The KITTI Vision Benchmark Suite

The bird's eye view benchmark consists of 7481 training images and 7518 test images as well as the corresponding point clouds, comprising a total of 80.256 labeled objects. For evaluation, we compute precision-recall curves. To rank the methods we compute average precision. We require that all methods use the same parameter set for all test pairs. Our development kit provides details about the data format as well as MATLAB / C++ utility functions for reading and writing the label files.

• Download left color images of object data set (12 GB)
• Download right color images, if you want to use stereo information (12 GB)
• Download the 3 temporally preceding frames (left color) (36 GB)
• Download the 3 temporally preceding frames (right color) (36 GB)
• Download Velodyne point clouds, if you want to use laser information (29 GB)
• Download camera calibration matrices of object data set (16 MB)
• Download training labels of object data set (5 MB)
• Download object development kit (1 MB) (including 3D object detection and bird's eye view evaluation code)
• Download pre-trained LSVM baseline models (5 MB) used in Joint 3D Estimation of Objects and Scene Layout (NIPS 2011). These models are referred to as LSVM-MDPM-sv (supervised version) and LSVM-MDPM-us (unsupervised version) in the tables below.
• Download reference detections (L-SVM) for training and test set (800 MB)
• Qianli Liao (NYU) has put together code to convert from KITTI to PASCAL VOC file format (documentation included, requires Emacs).
• Karl Rosaen (U.Mich) has released code to convert between KITTI, KITTI tracking, Pascal VOC, Udacity, CrowdAI and AUTTI formats.
• We thank David Stutz and Bo Li for developing the 3D object detection benchmark.

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

• Easy: Min. bounding box height: 40 Px, Max. occlusion level: Fully visible, Max. truncation: 15 %
• Moderate: Min. bounding box height: 25 Px, Max. occlusion level: Partly occluded, Max. truncation: 30 %
• Hard: Min. bounding box height: 25 Px, Max. occlusion level: Difficult to see, Max. truncation: 50 %

All methods are ranked based on the moderately difficult results.

Car

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	GraR-Po			92.12 %	95.79 %	87.11 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
2	TED			92.05 %	95.44 %	87.30 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
3	LIVOX_Det			92.05 %	95.60 %	89.22 %	n/a s	1 core @ 2.5 Ghz (Python + C/C++)
4	VPFNet			91.86 %	93.02 %	86.94 %	0.06 s	2 cores @ 2.5 Ghz (Python)
H. Zhu, J. Deng, Y. Zhang, J. Ji, Q. Mao, H. Li and Y. Zhang: VPFNet: Improving 3D Object Detection with Virtual Point based LiDAR and Stereo Data Fusion. 2021.
5	SFD		code	91.85 %	95.64 %	86.83 %	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.
6	SE-SSD		code	91.84 %	95.68 %	86.72 %	0.03 s	1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, L. Jiang and C. Fu: SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud. CVPR 2021.
7	GraR-Vo			91.72 %	95.27 %	86.51 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
8	PVT-SSD			91.63 %	95.23 %	86.43 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
9	CityBrainLab			91.62 %	94.78 %	86.68 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
10	SPANet			91.59 %	95.59 %	86.53 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
Y. Ye: SPANet: Spatial and Part-Aware Aggregation Network for 3D Object Detection. Pacific Rim International Conference on Artificial Intelligence 2021.
11	CasA			91.54 %	95.19 %	86.82 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
12	GraR-Pi			91.52 %	95.06 %	86.42 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
13	DGDNH			91.36 %	95.03 %	88.79 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
14	BADet		code	91.32 %	95.23 %	86.48 %	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.
15	Anonymous			91.32 %	95.42 %	88.38 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
16	CasA++			91.22 %	94.57 %	88.43 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
17	Anonymous			91.14 %	94.04 %	86.33 %	n/a s	1 core @ 2.5 Ghz (C/C++)
18	SGFusion			91.11 %	94.76 %	86.27 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
19	Anonymous			91.04 %	94.76 %	86.31 %	n/a s	1 core @ 2.5 Ghz (Python + C/C++)
20	SA-SSD		code	91.03 %	95.03 %	85.96 %	0.04 s	1 core @ 2.5 Ghz (Python)
C. He, H. Zeng, J. Huang, X. Hua and L. Zhang: Structure Aware Single-stage 3D Object Detection from Point Cloud. CVPR 2020.
21	anonymous			90.90 %	92.96 %	86.34 %	0.09 s	GPU @ 2.5 Ghz (Python)
22	MMLab PV-RCNN		code	90.65 %	94.98 %	86.14 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
23	VueronNet		code	90.56 %	94.67 %	85.31 %	0.06 s	1 core @ 2.0 Ghz (Python)
24	ST-RCNN			90.53 %	94.58 %	86.08 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
25	VPFNet		code	90.52 %	93.94 %	86.25 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
C. Wang, H. Chen and L. Fu: VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection. 2021.
26	PDV		code	90.48 %	94.56 %	86.23 %	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.
27	VCRCNN			90.42 %	94.55 %	86.11 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
28	M3DeTR		code	90.37 %	94.41 %	85.98 %	n/a s	GPU @ 1.0 Ghz (Python)
T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis and D. Manocha: M3DeTR: Multi-representation, Multi- scale, Mutual-relation 3D Object Detection with Transformers. 2021.
29	TBD			90.37 %	93.82 %	87.72 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
30	DDet			90.34 %	94.16 %	86.01 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
31	VoTr-TSD		code	90.34 %	94.03 %	86.14 %	0.07 s	1 core @ 2.5 Ghz (C/C++)
J. Mao, Y. Xue, M. Niu, H. Bai, J. Feng, X. Liang, H. Xu and C. Xu: Voxel Transformer for 3D Object Detection. ICCV 2021.
32	DSA-PV-RCNN		code	90.13 %	92.42 %	85.93 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
33	XView			90.12 %	92.27 %	85.94 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
34	GraR-VoI			90.10 %	95.69 %	86.85 %	0.07 s	1 core @ 2.5 Ghz (Python + C/C++)
35	CAT-Det			90.07 %	92.59 %	85.82 %	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.
36	IKT3D			90.06 %	92.14 %	85.74 %	0.05 s	1 core @ 2.5 Ghz (Python)
37	FPV-SSD			89.93 %	91.45 %	85.21 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
38	SVGA-Net			89.88 %	92.07 %	85.59 %	0.03s	1 core @ 2.5 Ghz (Python + C/C++)
Q. He, Z. Wang, H. Zeng, Y. Zeng and Y. Liu: SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds. AAAI 2022.
39	EBM3DOD		code	89.86 %	95.64 %	84.56 %	0.12 s	1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.
40	CIA-SSD		code	89.84 %	93.74 %	82.39 %	0.03 s	1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, S. Chen, L. Jiang and C. Fu: CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud. AAAI 2021.
41	CLOCs_PVCas		code	89.80 %	93.05 %	86.57 %	0.1 s	1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection . 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
42	PE-RCVN			89.79 %	95.55 %	84.78 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
43	Anonymous			89.76 %	95.41 %	86.42 %	0.03 s	1 core @ 2.5 Ghz (Python + C/C++)
44	GLENet-VR			89.76 %	93.48 %	84.89 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
45	EBM3DOD baseline		code	89.63 %	95.44 %	84.34 %	0.05 s	1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.
46	HCPVF			89.62 %	93.20 %	86.72 %	0.07 s	1 core @ 2.5 Ghz (Python + C/C++)
47	DSASNet			89.59 %	93.41 %	84.81 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
48	3SNet			89.58 %	93.26 %	84.80 %	0.07 s	GPU @ 2.5 Ghz (Python)
49	CAD			89.57 %	93.03 %	84.71 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
50	3D-CVF at SPA			89.56 %	93.52 %	82.45 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
J. Yoo, Y. Kim, J. Kim and J. Choi: 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection. ECCV 2020.
51	ImpDet			89.55 %	92.74 %	84.41 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
52	Struc info fusion II			89.54 %	95.26 %	82.31 %	0.05 s	GPU @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.
53	KpNet			89.53 %	93.34 %	81.95 %	0.42 s	1 core @ 2.5 Ghz (C/C++)
54	SASA		code	89.51 %	92.87 %	86.35 %	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.
55	Fast-CLOCs			89.49 %	93.03 %	86.40 %	0.1 s	GPU @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: Fast-CLOCs: Fast Camera-LiDAR Object Candidates Fusion for 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022.
56	KpNet			89.49 %	93.29 %	81.92 %	42 s	1 core @ 2.5 Ghz (C/C++)
57	IA-SSD (single)		code	89.48 %	93.14 %	84.42 %	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.
58	CLOCs		code	89.48 %	92.91 %	86.42 %	0.1 s	1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection . 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
59	SA3DNet			89.46 %	93.11 %	84.60 %	0.05 s	GPU @ 2.5 Ghz (Python)
60	DVF-V			89.42 %	93.12 %	86.50 %	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.
61	Struc info fusion I			89.38 %	94.91 %	84.29 %	0.05 s	1 core @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.
62	JPVNet			89.36 %	92.78 %	84.37 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
63	BtcDet		code	89.34 %	92.81 %	84.55 %	0.09 s	GPU @ 2.5 Ghz (Python + C/C++)
Q. Xu, Y. Zhong and U. Neumann: Behind the Curtain: Learning Occluded Shapes for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2022.
64	IA-SSD (multi)		code	89.33 %	92.79 %	84.35 %	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.
65	Anonymous			89.27 %	92.79 %	86.53 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
66	TBD			89.24 %	92.59 %	85.99 %	0.1 s	1 core @ 2.5 Ghz (Python)
67	ATT_SSD			89.22 %	92.82 %	85.90 %	0.01 s	1 core @ 2.5 Ghz (Python)
68	TBD		code	89.21 %	92.88 %	85.87 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
69	DVF-PV			89.20 %	93.08 %	86.28 %	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.
70	TF3D			89.19 %	93.10 %	84.41 %	0.1 s	2 cores @ 3.0 Ghz (Python)
71	STD		code	89.19 %	94.74 %	86.42 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
72	FS-Net			89.18 %	92.88 %	84.39 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
73	Point-GNN		code	89.17 %	93.11 %	83.90 %	0.6 s	GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
74	HMFI		code	89.17 %	93.04 %	86.37 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
75	SSL-PointGNN		code	89.16 %	92.92 %	83.99 %	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.
76	Anonymous			89.15 %	92.47 %	85.97 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
77	SGNet			89.14 %	93.04 %	86.54 %	0.09 s	GPU @ 2.5 Ghz (Python)
78	DGCN			89.14 %	92.62 %	83.90 %	0.1 s	GPU @ 2.5 Ghz (Python)
79	USVLab BSAODet			89.13 %	92.92 %	86.41 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
80	SPG_mini		code	89.12 %	92.80 %	86.27 %	0.09 s	GPU @ 2.5 Ghz (Python)
Q. Xu, Y. Zhou, W. Wang, C. Qi and D. Anguelov: SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation. Proceedings of the IEEE conference on computer vision and pattern recognition (ICCV) 2021.
81	ITCA-SSD		code	89.12 %	93.19 %	83.99 %	0.05 s	1 core @ 2.5 Ghz (Python)
82	EQ-PVRCNN		code	89.09 %	94.55 %	86.42 %	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.
83	SPT			89.09 %	94.87 %	84.38 %	0.1 s	GPU @ 2.5 Ghz (Python)
84	MSADet			89.08 %	92.76 %	85.66 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
85	VoxSeT		code	89.07 %	92.70 %	86.29 %	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.
86	3DSSD		code	89.02 %	92.66 %	85.86 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
87	EPNet++			89.00 %	95.41 %	85.73 %	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.
88	Focals Conv		code	89.00 %	92.67 %	86.33 %	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.
89	SECOND			88.98 %	92.01 %	83.67 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
90	LGNet			88.98 %	92.83 %	86.26 %	0.07 s	1 core @ 2.5 Ghz (C/C++)
91	ISE-RCNN			88.97 %	92.86 %	86.28 %	0.09 s	1 core @ 2.5 Ghz (Python + C/C++)
92	PTA-RCNN			88.94 %	92.32 %	85.63 %	0.08 s	1 core @ 2.5 Ghz (Python)
93	GV-RCNN		code	88.94 %	94.52 %	86.24 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
94	TBD			88.94 %	92.03 %	86.35 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
95	SPNet		code	88.92 %	92.29 %	86.16 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
96	Sem-Aug v1		code	88.92 %	92.59 %	84.29 %	0.04 s	GPU @ 3.5 Ghz (Python)
97	H^23D R-CNN		code	88.87 %	92.85 %	86.07 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
J. Deng, W. Zhou, Y. Zhang and H. Li: From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2021.
98	Pyramid R-CNN			88.84 %	92.19 %	86.21 %	0.07 s	1 core @ 2.5 Ghz (C/C++)
J. Mao, M. Niu, H. Bai, X. Liang, H. Xu and C. Xu: Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection. ICCV 2021.
99	CityBrainLab-CT3D		code	88.83 %	92.36 %	84.07 %	0.07 s	1 core @ 2.5 Ghz (Python + C/C++)
H. Sheng, S. Cai, Y. Liu, B. Deng, J. Huang, X. Hua and M. Zhao: Improving 3D Object Detection with Channel- wise Transformer. ICCV 2021.
100	Voxel R-CNN		code	88.83 %	94.85 %	86.13 %	0.04 s	GPU @ 3.0 Ghz (C/C++)
J. Deng, S. Shi, P. Li, W. Zhou, Y. Zhang and H. Li: Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection . AAAI 2021.
101	HVNet			88.82 %	92.83 %	83.38 %	0.03 s	GPU @ 2.0 Ghz (Python)
M. Ye, S. Xu and T. Cao: HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection. CVPR 2020.
102	GLENet			88.81 %	92.22 %	84.13 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
103	AGS-SSD[la]			88.80 %	92.61 %	85.44 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
104	FV2P v2			88.80 %	92.22 %	84.24 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
105	mbdf-netv1		code	88.77 %	94.45 %	83.90 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
106	SRIF-RCNN			88.77 %	92.10 %	86.06 %	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.
107	Anonymous			88.75 %	92.09 %	84.06 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
108	PV-RCNN++		code	88.74 %	92.66 %	85.97 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
109	SPG		code	88.70 %	94.33 %	85.98 %	0.09 s	1 core @ 2.5 Ghz (Python + C/C++)
Q. Xu, Y. Zhou, W. Wang, C. Qi and D. Anguelov: SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation. Proceedings of the IEEE conference on computer vision and pattern recognition (ICCV) 2021.
110	MVMM		code	88.70 %	92.17 %	85.47 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
111	ISE-RCNN-PV			88.69 %	92.31 %	86.10 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
112	DCAN-Second		code	88.68 %	92.76 %	85.32 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
113	SIENet		code	88.65 %	92.38 %	86.03 %	0.08 s	1 core @ 2.5 Ghz (Python)
Z. Li, Y. Yao, Z. Quan, W. Yang and J. Xie: SIENet: Spatial Information Enhancement Network for 3D Object Detection from Point Cloud. 2021.
114	P2V-RCNN			88.63 %	92.72 %	86.14 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.
115	FromVoxelToPoint		code	88.61 %	92.23 %	86.11 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to- Point Decoder. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
116	RangeIoUDet			88.59 %	92.28 %	85.83 %	0.02 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, Z. Zhang, M. Zhang, X. Zhao and S. Pu: RangeIoUDet: Range Image Based Real-Time 3D Object Detector Optimized by Intersection Over Union. CVPR 2021.
117	WGVRF			88.56 %	92.45 %	85.69 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
118	DCCA			88.55 %	92.29 %	85.85 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
119	GVNet-V2			88.54 %	92.26 %	85.71 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
120	EPNet		code	88.47 %	94.22 %	83.69 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
T. Huang, Z. Liu, X. Chen and X. Bai: EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection. ECCV 2020.
121	GT3D			88.46 %	92.22 %	83.81 %	0.1 s	1 core @ 2.5 Ghz (Python)
122	CenterNet3D			88.46 %	91.80 %	83.62 %	0.04 s	GPU @ 1.5 Ghz (Python)
G. Wang, B. Tian, Y. Ai, T. Xu, L. Chen and D. Cao: CenterNet3D:An Anchor free Object Detector for Autonomous Driving. 2020.
123	GVNet		code	88.43 %	92.19 %	85.63 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
124	USVLab BSAODet (S)			88.42 %	92.19 %	85.55 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
125	RangeRCNN			88.40 %	92.15 %	85.74 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, M. Zhang, Z. Zhang, X. Zhao and S. Pu: RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representation. arXiv preprint arXiv:2009.00206 2020.
126	Patches			88.39 %	92.72 %	83.19 %	0.15 s	GPU @ 2.0 Ghz
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
127	3D IoU-Net			88.38 %	94.76 %	81.93 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, S. Krylov, Y. Ding and L. Shao: 3D IoU-Net: IoU Guided 3D Object Detector for Point Clouds. arXiv preprint arXiv:2004.04962 2020.
128	StructuralIF			88.38 %	91.78 %	85.67 %	0.02 s	8 cores @ 2.5 Ghz (Python)
J. Pei An: Deep structural information fusion for 3D object detection on LiDAR-camera system. Accepted in CVIU 2021.
129	CSVoxel-RCNN			88.37 %	92.07 %	85.51 %	0.03 s	GPU @ 1.0 Ghz (Python)
130	NV-RCNN			88.36 %	91.41 %	85.72 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
131	DKDet			88.32 %	92.21 %	85.46 %	0.03 s	GPU @ 2.5 Ghz (Python + C/C++)
132	CenterFuse			88.31 %	91.54 %	83.39 %	0.059 sec/frame	2 x V100
133	SARFE			88.28 %	92.35 %	85.50 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
134	FusionDetv2-v4			88.27 %	92.05 %	85.38 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
135	TBD			88.26 %	91.44 %	85.44 %	0.06 s	GPU @ 2.5 Ghz (Python)
136	KPP3D		code	88.25 %	93.93 %	83.26 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
137	CLOCs_SecCas			88.23 %	91.16 %	82.63 %	0.1 s	1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
138	SPVB-SSD			88.23 %	91.82 %	85.46 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
139	UberATG-MMF			88.21 %	93.67 %	81.99 %	0.08 s	GPU @ 2.5 Ghz (Python)
M. Liang*, B. Yang*, Y. Chen, R. Hu and R. Urtasun: Multi-Task Multi-Sensor Fusion for 3D Object Detection. CVPR 2019.
140	Patches - EMP			88.17 %	94.49 %	84.75 %	0.5 s	GPU @ 2.5 Ghz (Python)
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
141	SRDL			88.17 %	92.01 %	85.43 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
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142	CF-cd-io-tv			88.16 %	91.32 %	83.26 %	1 s	1 core @ 2.5 Ghz (C/C++)
143	FusionDetv1			88.13 %	91.91 %	85.40 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
144	PSA-Det3D			88.13 %	92.08 %	85.35 %	0.1 s	GPU @ 2.5 Ghz (Python)
145	PointPainting			88.11 %	92.45 %	83.36 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
146	SERCNN			88.10 %	94.11 %	83.43 %	0.1 s	1 core @ 2.5 Ghz (Python)
D. Zhou, J. Fang, X. Song, L. Liu, J. Yin, Y. Dai, H. Li and R. Yang: Joint 3D Instance Segmentation and Object Detection for Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020.
147	Associate-3Ddet		code	88.09 %	91.40 %	82.96 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
L. Du, X. Ye, X. Tan, J. Feng, Z. Xu, E. Ding and S. Wen: Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection. The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
148	HotSpotNet			88.09 %	94.06 %	83.24 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
149	Faraway-Frustum		code	88.08 %	91.90 %	85.35 %	0.1 s	GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-frustum: Dealing with lidar sparsity for 3D object detection using fusion. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) 2021.
150	NV2P-RCNN			88.08 %	93.44 %	85.32 %	0.1 s	GPU @ 2.5 Ghz (Python)
151	VPN			88.06 %	90.94 %	83.24 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
152	TBD			88.04 %	91.31 %	84.79 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
153	SC-Voxel-RCNN			88.02 %	91.45 %	85.22 %	0.12 s	GPU @ 1.0 Ghz (Python)
154	CZY			88.00 %	91.85 %	85.22 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
155	UberATG-HDNET			87.98 %	93.13 %	81.23 %	0.05 s	GPU @ 2.5 Ghz (Python)
B. Yang, M. Liang and R. Urtasun: HDNET: Exploiting HD Maps for 3D Object Detection. 2nd Conference on Robot Learning (CoRL) 2018.
156	TCDVF			87.94 %	91.21 %	84.66 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
157	DGT-Det3D			87.88 %	91.70 %	85.14 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
158	FusionDetv2-v5			87.86 %	91.92 %	83.07 %	0.05 s	1 core @ 2.5 Ghz (Java + C/C++)
159	Fast Point R-CNN			87.84 %	90.87 %	80.52 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, S. Liu, X. Shen and J. Jia: Fast Point R-CNN. Proceedings of the IEEE international conference on computer vision (ICCV) 2019.
160	CSNet			87.84 %	92.23 %	82.93 %	0.1 s	1 core @ 2.5 Ghz (Python)
161	CF-ctdep-tv-ta			87.81 %	90.73 %	84.97 %	1 s	1 core @ 2.5 Ghz (C/C++)
162	MMLab-PartA^2		code	87.79 %	91.70 %	84.61 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
163	cp-tv-kp-io-sc			87.78 %	90.98 %	84.04 %	1 s	1 core @ 2.5 Ghz (C/C++)
164	SIF			87.76 %	91.44 %	85.15 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
165	MVAF-Net		code	87.73 %	91.95 %	85.00 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
G. Wang, B. Tian, Y. Zhang, L. Chen, D. Cao and J. Wu: Multi-View Adaptive Fusion Network for 3D Object Detection. arXiv preprint arXiv:2011.00652 2020.
166	Reprod-Two-Branch			87.69 %	90.69 %	84.72 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
167	DKAnet			87.68 %	91.07 %	84.03 %	0.05 s	1 core @ 2.0 Ghz (Python)
168	DVFENet			87.68 %	90.93 %	84.60 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection. Neurocomputing 2021.
169	S-AT GCN			87.68 %	90.85 %	84.20 %	0.02 s	GPU @ 2.0 Ghz (Python)
L. Wang, C. Wang, X. Zhang, T. Lan and J. Li: S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection. CoRR 2021.
170	TBD			87.67 %	91.02 %	82.42 %	0.1 s	1 core @ 2.5 Ghz (Python)
171	CFF-tv-v2			87.67 %	90.70 %	84.58 %	1 s	1 core @ 2.5 Ghz (C/C++)
172	CFF-ep25			87.66 %	90.60 %	84.71 %	1 s	1 core @ 2.5 Ghz (C/C++)
173	TBD			87.62 %	90.86 %	82.29 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
174	CF-base-tv			87.60 %	90.28 %	84.52 %	1 s	1 core @ 2.5 Ghz (C/C++)
175	AutoAlign			87.60 %	91.72 %	84.44 %	0.1 s	1 core @ 2.5 Ghz (Python)
176	KeyFuse2B			87.59 %	90.70 %	84.58 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
177	MODet			87.56 %	90.80 %	82.69 %	0.05 s	GTX1080Ti
Y. Zhang, Z. Xiang, C. Qiao and S. Chen: Accurate and Real-Time Object Detection Based on Bird's Eye View on 3D Point Clouds. 2019 International Conference on 3D Vision (3DV) 2019.
178	CFF-tv			87.55 %	90.56 %	84.59 %	1 s	1 core @ 2.5 Ghz (C/C++)
179	cff-tv-v2-ep25			87.55 %	90.26 %	84.53 %	1 s	1 core @ 2.5 Ghz (C/C++)
180	AB3DMOT		code	87.53 %	91.99 %	81.03 %	0.0047s	1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
181	TBD			87.51 %	90.76 %	80.15 %	0.1 s	1 core @ 2.5 Ghz (Python)
182	DTFI			87.51 %	91.01 %	84.25 %	0.03 s	1 core @ 2.5 Ghz (Python)
183	CF-ctdep-tv			87.50 %	90.56 %	84.65 %	1 s	1 core @ 2.5 Ghz (C/C++)
184	PointRGCN			87.49 %	91.63 %	80.73 %	0.26 s	GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
185	Anonymous			87.48 %	90.98 %	84.22 %	1	1 core @ 2.5 Ghz (Python)
186	MGAF-3DSSD		code	87.47 %	92.70 %	82.19 %	0.1 s	1 core @ 2.5 Ghz (Python)
J. Li, H. Dai, L. Shao and Y. Ding: Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
187	PC-CNN-V2			87.40 %	91.19 %	79.35 %	0.5 s	GPU @ 2.5 Ghz (Matlab + C/C++)
X. Du, M. Ang, S. Karaman and D. Rus: A General Pipeline for 3D Detection of Vehicles. 2018 IEEE International Conference on Robotics and Automation (ICRA) 2018.
188	HS3D		code	87.40 %	91.97 %	82.85 %	0.12 s	1 core @ 2.5 Ghz (Python + C/C++)
189	PVTr			87.39 %	91.21 %	84.77 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
190	MMLab-PointRCNN		code	87.39 %	92.13 %	82.72 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
191	Sem-Aug			87.37 %	93.35 %	82.43 %	0.08 s	GPU @ 2.5 Ghz (Python)
192	MAFF-Net(DAF-Pillar)			87.34 %	90.79 %	77.66 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
Z. Zhang, Z. Liang, M. Zhang, X. Zhao, Y. Ming, T. Wenming and S. Pu: MAFF-Net: Filter False Positive for 3D Vehicle Detection with Multi-modal Adaptive Feature Fusion. arXiv preprint arXiv:2009.10945 2020.
193	KeyPoint-IoUHead			87.32 %	90.36 %	83.23 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
194	ZMMPP			87.25 %	90.47 %	82.42 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
195	HRI-VoxelFPN			87.21 %	92.75 %	79.82 %	0.02 s	GPU @ 2.5 Ghz (Python + C/C++)
H. Kuang, B. Wang, J. An, M. Zhang and Z. Zhang: Voxel-FPN:multi-scale voxel feature aggregation in 3D object detection from point clouds. sensors 2020.
196	epBRM		code	87.13 %	90.70 %	81.92 %	0.1 s	GPU @ >3.5 Ghz (Python + C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
197	3D_att			87.09 %	93.14 %	81.92 %	0.17 s	GPU @ 2.5 Ghz (Python)
198	Contrastive PP		code	87.06 %	92.99 %	81.96 %	0.01 s	1 core @ 2.5 Ghz (Python)
199	DVF			87.05 %	92.76 %	84.13 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
200	T_PVRCNN			86.97 %	91.63 %	82.20 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
201	SARPNET			86.92 %	92.21 %	81.68 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
Y. Ye, H. Chen, C. Zhang, X. Hao and Z. Zhang: SARPNET: Shape Attention Regional Proposal Network for LiDAR-based 3D Object Detection. Neurocomputing 2019.
202	cff-tv-t			86.92 %	91.04 %	80.46 %	1 s	1 core @ 2.5 Ghz (C/C++)
203	CZY_3917			86.90 %	90.69 %	82.22 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
204	CF-base-train			86.88 %	90.03 %	83.16 %	1 s	1 core @ 2.5 Ghz (C/C++)
205	Self-Calib Conv			86.86 %	90.00 %	83.88 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
206	T_PVRCNN_V2			86.85 %	91.54 %	81.82 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
207	ARPNET			86.81 %	90.06 %	79.41 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
208	C-GCN			86.78 %	91.11 %	80.09 %	0.147 s	GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
209	IoU-2B			86.74 %	90.92 %	80.40 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
210	cp-tv-kp			86.58 %	89.58 %	83.64 %	1 s	1 core @ 2.5 Ghz (C/C++)
211	PointPillars		code	86.56 %	90.07 %	82.81 %	16 ms	1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
212	TANet		code	86.54 %	91.58 %	81.19 %	0.035s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
213	cp-tv			86.52 %	89.55 %	83.45 %	1 s	1 core @ 2.5 Ghz (C/C++)
214	SCNet			86.48 %	90.07 %	81.30 %	0.04 s	GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
215	CF-ctdep-train			86.46 %	89.57 %	82.03 %	1 s	1 core @ 2.5 Ghz (C/C++)
216	CSNet8306		code	86.44 %	92.57 %	81.36 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
217	SegVoxelNet			86.37 %	91.62 %	83.04 %	0.04 s	1 core @ 2.5 Ghz (Python)
H. Yi, S. Shi, M. Ding, J. Sun, K. Xu, H. Zhou, Z. Wang, S. Li and G. Wang: SegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloud. ICRA 2020.
218	Dune-DCF-e09			86.36 %	89.33 %	81.77 %	1 s	1 core @ 2.5 Ghz (C/C++)
219	Dune-DCF-e11			86.32 %	89.32 %	81.78 %	1 s	1 core @ 2.5 Ghz (C/C++)
220	PP-PCdet		code	86.32 %	89.86 %	81.62 %	0.01 s	1 core @ 2.5 Ghz (Python)
221	3D IoU Loss			86.22 %	91.36 %	81.20 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
D. Zhou, J. Fang, X. Song, C. Guan, J. Yin, Y. Dai and R. Yang: IoU Loss for 2D/3D Object Detection. International Conference on 3D Vision (3DV) 2019.
222	Dune-DCF-e15			86.21 %	88.99 %	81.62 %	1 s	1 core @ 2.5 Ghz (C/C++)
223	TBD_BD		code	86.12 %	91.00 %	81.66 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
224	CrazyTensor-CF			86.10 %	89.13 %	81.61 %	1 s	1 core @ 2.5 Ghz (C/C++)
225	City-CF-fixed			86.09 %	89.94 %	81.73 %	1 s	1 core @ 2.5 Ghz (C/C++)
226	R-GCN			86.05 %	91.91 %	81.05 %	0.16 s	GPU @ 2.5 Ghz (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
227	UberATG-PIXOR++			86.01 %	93.28 %	80.11 %	0.035 s	GPU @ 2.5 Ghz (Python)
B. Yang, M. Liang and R. Urtasun: HDNET: Exploiting HD Maps for 3D Object Detection. 2nd Conference on Robot Learning (CoRL) 2018.
228	SSL_PP		code	85.93 %	92.19 %	80.40 %	16ms	GPU @ 1.5 Ghz (Python)
229	CSNet8299		code	85.91 %	91.64 %	80.95 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
230	Sem-Aug-PointRCNN++			85.88 %	91.68 %	83.37 %	0.1 s	8 cores @ 3.0 Ghz (Python)
231	DASS			85.85 %	91.74 %	80.97 %	0.09 s	1 core @ 2.0 Ghz (Python)
O. Unal, L. Van Gool and D. Dai: Improving Point Cloud Semantic Segmentation by Learning 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2021.
232	F-ConvNet		code	85.84 %	91.51 %	76.11 %	0.47 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
233	City-CF			85.83 %	89.20 %	81.61 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
234	PI-RCNN			85.81 %	91.44 %	81.00 %	0.1 s	1 core @ 2.5 Ghz (Python)
L. Xie, C. Xiang, Z. Yu, G. Xu, Z. Yang, D. Cai and X. He: PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module. AAAI 2020 : The Thirty-Fourth AAAI Conference on Artificial Intelligence 2020.
235	LazyTorch-CP-Infer-O			85.74 %	89.19 %	81.35 %	1 s	1 core @ 2.5 Ghz (C/C++)
236	PointRGBNet			85.73 %	91.39 %	80.68 %	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.
237	AFTD			85.63 %	90.61 %	82.28 %	1 s	1 core @ 2.5 Ghz (Python + C/C++)
238	LazyTorch-CP-Small-P			85.63 %	89.10 %	81.27 %	1 s	1 core @ 2.5 Ghz (C/C++)
239	CrazyTensor-CP			85.55 %	87.94 %	82.63 %	1 s	1 core @ 2.5 Ghz (Python)
240	Sem-Aug-PointRCNN		code	85.50 %	89.75 %	83.13 %	0.1 s	GPU @ 3.5 Ghz (C/C++)
241	variance_point			85.39 %	91.90 %	81.13 %	0.05 s	1 core @ 2.5 Ghz (Python)
242	UberATG-ContFuse			85.35 %	94.07 %	75.88 %	0.06 s	GPU @ 2.5 Ghz (Python)
M. Liang, B. Yang, S. Wang and R. Urtasun: Deep Continuous Fusion for Multi-Sensor 3D Object Detection. ECCV 2018.
243	new_stereo			85.24 %	90.74 %	82.10 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
244	PSM_stereo			85.12 %	90.26 %	80.21 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
245	PFF3D		code	85.08 %	89.61 %	80.42 %	0.05 s	GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
246	CenterPoint (pcdet)			85.05 %	88.47 %	81.19 %	0.051 sec/frame	2 x V100
247	AVOD		code	84.95 %	89.75 %	78.32 %	0.08 s	Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
248	WS3D			84.93 %	90.96 %	77.96 %	0.1 s	GPU @ 2.5 Ghz (Python)
Q. Meng, W. Wang, T. Zhou, J. Shen, L. Van Gool and D. Dai: Weakly Supervised 3D Object Detection from Lidar Point Cloud. 2020.
249	AVOD-FPN		code	84.82 %	90.99 %	79.62 %	0.1 s	Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
250	MF			84.72 %	88.58 %	78.17 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
251	F-PointNet		code	84.67 %	91.17 %	74.77 %	0.17 s	GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
252	FusionDetv2-baseline			84.31 %	90.38 %	79.23 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
253	3DBN			83.94 %	89.66 %	76.50 %	0.13s	1080Ti (Python+C/C++)
X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.
254	MLOD		code	82.68 %	90.25 %	77.97 %	0.12 s	GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
255	BirdNet+		code	81.85 %	87.43 %	75.36 %	0.11 s	Titan Xp (PyTorch)
A. Barrera, J. Beltrán, C. Guindel, J. Iglesias and F. García: BirdNet+: Two-Stage 3D Object Detection in LiDAR through a Sparsity-Invariant Bird’s Eye View. IEEE Access 2021.
256	TBD			81.53 %	87.90 %	74.26 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
257	FD			81.47 %	88.34 %	75.07 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
258	CZY			81.21 %	89.10 %	76.13 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
259	AEC3D			80.37 %	86.81 %	74.26 %	18 ms	GPU @ 2.5 Ghz (Python)
260	DMF			80.29 %	84.64 %	76.05 %	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.
261	UberATG-PIXOR			80.01 %	83.97 %	74.31 %	0.035 s	TITAN Xp (Python)
B. Yang, W. Luo and R. Urtasun: PIXOR: Real-time 3D Object Detection from Point Clouds. CVPR 2018.
262	MV3D (LIDAR)			78.98 %	86.49 %	72.23 %	0.24 s	GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
263	DSGN++		code	78.94 %	88.55 %	69.74 %	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.
264	MV3D			78.93 %	86.62 %	69.80 %	0.36 s	GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
265	StereoDistill			78.59 %	89.03 %	69.34 %	0.4 s	1 core @ 2.5 Ghz (Python)
266	Anonymous			77.40 %	90.76 %	70.00 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
267	SD3DOD			76.96 %	86.82 %	70.05 %	0.04 s	GPU @ 2.5 Ghz (Python)
268	MMLAB LIGA-Stereo		code	76.78 %	88.15 %	67.40 %	0.4 s	1 core @ 2.5 Ghz (Python + C/C++)
X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
269	RCD			75.83 %	82.26 %	69.61 %	0.1 s	GPU @ 2.5 Ghz (Python)
A. Bewley, P. Sun, T. Mensink, D. Anguelov and C. Sminchisescu: Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection. Conference on Robot Learning (CoRL) 2020.
270	LaserNet			74.52 %	79.19 %	68.45 %	12 ms	GPU @ 2.5 Ghz (C/C++)
G. Meyer, A. Laddha, E. Kee, C. Vallespi-Gonzalez and C. Wellington: LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
271	PL++ (SDN+GDC)		code	73.80 %	84.61 %	65.59 %	0.6 s	GPU @ 2.5 Ghz (C/C++)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.
272	SNVC		code	73.61 %	86.88 %	64.49 %	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.
273	A3DODWTDA		code	73.26 %	79.58 %	62.77 %	0.08 s	GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
274	Anonymous			71.23 %	86.67 %	64.08 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
275	ppt			70.21 %	72.17 %	65.26 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
276	Complexer-YOLO			68.96 %	77.24 %	64.95 %	0.06 s	GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
277	PS++		code	68.36 %	84.64 %	59.01 %	PS++ s	1 core @ 2.5 Ghz (C/C++)
278	TopNet-Retina			68.16 %	80.16 %	63.43 %	52ms	GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
279	CG-Stereo			66.44 %	85.29 %	58.95 %	0.57 s	GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
280	PLUME			66.27 %	82.97 %	56.70 %	0.15 s	GPU @ 2.5 Ghz (Python)
Y. Wang, B. Yang, R. Hu, M. Liang and R. Urtasun: PLUME: Efficient 3D Object Detection from Stereo Images. IROS 2021.
281	CDN		code	66.24 %	83.32 %	57.65 %	0.6 s	GPU @ 2.5 Ghz (Python)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems (NeurIPS) 2020.
282	PS		code	65.33 %	83.75 %	56.14 %	PS s	1 core @ 2.5 Ghz (C/C++)
283	DSGN		code	65.05 %	82.90 %	56.60 %	0.67 s	NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
284	TopNet-DecayRate			64.60 %	79.74 %	58.04 %	92 ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
285	UPF_3D			63.58 %	85.53 %	56.56 %	0.29 s	1 core @ 2.5 Ghz (Python)
286	BirdNet+ (legacy)		code	63.33 %	84.80 %	61.23 %	0.1 s	Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird’s Eye View. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
287	3D FCN			61.67 %	70.62 %	55.61 %	>5 s	1 core @ 2.5 Ghz (C/C++)
B. Li: 3D Fully Convolutional Network for Vehicle Detection in Point Cloud. IROS 2017.
288	CDN-PL++			61.04 %	81.27 %	52.84 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems 2020.
289	BirdNet			59.83 %	84.17 %	57.35 %	0.11 s	Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
290	TopNet-UncEst			59.67 %	72.05 %	51.67 %	0.09 s	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
291	RT3D-GMP			59.00 %	69.14 %	45.49 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
292	Disp R-CNN (velo)		code	58.62 %	79.76 %	47.73 %	0.387 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
293	ESGN			58.12 %	78.10 %	49.28 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
294	Pseudo-LiDAR++		code	58.01 %	78.31 %	51.25 %	0.4 s	GPU @ 2.5 Ghz (Python)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.
295	Disp R-CNN		code	57.98 %	79.61 %	47.09 %	0.387 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
296	ZoomNet		code	54.91 %	72.94 %	44.14 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
L. Z. Xu: ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2020.
297	ART			54.23 %	75.05 %	48.19 %	20ms s	1 core @ 2.5 Ghz (C/C++)
298	VoxelJones		code	53.96 %	66.21 %	47.66 %	.18 s	1 core @ 2.5 Ghz (Python + C/C++)
M. Motro and J. Ghosh: Vehicular Multi-object Tracking with Persistent Detector Failures. arXiv preprint arXiv:1907.11306 2019.
299	TopNet-HighRes			53.05 %	67.84 %	46.99 %	101ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
300	OC Stereo		code	51.47 %	68.89 %	42.97 %	0.35 s	1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
301	YOLOStereo3D		code	50.28 %	76.10 %	36.86 %	0.1 s	GPU 1080Ti
Y. Liu, L. Wang and M. Liu: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
302	RT3DStereo			46.82 %	58.81 %	38.38 %	0.08 s	GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
303	Pseudo-Lidar		code	45.00 %	67.30 %	38.40 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Wang, W. Chao, D. Garg, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
304	RT3D			44.00 %	56.44 %	42.34 %	0.09 s	GPU @ 1.8Ghz
Y. Zeng, Y. Hu, S. Liu, J. Ye, Y. Han, X. Li and N. Sun: RT3D: Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving. IEEE Robotics and Automation Letters 2018.
305	Stereo CenterNet			42.12 %	62.97 %	35.37 %	0.04 s	GPU @ 2.5 Ghz (Python)
Y. Shi, Y. Guo, Z. Mi and X. Li: Stereo CenterNet-based 3D object detection for autonomous driving. Neurocomputing 2022.
306	Stereo R-CNN		code	41.31 %	61.92 %	33.42 %	0.3 s	GPU @ 2.5 Ghz (Python)
P. Li, X. Chen and S. Shen: Stereo R-CNN based 3D Object Detection for Autonomous Driving. CVPR 2019.
307	SparseLiDAR_fusion			38.99 %	52.57 %	32.86 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
308	GCDR			37.34 %	50.85 %	30.51 %	0.28 s	1 core @ 2.5 Ghz (Python)
309	VMDet_Boost			33.13 %	46.17 %	28.80 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
310	StereoFENet			32.96 %	49.29 %	25.90 %	0.15 s	1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
311	Anonymous			30.81 %	43.11 %	26.81 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
312	Digging_M3D			28.84 %	39.74 %	26.08 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
313	Mobile Stereo R-CNN			28.78 %	44.51 %	22.30 %	1.8 s	NVIDIA Jetson TX2
M. Hussein, M. Khalil and B. Abdullah: 3D Object Detection using Mobile Stereo R- CNN on Nvidia Jetson TX2. International Conference on Advanced Engineering, Technology and Applications (ICAETA) 2021.
314	VMDet			28.50 %	41.41 %	23.88 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
315	Anonymous			27.70 %	37.81 %	24.61 %	40 s	1 core @ 2.5 Ghz (C/C++)
316	SARM3D			26.81 %	34.17 %	23.68 %	0.03 s	GPU @ 2.5 Ghz (Python)
317	CMKD*			25.82 %	38.98 %	22.80 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
318	MoGDE			25.60 %	38.38 %	22.91 %	0.03 s	GPU @ 2.5 Ghz (Python)
319	LPCG-Monoflex			24.81 %	35.96 %	21.86 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
320	Anonymous			24.78 %	33.38 %	22.00 %	40 s	1 core @ 2.5 Ghz (C/C++)
321	DD3Dv2		code	24.67 %	35.70 %	21.73 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
322	Mix-Teaching			24.23 %	35.74 %	20.80 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
323	MonoInsight			24.23 %	34.85 %	20.87 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
324	anonymity			23.92 %	36.92 %	21.68 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
325	PS-fld		code	23.76 %	32.64 %	20.64 %	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.
326	SCSTSV-MonoFlex			23.71 %	34.59 %	20.41 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
327	anonymity			23.61 %	36.80 %	21.31 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
328	MonoDDE			23.46 %	33.58 %	20.37 %	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.
329	DD3D		code	23.41 %	32.35 %	20.42 %	n/a s	1 core @ 2.5 Ghz (C/C++)
D. Park, R. Ambrus, V. Guizilini, J. Li and A. Gaidon: Is Pseudo-Lidar needed for Monocular 3D Object detection?. IEEE/CVF International Conference on Computer Vision (ICCV) .
330	DID-M3D			22.76 %	32.95 %	19.83 %	0.04 s	1 core @ 2.5 Ghz (Python)
331	MonoDistill			22.59 %	31.87 %	19.72 %	0.04 s	1 core @ 2.5 Ghz (Python)
332	zongmuDistill			22.56 %	33.48 %	19.88 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
333	OPA-3D		code	22.53 %	33.54 %	19.22 %	0.04 s	1 core @ 3.5 Ghz (Python)
334	Shape-Aware			22.13 %	32.55 %	18.94 %	0.05 s	1 core @ 2.5 Ghz (Python)
335	MonoCon		code	22.10 %	31.12 %	19.00 %	0.02 s	GPU @ 2.5 Ghz (Python)
T. Xianpeng Liu: Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection. AAAI 2022.
336	Anonymous			22.05 %	31.75 %	19.44 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
337	gupnet_se			21.98 %	32.82 %	18.70 %	0.03s	1 core @ 2.5 Ghz (C/C++)
338	ZongmuMono3d		code	21.78 %	33.18 %	18.71 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
339	MDNet			21.71 %	33.31 %	18.49 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
340	Lite-FPN-GUPNet			21.53 %	31.68 %	18.38 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
341	DDS		code	21.50 %	32.55 %	18.25 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
342	MonoDETR		code	21.45 %	32.20 %	18.68 %	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.
343	OBMO_GUPNet			21.41 %	30.81 %	18.37 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
344	M3DGAF			21.39 %	31.34 %	19.28 %	0.07 s	1 core @ 2.5 Ghz (Python)
345	mono3d		code	21.39 %	32.17 %	18.47 %	TBD	TBD
346	SGM3D			21.37 %	31.49 %	18.43 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
Z. Zhou, L. Du, X. Ye, Z. Zou, X. Tan, E. Ding, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection. 2021.
347	monopd		code	21.29 %	32.12 %	18.08 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
348	DEPT			21.22 %	30.85 %	18.47 %	0.03 s	1 core @ 2.5 Ghz (Python)
349	GUPNet		code	21.19 %	30.29 %	18.20 %	NA s	1 core @ 2.5 Ghz (Python + C/C++)
Y. Lu, X. Ma, L. Yang, T. Zhang, Y. Liu, Q. Chu, J. Yan and W. Ouyang: Geometry Uncertainty Projection Network for Monocular 3D Object Detection. arXiv preprint arXiv:2107.13774 2021.
350	HBD			20.91 %	29.87 %	18.22 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
351	GPENet		code	20.79 %	30.31 %	18.21 %	0.02 s	GPU @ 2.5 Ghz (Python)
352	mono3d			20.75 %	31.58 %	17.66 %	0.03 s	GPU @ 2.5 Ghz (Python)
353	LT-M3OD			20.74 %	29.40 %	17.83 %	0.03 s	1 core @ 2.5 Ghz (Python)
354	HomoLoss(monoflex)		code	20.68 %	29.60 %	17.81 %	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.
355	MonoFlex			20.67 %	30.95 %	17.72 %	0.03 s	1 core @ 2.5 Ghz (Python)
356	Anonymous			20.47 %	33.17 %	17.31 %	40 s	1 core @ 2.5 Ghz (C/C++)
357	MonoGround			20.47 %	30.07 %	17.74 %	0.03 s	1 core @ 2.5 Ghz (Python)
358	EW		code	20.38 %	28.88 %	17.59 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
359	MonoDTR			20.38 %	28.59 %	17.14 %	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.
360	MonoEdge			20.35 %	28.80 %	17.57 %	0.05 s	GPU @ 2.5 Ghz (Python)
361	SAIC_ADC_Mono3D		code	20.20 %	27.09 %	18.78 %	50 s	GPU @ 2.5 Ghz (Python)
362	MonoEdge-Rotate			20.16 %	31.19 %	17.35 %	0.05 s	GPU @ 2.5 Ghz (Python)
363	MDSNet			20.14 %	32.81 %	15.77 %	0.07 s	1 core @ 2.5 Ghz (Python)
364	AutoShape		code	20.08 %	30.66 %	15.95 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
Z. Liu, D. Zhou, F. Lu, J. Fang and L. Zhang: AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.
365	MonoEdge-RCNN			20.07 %	27.62 %	16.34 %	0.05 s	1 core @ 2.5 Ghz (Python)
366	M3DSSD++		code	20.03 %	32.18 %	16.47 %	0.16s	1 core @ 2.5 Ghz (C/C++)
367	MAOLoss		code	19.95 %	28.29 %	16.94 %	0.05 s	1 core @ 2.5 Ghz (Python)
368	ANM			19.82 %	29.89 %	16.77 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
369	EM		code	19.80 %	30.61 %	16.55 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
370	MonoFlex			19.75 %	28.23 %	16.89 %	0.03 s	GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.
371	MonoEF			19.70 %	29.03 %	17.26 %	0.03 s	1 core @ 2.5 Ghz (Python)
Y. Zhou, Y. He, H. Zhu, C. Wang, H. Li and Q. Jiang: Monocular 3D Object Detection: An Extrinsic Parameter Free Approach. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
372	K3D			19.60 %	28.31 %	17.62 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
373	HomoLoss(imvoxelnet)		code	19.25 %	29.18 %	16.21 %	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.
374	MonoAug			19.19 %	28.20 %	16.15 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
375	MK3D			19.18 %	29.11 %	15.78 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
376	DFR-Net			19.17 %	28.17 %	14.84 %	0.18 s	1080 Ti (Pytorch)
Z. Zou, X. Ye, L. Du, X. Cheng, X. Tan, L. Zhang, J. Feng, X. Xue and E. Ding: The devil is in the task: Exploiting reciprocal appearance-localization features for monocular 3d object detection . ICCV 2021.
377	SwinMono3D			19.15 %	29.65 %	14.09 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
378	DLE		code	19.05 %	31.09 %	14.13 %	0.06 s	NVIDIA Tesla V100
C. Liu, S. Gu, L. Gool and R. Timofte: Deep Line Encoding for Monocular 3D Object Detection and Depth Prediction. Proceedings of the British Machine Vision Conference (BMVC) 2021.
379	PCT		code	19.03 %	29.65 %	15.92 %	0.045 s	1 core @ 2.5 Ghz (C/C++)
L. Wang, L. Zhang, Y. Zhu, Z. Zhang, T. He, M. Li and X. Xue: Progressive Coordinate Transforms for Monocular 3D Object Detection. NeurIPS 2021.
380	Anonymous		code	18.96 %	26.54 %	16.01 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
381	CaDDN		code	18.91 %	27.94 %	17.19 %	0.63 s	GPU @ 2.5 Ghz (Python)
C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.
382	monodle		code	18.89 %	24.79 %	16.00 %	0.04 s	GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for Monocular 3D Object Detection. CVPR 2021 .
383	MonoFar			18.68 %	25.89 %	16.30 %	0.04 s	1 core @ 2.5 Ghz (Python)
384	none			18.66 %	26.19 %	15.79 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
385	Neighbor-Vote			18.65 %	27.39 %	16.54 %	0.1 s	GPU @ 2.5 Ghz (Python)
X. Chu, J. Deng, Y. Li, Z. Yuan, Y. Zhang, J. Ji and Y. Zhang: Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance Voting. ACM MM 2021.
386	GrooMeD-NMS		code	18.27 %	26.19 %	14.05 %	0.12 s	1 core @ 2.5 Ghz (Python)
A. Kumar, G. Brazil and X. Liu: GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection. CVPR 2021.
387	MonoRCNN		code	18.11 %	25.48 %	14.10 %	0.07 s	GPU @ 2.5 Ghz (Python)
X. Shi, Q. Ye, X. Chen, C. Chen, Z. Chen and T. Kim: Geometry-based Distance Decomposition for Monocular 3D Object Detection. ICCV 2021.
388	Ground-Aware		code	17.98 %	29.81 %	13.08 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
Y. Liu, Y. Yuan and M. Liu: Ground-aware Monocular 3D Object Detection for Autonomous Driving. IEEE Robotics and Automation Letters 2021.
389	MP-Mono			17.96 %	25.36 %	13.84 %	0.16 s	GPU @ 2.5 Ghz (Python)
390	Aug3D-RPN			17.89 %	26.00 %	14.18 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth. 2021.
391	DDMP-3D			17.89 %	28.08 %	13.44 %	0.18 s	1 core @ 2.5 Ghz (Python)
L. Wang, L. Du, X. Ye, Y. Fu, G. Guo, X. Xue, J. Feng and L. Zhang: Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection. CVPR 2020.
392	IAFA			17.88 %	25.88 %	15.35 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
D. Zhou, X. Song, Y. Dai, J. Yin, F. Lu, M. Liao, J. Fang and L. Zhang: IAFA: Instance-Aware Feature Aggregation for 3D Object Detection from a Single Image. Proceedings of the Asian Conference on Computer Vision 2020.
393	RefinedMPL			17.60 %	28.08 %	13.95 %	0.15 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
394	Lite-FPN			17.58 %	26.67 %	14.61 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
395	Kinematic3D		code	17.52 %	26.69 %	13.10 %	0.12 s	1 core @ 1.5 Ghz (C/C++)
G. Brazil, G. Pons-Moll, X. Liu and B. Schiele: Kinematic 3D Object Detection in Monocular Video. ECCV 2020 .
396	MonoRUn		code	17.34 %	27.94 %	15.24 %	0.07 s	GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
397	RetinaMono			17.33 %	26.12 %	15.26 %	0.02 s	1 core @ 2.5 Ghz (Python)
398	AM3D			17.32 %	25.03 %	14.91 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
X. Ma, Z. Wang, H. Li, P. Zhang, W. Ouyang and X. Fan: Accurate Monocular Object Detection via Color- Embedded 3D Reconstruction for Autonomous Driving. Proceedings of the IEEE international Conference on Computer Vision (ICCV) 2019.
399	YoloMono3D		code	17.15 %	26.79 %	12.56 %	0.05 s	GPU @ 2.5 Ghz (Python)
Y. Liu, L. Wang and L. Ming: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
400	CMAN			17.04 %	25.89 %	12.88 %	0.15 s	1 core @ 2.5 Ghz (Python)
401	GAC3D			16.93 %	25.80 %	12.50 %	0.25 s	1 core @ 2.5 Ghz (Python)
M. Bui, D. Ngo, H. Pham and D. Nguyen: GAC3D: improving monocular 3D object detection with ground-guide model and adaptive convolution. 2021.
402	PatchNet		code	16.86 %	22.97 %	14.97 %	0.4 s	1 core @ 2.5 Ghz (C/C++)
X. Ma, S. Liu, Z. Xia, H. Zhang, X. Zeng and W. Ouyang: Rethinking Pseudo-LiDAR Representation. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
403	MonoAug			16.71 %	24.39 %	13.83 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
404	PGD-FCOS3D		code	16.51 %	26.89 %	13.49 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
T. Wang, X. Zhu, J. Pang and D. Lin: Probabilistic and Geometric Depth: Detecting Objects in Perspective. Conference on Robot Learning (CoRL) 2021.
405	ImVoxelNet		code	16.37 %	25.19 %	13.58 %	0.2 s	GPU @ 2.5 Ghz (Python)
D. Rukhovich, A. Vorontsova and A. Konushin: ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection. arXiv preprint arXiv:2106.01178 2021.
406	KM3D		code	16.20 %	23.44 %	14.47 %	0.03 s	1 core @ 2.5 Ghz (Python)
P. Li: Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training. 2020.
407	MM			16.09 %	24.65 %	13.99 %	1 s	1 core @ 2.5 Ghz (C/C++)
408	D4LCN		code	16.02 %	22.51 %	12.55 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
409	Keypoint-3D			15.54 %	23.16 %	11.83 %	14 s	1 core @ 2.5 Ghz (C/C++)
410	COF3D			15.39 %	25.36 %	11.34 %	200 s	1 core @ 2.5 Ghz (Python + C/C++)
411	MonoPair			14.83 %	19.28 %	12.89 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
412	Decoupled-3D			14.82 %	23.16 %	11.25 %	0.08 s	GPU @ 2.5 Ghz (C/C++)
Y. Cai, B. Li, Z. Jiao, H. Li, X. Zeng and X. Wang: Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation. AAAI 2020.
413	QD-3DT		code	14.71 %	20.16 %	12.76 %	0.03 s	GPU @ 2.5 Ghz (Python)
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.
414	SMOKE		code	14.49 %	20.83 %	12.75 %	0.03 s	GPU @ 2.5 Ghz (Python)
Z. Liu, Z. Wu and R. Tóth: SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation. 2020.
415	RTM3D		code	14.20 %	19.17 %	11.99 %	0.05 s	GPU @ 1.0 Ghz (Python)
P. Li, H. Zhao, P. Liu and F. Cao: RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving. 2020.
416	Mono3D_PLiDAR		code	13.92 %	21.27 %	11.25 %	0.1 s	NVIDIA GeForce 1080 (pytorch)
X. Weng and K. Kitani: Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud. arXiv:1903.09847 2019.
417	M3D-RPN		code	13.67 %	21.02 %	10.23 %	0.16 s	GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
418	CSoR			13.07 %	18.67 %	10.34 %	3.5 s	4 cores @ >3.5 Ghz (Python + C/C++)
L. Plotkin: PyDriver: Entwicklung eines Frameworks für räumliche Detektion und Klassifikation von Objekten in Fahrzeugumgebung. 2015.
419	MonoPSR		code	12.58 %	18.33 %	9.91 %	0.2 s	GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
420	MonoCInIS			11.64 %	22.28 %	9.95 %	0,13 s	GPU @ 2.5 Ghz (C/C++)
J. Heylen, M. De Wolf, B. Dawagne, M. Proesmans, L. Van Gool, W. Abbeloos, H. Abdelkawy and D. Reino: MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.
421	SS3D			11.52 %	16.33 %	9.93 %	48 ms	Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
422	MonoGRNet		code	11.17 %	18.19 %	8.73 %	0.04s	NVIDIA P40
Z. Qin, J. Wang and Y. Lu: MonoGRNet: A Geometric Reasoning Network for 3D Object Localization. The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) 2019.
423	MonoFENet			11.03 %	17.03 %	9.05 %	0.15 s	1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
424	MonoCInIS			10.96 %	20.42 %	9.23 %	0,14 s	GPU @ 2.5 Ghz (Python)
J. Heylen, M. De Wolf, B. Dawagne, M. Proesmans, L. Van Gool, W. Abbeloos, H. Abdelkawy and D. Reino: MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.
425	A3DODWTDA (image)		code	8.66 %	10.37 %	7.06 %	0.8 s	GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
426	TLNet (Stereo)		code	7.69 %	13.71 %	6.73 %	0.1 s	1 core @ 2.5 Ghz (Python)
Z. Qin, J. Wang and Y. Lu: Triangulation Learning Network: from Monocular to Stereo 3D Object Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
427	Shift R-CNN (mono)		code	6.82 %	11.84 %	5.27 %	0.25 s	GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
428	SparVox3D			6.39 %	10.20 %	5.06 %	0.05 s	GPU @ 2.0 Ghz (Python)
E. Balatkan and F. Kıraç: Improving Regression Performance on Monocular 3D Object Detection Using Bin-Mixing and Sparse Voxel Data. 2021 6th International Conference on Computer Science and Engineering (UBMK) 2021.
429	GS3D			6.08 %	8.41 %	4.94 %	2 s	1 core @ 2.5 Ghz (C/C++)
B. Li, W. Ouyang, L. Sheng, X. Zeng and X. Wang: GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
430	MVRA + I-FRCNN+			5.84 %	9.05 %	4.50 %	0.18 s	GPU @ 2.5 Ghz (Python)
H. Choi, H. Kang and Y. Hyun: Multi-View Reprojection Architecture for Orientation Estimation. The IEEE International Conference on Computer Vision (ICCV) Workshops 2019.
431	WeakM3D		code	5.66 %	11.82 %	4.08 %	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.
432	ROI-10D			4.91 %	9.78 %	3.74 %	0.2 s	GPU @ 3.5 Ghz (Python)
F. Manhardt, W. Kehl and A. Gaidon: ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape. Computer Vision and Pattern Recognition (CVPR) 2019.
433	CDTrack3D		code	4.61 %	7.02 %	3.73 %	0.0106 s	NVIDIA RTX 3090 GPU, i9 10850k CPU
434	3D-GCK			4.57 %	5.79 %	3.64 %	24 ms	Tesla V100
N. Gählert, J. Wan, N. Jourdan, J. Finkbeiner, U. Franke and J. Denzler: Single-Shot 3D Detection of Vehicles from Monocular RGB Images via Geometrically Constrained Keypoints in Real-Time. 2020 IEEE Intelligent Vehicles Symposium (IV) 2020.
435	FQNet			3.23 %	5.40 %	2.46 %	0.5 s	1 core @ 2.5 Ghz (Python)
L. Liu, J. Lu, C. Xu, Q. Tian and J. Zhou: Deep Fitting Degree Scoring Network for Monocular 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
436	3D-SSMFCNN		code	2.63 %	3.20 %	2.40 %	0.1 s	GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.
437	VeloFCN			0.14 %	0.02 %	0.21 %	1 s	GPU @ 2.5 Ghz (Python + C/C++)
B. Li, T. Zhang and T. Xia: Vehicle Detection from 3D Lidar Using Fully Convolutional Network. RSS 2016 .
438	MonoDET		code	0.14 %	0.25 %	0.10 %	0.04 s	1 core @ 2.5 Ghz (Python)
439	test		code	0.09 %	0.04 %	0.11 %	50 s	1 core @ 2.5 Ghz (Python)
440	multi-task CNN			0.00 %	0.00 %	0.00 %	25.1 ms	GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
441	mBoW			0.00 %	0.00 %	0.00 %	10 s	1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.

Pedestrian

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	PiFeNet			53.92 %	63.25 %	50.53 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
D. Le, H. Shi, H. Rezatofighi and J. Cai: PiFeNet: Pillar-Feature Network for Real-Time 3D Pedestrian Detection from Point Cloud. 2021.
2	CasA++			53.84 %	60.14 %	51.35 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
3	TED			53.48 %	60.13 %	50.89 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
4	DCAN-Second		code	53.18 %	60.92 %	50.56 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
5	EQ-PVRCNN		code	52.81 %	61.73 %	49.87 %	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.
6	PV-RCNN++		code	52.43 %	59.73 %	48.73 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
7	VPFNet		code	52.41 %	60.07 %	50.28 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
C. Wang, H. Chen and L. Fu: VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection. 2021.
8	Frustum-PointPillars		code	52.23 %	60.98 %	48.30 %	0.06 s	4 cores @ 3.0 Ghz (Python)
A. Paigwar, D. Sierra-Gonzalez, \. Erkent and C. Laugier: Frustum-PointPillars: A Multi-Stage Approach for 3D Object Detection using RGB Camera and LiDAR. International Conference on Computer Vision, ICCV, Workshop on Autonomous Vehicle Vision 2021.
9	CAD			52.20 %	60.23 %	49.54 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
10	TANet		code	51.38 %	60.85 %	47.54 %	0.035s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
11	CasA			51.37 %	57.95 %	49.08 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
12	ISE-RCNN			51.06 %	55.64 %	47.76 %	0.09 s	1 core @ 2.5 Ghz (Python + C/C++)
13	MMLab PV-RCNN		code	50.57 %	59.86 %	46.74 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
14	HotSpotNet			50.53 %	57.39 %	46.65 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
15	VMVS			50.34 %	60.34 %	46.45 %	0.25 s	GPU @ 2.5 Ghz (Python)
J. Ku, A. Pon, S. Walsh and S. Waslander: Improving 3D object detection for pedestrians with virtual multi-view synthesis orientation estimation. IROS 2019.
16	AVOD-FPN		code	50.32 %	58.49 %	46.98 %	0.1 s	Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
17	SPT			50.22 %	56.54 %	46.72 %	0.1 s	GPU @ 2.5 Ghz (Python)
18	variance_point			50.03 %	57.72 %	46.27 %	0.05 s	1 core @ 2.5 Ghz (Python)
19	3DSSD		code	49.94 %	60.54 %	45.73 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
20	PointPainting			49.93 %	58.70 %	46.29 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
21	SemanticVoxels			49.93 %	58.91 %	47.31 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Fei, W. Chen, P. Heidenreich, S. Wirges and C. Stiller: SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentation. MFI 2020.
22	MMLab-PartA^2		code	49.81 %	59.04 %	45.92 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
23	TBD			49.59 %	58.17 %	47.08 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
24	F-PointNet		code	49.57 %	57.13 %	45.48 %	0.17 s	GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
25	TBD			49.56 %	58.10 %	47.05 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
26	CFF-tv			49.29 %	57.83 %	46.70 %	1 s	1 core @ 2.5 Ghz (C/C++)
27	AutoAlign			49.27 %	59.28 %	46.14 %	0.1 s	1 core @ 2.5 Ghz (Python)
28	VPN			49.19 %	57.98 %	45.26 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
29	F-ConvNet		code	48.96 %	57.04 %	44.33 %	0.47 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
30	HVNet			48.86 %	54.84 %	46.33 %	0.03 s	GPU @ 2.0 Ghz (Python)
M. Ye, S. Xu and T. Cao: HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection. CVPR 2020.
31	CAT-Det			48.78 %	57.13 %	45.56 %	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.
32	PE-RCVN			48.72 %	54.09 %	46.71 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
33	STD		code	48.72 %	60.02 %	44.55 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
34	Reprod-Two-Branch			48.71 %	57.25 %	45.75 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
35	KeyFuse2B			48.64 %	56.16 %	46.20 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
36	PointPillars		code	48.64 %	57.60 %	45.78 %	16 ms	1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
37	USVLab BSAODet			48.61 %	55.76 %	46.08 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
38	FV2P v2			48.58 %	54.90 %	45.11 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
39	FS-Net			48.50 %	54.91 %	46.29 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
40	EPNet++			48.47 %	56.24 %	45.73 %	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.
41	MGAF-3DSSD		code	48.46 %	56.09 %	44.90 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
42	CFF-ep25			48.31 %	56.34 %	45.58 %	1 s	1 core @ 2.5 Ghz (C/C++)
43	Fast-CLOCs			48.27 %	57.19 %	44.55 %	0.1 s	GPU @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: Fast-CLOCs: Fast Camera-LiDAR Object Candidates Fusion for 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022.
44	FromVoxelToPoint		code	48.15 %	56.54 %	45.63 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to- Point Decoder. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
45	cff-tv-v2-ep25			48.13 %	56.48 %	45.66 %	1 s	1 core @ 2.5 Ghz (C/C++)
46	USVLab BSAODet (S)			48.10 %	54.96 %	45.65 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
47	ISE-RCNN-PV			47.85 %	55.63 %	45.80 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
48	tbd			47.84 %	57.69 %	43.99 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
49	HMFI		code	47.77 %	55.61 %	45.17 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
50	TBD			47.77 %	55.61 %	45.17 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
51	CFF-tv-v2			47.59 %	55.46 %	45.09 %	1 s	1 core @ 2.5 Ghz (C/C++)
52	Anonymous			47.47 %	52.94 %	45.41 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
53	CF-ctdep-tv-ta			47.46 %	54.36 %	45.07 %	1 s	1 core @ 2.5 Ghz (C/C++)
54	P2V-RCNN			47.36 %	54.15 %	45.10 %	0.1 s	2 cores @ 2.5 Ghz (Python)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.
55	SGNet			47.29 %	53.84 %	44.10 %	0.09 s	GPU @ 2.5 Ghz (Python)
56	CF-base-tv			47.28 %	54.77 %	44.81 %	1 s	1 core @ 2.5 Ghz (C/C++)
57	Self-Calib Conv			47.17 %	54.20 %	44.84 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
58	TCDVF			47.11 %	55.26 %	44.53 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
59	Point-GNN		code	47.07 %	55.36 %	44.61 %	0.6 s	GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
60	MVMM		code	46.84 %	53.75 %	44.87 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
61	SCNet			46.73 %	56.87 %	42.74 %	0.04 s	GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
62	cp-tv-kp			46.71 %	53.73 %	44.45 %	1 s	1 core @ 2.5 Ghz (C/C++)
63	PSA-Det3D			46.36 %	53.26 %	43.73 %	0.1 s	GPU @ 2.5 Ghz (Python)
64	CF-ctdep-tv			46.36 %	53.50 %	44.01 %	1 s	1 core @ 2.5 Ghz (C/C++)
65	MSADet			46.27 %	55.91 %	43.83 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
66	3SNet			46.25 %	52.22 %	42.89 %	0.07 s	GPU @ 2.5 Ghz (Python)
67	DGT-Det3D			46.22 %	53.98 %	43.85 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
68	MMLab-PointRCNN		code	46.13 %	54.77 %	42.84 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
69	ARPNET			45.92 %	55.48 %	42.54 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
70	CenterFuse			45.84 %	55.20 %	43.46 %	0.059 sec/frame	2 x V100
71	DSA-PV-RCNN		code	45.82 %	52.03 %	43.81 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
72	cp-tv			45.75 %	52.90 %	43.49 %	1 s	1 core @ 2.5 Ghz (C/C++)
73	SVGA-Net			45.68 %	53.09 %	43.30 %	0.03s	1 core @ 2.5 Ghz (Python + C/C++)
Q. He, Z. Wang, H. Zeng, Y. Zeng and Y. Liu: SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds. AAAI 2022.
74	SARFE			45.60 %	51.45 %	43.49 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
75	TBD			45.57 %	52.08 %	42.35 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
76	epBRM		code	45.49 %	52.48 %	42.75 %	0.10 s	1 core @ 2.5 Ghz (C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
77	TBD		code	45.46 %	52.72 %	42.53 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
78	PDV		code	45.45 %	51.95 %	43.33 %	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.
79	MLOD		code	45.40 %	55.09 %	41.42 %	0.12 s	GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
80	cp-tv-kp-io-sc			45.30 %	53.84 %	42.12 %	1 s	1 core @ 2.5 Ghz (C/C++)
81	IA-SSD (single)		code	45.07 %	52.73 %	42.75 %	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.
82	TBD			44.99 %	50.41 %	42.85 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
83	FusionDetv1			44.85 %	52.42 %	42.56 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
84	SRDL			44.84 %	52.42 %	42.56 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
85	M3DeTR		code	44.78 %	50.63 %	42.57 %	n/a s	GPU @ 1.0 Ghz (Python)
T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis and D. Manocha: M3DeTR: Multi-representation, Multi- scale, Mutual-relation 3D Object Detection with Transformers. 2021.
86	WGVRF			44.75 %	50.80 %	42.78 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
87	AFTD			44.74 %	53.94 %	42.36 %	1 s	1 core @ 2.5 Ghz (Python + C/C++)
88	AGS-SSD[la]			44.65 %	51.26 %	41.57 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
89	FusionDetv2-v5			44.64 %	51.44 %	42.32 %	0.05 s	1 core @ 2.5 Ghz (Java + C/C++)
90	Dune-DCF-e11			44.58 %	52.44 %	41.75 %	1 s	1 core @ 2.5 Ghz (C/C++)
91	ATT_SSD			44.57 %	51.26 %	42.33 %	0.01 s	1 core @ 2.5 Ghz (Python)
92	CF-cd-io-tv			44.54 %	53.64 %	41.21 %	1 s	1 core @ 2.5 Ghz (C/C++)
93	Dune-DCF-e09			44.50 %	52.64 %	41.86 %	1 s	1 core @ 2.5 Ghz (C/C++)
94	FusionDetv2-v4			44.47 %	50.88 %	42.18 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
95	IKT3D			44.45 %	49.50 %	42.58 %	0.05 s	1 core @ 2.5 Ghz (Python)
96	SIF			44.28 %	52.05 %	42.03 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
97	LazyTorch-CP-Infer-O			44.27 %	51.92 %	41.99 %	1 s	1 core @ 2.5 Ghz (C/C++)
98	KeyPoint-IoUHead			44.27 %	53.12 %	41.83 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
99	LazyTorch-CP-Small-P			44.25 %	51.84 %	41.97 %	1 s	1 core @ 2.5 Ghz (C/C++)
100	DDet			44.24 %	50.01 %	42.38 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
101	IoU-2B			44.19 %	55.31 %	40.33 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
102	ST-RCNN			44.14 %	49.78 %	41.95 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
103	DVFENet			44.12 %	50.98 %	41.62 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection. Neurocomputing 2021.
104	VCRCNN			44.09 %	48.82 %	42.19 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
105	CenterPoint (pcdet)			44.08 %	51.76 %	41.80 %	0.051 sec/frame	2 x V100
106	CrazyTensor-CP			44.06 %	51.25 %	41.50 %	1 s	1 core @ 2.5 Ghz (Python)
107	cff-tv-t			44.00 %	54.42 %	41.46 %	1 s	1 core @ 2.5 Ghz (C/C++)
108	DSASNet			43.98 %	50.55 %	40.63 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
109	CF-base-train			43.90 %	51.40 %	41.24 %	1 s	1 core @ 2.5 Ghz (C/C++)
110	City-CF-fixed			43.86 %	51.92 %	41.33 %	1 s	1 core @ 2.5 Ghz (C/C++)
111	Faraway-Frustum		code	43.85 %	52.15 %	41.68 %	0.1 s	GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-frustum: Dealing with lidar sparsity for 3D object detection using fusion. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) 2021.
112	Dune-DCF-e15			43.63 %	51.18 %	41.11 %	1 s	1 core @ 2.5 Ghz (C/C++)
113	S-AT GCN			43.43 %	50.63 %	41.58 %	0.02 s	GPU @ 2.0 Ghz (Python)
L. Wang, C. Wang, X. Zhang, T. Lan and J. Li: S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection. CoRR 2021.
114	CF-ctdep-train			43.20 %	50.14 %	40.69 %	1 s	1 core @ 2.5 Ghz (C/C++)
115	FPV-SSD			43.19 %	50.37 %	40.95 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
116	City-CF			42.95 %	49.91 %	40.61 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
117	BirdNet+		code	42.87 %	48.90 %	40.59 %	0.11 s	Titan Xp (PyTorch)
A. Barrera, J. Beltrán, C. Guindel, J. Iglesias and F. García: BirdNet+: Two-Stage 3D Object Detection in LiDAR through a Sparsity-Invariant Bird’s Eye View. IEEE Access 2021.
118	CZY			42.80 %	49.42 %	40.83 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
119	TBD			42.76 %	50.17 %	39.75 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
120	IA-SSD (multi)		code	42.61 %	51.76 %	40.51 %	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.
121	HS3D		code	42.60 %	51.58 %	39.27 %	0.12 s	1 core @ 2.5 Ghz (Python + C/C++)
122	NV-RCNN			42.58 %	49.00 %	40.39 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
123	FusionDetv2-baseline			42.53 %	47.08 %	40.71 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
124	XView			42.42 %	47.24 %	39.96 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
125	PVTr			42.26 %	48.79 %	40.27 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
126	T_PVRCNN_V2			42.21 %	50.58 %	39.81 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
127	T_PVRCNN			41.87 %	49.87 %	39.44 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
128	PFF3D		code	40.94 %	48.74 %	38.54 %	0.05 s	GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
129	CrazyTensor-CF			40.78 %	48.79 %	38.16 %	1 s	1 core @ 2.5 Ghz (C/C++)
130	NV2P-RCNN			40.71 %	46.83 %	38.86 %	0.1 s	GPU @ 2.5 Ghz (Python)
131	TBD_BD		code	40.41 %	48.27 %	38.45 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
132	ZMMPP			39.11 %	46.50 %	37.04 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
133	DSGN++		code	38.92 %	50.26 %	35.12 %	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.
134	AB3DMOT		code	38.79 %	47.51 %	35.85 %	0.0047s	1 core @ 2.5 Ghz (python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
135	BirdNet+ (legacy)		code	38.28 %	45.53 %	35.37 %	0.1 s	Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird’s Eye View. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
136	PP-PCdet		code	38.21 %	45.14 %	36.04 %	0.01 s	1 core @ 2.5 Ghz (Python)
137	CSW3D			37.96 %	49.27 %	33.83 %	0.03 s	4 cores @ 2.5 Ghz (C/C++)
J. Hu, T. Wu, H. Fu, Z. Wang and K. Ding: Cascaded Sliding Window Based Real-Time 3D Region Proposal for Pedestrian Detection. ROBIO 2019.
138	KPP3D		code	37.82 %	45.25 %	35.36 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
139	StereoDistill			37.75 %	50.79 %	34.28 %	0.4 s	1 core @ 2.5 Ghz (Python)
140	Contrastive PP		code	37.68 %	44.10 %	35.84 %	0.01 s	1 core @ 2.5 Ghz (Python)
141	CZY_3917			37.26 %	44.66 %	34.56 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
142	GT3D			36.37 %	46.02 %	32.62 %	0.1 s	1 core @ 2.5 Ghz (Python)
143	DMF			34.92 %	42.08 %	32.69 %	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	34.15 %	43.33 %	31.78 %	0.13 s	8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
145	MMLAB LIGA-Stereo		code	34.13 %	44.71 %	30.42 %	0.4 s	1 core @ 2.5 Ghz (Python + C/C++)
X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
146	AVOD		code	33.57 %	42.58 %	30.14 %	0.08 s	Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
147	SparsePool		code	33.22 %	41.55 %	29.66 %	0.13 s	8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
148	PS++		code	32.38 %	43.37 %	28.66 %	PS++ s	1 core @ 2.5 Ghz (C/C++)
149	CZY			32.05 %	39.50 %	29.90 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
150	PS		code	31.13 %	41.55 %	27.50 %	PS s	1 core @ 2.5 Ghz (C/C++)
151	CG-Stereo			29.56 %	39.24 %	25.87 %	0.57 s	GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
152	PointRGBNet			29.32 %	38.07 %	26.94 %	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.
153	Disp R-CNN		code	29.12 %	42.72 %	25.09 %	0.387 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
154	Disp R-CNN (velo)		code	28.34 %	40.21 %	24.46 %	0.387 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
155	BirdNet			23.06 %	28.20 %	21.65 %	0.11 s	Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
156	AEC3D			22.40 %	28.59 %	20.67 %	18 ms	GPU @ 2.5 Ghz (Python)
157	OC Stereo		code	20.80 %	29.79 %	18.62 %	0.35 s	1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
158	YOLOStereo3D		code	20.76 %	31.01 %	18.41 %	0.1 s	GPU 1080Ti
Y. Liu, L. Wang and M. Liu: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
159	DSGN		code	20.75 %	26.61 %	18.86 %	0.67 s	NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
160	Complexer-YOLO			18.26 %	21.42 %	17.06 %	0.06 s	GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
161	TopNet-Retina			14.57 %	18.04 %	12.48 %	52ms	GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
162	RT3D-GMP			14.22 %	19.92 %	12.83 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
163	TopNet-HighRes			13.50 %	19.43 %	11.93 %	101ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
164	Anonymous			13.47 %	20.42 %	11.64 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
165	ESGN			13.03 %	17.94 %	11.54 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
166	DD3D		code	12.51 %	18.58 %	10.65 %	n/a s	1 core @ 2.5 Ghz (C/C++)
D. Park, R. Ambrus, V. Guizilini, J. Li and A. Gaidon: Is Pseudo-Lidar needed for Monocular 3D Object detection?. IEEE/CVF International Conference on Computer Vision (ICCV) .
167	DEPT			12.29 %	18.05 %	10.50 %	0.03 s	1 core @ 2.5 Ghz (Python)
168	PS-fld		code	12.23 %	19.03 %	10.53 %	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.
169	DD3Dv2		code	12.16 %	17.74 %	10.49 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
170	anonymity			12.00 %	18.98 %	10.21 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
171	OPA-3D		code	11.01 %	17.14 %	9.94 %	0.04 s	1 core @ 3.5 Ghz (Python)
172	GCDR			10.92 %	15.65 %	9.86 %	0.28 s	1 core @ 2.5 Ghz (Python)
173	LT-M3OD			10.89 %	16.63 %	9.20 %	0.03 s	1 core @ 2.5 Ghz (Python)
174	ZongmuMono3d		code	10.65 %	16.19 %	9.04 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
175	MonoDTR			10.59 %	16.66 %	9.00 %	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.
176	mono3d		code	10.41 %	16.66 %	9.22 %	TBD	TBD
177	GUPNet		code	10.37 %	15.62 %	8.79 %	NA s	1 core @ 2.5 Ghz (Python + C/C++)
Y. Lu, X. Ma, L. Yang, T. Zhang, Y. Liu, Q. Chu, J. Yan and W. Ouyang: Geometry Uncertainty Projection Network for Monocular 3D Object Detection. arXiv preprint arXiv:2107.13774 2021.
178	CMKD*			10.28 %	16.03 %	8.85 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
179	Lite-FPN-GUPNet			10.08 %	15.73 %	8.52 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
180	MonoInsight			9.98 %	15.20 %	8.90 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
181	GPENet		code	9.96 %	15.47 %	8.55 %	0.02 s	GPU @ 2.5 Ghz (Python)
182	gupnet_se			9.85 %	14.65 %	8.32 %	0.03s	1 core @ 2.5 Ghz (C/C++)
183	SwinMono3D			9.82 %	14.55 %	8.31 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
184	HBD			9.66 %	15.26 %	8.17 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
185	SCSTSV-MonoFlex			9.62 %	14.45 %	8.14 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
186	SARM3D			9.42 %	14.32 %	8.15 %	0.03 s	GPU @ 2.5 Ghz (Python)
187	CaDDN		code	9.41 %	14.72 %	8.17 %	0.63 s	GPU @ 2.5 Ghz (Python)
C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.
188	SGM3D			9.39 %	15.39 %	8.61 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
Z. Zhou, L. Du, X. Ye, Z. Zou, X. Tan, E. Ding, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection. 2021.
189	MonoGround			9.11 %	13.67 %	7.68 %	0.03 s	1 core @ 2.5 Ghz (Python)
190	K3D			9.06 %	14.56 %	7.59 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
191	M3DGAF			8.93 %	13.42 %	7.58 %	0.07 s	1 core @ 2.5 Ghz (Python)
192	MonoFlex			8.91 %	13.26 %	7.38 %	0.03 s	1 core @ 2.5 Ghz (Python)
193	MonoFar			8.89 %	12.73 %	7.72 %	0.04 s	1 core @ 2.5 Ghz (Python)
194	SAIC_ADC_Mono3D		code	8.87 %	13.92 %	7.55 %	50 s	GPU @ 2.5 Ghz (Python)
195	MonoEdge			8.87 %	13.33 %	7.50 %	0.05 s	GPU @ 2.5 Ghz (Python)
196	HomoLoss(monoflex)		code	8.81 %	13.26 %	7.41 %	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.
197	MonoCon		code	8.73 %	13.55 %	7.83 %	0.02 s	GPU @ 2.5 Ghz (Python)
T. Xianpeng Liu: Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection. AAAI 2022.
198	MonoDDE			8.41 %	12.38 %	7.16 %	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	Mix-Teaching			8.40 %	12.34 %	7.06 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
200	ANM			8.28 %	12.83 %	7.59 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
201	MDSNet			8.18 %	12.05 %	7.03 %	0.07 s	1 core @ 2.5 Ghz (Python)
202	DCD		code	8.08 %	11.76 %	6.61 %	1 s	1 core @ 2.5 Ghz (C/C++)
203	mono3d			7.95 %	11.89 %	6.75 %	0.03 s	GPU @ 2.5 Ghz (Python)
204	MonoAug			7.94 %	12.66 %	6.64 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
205	LPCG-Monoflex			7.92 %	12.11 %	6.61 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
206	RefinedMPL			7.92 %	13.09 %	7.25 %	0.15 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
207	Shape-Aware			7.65 %	11.69 %	6.35 %	0.05 s	1 core @ 2.5 Ghz (Python)
208	MonoRUn		code	7.59 %	11.70 %	6.34 %	0.07 s	GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
209	MonoEdge-Rotate			7.53 %	11.62 %	6.79 %	0.05 s	GPU @ 2.5 Ghz (Python)
210	MonoFlex			7.36 %	10.36 %	6.29 %	0.03 s	GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.
211	MonoPair			7.04 %	10.99 %	6.29 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
212	monodle		code	6.96 %	10.73 %	6.20 %	0.04 s	GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for Monocular 3D Object Detection. CVPR 2021 .
213	MonoAug			6.87 %	10.81 %	5.66 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
214	TopNet-DecayRate			6.59 %	8.78 %	6.25 %	92 ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
215	M3DSSD++		code	6.19 %	9.24 %	5.54 %	0.16s	1 core @ 2.5 Ghz (C/C++)
216	MDNet			6.18 %	9.48 %	5.63 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
217	MK3D			6.15 %	8.76 %	5.14 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
218	Shift R-CNN (mono)		code	5.66 %	8.58 %	4.49 %	0.25 s	GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
219	MM			5.63 %	9.20 %	4.78 %	1 s	1 core @ 2.5 Ghz (C/C++)
220	Aug3D-RPN			5.22 %	7.14 %	4.21 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth. 2021.
221	Lite-FPN			4.79 %	7.13 %	4.26 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
222	COF3D			4.78 %	7.20 %	4.55 %	200 s	1 core @ 2.5 Ghz (Python + C/C++)
223	MAOLoss		code	4.74 %	6.63 %	4.19 %	0.05 s	1 core @ 2.5 Ghz (Python)
224	TopNet-UncEst			4.60 %	6.88 %	3.79 %	0.09 s	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
225	MP-Mono			4.59 %	6.04 %	3.96 %	0.16 s	GPU @ 2.5 Ghz (Python)
226	MonoPSR		code	4.56 %	7.24 %	4.11 %	0.2 s	GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
227	DFR-Net			4.52 %	6.66 %	3.71 %	0.18 s	1080 Ti (Pytorch)
Z. Zou, X. Ye, L. Du, X. Cheng, X. Tan, L. Zhang, J. Feng, X. Xue and E. Ding: The devil is in the task: Exploiting reciprocal appearance-localization features for monocular 3d object detection . ICCV 2021.
228	MoGDE			4.51 %	7.22 %	3.83 %	0.03 s	GPU @ 2.5 Ghz (Python)
229	QD-3DT		code	4.23 %	6.62 %	3.39 %	0.03 s	GPU @ 2.5 Ghz (Python)
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.
230	M3D-RPN		code	4.05 %	5.65 %	3.29 %	0.16 s	GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
231	DDMP-3D			4.02 %	5.53 %	3.36 %	0.18 s	1 core @ 2.5 Ghz (Python)
L. Wang, L. Du, X. Ye, Y. Fu, G. Guo, X. Xue, J. Feng and L. Zhang: Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection. CVPR 2020.
232	CMAN			3.96 %	5.24 %	3.18 %	0.15 s	1 core @ 2.5 Ghz (Python)
233	D4LCN		code	3.86 %	5.06 %	3.59 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
234	RT3DStereo			3.65 %	4.72 %	3.00 %	0.08 s	GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
235	MonoEF			3.05 %	4.61 %	2.85 %	0.03 s	1 core @ 2.5 Ghz (Python)
Y. Zhou, Y. He, H. Zhu, C. Wang, H. Li and Q. Jiang: Monocular 3D Object Detection: An Extrinsic Parameter Free Approach. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
236	SS3D			2.09 %	2.48 %	1.61 %	48 ms	Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
237	SparVox3D			2.05 %	2.90 %	1.69 %	0.05 s	GPU @ 2.0 Ghz (Python)
E. Balatkan and F. Kıraç: Improving Regression Performance on Monocular 3D Object Detection Using Bin-Mixing and Sparse Voxel Data. 2021 6th International Conference on Computer Science and Engineering (UBMK) 2021.
238	CDTrack3D		code	1.91 %	2.56 %	1.49 %	0.0106 s	NVIDIA RTX 3090 GPU, i9 10850k CPU
239	PGD-FCOS3D		code	1.88 %	2.82 %	1.54 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
T. Wang, X. Zhu, J. Pang and D. Lin: Probabilistic and Geometric Depth: Detecting Objects in Perspective. Conference on Robot Learning (CoRL) 2021.
240	EM		code	1.25 %	1.18 %	0.89 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
241	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.