame	2 x V100
152	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.
153	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.
154	CSNet			78.42 %	87.39 %	71.75 %	0.1 s	1 core @ 2.5 Ghz (Python)
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	CZY			78.36 %	87.00 %	73.84 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
157	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.
158	FusionDetv2-v5			78.30 %	86.94 %	73.44 %	0.05 s	1 core @ 2.5 Ghz (Java + C/C++)
159	Sem-Aug-PointRCNN++			78.06 %	86.69 %	73.85 %	0.1 s	8 cores @ 3.0 Ghz (Python)
160	CF-cd-io-tv			78.05 %	86.38 %	73.29 %	1 s	1 core @ 2.5 Ghz (C/C++)
161	VPN			77.93 %	85.02 %	72.97 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
162	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.
163	TBD			77.85 %	86.46 %	72.39 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
164	CF-ctdep-tv-ta			77.75 %	85.27 %	74.83 %	1 s	1 core @ 2.5 Ghz (C/C++)
165	IoU-2B			77.74 %	85.65 %	71.30 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
166	Reprod-Two-Branch			77.73 %	85.60 %	74.24 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
167	AutoAlign			77.58 %	86.84 %	73.23 %	0.1 s	1 core @ 2.5 Ghz (Python)
168	TBD			77.56 %	85.38 %	72.32 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
169	CFF-tv			77.53 %	85.01 %	74.01 %	1 s	1 core @ 2.5 Ghz (C/C++)
170	TCDVF			77.49 %	85.55 %	72.29 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
171	CFF-ep25			77.48 %	84.84 %	72.96 %	1 s	1 core @ 2.5 Ghz (C/C++)
172	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.
173	CFF-tv-v2			77.41 %	85.18 %	72.81 %	1 s	1 core @ 2.5 Ghz (C/C++)
174	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.
175	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.
176	cff-tv-v2-ep25			77.38 %	84.44 %	72.82 %	1 s	1 core @ 2.5 Ghz (C/C++)
177	3D_att			77.27 %	88.46 %	70.11 %	0.17 s	GPU @ 2.5 Ghz (Python)
178	cp-tv-kp-io-sc			77.25 %	85.41 %	72.42 %	1 s	1 core @ 2.5 Ghz (C/C++)
179	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.
180	CF-ctdep-tv			77.12 %	84.71 %	74.02 %	1 s	1 core @ 2.5 Ghz (C/C++)
181	CF-base-tv			77.09 %	83.72 %	73.71 %	1 s	1 core @ 2.5 Ghz (C/C++)
182	Sem-Aug-PointRCNN		code	77.04 %	82.75 %	73.21 %	0.1 s	GPU @ 3.5 Ghz (C/C++)
183	KeyFuse2B			76.95 %	84.86 %	72.53 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
184	KeyPoint-IoUHead			76.81 %	84.61 %	72.16 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
185	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.
186	DKAnet			76.70 %	84.57 %	71.54 %	0.05 s	1 core @ 2.0 Ghz (Python)
187	cff-tv-t			76.68 %	85.58 %	70.69 %	1 s	1 core @ 2.5 Ghz (C/C++)
188	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.
189	Anonymous			76.60 %	85.29 %	71.77 %	1	1 core @ 2.5 Ghz (Python)
190	DTFI			76.59 %	85.29 %	71.78 %	0.03 s	1 core @ 2.5 Ghz (Python)
191	CSNet8299		code	76.55 %	86.49 %	71.37 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
192	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.
193	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.
194	variance_point			76.27 %	87.44 %	72.03 %	0.05 s	1 core @ 2.5 Ghz (Python)
195	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.
196	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.
197	KPP3D		code	76.00 %	86.66 %	71.07 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
198	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.
199	CF-base-train			75.93 %	83.47 %	71.22 %	1 s	1 core @ 2.5 Ghz (C/C++)
200	cp-tv-kp			75.85 %	83.50 %	72.54 %	1 s	1 core @ 2.5 Ghz (C/C++)
201	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.
202	cp-tv			75.67 %	83.31 %	72.24 %	1 s	1 core @ 2.5 Ghz (C/C++)
203	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.
204	Self-Calib Conv			75.59 %	83.54 %	71.96 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
205	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.
206	CF-ctdep-train			75.43 %	83.03 %	71.31 %	1 s	1 core @ 2.5 Ghz (C/C++)
207	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.
208	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.
209	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.
210	T_PVRCNN			74.93 %	84.79 %	69.95 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
211	T_PVRCNN_V2			74.90 %	84.74 %	69.60 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
212	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.
213	MF			74.70 %	83.42 %	66.51 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
214	LazyTorch-CP-Infer-O			74.57 %	81.82 %	70.24 %	1 s	1 core @ 2.5 Ghz (C/C++)
215	LazyTorch-CP-Small-P			74.44 %	81.73 %	70.14 %	1 s	1 core @ 2.5 Ghz (C/C++)
216	City-CF-fixed			74.37 %	83.23 %	69.65 %	1 s	1 core @ 2.5 Ghz (C/C++)
217	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.
218	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.
219	CenterPoint (pcdet)			73.96 %	81.17 %	69.48 %	0.051 sec/frame	2 x V100
220	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.
221	ZMMPP			73.78 %	82.48 %	68.80 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
222	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.
223	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.
224	Dune-DCF-e11			73.51 %	80.89 %	68.89 %	1 s	1 core @ 2.5 Ghz (C/C++)
225	CrazyTensor-CP			73.50 %	81.04 %	69.87 %	1 s	1 core @ 2.5 Ghz (Python)
226	PointRGBNet			73.49 %	83.99 %	68.56 %	0.08 s	4 cores @ 2.5 Ghz (Python + C/C++)
P. Xie Desheng: Real-time Detection of 3D Objects Based on Multi-Sensor Information Fusion. Automotive Engineering 2022.
227	City-CF			73.48 %	80.85 %	69.07 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
228	PSM_stereo			73.43 %	81.28 %	66.98 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
229	Dune-DCF-e15			73.29 %	80.34 %	68.54 %	1 s	1 core @ 2.5 Ghz (C/C++)
230	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.
231	Dune-DCF-e09			73.15 %	80.40 %	68.57 %	1 s	1 core @ 2.5 Ghz (C/C++)
232	AFTD			73.12 %	82.71 %	68.09 %	1 s	1 core @ 2.5 Ghz (Python + C/C++)
233	PP-PCdet		code	73.07 %	83.32 %	68.18 %	0.01 s	1 core @ 2.5 Ghz (Python)
234	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.
235	CrazyTensor-CF			72.92 %	79.87 %	68.41 %	1 s	1 core @ 2.5 Ghz (C/C++)
236	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.
237	HS3D		code	72.25 %	83.57 %	67.49 %	0.12 s	1 core @ 2.5 Ghz (Python + C/C++)
238	TBD_BD		code	72.16 %	83.36 %	66.87 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
239	SSL_PP		code	72.02 %	83.74 %	64.95 %	16ms	GPU @ 1.5 Ghz (Python)
240	TBD			71.94 %	83.20 %	66.83 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
241	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.
242	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.
243	Contrastive PP		code	71.64 %	84.80 %	66.49 %	0.01 s	1 core @ 2.5 Ghz (Python)
244	CZY_3917			71.48 %	82.24 %	66.59 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
245	new_stereo			70.79 %	80.05 %	66.04 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
246	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.
247	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.
248	FusionDetv2-baseline			68.87 %	79.05 %	63.68 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
249	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.
250	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.
251	DSGN++		code	67.37 %	83.21 %	59.91 %	0.2 s	GeForce RTX 2080Ti
Y. Chen, S. Huang, S. Liu, B. Yu and J. Jia: DSGN++: Exploiting Visual-Spatial Relation for Stereo-based 3D Detectors. arXiv preprint arXiv:2204.03039 2022.
252	DMF			67.33 %	77.55 %	62.44 %	0.2 s	1 core @ 2.5 Ghz (Python + C/C++)
X. J. Chen and W. Xu: Disparity-Based Multiscale Fusion Network for Transportation Detection. IEEE Transactions on Intelligent Transportation Systems 2022.
253	Anonymous			66.97 %	83.77 %	58.41 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
254	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.
255	StereoDistill			66.39 %	81.66 %	57.39 %	0.4 s	1 core @ 2.5 Ghz (Python)
256	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.
257	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.
258	CZY			63.68 %	77.56 %	57.82 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
259	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.
260	SD3DOD			62.00 %	76.09 %	55.46 %	0.04 s	GPU @ 2.5 Ghz (Python)
261	AEC3D			61.99 %	72.16 %	57.11 %	18 ms	GPU @ 2.5 Ghz (Python)
262	SNVC		code	61.34 %	78.54 %	54.23 %	1 s	GPU @ 1.0 Ghz (Python)
S. Li, Z. Liu, Z. Shen and K. Cheng: Stereo Neural Vernier Caliper. Proceedings of the AAAI Conference on Artificial Intelligence 2022.
263	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.
264	Anonymous			58.57 %	77.81 %	52.13 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
265	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.
266	FD			56.40 %	73.05 %	52.25 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
267	PS++		code	55.28 %	74.80 %	46.70 %	PS++ s	1 core @ 2.5 Ghz (C/C++)
268	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.
269	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.
270	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.
271	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.
272	PS		code	52.88 %	74.41 %	44.38 %	PS s	1 core @ 2.5 Ghz (C/C++)
273	UPF_3D			52.83 %	78.24 %	46.12 %	0.29 s	1 core @ 2.5 Ghz (Python)
274	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.
275	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.
276	ppt			50.41 %	54.19 %	45.14 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
277	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.
278	ESGN			46.39 %	65.80 %	38.42 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
279	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.
280	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.
281	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.
282	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.
283	ART			42.42 %	63.38 %	36.44 %	20ms s	1 core @ 2.5 Ghz (C/C++)
284	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.
285	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.
286	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.
287	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.
288	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.
289	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.
290	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.
291	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.
292	SparseLiDAR_fusion			26.23 %	36.85 %	21.45 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
293	VMDet_Boost			25.02 %	35.96 %	21.47 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
294	GCDR			23.92 %	34.89 %	19.59 %	0.28 s	1 core @ 2.5 Ghz (Python)
295	Anonymous			23.79 %	33.89 %	20.33 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
296	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.
297	Digging_M3D			21.24 %	29.15 %	19.18 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
298	VMDet			20.95 %	31.55 %	17.83 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
299	Anonymous			20.23 %	28.29 %	17.55 %	40 s	1 core @ 2.5 Ghz (C/C++)
300	SARM3D			19.70 %	25.20 %	17.35 %	0.03 s	GPU @ 2.5 Ghz (Python)
301	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.
302	MonoInsight			19.04 %	27.71 %	16.03 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
303	CMKD*			18.69 %	28.55 %	16.77 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
304	Mix-Teaching			18.54 %	26.89 %	15.79 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
305	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.
306	anonymity			18.00 %	28.10 %	15.60 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
307	SCSTSV-MonoFlex			17.91 %	27.38 %	15.58 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
308	MoGDE			17.88 %	27.07 %	15.66 %	0.03 s	GPU @ 2.5 Ghz (Python)
309	LPCG-Monoflex			17.80 %	25.56 %	15.38 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
310	PS-fld		code	17.74 %	23.74 %	15.14 %	0.25 s	1 core @ 2.5 Ghz (C/C++)
Y. Chen, H. Dai and Y. Ding: Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
311	DD3Dv2		code	17.61 %	26.36 %	15.32 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
312	MonoDDE			17.14 %	24.93 %	15.10 %	0.04 s	1 core @ 2.5 Ghz (Python)
Z. Li, Z. Qu, Y. Zhou, J. Liu, H. Wang and L. Jiang: Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection. CVPR 2022.
313	Anonymous			17.08 %	24.43 %	15.25 %	40 s	1 core @ 2.5 Ghz (C/C++)
314	OPA-3D		code	17.05 %	24.60 %	14.25 %	0.04 s	1 core @ 3.5 Ghz (Python)
315	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.
316	anonymity			16.99 %	27.20 %	15.08 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
317	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) .
318	Shape-Aware			16.52 %	23.84 %	13.88 %	0.05 s	1 core @ 2.5 Ghz (Python)
319	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.
320	DID-M3D			16.29 %	24.40 %	13.75 %	0.04 s	1 core @ 2.5 Ghz (Python)
321	MonoDETR		code	16.26 %	24.52 %	13.93 %	0.04 s	1 core @ 2.5 Ghz (Python)
R. Zhang, H. Qiu, T. Wang, X. Xu, Z. Guo, Y. Qiao, P. Gao and H. Li: MonoDETR: Depth-aware Transformer for Monocular 3D Object Detection. arXiv preprint arXiv:2203.13310 2022.
322	Lite-FPN-GUPNet			16.20 %	23.58 %	13.56 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
323	gupnet_se			16.10 %	23.62 %	13.41 %	0.03s	1 core @ 2.5 Ghz (C/C++)
324	zongmuDistill			16.08 %	25.11 %	13.69 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
325	MonoDistill			16.03 %	22.97 %	13.60 %	0.04 s	1 core @ 2.5 Ghz (Python)
326	MDNet			16.01 %	24.59 %	13.49 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
327	DDS		code	15.90 %	23.81 %	13.21 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
328	mono3d		code	15.73 %	23.96 %	13.35 %	TBD	TBD
329	monopd		code	15.72 %	23.51 %	13.07 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
330	OBMO_GUPNet			15.70 %	22.71 %	13.23 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
331	GPENet		code	15.44 %	22.41 %	12.84 %	0.02 s	GPU @ 2.5 Ghz (Python)
332	MonoDTR			15.39 %	21.99 %	12.73 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
K. Huang, T. Wu, H. Su and W. Hsu: MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer. CVPR 2022.
333	mono3d			15.26 %	23.41 %	12.80 %	0.03 s	GPU @ 2.5 Ghz (Python)
334	HBD			15.17 %	21.71 %	13.06 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
335	EW		code	15.13 %	21.16 %	12.81 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
336	ZongmuMono3d		code	15.08 %	23.79 %	13.25 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
337	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.
338	HomoLoss(monoflex)		code	14.94 %	21.75 %	13.07 %	0.04 s	1 core @ 2.5 Ghz (Python)
J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homography Loss for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
339	Anonymous			14.87 %	23.93 %	12.45 %	40 s	1 core @ 2.5 Ghz (C/C++)
340	Anonymous			14.84 %	22.73 %	13.08 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
341	M3DSSD++		code	14.75 %	23.61 %	11.80 %	0.16s	1 core @ 2.5 Ghz (C/C++)
342	MonoFlex			14.73 %	22.29 %	12.77 %	0.03 s	1 core @ 2.5 Ghz (Python)
343	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.
344	Anonymous		code	14.56 %	20.65 %	11.92 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
345	SAIC_ADC_Mono3D		code	14.54 %	18.98 %	12.86 %	50 s	GPU @ 2.5 Ghz (Python)
346	MonoEdge			14.47 %	21.08 %	12.73 %	0.05 s	GPU @ 2.5 Ghz (Python)
347	MDSNet			14.46 %	24.30 %	11.12 %	0.07 s	1 core @ 2.5 Ghz (Python)
348	MonoGround			14.36 %	21.37 %	12.62 %	0.03 s	1 core @ 2.5 Ghz (Python)
349	MonoEdge-RCNN			14.35 %	19.74 %	11.94 %	0.05 s	1 core @ 2.5 Ghz (Python)
350	ANM			14.33 %	20.84 %	11.61 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
351	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.
352	SwinMono3D			14.24 %	22.61 %	10.11 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
353	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.
354	MonoEdge-Rotate			14.13 %	21.60 %	12.27 %	0.05 s	GPU @ 2.5 Ghz (Python)
355	EM		code	14.00 %	22.93 %	11.26 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
356	MAOLoss		code	14.00 %	20.05 %	11.81 %	0.05 s	1 core @ 2.5 Ghz (Python)
357	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.
358	MonoEF			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.
359	MonoAug			13.85 %	20.06 %	11.43 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
360	DEPT			13.83 %	20.43 %	11.66 %	0.03 s	1 core @ 2.5 Ghz (Python)
361	K3D			13.80 %	20.04 %	11.67 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
362	none			13.79 %	18.84 %	11.52 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
363	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.
364	MonoFar			13.52 %	18.08 %	11.58 %	0.04 s	1 core @ 2.5 Ghz (Python)
365	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.
366	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.
367	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.
368	MK3D			13.19 %	20.48 %	11.10 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
369	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.
370	HomoLoss(imvoxelnet)		code	12.99 %	20.10 %	10.50 %	0.20 s	1 core @ 2.5 Ghz (Python)
J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homogrpahy Loss for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
371	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.
372	RetinaMono			12.73 %	19.41 %	10.45 %	0.02 s	1 core @ 2.5 Ghz (Python)
373	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 .
374	M3DGAF			12.66 %	19.48 %	10.99 %	0.07 s	1 core @ 2.5 Ghz (Python)
375	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.
376	MP-Mono			12.37 %	17.89 %	9.58 %	0.16 s	GPU @ 2.5 Ghz (Python)
377	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.
378	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.
379	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 .
380	LT-M3OD			12.26 %	18.15 %	10.05 %	0.03 s	1 core @ 2.5 Ghz (Python)
381	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.
382	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.
383	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.
384	CMAN			11.87 %	17.77 %	9.16 %	0.15 s	1 core @ 2.5 Ghz (Python)
385	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.
386	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.
387	MonoAug			11.47 %	16.40 %	9.26 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
388	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.
389	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.
390	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.
391	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.
392	COF3D			10.91 %	17.86 %	8.20 %	200 s	1 core @ 2.5 Ghz (Python + C/C++)
393	MM			10.74 %	15.80 %	8.64 %	1 s	1 core @ 2.5 Ghz (C/C++)
394	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.
395	Lite-FPN			10.64 %	15.32 %	8.59 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
396	Keypoint-3D			10.42 %	15.97 %	7.91 %	14 s	1 core @ 2.5 Ghz (C/C++)
397	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.
398	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.
399	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.
400	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.
401	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 .
402	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.
403	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.
404	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.
405	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.
406	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.
407	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.
408	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.
409	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.
410	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.
411	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.
412	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.
413	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.
414	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.
415	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.
416	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.
417	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.
418	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.
419	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.
420	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.
421	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.
422	WeakM3D		code	2.26 %	5.03 %	1.63 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
L. Peng, S. Yan, B. Wu, Z. Yang, X. He and D. Cai: WeakM3D: Towards Weakly Supervised Monocular 3D Object Detection. ICLR 2022.
423	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.
424	CDTrack3D		code	1.92 %	3.20 %	1.63 %	0.0106 s	NVIDIA RTX 3090 GPU, i9 10850k CPU
425	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.
426	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.
427	test		code	0.03 %	0.01 %	0.03 %	50 s	1 core @ 2.5 Ghz (Python)
428	MonoDET		code	0.01 %	0.03 %	0.01 %	0.04 s	1 core @ 2.5 Ghz (Python)
429	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	CasA++			49.29 %	56.33 %	46.70 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
2	TED			49.21 %	55.85 %	46.52 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
3	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.
4	CAD			47.91 %	55.98 %	44.63 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
5	DCAN-Second		code	47.38 %	55.12 %	44.59 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
6	PV-RCNN++		code	47.19 %	54.29 %	43.49 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
7	CasA			47.09 %	54.04 %	44.56 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
8	EQ-PVRCNN		code	47.02 %	55.84 %	42.94 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, L. Jiang, Y. Sun, B. Schiele and J. Jia: A Unified Query-based Paradigm for Point Cloud Understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.
9	PiFeNet			46.71 %	56.39 %	42.71 %	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.
10	ISE-RCNN			45.66 %	51.44 %	42.43 %	0.09 s	1 core @ 2.5 Ghz (Python + C/C++)
11	CAT-Det			45.44 %	54.26 %	41.94 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Zhang, J. Chen and D. Huang: CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection. CVPR 2022.
12	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.
13	PE-RCVN			45.01 %	50.29 %	41.85 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
14	variance_point			44.89 %	53.72 %	41.21 %	0.05 s	1 core @ 2.5 Ghz (Python)
15	SPT			44.72 %	51.35 %	41.38 %	0.1 s	GPU @ 2.5 Ghz (Python)
16	VPN			44.56 %	54.13 %	41.73 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
17	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.
18	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.
19	CFF-tv			44.33 %	52.72 %	41.11 %	1 s	1 core @ 2.5 Ghz (C/C++)
20	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.
21	AutoAlign			44.08 %	53.99 %	40.82 %	0.1 s	1 core @ 2.5 Ghz (Python)
22	ISE-RCNN-PV			43.78 %	50.03 %	40.50 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
23	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.
24	USVLab BSAODet			43.63 %	51.71 %	41.09 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
25	CFF-ep25			43.47 %	51.85 %	40.69 %	1 s	1 core @ 2.5 Ghz (C/C++)
26	FV2P v2			43.47 %	50.64 %	40.09 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
27	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.
28	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.
29	FS-Net			43.31 %	49.82 %	40.89 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
30	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.
31	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.
32	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.
33	cff-tv-v2-ep25			43.25 %	51.40 %	40.23 %	1 s	1 core @ 2.5 Ghz (C/C++)
34	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.
35	KeyFuse2B			43.18 %	51.49 %	40.70 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
36	CF-ctdep-tv-ta			43.11 %	50.40 %	40.51 %	1 s	1 core @ 2.5 Ghz (C/C++)
37	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.
38	Reprod-Two-Branch			43.07 %	52.07 %	40.40 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
39	SGNet			43.00 %	49.68 %	40.45 %	0.09 s	GPU @ 2.5 Ghz (Python)
40	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.
41	PSA-Det3D			42.81 %	49.72 %	39.58 %	0.1 s	GPU @ 2.5 Ghz (Python)
42	CFF-tv-v2			42.77 %	51.08 %	40.15 %	1 s	1 core @ 2.5 Ghz (C/C++)
43	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.
44	CF-base-tv			42.66 %	50.01 %	39.76 %	1 s	1 core @ 2.5 Ghz (C/C++)
45	HMFI		code	42.65 %	50.88 %	39.78 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
46	TBD			42.65 %	50.88 %	39.78 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
47	USVLab BSAODet (S)			42.62 %	49.52 %	39.12 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
48	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.
49	Anonymous			42.32 %	47.96 %	39.69 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
50	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.
51	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.
52	TBD			42.19 %	49.89 %	39.34 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
53	TBD			42.19 %	49.89 %	39.34 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
54	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.
55	3SNet			42.02 %	48.91 %	39.39 %	0.07 s	GPU @ 2.5 Ghz (Python)
56	CF-ctdep-tv			41.98 %	49.14 %	39.03 %	1 s	1 core @ 2.5 Ghz (C/C++)
57	Self-Calib Conv			41.95 %	48.88 %	39.52 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
58	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.
59	CenterFuse			41.80 %	49.77 %	38.49 %	0.059 sec/frame	2 x V100
60	cp-tv-kp			41.70 %	48.77 %	39.41 %	1 s	1 core @ 2.5 Ghz (C/C++)
61	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.
62	TCDVF			41.47 %	49.44 %	38.41 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
63	DGT-Det3D			41.40 %	49.06 %	38.43 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
64	tbd			41.10 %	50.56 %	37.49 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
65	IA-SSD (single)		code	41.03 %	47.90 %	37.98 %	0.013 s	1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
66	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.
67	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.
68	SARFE			40.79 %	47.29 %	38.25 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
69	IoU-2B			40.62 %	50.33 %	36.94 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
70	MSADet			40.58 %	49.54 %	38.19 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
71	TBD			40.57 %	47.65 %	38.33 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
72	PDV		code	40.56 %	47.80 %	38.46 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
J. Hu, T. Kuai and S. Waslander: Point Density-Aware Voxels for LiDAR 3D Object Detection. CVPR 2022.
73	cp-tv			40.55 %	47.71 %	38.23 %	1 s	1 core @ 2.5 Ghz (C/C++)
74	MVMM		code	40.49 %	47.54 %	38.36 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
75	cff-tv-t			40.41 %	49.46 %	37.02 %	1 s	1 core @ 2.5 Ghz (C/C++)
76	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.
77	KeyPoint-IoUHead			40.15 %	47.86 %	37.01 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
78	TBD			40.07 %	46.11 %	37.87 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
79	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.
80	cp-tv-kp-io-sc			39.92 %	48.06 %	37.68 %	1 s	1 core @ 2.5 Ghz (C/C++)
81	FusionDetv2-v5			39.91 %	47.50 %	37.39 %	0.05 s	1 core @ 2.5 Ghz (Java + C/C++)
82	DDet			39.87 %	45.82 %	38.00 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
83	CF-cd-io-tv			39.82 %	48.67 %	36.45 %	1 s	1 core @ 2.5 Ghz (C/C++)
84	FusionDetv2-v4			39.68 %	46.93 %	37.31 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
85	DSASNet			39.65 %	47.14 %	37.05 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
86	VCRCNN			39.64 %	45.19 %	37.55 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
87	WGVRF			39.52 %	45.98 %	37.56 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
88	AFTD			39.45 %	48.28 %	36.07 %	1 s	1 core @ 2.5 Ghz (Python + C/C++)
89	TBD		code	39.45 %	47.08 %	37.12 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
90	Dune-DCF-e09			39.43 %	47.29 %	36.74 %	1 s	1 core @ 2.5 Ghz (C/C++)
91	LazyTorch-CP-Infer-O			39.43 %	47.38 %	36.96 %	1 s	1 core @ 2.5 Ghz (C/C++)
92	SRDL			39.43 %	47.30 %	36.99 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
93	FusionDetv1			39.42 %	47.30 %	36.97 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
94	AGS-SSD[la]			39.41 %	46.04 %	36.28 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
95	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.
96	ST-RCNN			39.36 %	44.96 %	37.09 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
97	LazyTorch-CP-Small-P			39.33 %	47.27 %	36.89 %	1 s	1 core @ 2.5 Ghz (C/C++)
98	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.
99	CenterPoint (pcdet)			39.28 %	47.25 %	36.78 %	0.051 sec/frame	2 x V100
100	CZY			39.26 %	45.08 %	36.38 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
101	Dune-DCF-e11			39.26 %	47.32 %	36.63 %	1 s	1 core @ 2.5 Ghz (C/C++)
102	ATT_SSD			39.24 %	45.92 %	36.35 %	0.01 s	1 core @ 2.5 Ghz (Python)
103	City-CF-fixed			39.22 %	47.68 %	36.55 %	1 s	1 core @ 2.5 Ghz (C/C++)
104	IA-SSD (multi)		code	39.03 %	46.51 %	35.61 %	0.014 s	1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
105	SIF			38.74 %	46.23 %	36.06 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
106	CrazyTensor-CP			38.67 %	46.58 %	36.15 %	1 s	1 core @ 2.5 Ghz (Python)
107	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.
108	Dune-DCF-e15			38.61 %	46.41 %	36.02 %	1 s	1 core @ 2.5 Ghz (C/C++)
109	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. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) 2021.
110	IKT3D			38.58 %	44.18 %	36.56 %	0.05 s	1 core @ 2.5 Ghz (Python)
111	FPV-SSD			38.45 %	45.83 %	36.03 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
112	CF-base-train			38.44 %	45.89 %	35.55 %	1 s	1 core @ 2.5 Ghz (C/C++)
113	TBD			38.27 %	46.35 %	36.39 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
114	City-CF			38.04 %	45.42 %	35.40 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
115	CF-ctdep-train			38.03 %	44.75 %	35.49 %	1 s	1 core @ 2.5 Ghz (C/C++)
116	NV-RCNN			37.82 %	44.38 %	35.55 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
117	PVTr			37.58 %	43.99 %	35.48 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
118	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.
119	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.
120	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.
121	T_PVRCNN			37.12 %	45.20 %	34.04 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
122	HS3D		code	36.86 %	45.62 %	33.67 %	0.12 s	1 core @ 2.5 Ghz (Python + C/C++)
123	T_PVRCNN_V2			36.79 %	44.81 %	33.83 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
124	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.
125	FusionDetv2-baseline			36.66 %	41.34 %	34.60 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
126	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.
127	NV2P-RCNN			35.98 %	43.18 %	33.88 %	0.1 s	GPU @ 2.5 Ghz (Python)
128	CrazyTensor-CF			35.83 %	43.50 %	33.23 %	1 s	1 core @ 2.5 Ghz (C/C++)
129	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.
130	TBD_BD		code	34.86 %	42.56 %	32.49 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
131	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.
132	GT3D			33.56 %	42.96 %	29.93 %	0.1 s	1 core @ 2.5 Ghz (Python)
133	KPP3D		code	32.91 %	41.34 %	30.45 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
134	DSGN++		code	32.74 %	43.05 %	29.54 %	0.2 s	GeForce RTX 2080Ti
Y. Chen, S. Huang, S. Liu, B. Yu and J. Jia: DSGN++: Exploiting Visual-Spatial Relation for Stereo-based 3D Detectors. arXiv preprint arXiv:2204.03039 2022.
135	ZMMPP			32.38 %	39.54 %	30.25 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
136	StereoDistill			32.23 %	44.12 %	28.95 %	0.4 s	1 core @ 2.5 Ghz (Python)
137	PP-PCdet		code	32.04 %	39.23 %	29.79 %	0.01 s	1 core @ 2.5 Ghz (Python)
138	Contrastive PP		code	31.64 %	38.47 %	29.30 %	0.01 s	1 core @ 2.5 Ghz (Python)
139	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.
140	CZY_3917			31.06 %	38.57 %	28.83 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
141	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.
142	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.
143	DMF			29.77 %	37.21 %	27.62 %	0.2 s	1 core @ 2.5 Ghz (Python + C/C++)
X. J. Chen and W. Xu: Disparity-Based Multiscale Fusion Network for Transportation Detection. IEEE Transactions on Intelligent Transportation Systems 2022.
144	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.
145	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.
146	PS++		code	27.45 %	36.89 %	24.01 %	PS++ s	1 core @ 2.5 Ghz (C/C++)
147	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.
148	PointRGBNet			26.40 %	34.77 %	24.03 %	0.08 s	4 cores @ 2.5 Ghz (Python + C/C++)
P. Xie Desheng: Real-time Detection of 3D Objects Based on Multi-Sensor Information Fusion. Automotive Engineering 2022.
149	PS		code	26.01 %	35.52 %	23.24 %	PS s	1 core @ 2.5 Ghz (C/C++)
150	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.
151	CZY			25.47 %	32.33 %	23.60 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
152	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.
153	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.
154	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.
155	AEC3D			19.00 %	24.39 %	17.43 %	18 ms	GPU @ 2.5 Ghz (Python)
156	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.
157	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.
158	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.
159	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.
160	Anonymous			11.69 %	17.79 %	10.09 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
161	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.
162	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) .
163	DD3Dv2		code	10.82 %	16.25 %	9.24 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
164	PS-fld		code	10.82 %	16.95 %	9.26 %	0.25 s	1 core @ 2.5 Ghz (C/C++)
Y. Chen, H. Dai and Y. Ding: Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
165	DEPT			10.81 %	16.28 %	9.15 %	0.03 s	1 core @ 2.5 Ghz (Python)
166	OPA-3D		code	10.49 %	15.65 %	8.80 %	0.04 s	1 core @ 3.5 Ghz (Python)
167	anonymity			10.39 %	16.89 %	9.29 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
168	ESGN			10.27 %	14.05 %	9.02 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
169	MonoDTR			10.18 %	15.33 %	8.61 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
K. Huang, T. Wu, H. Su and W. Hsu: MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer. CVPR 2022.
170	LT-M3OD			9.99 %	14.85 %	8.52 %	0.03 s	1 core @ 2.5 Ghz (Python)
171	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.
172	MonoInsight			9.42 %	14.41 %	7.96 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
173	GPENet		code	9.36 %	14.61 %	7.91 %	0.02 s	GPU @ 2.5 Ghz (Python)
174	Lite-FPN-GUPNet			9.32 %	14.13 %	7.93 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
175	mono3d		code	9.20 %	14.53 %	7.82 %	TBD	TBD
176	ZongmuMono3d		code	9.18 %	14.23 %	7.82 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
177	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.
178	CMKD*			8.79 %	13.94 %	7.42 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
179	SCSTSV-MonoFlex			8.75 %	13.10 %	7.38 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
180	gupnet_se			8.65 %	13.40 %	7.78 %	0.03s	1 core @ 2.5 Ghz (C/C++)
181	SwinMono3D			8.54 %	12.96 %	7.19 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
182	SARM3D			8.48 %	12.95 %	7.25 %	0.03 s	GPU @ 2.5 Ghz (Python)
183	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.
184	HBD			8.33 %	13.47 %	6.99 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
185	MonoEdge			8.33 %	12.11 %	7.03 %	0.05 s	GPU @ 2.5 Ghz (Python)
186	GCDR			8.27 %	11.50 %	7.37 %	0.28 s	1 core @ 2.5 Ghz (Python)
187	MonoFlex			8.16 %	11.89 %	6.81 %	0.03 s	1 core @ 2.5 Ghz (Python)
188	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.
189	MonoGround			7.89 %	12.37 %	7.13 %	0.03 s	1 core @ 2.5 Ghz (Python)
190	HomoLoss(monoflex)		code	7.66 %	11.87 %	6.82 %	0.04 s	1 core @ 2.5 Ghz (Python)
J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homography Loss for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
191	MonoFar			7.62 %	11.21 %	6.47 %	0.04 s	1 core @ 2.5 Ghz (Python)
192	K3D			7.60 %	12.58 %	6.73 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
193	ANM			7.54 %	11.92 %	6.37 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
194	SAIC_ADC_Mono3D		code	7.54 %	12.06 %	6.41 %	50 s	GPU @ 2.5 Ghz (Python)
195	Mix-Teaching			7.47 %	11.67 %	6.61 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
196	M3DGAF			7.42 %	11.68 %	6.65 %	0.07 s	1 core @ 2.5 Ghz (Python)
197	LPCG-Monoflex			7.33 %	10.82 %	6.18 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
198	MonoDDE			7.32 %	11.13 %	6.67 %	0.04 s	1 core @ 2.5 Ghz (Python)
Z. Li, Z. Qu, Y. Zhou, J. Liu, H. Wang and L. Jiang: Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection. CVPR 2022.
199	MonoAug			7.31 %	11.31 %	6.12 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
200	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.
201	Shape-Aware			7.16 %	10.40 %	5.93 %	0.05 s	1 core @ 2.5 Ghz (Python)
202	MDSNet			7.09 %	10.68 %	6.06 %	0.07 s	1 core @ 2.5 Ghz (Python)
203	MonoEdge-Rotate			7.02 %	10.47 %	5.84 %	0.05 s	GPU @ 2.5 Ghz (Python)
204	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.
205	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.
206	DCD		code	6.73 %	10.37 %	6.28 %	1 s	1 core @ 2.5 Ghz (C/C++)
207	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.
208	mono3d			6.62 %	10.10 %	5.46 %	0.03 s	GPU @ 2.5 Ghz (Python)
209	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 .
210	MonoAug			6.36 %	9.59 %	5.24 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
211	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.
212	MDNet			5.66 %	8.24 %	4.74 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
213	M3DSSD++		code	5.65 %	8.10 %	4.72 %	0.16s	1 core @ 2.5 Ghz (C/C++)
214	MK3D			5.00 %	7.29 %	4.54 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
215	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.
216	MM			4.70 %	7.81 %	4.01 %	1 s	1 core @ 2.5 Ghz (C/C++)
217	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.
218	Lite-FPN			4.38 %	6.57 %	3.56 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
219	COF3D			4.37 %	6.02 %	3.55 %	200 s	1 core @ 2.5 Ghz (Python + C/C++)
220	MAOLoss		code	4.18 %	5.81 %	3.67 %	0.05 s	1 core @ 2.5 Ghz (Python)
221	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.
222	MP-Mono			3.75 %	5.09 %	3.50 %	0.16 s	GPU @ 2.5 Ghz (Python)
223	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.
224	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.
225	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 .
226	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.
227	MoGDE			3.42 %	5.57 %	2.73 %	0.03 s	GPU @ 2.5 Ghz (Python)
228	CMAN			3.41 %	4.62 %	2.87 %	0.15 s	1 core @ 2.5 Ghz (Python)
229	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.
230	MonoEF			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.
231	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.
232	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.
233	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.
234	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.
235	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.
236	EM		code	1.18 %	1.09 %	0.80 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
237	CDTrack3D		code	1.01 %	1.48 %	0.69 %	0.0106 s	NVIDIA RTX 3090 GPU, i9 10850k CPU
238	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.