The KITTI Vision Benchmark Suite

The 3D object detection benchmark consists of 7481 training images and 7518 test images as well as the corresponding point clouds, comprising a total of 80.256 labeled objects. For evaluation, we compute precision-recall curves. To rank the methods we compute average precision. We require that all methods use the same parameter set for all test pairs. Our development kit provides details about the data format as well as MATLAB / C++ utility functions for reading and writing the label files.

• Download left color images of object data set (12 GB)
• Download right color images, if you want to use stereo information (12 GB)
• Download the 3 temporally preceding frames (left color) (36 GB)
• Download the 3 temporally preceding frames (right color) (36 GB)
• Download Velodyne point clouds, if you want to use laser information (29 GB)
• Download camera calibration matrices of object data set (16 MB)
• Download training labels of object data set (5 MB)
• Download object development kit (1 MB) (including 3D object detection and bird's eye view evaluation code)
• Download pre-trained LSVM baseline models (5 MB) used in Joint 3D Estimation of Objects and Scene Layout (NIPS 2011). These models are referred to as LSVM-MDPM-sv (supervised version) and LSVM-MDPM-us (unsupervised version) in the tables below.
• Download reference detections (L-SVM) for training and test set (800 MB)
• Qianli Liao (NYU) has put together code to convert from KITTI to PASCAL VOC file format (documentation included, requires Emacs).
• Karl Rosaen (U.Mich) has released code to convert between KITTI, KITTI tracking, Pascal VOC, Udacity, CrowdAI and AUTTI formats.
• Jonas Heylen (TRACE vzw) has released pixel accurate instance segmentations for all 7481 training images.
• We thank David Stutz and Bo Li for developing the 3D object detection benchmark.

We evaluate 3D object detection performance using the PASCAL criteria also used for 2D object detection. Far objects are thus filtered based on their bounding box height in the image plane. As only objects also appearing on the image plane are labeled, objects in don't car areas do not count as false positives. We note that the evaluation does not take care of ignoring detections that are not visible on the image plane — these detections might give rise to false positives. For cars we require an 3D bounding box overlap of 70%, while for pedestrians and cyclists we require a 3D bounding box overlap of 50%. Difficulties are defined as follows:

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

All methods are ranked based on the moderately difficult results.

Car

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	TED			85.28 %	91.61 %	80.68 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
2	LIVOX_Det			84.94 %	91.72 %	80.10 %	n/a s	1 core @ 2.5 Ghz (Python + C/C++)
3	SFD			84.76 %	91.73 %	77.92 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
X. Wu, L. Peng, H. Yang, L. Xie, C. Huang, C. Deng, H. Liu and D. Cai: Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completion. CVPR 2022.
4	Anonymous			84.76 %	91.99 %	79.81 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
5	CasA++			84.04 %	90.68 %	79.69 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
6	Anonymous			83.96 %	90.83 %	77.47 %	n/a s	1 core @ 2.5 Ghz (Python + C/C++)
7	DGDNH			83.88 %	90.69 %	79.50 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
8	Anonymous			83.51 %	89.08 %	78.94 %	n/a s	1 core @ 2.5 Ghz (C/C++)
9	GraR-VoI			83.27 %	91.89 %	77.78 %	0.07 s	1 core @ 2.5 Ghz (Python + C/C++)
10	GLENet-VR			83.23 %	91.67 %	78.43 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
11	VPFNet			83.21 %	91.02 %	78.20 %	0.06 s	2 cores @ 2.5 Ghz (Python)
H. Zhu, J. Deng, Y. Zhang, J. Ji, Q. Mao, H. Li and Y. Zhang: VPFNet: Improving 3D Object Detection with Virtual Point based LiDAR and Stereo Data Fusion. 2021.
12	GraR-Po			83.18 %	91.79 %	77.98 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
13	CasA			83.06 %	91.58 %	80.08 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
14	BtcDet		code	82.86 %	90.64 %	78.09 %	0.09 s	GPU @ 2.5 Ghz (Python + C/C++)
Q. Xu, Y. Zhong and U. Neumann: Behind the Curtain: Learning Occluded Shapes for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2022.
15	Anonymous			82.79 %	91.30 %	78.07 %	n/a s	1 core @ 2.5 Ghz (C/C++)
16	GraR-Vo			82.77 %	91.29 %	77.20 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
17	3SNet			82.70 %	89.41 %	78.03 %	0.07 s	GPU @ 2.5 Ghz (Python)
18	PE-RCVN			82.69 %	91.51 %	77.75 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
19	CAD			82.68 %	88.96 %	77.91 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
20	SPG_mini		code	82.66 %	90.64 %	77.91 %	0.09 s	GPU @ 2.5 Ghz (Python)
Q. Xu, Y. Zhou, W. Wang, C. Qi and D. Anguelov: SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation. Proceedings of the IEEE conference on computer vision and pattern recognition (ICCV) 2021.
21	HCPVF			82.63 %	89.34 %	77.72 %	0.07 s	1 core @ 2.5 Ghz (Python + C/C++)
22	DSASNet			82.63 %	89.48 %	77.94 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
23	SA3DNet			82.57 %	90.49 %	77.88 %	0.05 s	GPU @ 2.5 Ghz (Python)
24	SE-SSD		code	82.54 %	91.49 %	77.15 %	0.03 s	1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, L. Jiang and C. Fu: SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud. CVPR 2021.
25	TF3D			82.46 %	89.10 %	77.78 %	0.1 s	2 cores @ 3.0 Ghz (Python)
26	DVF-V			82.45 %	89.40 %	77.56 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
A. Mahmoud, J. Hu and S. Waslander: Dense Voxel Fusion for 3D Object Detection. 2022.
27	GraR-Pi			82.42 %	90.94 %	77.00 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
28	DVF-PV			82.40 %	90.99 %	77.37 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
A. Mahmoud, J. Hu and S. Waslander: Dense Voxel Fusion for 3D Object Detection. 2022.
29	Anonymous			82.30 %	90.88 %	76.89 %	0.03 s	1 core @ 2.5 Ghz (Python + C/C++)
30	PVT-SSD			82.29 %	90.65 %	76.85 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
31	Focals Conv		code	82.28 %	90.55 %	77.59 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
Y. Chen, Y. Li, X. Zhang, J. Sun and J. Jia: Focal Sparse Convolutional Networks for 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.
32	CLOCs		code	82.28 %	89.16 %	77.23 %	0.1 s	1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection . 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
33	TBD			82.23 %	88.76 %	77.48 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
34	CityBrainLab			82.22 %	90.54 %	77.19 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
35	SASA		code	82.16 %	88.76 %	77.16 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
C. Chen, Z. Chen, J. Zhang and D. Tao: SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object Detection. arXiv preprint arXiv:2201.01976 2022.
36	ImpDet			82.14 %	88.39 %	76.98 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
37	SPG		code	82.13 %	90.50 %	78.90 %	0.09 s	1 core @ 2.5 Ghz (Python + C/C++)
Q. Xu, Y. Zhou, W. Wang, C. Qi and D. Anguelov: SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation. Proceedings of the IEEE conference on computer vision and pattern recognition (ICCV) 2021.
38	VoTr-TSD		code	82.09 %	89.90 %	79.14 %	0.07 s	1 core @ 2.5 Ghz (C/C++)
J. Mao, Y. Xue, M. Niu, H. Bai, J. Feng, X. Liang, H. Xu and C. Xu: Voxel Transformer for 3D Object Detection. ICCV 2021.
39	TBD			82.09 %	89.50 %	79.29 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
40	Pyramid R-CNN			82.08 %	88.39 %	77.49 %	0.07 s	1 core @ 2.5 Ghz (C/C++)
J. Mao, M. Niu, H. Bai, X. Liang, H. Xu and C. Xu: Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection. ICCV 2021.
41	FS-Net			82.07 %	88.68 %	77.42 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
42	VoxSeT		code	82.06 %	88.53 %	77.46 %	33 ms	1 core @ 2.5 Ghz (C/C++)
C. He, R. Li, S. Li and L. Zhang: Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds. CVPR 2022.
43	SRIF-RCNN			82.04 %	88.45 %	77.54 %	0.0947 s	1 core @ 2.5 Ghz (C/C++)
X. Li and D. Kong: SRIF-RCNN: Sparsely Represented Inputs Fusion of Different Sensors for 3D Object Detection. Applied Intelligence 2022.
44	LGNet			82.02 %	90.65 %	77.34 %	0.07 s	1 core @ 2.5 Ghz (C/C++)
45	EQ-PVRCNN		code	82.01 %	90.13 %	77.53 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, L. Jiang, Y. Sun, B. Schiele and J. Jia: A Unified Query-based Paradigm for Point Cloud Understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.
46	anonymous			81.99 %	88.82 %	77.26 %	0.09 s	GPU @ 2.5 Ghz (Python)
47	Anonymous			81.96 %	89.90 %	77.20 %	0.1s	1 core @ 2.5 Ghz (C/C++)
48	EPNet++			81.96 %	91.37 %	76.71 %	0.1 s	GPU @ 2.5 Ghz (Python)
Z. Liu, H. tengteng, B. Li, X. Chen, X. Wang and X. Bai: EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object Detection. arXiv preprint arXiv:2112.11088 2021.
49	HMFI		code	81.93 %	88.90 %	77.30 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
50	PV-RCNN++		code	81.88 %	90.14 %	77.15 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
51	GLENet			81.86 %	89.87 %	77.32 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
52	PDV		code	81.86 %	90.43 %	77.36 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
J. Hu, T. Kuai and S. Waslander: Point Density-Aware Voxels for LiDAR 3D Object Detection. CVPR 2022.
53	SGNet			81.85 %	88.83 %	77.47 %	0.09 s	GPU @ 2.5 Ghz (Python)
54	Anonymous			81.85 %	89.96 %	76.51 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
55	ST-RCNN			81.84 %	90.50 %	77.22 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
56	ST-RCNN (SNLW-RCNN)		code	81.84 %	90.50 %	77.22 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
57	ISE-RCNN			81.83 %	89.12 %	77.29 %	0.09 s	1 core @ 2.5 Ghz (Python + C/C++)
58	FV2P v2			81.81 %	88.17 %	77.43 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
59	Anonymous			81.80 %	89.86 %	77.26 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
60	SqueezeRCNN			81.80 %	88.72 %	77.10 %	0.08 s	1 core @ 2.5 Ghz (Python)
61	CityBrainLab-CT3D		code	81.77 %	87.83 %	77.16 %	0.07 s	1 core @ 2.5 Ghz (Python + C/C++)
H. Sheng, S. Cai, Y. Liu, B. Deng, J. Huang, X. Hua and M. Zhao: Improving 3D Object Detection with Channel- wise Transformer. ICCV 2021.
62	USVLab BSAODet			81.74 %	88.89 %	77.14 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
63	JPVNet			81.73 %	88.66 %	76.94 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
64	TBD			81.73 %	89.48 %	79.05 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
65	M3DeTR		code	81.73 %	90.28 %	76.96 %	n/a s	GPU @ 1.0 Ghz (Python)
T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis and D. Manocha: M3DeTR: Multi-representation, Multi- scale, Mutual-relation 3D Object Detection with Transformers. 2021.
66	SIENet		code	81.71 %	88.22 %	77.22 %	0.08 s	1 core @ 2.5 Ghz (Python)
Z. Li, Y. Yao, Z. Quan, W. Yang and J. Xie: SIENet: Spatial Information Enhancement Network for 3D Object Detection from Point Cloud. 2021.
67	TBD			81.71 %	88.46 %	76.63 %	0.1 s	1 core @ 2.5 Ghz (Python)
68	DCCA			81.70 %	88.42 %	77.18 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
69	VCRCNN			81.68 %	90.52 %	77.26 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
70	TBD			81.68 %	87.93 %	76.92 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
71	Voxel R-CNN		code	81.62 %	90.90 %	77.06 %	0.04 s	GPU @ 3.0 Ghz (C/C++)
J. Deng, S. Shi, P. Li, W. Zhou, Y. Zhang and H. Li: Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection . AAAI 2021.
72	BADet		code	81.61 %	89.28 %	76.58 %	0.14 s	1 core @ 2.5 Ghz (C/C++)
R. Qian, X. Lai and X. Li: BADet: Boundary-Aware 3D Object Detection from Point Clouds. Pattern Recognition 2022.
73	SARFE			81.59 %	88.88 %	76.74 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
74	FromVoxelToPoint		code	81.58 %	88.53 %	77.37 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to- Point Decoder. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
75	H^23D R-CNN		code	81.55 %	90.43 %	77.22 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
J. Deng, W. Zhou, Y. Zhang and H. Li: From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2021.
76	Anonymous			81.55 %	87.90 %	77.03 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
77	DSA-PV-RCNN		code	81.46 %	88.25 %	76.96 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
78	P2V-RCNN			81.45 %	88.34 %	77.20 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.
79	MMLab PV-RCNN		code	81.43 %	90.25 %	76.82 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
80	TPCG			81.41 %	89.16 %	76.90 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
81	DDet			81.38 %	89.63 %	78.57 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
82	XView			81.35 %	89.21 %	76.87 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
83	ISE-RCNN-PV			81.34 %	88.05 %	76.99 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
84	RangeRCNN			81.33 %	88.47 %	77.09 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, M. Zhang, Z. Zhang, X. Zhao and S. Pu: RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representation. arXiv preprint arXiv:2009.00206 2020.
85	CAT-Det			81.32 %	89.87 %	76.68 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Zhang, J. Chen and D. Huang: CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection. CVPR 2022.
86	IKT3D			80.97 %	87.84 %	76.43 %	0.05 s	1 core @ 2.5 Ghz (Python)
87	VPFNet		code	80.97 %	88.51 %	76.74 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
C. Wang, H. Chen and L. Fu: VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection. 2021.
88	GVNet-V2			80.96 %	87.57 %	76.32 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
89	VueronNet		code	80.96 %	90.06 %	73.72 %	0.06 s	1 core @ 2.0 Ghz (Python)
90	DKDet			80.94 %	87.66 %	76.23 %	0.03 s	GPU @ 2.5 Ghz (Python + C/C++)
91	FusionDetv2-v4			80.93 %	87.75 %	76.12 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
92	sa-voxel-centernet		code	80.77 %	87.39 %	76.45 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
93	Sem-Aug			80.77 %	89.41 %	75.90 %	0.08 s	GPU @ 2.5 Ghz (Python)
94	CM3DV			80.77 %	87.28 %	76.51 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
95	Associate-3Ddet_v2			80.77 %	91.53 %	75.23 %	0.04 s	1 core @ 2.5 Ghz (Python)
96	CSVoxel-RCNN			80.73 %	87.44 %	76.18 %	0.03 s	GPU @ 1.0 Ghz (Python)
97	FusionDetv2-v3			80.70 %	88.05 %	76.10 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
98	StructuralIF			80.69 %	87.15 %	76.26 %	0.02 s	8 cores @ 2.5 Ghz (Python)
J. Pei An: Deep structural information fusion for 3D object detection on LiDAR-camera system. Accepted in CVIU 2021.
99	SPVB-SSD			80.68 %	86.99 %	76.23 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
100	CLOCs_PVCas		code	80.67 %	88.94 %	77.15 %	0.1 s	1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection . 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
101	GVNet		code	80.52 %	87.63 %	75.99 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
102	SVGA-Net			80.47 %	87.33 %	75.91 %	0.03s	1 core @ 2.5 Ghz (Python + C/C++)
Q. He, Z. Wang, H. Zeng, Y. Zeng and Y. Liu: SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds. AAAI 2022.
103	SC-Voxel-RCNN			80.46 %	86.94 %	75.85 %	0.12 s	GPU @ 1.0 Ghz (Python)
104	TBD			80.44 %	88.83 %	73.18 %	0.1 s	1 core @ 2.5 Ghz (Python)
105	Sem-Aug v1		code	80.40 %	88.92 %	77.37 %	0.04 s	GPU @ 3.5 Ghz (Python)
106	SRDL			80.38 %	87.73 %	76.27 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
107	Fast-CLOCs			80.35 %	89.10 %	76.99 %	0.1 s	GPU @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: Fast-CLOCs: Fast Camera-LiDAR Object Candidates Fusion for 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022.
108	FPV-SSD			80.34 %	87.72 %	75.40 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
109	SPANet			80.34 %	91.05 %	74.89 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
Y. Ye: SPANet: Spatial and Part-Aware Aggregation Network for 3D Object Detection. Pacific Rim International Conference on Artificial Intelligence 2021.
110	IA-SSD (single)		code	80.32 %	88.87 %	75.10 %	0.013 s	1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
111	TBD			80.29 %	87.37 %	73.05 %	0.1 s	1 core @ 2.5 Ghz (Python)
112	CIA-SSD		code	80.28 %	89.59 %	72.87 %	0.03 s	1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, S. Chen, L. Jiang and C. Fu: CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud. AAAI 2021.
113	FusionDetv1			80.28 %	87.45 %	76.21 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
114	DVF			80.21 %	88.97 %	75.22 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
115	VCT			80.19 %	89.12 %	77.19 %	0.2 s	1 core @ 2.5 Ghz (Python)
116	PVTr			80.16 %	86.90 %	75.98 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
117	IA-SSD (multi)		code	80.13 %	88.34 %	75.04 %	0.014 s	1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
118	TBD			80.12 %	88.30 %	75.29 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
119	EBM3DOD		code	80.12 %	91.05 %	72.78 %	0.12 s	1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.
120	TBD			80.12 %	86.50 %	75.72 %	0.06 s	GPU @ 2.5 Ghz (Python)
121	ATT_SSD			80.11 %	88.94 %	74.91 %	0.01 s	1 core @ 2.5 Ghz (Python)
122	TBD		code	80.06 %	88.75 %	74.84 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
123	3D-CVF at SPA			80.05 %	89.20 %	73.11 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
J. Yoo, Y. Kim, J. Kim and J. Choi: 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection. ECCV 2020.
124	TBD			80.02 %	88.45 %	74.85 %	TBD	GPU @ 2.5 Ghz (Python + C/C++)
125	MVOD			80.01 %	88.53 %	77.24 %	0.16 s	1 core @ 2.5 Ghz (C/C++)
126	SIF			79.88 %	86.84 %	75.89 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
127	RangeIoUDet			79.80 %	88.60 %	76.76 %	0.02 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, Z. Zhang, M. Zhang, X. Zhao and S. Pu: RangeIoUDet: Range Image Based Real-Time 3D Object Detector Optimized by Intersection Over Union. CVPR 2021.
128	SA-SSD		code	79.79 %	88.75 %	74.16 %	0.04 s	1 core @ 2.5 Ghz (Python)
C. He, H. Zeng, J. Huang, X. Hua and L. Zhang: Structure Aware Single-stage 3D Object Detection from Point Cloud. CVPR 2020.
129	DGT-Det3D			79.78 %	86.76 %	75.73 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
130	KpNet			79.75 %	88.92 %	72.17 %	0.42 s	1 core @ 2.5 Ghz (C/C++)
131	KpNet			79.74 %	88.88 %	72.13 %	42 s	1 core @ 2.5 Ghz (C/C++)
132	STD		code	79.71 %	87.95 %	75.09 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
133	MGAF-3DSSD		code	79.68 %	88.16 %	72.39 %	0.1 s	1 core @ 2.5 Ghz (Python)
J. Li, H. Dai, L. Shao and Y. Ding: Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
134	TBD			79.67 %	88.33 %	74.30 %	0.03 s	GPU @ 2.5 Ghz (Python)
135	mbdf-netv1		code	79.66 %	90.19 %	74.76 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
136	PTA-RCNN			79.61 %	87.84 %	74.43 %	0.08 s	1 core @ 2.5 Ghz (Python)
137	Struc info fusion II			79.59 %	88.97 %	72.51 %	0.05 s	GPU @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.
138	3DSSD		code	79.57 %	88.36 %	74.55 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
139	EBM3DOD baseline		code	79.52 %	88.80 %	72.30 %	0.05 s	1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.
140	Struc info fusion I			79.49 %	88.70 %	74.25 %	0.05 s	1 core @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.
141	Point-GNN		code	79.47 %	88.33 %	72.29 %	0.6 s	GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
142	SECOND			79.46 %	87.44 %	73.97 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
143	AGS-SSD[la]			79.39 %	88.13 %	74.11 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
144	SSL-PointGNN		code	79.36 %	87.78 %	74.15 %	0.56 s	GPU @ 1.5 Ghz (Python)
E. Erçelik, E. Yurtsever, M. Liu, Z. Yang, H. Zhang, P. Topçam, M. Listl, Y. Çaylı and A. Knoll: 3D Object Detection with a Self-supervised Lidar Scene Flow Backbone. arXiv preprint arXiv:2205.00705 2022.
145	NV-RCNN			79.32 %	87.58 %	74.74 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
146	USVLab BSAODet (S)			79.30 %	88.02 %	76.05 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
147	EPNet		code	79.28 %	89.81 %	74.59 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
T. Huang, Z. Liu, X. Chen and X. Bai: EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection. ECCV 2020.
148	DVFENet			79.18 %	86.20 %	74.58 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection. Neurocomputing 2021.
149	ITCA-SSD		code	79.11 %	88.66 %	72.12 %	0.05 s	1 core @ 2.5 Ghz (Python)
150	Faraway-Frustum		code	79.05 %	87.45 %	76.14 %	0.1 s	GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-frustum: Dealing with lidar sparsity for 3D object detection using fusion. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) 2021.
151	3D IoU-Net			79.03 %	87.96 %	72.78 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, S. Krylov, Y. Ding and L. Shao: 3D IoU-Net: IoU Guided 3D Object Detector for Point Clouds. arXiv preprint arXiv:2004.04962 2020.
152	SERCNN			78.96 %	87.74 %	74.30 %	0.1 s	1 core @ 2.5 Ghz (Python)
D. Zhou, J. Fang, X. Song, L. Liu, J. Yin, Y. Dai, H. Li and R. Yang: Joint 3D Instance Segmentation and Object Detection for Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020.
153	NV2P-RCNN			78.92 %	87.36 %	74.16 %	0.1 s	GPU @ 2.5 Ghz (Python)
154	MSADet			78.81 %	88.31 %	73.82 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
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155	CSNet8306		code	78.74 %	89.57 %	72.09 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
156	MVAF-Net		code	78.71 %	87.87 %	75.48 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
G. Wang, B. Tian, Y. Zhang, L. Chen, D. Cao and J. Wu: Multi-View Adaptive Fusion Network for 3D Object Detection. arXiv preprint arXiv:2011.00652 2020.
157	CenterFuse			78.70 %	86.92 %	73.87 %	0.059 sec/frame	2 x V100
158	MMLab-PartA^2		code	78.49 %	87.81 %	73.51 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
159	CLOCs_SecCas			78.45 %	86.38 %	72.45 %	0.1 s	1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
160	CSNet			78.42 %	87.39 %	71.75 %	0.1 s	1 core @ 2.5 Ghz (Python)
161	FusionDetv2-v2			78.42 %	86.59 %	73.87 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
162	Patches - EMP			78.41 %	89.84 %	73.15 %	0.5 s	GPU @ 2.5 Ghz (Python)
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
163	HotSpotNet			78.31 %	87.60 %	73.34 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
164	FusionDetv2-v5			78.30 %	86.94 %	73.44 %	0.05 s	1 core @ 2.5 Ghz (Java + C/C++)
165	Sem-Aug-PointRCNN++			78.06 %	86.69 %	73.85 %	0.1 s	8 cores @ 3.0 Ghz (Python)
166	CF-cd-io-tv			78.05 %	86.38 %	73.29 %	1 s	1 core @ 2.5 Ghz (C/C++)
167	3D-VDNet			78.05 %	87.13 %	72.90 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
168	VPN			77.93 %	85.02 %	72.97 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
169	CenterNet3D			77.90 %	86.20 %	73.03 %	0.04 s	GPU @ 1.5 Ghz (Python)
G. Wang, B. Tian, Y. Ai, T. Xu, L. Chen and D. Cao: CenterNet3D:An Anchor free Object Detector for Autonomous Driving. 2020.
170	TBD			77.85 %	86.46 %	72.39 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
171	CF-ctdep-tv-ta			77.75 %	85.27 %	74.83 %	1 s	1 core @ 2.5 Ghz (C/C++)
172	IoU-2B			77.74 %	85.65 %	71.30 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
173	Reprod-Two-Branch			77.73 %	85.60 %	74.24 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
174	CVFNet			77.70 %	88.75 %	71.95 %	28.1ms	1 core @ 2.5 Ghz (Python)
175	AutoAlign			77.58 %	86.84 %	73.23 %	0.1 s	1 core @ 2.5 Ghz (Python)
176	TBD			77.56 %	85.38 %	72.32 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
177	CFF-tv			77.53 %	85.01 %	74.01 %	1 s	1 core @ 2.5 Ghz (C/C++)
178	TCDVF			77.49 %	85.55 %	72.29 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
179	CFF-ep25			77.48 %	84.84 %	72.96 %	1 s	1 core @ 2.5 Ghz (C/C++)
180	UberATG-MMF			77.43 %	88.40 %	70.22 %	0.08 s	GPU @ 2.5 Ghz (Python)
M. Liang*, B. Yang*, Y. Chen, R. Hu and R. Urtasun: Multi-Task Multi-Sensor Fusion for 3D Object Detection. CVPR 2019.
181	CFF-tv-v2			77.41 %	85.18 %	72.81 %	1 s	1 core @ 2.5 Ghz (C/C++)
182	Associate-3Ddet		code	77.40 %	85.99 %	70.53 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
L. Du, X. Ye, X. Tan, J. Feng, Z. Xu, E. Ding and S. Wen: Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection. The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
183	Fast Point R-CNN			77.40 %	85.29 %	70.24 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, S. Liu, X. Shen and J. Jia: Fast Point R-CNN. Proceedings of the IEEE international conference on computer vision (ICCV) 2019.
184	cff-tv-v2-ep25			77.38 %	84.44 %	72.82 %	1 s	1 core @ 2.5 Ghz (C/C++)
185	3D_att			77.27 %	88.46 %	70.11 %	0.17 s	GPU @ 2.5 Ghz (Python)
186	cp-tv-kp-io-sc			77.25 %	85.41 %	72.42 %	1 s	1 core @ 2.5 Ghz (C/C++)
187	Patches			77.20 %	88.67 %	71.82 %	0.15 s	GPU @ 2.0 Ghz
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
188	CF-ctdep-tv			77.12 %	84.71 %	74.02 %	1 s	1 core @ 2.5 Ghz (C/C++)
189	CF-base-tv			77.09 %	83.72 %	73.71 %	1 s	1 core @ 2.5 Ghz (C/C++)
190	Sem-Aug-PointRCNN		code	77.04 %	82.75 %	73.21 %	0.1 s	GPU @ 3.5 Ghz (C/C++)
191	KeyFuse2B			76.95 %	84.86 %	72.53 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
192	KeyPoint-IoUHead			76.81 %	84.61 %	72.16 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
193	HRI-VoxelFPN			76.70 %	85.64 %	69.44 %	0.02 s	GPU @ 2.5 Ghz (Python + C/C++)
H. Kuang, B. Wang, J. An, M. Zhang and Z. Zhang: Voxel-FPN:multi-scale voxel feature aggregation in 3D object detection from point clouds. sensors 2020.
194	DKAnet			76.70 %	84.57 %	71.54 %	0.05 s	1 core @ 2.0 Ghz (Python)
195	cff-tv-t			76.68 %	85.58 %	70.69 %	1 s	1 core @ 2.5 Ghz (C/C++)
196	SARPNET			76.64 %	85.63 %	71.31 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
Y. Ye, H. Chen, C. Zhang, X. Hao and Z. Zhang: SARPNET: Shape Attention Regional Proposal Network for LiDAR-based 3D Object Detection. Neurocomputing 2019.
197	Anonymous			76.60 %	85.29 %	71.77 %	1	1 core @ 2.5 Ghz (Python)
198	DTFI			76.59 %	85.29 %	71.78 %	0.03 s	1 core @ 2.5 Ghz (Python)
199	CSNet8299		code	76.55 %	86.49 %	71.37 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
200	3D IoU Loss			76.50 %	86.16 %	71.39 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
D. Zhou, J. Fang, X. Song, C. Guan, J. Yin, Y. Dai and R. Yang: IoU Loss for 2D/3D Object Detection. International Conference on 3D Vision (3DV) 2019.
201	HNet-3DSSD		code	76.48 %	86.06 %	69.71 %	0.05 s	GPU @ 2.5 Ghz (Python)
202	F-ConvNet		code	76.39 %	87.36 %	66.69 %	0.47 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
203	variance_point			76.27 %	87.44 %	72.03 %	0.05 s	1 core @ 2.5 Ghz (Python)
204	SegVoxelNet			76.13 %	86.04 %	70.76 %	0.04 s	1 core @ 2.5 Ghz (Python)
H. Yi, S. Shi, M. Ding, J. Sun, K. Xu, H. Zhou, Z. Wang, S. Li and G. Wang: SegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloud. ICRA 2020.
205	S-AT GCN			76.04 %	83.20 %	71.17 %	0.02 s	GPU @ 2.0 Ghz (Python)
L. Wang, C. Wang, X. Zhang, T. Lan and J. Li: S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection. CoRR 2021.
206	KPP3D		code	76.00 %	86.66 %	71.07 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
207	TANet		code	75.94 %	84.39 %	68.82 %	0.035s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
208	CF-base-train			75.93 %	83.47 %	71.22 %	1 s	1 core @ 2.5 Ghz (C/C++)
209	cp-tv-kp			75.85 %	83.50 %	72.54 %	1 s	1 core @ 2.5 Ghz (C/C++)
210	PointRGCN			75.73 %	85.97 %	70.60 %	0.26 s	GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
211	cp-tv			75.67 %	83.31 %	72.24 %	1 s	1 core @ 2.5 Ghz (C/C++)
212	MMLab-PointRCNN		code	75.64 %	86.96 %	70.70 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
213	Self-Calib Conv			75.59 %	83.54 %	71.96 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
214	AB3DMOT		code	75.43 %	86.10 %	68.88 %	0.0047s	1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
215	CF-ctdep-train			75.43 %	83.03 %	71.31 %	1 s	1 core @ 2.5 Ghz (C/C++)
216	R-GCN			75.26 %	83.42 %	68.73 %	0.16 s	GPU @ 2.5 Ghz (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
217	epBRM		code	75.15 %	85.00 %	69.84 %	0.1 s	GPU @ >3.5 Ghz (Python + C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
218	MAFF-Net(DAF-Pillar)			75.04 %	85.52 %	67.61 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
Z. Zhang, Z. Liang, M. Zhang, X. Zhao, Y. Ming, T. Wenming and S. Pu: MAFF-Net: Filter False Positive for 3D Vehicle Detection with Multi-modal Adaptive Feature Fusion. arXiv preprint arXiv:2009.10945 2020.
219	sscl-20p			74.82 %	86.06 %	69.87 %	0.02 s	1 core @ 2.5 Ghz (Python)
220	PI-RCNN			74.82 %	84.37 %	70.03 %	0.1 s	1 core @ 2.5 Ghz (Python)
L. Xie, C. Xiang, Z. Yu, G. Xu, Z. Yang, D. Cai and X. He: PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module. AAAI 2020 : The Thirty-Fourth AAAI Conference on Artificial Intelligence 2020.
221	MF			74.70 %	83.42 %	66.51 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
222	LazyTorch-CP-Infer-O			74.57 %	81.82 %	70.24 %	1 s	1 core @ 2.5 Ghz (C/C++)
223	LazyTorch-CP-Small-P			74.44 %	81.73 %	70.14 %	1 s	1 core @ 2.5 Ghz (C/C++)
224	City-CF-fixed			74.37 %	83.23 %	69.65 %	1 s	1 core @ 2.5 Ghz (C/C++)
225	PointPillars		code	74.31 %	82.58 %	68.99 %	16 ms	1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
226	ARPNET			74.04 %	84.69 %	68.64 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
227	CenterPoint (pcdet)			73.96 %	81.17 %	69.48 %	0.051 sec/frame	2 x V100
228	PC-CNN-V2			73.79 %	85.57 %	65.65 %	0.5 s	GPU @ 2.5 Ghz (Matlab + C/C++)
X. Du, M. Ang, S. Karaman and D. Rus: A General Pipeline for 3D Detection of Vehicles. 2018 IEEE International Conference on Robotics and Automation (ICRA) 2018.
229	C-GCN			73.62 %	83.49 %	67.01 %	0.147 s	GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
230	3DBN			73.53 %	83.77 %	66.23 %	0.13s	1080Ti (Python+C/C++)
X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.
231	Dune-DCF-e11			73.51 %	80.89 %	68.89 %	1 s	1 core @ 2.5 Ghz (C/C++)
232	CrazyTensor-CP			73.50 %	81.04 %	69.87 %	1 s	1 core @ 2.5 Ghz (Python)
233	PointRGBNet			73.49 %	83.99 %	68.56 %	0.08 s	4 cores @ 2.5 Ghz (Python + C/C++)
P. Xie Desheng: Real-time Detection of 3D Objects Based on Multi-Sensor Information Fusion. Automotive Engineering 2022.
234	City-CF			73.48 %	80.85 %	69.07 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
235	RangeDet		code	73.44 %	80.53 %	67.28 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
236	PSM_stereo			73.43 %	81.28 %	66.98 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
237	Dune-DCF-e15			73.29 %	80.34 %	68.54 %	1 s	1 core @ 2.5 Ghz (C/C++)
238	SCNet			73.17 %	83.34 %	67.93 %	0.04 s	GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
239	Dune-DCF-e09			73.15 %	80.40 %	68.57 %	1 s	1 core @ 2.5 Ghz (C/C++)
240	AFTD			73.12 %	82.71 %	68.09 %	1 s	1 core @ 2.5 Ghz (Python + C/C++)
241	PP-PCdet		code	73.07 %	83.32 %	68.18 %	0.01 s	1 core @ 2.5 Ghz (Python)
242	PFF3D		code	72.93 %	81.11 %	67.24 %	0.05 s	GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
243	CrazyTensor-CF			72.92 %	79.87 %	68.41 %	1 s	1 core @ 2.5 Ghz (C/C++)
244	DASS			72.31 %	81.85 %	65.99 %	0.09 s	1 core @ 2.0 Ghz (Python)
O. Unal, L. Van Gool and D. Dai: Improving Point Cloud Semantic Segmentation by Learning 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2021.
245	HS3D		code	72.25 %	83.57 %	67.49 %	0.12 s	1 core @ 2.5 Ghz (Python + C/C++)
246	TBD_BD		code	72.16 %	83.36 %	66.87 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
247	TBD			71.94 %	83.20 %	66.83 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
248	AVOD-FPN		code	71.76 %	83.07 %	65.73 %	0.1 s	Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
249	PointPainting			71.70 %	82.11 %	67.08 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
250	Contrastive PP		code	71.64 %	84.80 %	66.49 %	0.01 s	1 core @ 2.5 Ghz (Python)
251	new_stereo			70.79 %	80.05 %	66.04 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
252	WS3D			70.59 %	80.99 %	64.23 %	0.1 s	GPU @ 2.5 Ghz (Python)
Q. Meng, W. Wang, T. Zhou, J. Shen, L. Van Gool and D. Dai: Weakly Supervised 3D Object Detection from Lidar Point Cloud. 2020.
253	F-PointNet		code	69.79 %	82.19 %	60.59 %	0.17 s	GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
254	FusionDetv2-baseline			68.87 %	79.05 %	63.68 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
255	UberATG-ContFuse			68.78 %	83.68 %	61.67 %	0.06 s	GPU @ 2.5 Ghz (Python)
M. Liang, B. Yang, S. Wang and R. Urtasun: Deep Continuous Fusion for Multi-Sensor 3D Object Detection. ECCV 2018.
256	MLOD		code	67.76 %	77.24 %	62.05 %	0.12 s	GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
257	DSGN++		code	67.37 %	83.21 %	59.91 %	0.2 s	GeForce RTX 2080Ti
Y. Chen, S. Huang, S. Liu, B. Yu and J. Jia: DSGN++: Exploiting Visual-Spatial Relation for Stereo-based 3D Detectors. arXiv preprint arXiv:2204.03039 2022.
258	DisposalNet			67.33 %	77.55 %	62.44 %	0.2 s	1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
259	Anonymous			66.97 %	83.77 %	58.41 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
260	AVOD		code	66.47 %	76.39 %	60.23 %	0.08 s	Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
261	StereoDistill			66.39 %	81.66 %	57.39 %	0.4 s	1 core @ 2.5 Ghz (Python)
262	FusionDetv2-v1			65.65 %	75.21 %	60.65 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
263	MMLAB LIGA-Stereo		code	64.66 %	81.39 %	57.22 %	0.4 s	1 core @ 2.5 Ghz (Python + C/C++)
X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
264	BirdNet+		code	64.04 %	76.15 %	59.79 %	0.11 s	Titan Xp (PyTorch)
A. Barrera, J. Beltrán, C. Guindel, J. Iglesias and F. García: BirdNet+: Two-Stage 3D Object Detection in LiDAR through a Sparsity-Invariant Bird’s Eye View. IEEE Access 2021.
265	CZY			63.68 %	77.56 %	57.82 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
266	MV3D			63.63 %	74.97 %	54.00 %	0.36 s	GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
267	SD3DOD			62.00 %	76.09 %	55.46 %	0.04 s	GPU @ 2.5 Ghz (Python)
268	AEC3D			61.99 %	72.16 %	57.11 %	18 ms	GPU @ 2.5 Ghz (Python)
269	VN3D			61.41 %	72.37 %	56.86 %	0.02 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
270	SNVC		code	61.34 %	78.54 %	54.23 %	1 s	GPU @ 1.0 Ghz (Python)
S. Li, Z. Liu, Z. Shen and K. Cheng: Stereo Neural Vernier Caliper. Proceedings of the AAAI Conference on Artificial Intelligence 2022.
271	RCD			60.56 %	70.54 %	55.58 %	0.1 s	GPU @ 2.5 Ghz (Python)
A. Bewley, P. Sun, T. Mensink, D. Anguelov and C. Sminchisescu: Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection. Conference on Robot Learning (CoRL) 2020.
272	Anonymous			58.57 %	77.81 %	52.13 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
273	A3DODWTDA		code	56.82 %	62.84 %	48.12 %	0.08 s	GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
274	FD			56.40 %	73.05 %	52.25 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
275	PS++		code	55.28 %	74.80 %	46.70 %	PS++ s	1 core @ 2.5 Ghz (C/C++)
276	PL++ (SDN+GDC)		code	54.88 %	68.38 %	49.16 %	0.6 s	GPU @ 2.5 Ghz (C/C++)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.
277	MV3D (LIDAR)			54.54 %	68.35 %	49.16 %	0.24 s	GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
278	CDN		code	54.22 %	74.52 %	46.36 %	0.6 s	GPU @ 2.5 Ghz (Python)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems (NeurIPS) 2020.
279	CG-Stereo			53.58 %	74.39 %	46.50 %	0.57 s	GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
280	PS		code	52.88 %	74.41 %	44.38 %	PS s	1 core @ 2.5 Ghz (C/C++)
281	UPF_3D			52.83 %	78.24 %	46.12 %	0.29 s	1 core @ 2.5 Ghz (Python)
282	DSGN		code	52.18 %	73.50 %	45.14 %	0.67 s	NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
283	BirdNet+ (legacy)		code	51.85 %	70.14 %	50.03 %	0.1 s	Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird’s Eye View. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
284	ppt			50.41 %	54.19 %	45.14 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
285	Complexer-YOLO			47.34 %	55.93 %	42.60 %	0.06 s	GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
286	ESGN			46.39 %	65.80 %	38.42 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
287	Disp R-CNN (velo)		code	45.78 %	68.21 %	37.73 %	0.387 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
288	CDN-PL++			44.86 %	64.31 %	38.11 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems 2020.
289	Disp R-CNN		code	43.27 %	67.02 %	36.43 %	0.387 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
290	Pseudo-LiDAR++		code	42.43 %	61.11 %	36.99 %	0.4 s	GPU @ 2.5 Ghz (Python)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.
291	ART			42.42 %	63.38 %	36.44 %	20ms s	1 core @ 2.5 Ghz (C/C++)
292	YOLOStereo3D		code	41.25 %	65.68 %	30.42 %	0.1 s	GPU 1080Ti
Y. Liu, L. Wang and M. Liu: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
293	BEVC			40.72 %	50.05 %	36.42 %	35ms	GPU @ 1.5 Ghz (Python)
294	RT3D-GMP			38.76 %	45.79 %	30.00 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
295	ZoomNet		code	38.64 %	55.98 %	30.97 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
L. Z. Xu: ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2020.
296	OC Stereo		code	37.60 %	55.15 %	30.25 %	0.35 s	1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
297	Pseudo-Lidar		code	34.05 %	54.53 %	28.25 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Wang, W. Chao, D. Garg, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
298	Stereo CenterNet			31.30 %	49.94 %	25.62 %	0.04 s	GPU @ 2.5 Ghz (Python)
Y. Shi, Y. Guo, Z. Mi and X. Li: Stereo CenterNet-based 3D object detection for autonomous driving. Neurocomputing 2022.
299	Stereo R-CNN		code	30.23 %	47.58 %	23.72 %	0.3 s	GPU @ 2.5 Ghz (Python)
P. Li, X. Chen and S. Shen: Stereo R-CNN based 3D Object Detection for Autonomous Driving. CVPR 2019.
300	BirdNet			27.26 %	40.99 %	25.32 %	0.11 s	Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
301	SparseLiDAR_fusion			26.23 %	36.85 %	21.45 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
302	GCDR			23.92 %	34.89 %	19.59 %	0.28 s	1 core @ 2.5 Ghz (Python)
303	VMDet_boost			23.79 %	33.89 %	20.33 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
304	RT3DStereo			23.28 %	29.90 %	18.96 %	0.08 s	GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
305	Digging_M3D			21.24 %	29.15 %	19.18 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
306	VMDet			20.95 %	30.51 %	17.85 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
307	Anonymous			20.23 %	28.29 %	17.55 %	40 s	1 core @ 2.5 Ghz (C/C++)
308	SARM3D			19.70 %	25.20 %	17.35 %	0.03 s	GPU @ 2.5 Ghz (Python)
309	RT3D			19.14 %	23.74 %	18.86 %	0.09 s	GPU @ 1.8Ghz
Y. Zeng, Y. Hu, S. Liu, J. Ye, Y. Han, X. Li and N. Sun: RT3D: Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving. IEEE Robotics and Automation Letters 2018.
310	MonoInsight			19.04 %	27.71 %	16.03 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
311	CMKD*			18.69 %	28.55 %	16.77 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
312	Mix-Teaching			18.54 %	26.89 %	15.79 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
313	StereoFENet			18.41 %	29.14 %	14.20 %	0.15 s	1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
314	anonymity			18.00 %	28.10 %	15.60 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
315	SCSTSV-MonoFlex			17.91 %	27.38 %	15.58 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
316	MoGDE			17.88 %	27.07 %	15.66 %	0.03 s	GPU @ 2.5 Ghz (Python)
317	LPCG-Monoflex			17.80 %	25.56 %	15.38 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
318	PS-fld		code	17.74 %	23.74 %	15.14 %	0.25 s	1 core @ 2.5 Ghz (C/C++)
Y. Chen, H. Dai and Y. Ding: Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
319	MonoDDE			17.14 %	24.93 %	15.10 %	0.04 s	1 core @ 2.5 Ghz (Python)
Z. Li, Z. Qu, Y. Zhou, J. Liu, H. Wang and L. Jiang: Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection. CVPR 2022.
320	Anonymous			17.08 %	24.43 %	15.25 %	40 s	1 core @ 2.5 Ghz (C/C++)
321	OPA-3D		code	17.05 %	24.60 %	14.25 %	0.04 s	1 core @ 3.5 Ghz (Python)
322	Mobile Stereo R-CNN			17.04 %	26.97 %	13.26 %	1.8 s	NVIDIA Jetson TX2
M. Hussein, M. Khalil and B. Abdullah: 3D Object Detection using Mobile Stereo R- CNN on Nvidia Jetson TX2. International Conference on Advanced Engineering, Technology and Applications (ICAETA) 2021.
323	anonymity			16.99 %	27.20 %	15.08 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
324	DD3D		code	16.87 %	23.19 %	14.36 %	n/a s	1 core @ 2.5 Ghz (C/C++)
D. Park, R. Ambrus, V. Guizilini, J. Li and A. Gaidon: Is Pseudo-Lidar needed for Monocular 3D Object detection?. IEEE/CVF International Conference on Computer Vision (ICCV) .
325	Shape-Aware			16.52 %	23.84 %	13.88 %	0.05 s	1 core @ 2.5 Ghz (Python)
326	MonoCon		code	16.46 %	22.50 %	13.95 %	0.02 s	GPU @ 2.5 Ghz (Python)
T. Xianpeng Liu: Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection. AAAI 2022.
327	DID-M3D			16.29 %	24.40 %	13.75 %	0.04 s	1 core @ 2.5 Ghz (Python)
328	MonoDETR		code	16.26 %	24.52 %	13.93 %	0.04 s	1 core @ 2.5 Ghz (Python)
R. Zhang, H. Qiu, T. Wang, X. Xu, Z. Guo, Y. Qiao, P. Gao and H. Li: MonoDETR: Depth-aware Transformer for Monocular 3D Object Detection. arXiv preprint arXiv:2203.13310 2022.
329	Lite-FPN-GUPNet			16.20 %	23.58 %	13.56 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
330	gupnet_se			16.10 %	23.62 %	13.41 %	0.03s	1 core @ 2.5 Ghz (C/C++)
331	zongmuDistill			16.08 %	25.11 %	13.69 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
332	MonoDistill			16.03 %	22.97 %	13.60 %	0.04 s	1 core @ 2.5 Ghz (Python)
333	MDNet			16.01 %	24.59 %	13.49 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
334	DDS		code	15.90 %	23.81 %	13.21 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
335	mono3d		code	15.73 %	23.96 %	13.35 %	TBD	TBD
336	monopd		code	15.72 %	23.51 %	13.07 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
337	OBMO_GUPNet			15.70 %	22.71 %	13.23 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
338	GPENet		code	15.44 %	22.41 %	12.84 %	0.02 s	GPU @ 2.5 Ghz (Python)
339	MonoDTR			15.39 %	21.99 %	12.73 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
K. Huang, T. Wu, H. Su and W. Hsu: MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer. CVPR 2022.
340	mono3d			15.26 %	23.41 %	12.80 %	0.03 s	GPU @ 2.5 Ghz (Python)
341	HBD			15.17 %	21.71 %	13.06 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
342	EW		code	15.13 %	21.16 %	12.81 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
343	ZongmuMono3d		code	15.08 %	23.79 %	13.25 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
344	GUPNet		code	15.02 %	22.26 %	13.12 %	NA s	1 core @ 2.5 Ghz (Python + C/C++)
Y. Lu, X. Ma, L. Yang, T. Zhang, Y. Liu, Q. Chu, J. Yan and W. Ouyang: Geometry Uncertainty Projection Network for Monocular 3D Object Detection. arXiv preprint arXiv:2107.13774 2021.
345	HomoLoss(monoflex)		code	14.94 %	21.75 %	13.07 %	0.04 s	1 core @ 2.5 Ghz (Python)
J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homography Loss for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
346	Anonymous			14.87 %	23.93 %	12.45 %	40 s	1 core @ 2.5 Ghz (C/C++)
347	Anonymous			14.84 %	22.73 %	13.08 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
348	M3DSSD++		code	14.75 %	23.61 %	11.80 %	0.16s	1 core @ 2.5 Ghz (C/C++)
349	MonoFlex			14.73 %	22.29 %	12.77 %	0.03 s	1 core @ 2.5 Ghz (Python)
350	SGM3D			14.65 %	22.46 %	12.97 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
Z. Zhou, L. Du, X. Ye, Z. Zou, X. Tan, E. Ding, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection. 2021.
351	Anonymous		code	14.56 %	20.65 %	11.92 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
352	SAIC_ADC_Mono3D		code	14.54 %	18.98 %	12.86 %	50 s	GPU @ 2.5 Ghz (Python)
353	CA3D			14.49 %	20.89 %	12.19 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
354	MonoEdge			14.47 %	21.08 %	12.73 %	0.05 s	GPU @ 2.5 Ghz (Python)
355	MDSNet			14.46 %	24.30 %	11.12 %	0.07 s	1 core @ 2.5 Ghz (Python)
356	MonoGround			14.36 %	21.37 %	12.62 %	0.03 s	1 core @ 2.5 Ghz (Python)
357	MonoEdge-RCNN			14.35 %	19.74 %	11.94 %	0.05 s	1 core @ 2.5 Ghz (Python)
358	ANM			14.33 %	20.84 %	11.61 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
359	DLE		code	14.33 %	24.23 %	10.30 %	0.06 s	NVIDIA Tesla V100
C. Liu, S. Gu, L. Gool and R. Timofte: Deep Line Encoding for Monocular 3D Object Detection and Depth Prediction. Proceedings of the British Machine Vision Conference (BMVC) 2021.
360	SwinMono3D			14.24 %	22.61 %	10.11 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
361	AutoShape		code	14.17 %	22.47 %	11.36 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
Z. Liu, D. Zhou, F. Lu, J. Fang and L. Zhang: AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.
362	MonoEdge-Rotate			14.13 %	21.60 %	12.27 %	0.05 s	GPU @ 2.5 Ghz (Python)
363	EM		code	14.00 %	22.93 %	11.26 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
364	MAOLoss		code	14.00 %	20.05 %	11.81 %	0.05 s	1 core @ 2.5 Ghz (Python)
365	E2E-DA			13.97 %	19.73 %	11.82 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
366	MonoFlex			13.89 %	19.94 %	12.07 %	0.03 s	GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.
367	MonoEF			13.87 %	21.29 %	11.71 %	0.03 s	1 core @ 2.5 Ghz (Python)
Y. Zhou, Y. He, H. Zhu, C. Wang, H. Li and Q. Jiang: Monocular 3D Object Detection: An Extrinsic Parameter Free Approach. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
368	MonoAug			13.85 %	20.06 %	11.43 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
369	DEPT			13.83 %	20.43 %	11.66 %	0.03 s	1 core @ 2.5 Ghz (Python)
370	K3D			13.80 %	20.04 %	11.67 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
371	none			13.79 %	18.84 %	11.52 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
372	DFR-Net			13.63 %	19.40 %	10.35 %	0.18 s	1080 Ti (Pytorch)
Z. Zou, X. Ye, L. Du, X. Cheng, X. Tan, L. Zhang, J. Feng, X. Xue and E. Ding: The devil is in the task: Exploiting reciprocal appearance-localization features for monocular 3d object detection . ICCV 2021.
373	MonoFar			13.52 %	18.08 %	11.58 %	0.04 s	1 core @ 2.5 Ghz (Python)
374	CaDDN		code	13.41 %	19.17 %	11.46 %	0.63 s	GPU @ 2.5 Ghz (Python)
C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.
375	PCT		code	13.37 %	21.00 %	11.31 %	0.045 s	1 core @ 2.5 Ghz (C/C++)
L. Wang, L. Zhang, Y. Zhu, Z. Zhang, T. He, M. Li and X. Xue: Progressive Coordinate Transforms for Monocular 3D Object Detection. NeurIPS 2021.
376	Ground-Aware		code	13.25 %	21.65 %	9.91 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
Y. Liu, Y. Yuan and M. Liu: Ground-aware Monocular 3D Object Detection for Autonomous Driving. IEEE Robotics and Automation Letters 2021.
377	MK3D			13.19 %	20.48 %	11.10 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
378	Aug3D-RPN			12.99 %	17.82 %	9.78 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth. 2021.
379	HomoLoss(imvoxelnet)		code	12.99 %	20.10 %	10.50 %	0.20 s	1 core @ 2.5 Ghz (Python)
J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homogrpahy Loss for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
380	DDMP-3D			12.78 %	19.71 %	9.80 %	0.18 s	1 core @ 2.5 Ghz (Python)
L. Wang, L. Du, X. Ye, Y. Fu, G. Guo, X. Xue, J. Feng and L. Zhang: Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection. CVPR 2020.
381	RetinaMono			12.73 %	19.41 %	10.45 %	0.02 s	1 core @ 2.5 Ghz (Python)
382	Kinematic3D		code	12.72 %	19.07 %	9.17 %	0.12 s	1 core @ 1.5 Ghz (C/C++)
G. Brazil, G. Pons-Moll, X. Liu and B. Schiele: Kinematic 3D Object Detection in Monocular Video. ECCV 2020 .
383	M3DGAF			12.66 %	19.48 %	10.99 %	0.07 s	1 core @ 2.5 Ghz (Python)
384	MonoRCNN		code	12.65 %	18.36 %	10.03 %	0.07 s	GPU @ 2.5 Ghz (Python)
X. Shi, Q. Ye, X. Chen, C. Chen, Z. Chen and T. Kim: Geometry-based Distance Decomposition for Monocular 3D Object Detection. ICCV 2021.
385	MP-Mono			12.37 %	17.89 %	9.58 %	0.16 s	GPU @ 2.5 Ghz (Python)
386	GrooMeD-NMS		code	12.32 %	18.10 %	9.65 %	0.12 s	1 core @ 2.5 Ghz (Python)
A. Kumar, G. Brazil and X. Liu: GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection. CVPR 2021.
387	MonoRUn		code	12.30 %	19.65 %	10.58 %	0.07 s	GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
388	monodle		code	12.26 %	17.23 %	10.29 %	0.04 s	GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for Monocular 3D Object Detection. CVPR 2021 .
389	LT-M3OD			12.26 %	18.15 %	10.05 %	0.03 s	1 core @ 2.5 Ghz (Python)
390	PPTrans			12.06 %	19.79 %	10.48 %	0.2 s	GPU @ 2.5 Ghz (Python)
391	YoloMono3D		code	12.06 %	18.28 %	8.42 %	0.05 s	GPU @ 2.5 Ghz (Python)
Y. Liu, L. Wang and L. Ming: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
392	IAFA			12.01 %	17.81 %	10.61 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
D. Zhou, X. Song, Y. Dai, J. Yin, F. Lu, M. Liao, J. Fang and L. Zhang: IAFA: Instance-Aware Feature Aggregation for 3D Object Detection from a Single Image. Proceedings of the Asian Conference on Computer Vision 2020.
393	GAC3D			12.00 %	17.75 %	9.15 %	0.25 s	1 core @ 2.5 Ghz (Python)
M. Bui, D. Ngo, H. Pham and D. Nguyen: GAC3D: improving monocular 3D object detection with ground-guide model and adaptive convolution. 2021.
394	CMAN			11.87 %	17.77 %	9.16 %	0.15 s	1 core @ 2.5 Ghz (Python)
395	PGD-FCOS3D		code	11.76 %	19.05 %	9.39 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
T. Wang, X. Zhu, J. Pang and D. Lin: Probabilistic and Geometric Depth: Detecting Objects in Perspective. Conference on Robot Learning (CoRL) 2021.
396	D4LCN		code	11.72 %	16.65 %	9.51 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
397	MonoAug			11.47 %	16.40 %	9.26 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
398	KM3D		code	11.45 %	16.73 %	9.92 %	0.03 s	1 core @ 2.5 Ghz (Python)
P. Li: Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training. 2020.
399	RefinedMPL			11.14 %	18.09 %	8.94 %	0.15 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
400	PatchNet		code	11.12 %	15.68 %	10.17 %	0.4 s	1 core @ 2.5 Ghz (C/C++)
X. Ma, S. Liu, Z. Xia, H. Zhang, X. Zeng and W. Ouyang: Rethinking Pseudo-LiDAR Representation. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
401	ImVoxelNet		code	10.97 %	17.15 %	9.15 %	0.2 s	GPU @ 2.5 Ghz (Python)
D. Rukhovich, A. Vorontsova and A. Konushin: ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection. arXiv preprint arXiv:2106.01178 2021.
402	COF3D			10.91 %	17.86 %	8.20 %	200 s	1 core @ 2.5 Ghz (Python + C/C++)
403	MM			10.74 %	15.80 %	8.64 %	1 s	1 core @ 2.5 Ghz (C/C++)
404	AM3D			10.74 %	16.50 %	9.52 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
X. Ma, Z. Wang, H. Li, P. Zhang, W. Ouyang and X. Fan: Accurate Monocular Object Detection via Color- Embedded 3D Reconstruction for Autonomous Driving. Proceedings of the IEEE international Conference on Computer Vision (ICCV) 2019.
405	Lite-FPN			10.64 %	15.32 %	8.59 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
406	Keypoint-3D			10.42 %	15.97 %	7.91 %	14 s	1 core @ 2.5 Ghz (C/C++)
407	RTM3D		code	10.34 %	14.41 %	8.77 %	0.05 s	GPU @ 1.0 Ghz (Python)
P. Li, H. Zhao, P. Liu and F. Cao: RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving. 2020.
408	E2E-DA-Lite (Res18)			10.32 %	15.56 %	8.89 %	0.01 s	GPU @ 2.5 Ghz (Python)
409	MonoPair			9.99 %	13.04 %	8.65 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
410	Neighbor-Vote			9.90 %	15.57 %	8.89 %	0.1 s	GPU @ 2.5 Ghz (Python)
X. Chu, J. Deng, Y. Li, Z. Yuan, Y. Zhang, J. Ji and Y. Zhang: Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance Voting. ACM MM 2021.
411	SMOKE		code	9.76 %	14.03 %	7.84 %	0.03 s	GPU @ 2.5 Ghz (Python)
Z. Liu, Z. Wu and R. Tóth: SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation. 2020.
412	M3D-RPN		code	9.71 %	14.76 %	7.42 %	0.16 s	GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
413	QD-3DT		code	9.33 %	12.81 %	7.86 %	0.03 s	GPU @ 2.5 Ghz (Python)
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.
414	TopNet-HighRes			9.28 %	12.67 %	7.95 %	101ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
415	MonoCInIS			7.94 %	15.82 %	6.68 %	0,13 s	GPU @ 2.5 Ghz (C/C++)
J. Heylen, M. De Wolf, B. Dawagne, M. Proesmans, L. Van Gool, W. Abbeloos, H. Abdelkawy and D. Reino: MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.
416	Geo3D			7.70 %	11.52 %	6.80 %	0.04 s	GPU @ 2.5 Ghz (Python)
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417	SS3D			7.68 %	10.78 %	6.51 %	48 ms	Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
418	MonoCInIS			7.66 %	15.21 %	6.24 %	0,14 s	GPU @ 2.5 Ghz (Python)
J. Heylen, M. De Wolf, B. Dawagne, M. Proesmans, L. Van Gool, W. Abbeloos, H. Abdelkawy and D. Reino: MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.
419	Mono3D_PLiDAR		code	7.50 %	10.76 %	6.10 %	0.1 s	NVIDIA GeForce 1080 (pytorch)
X. Weng and K. Kitani: Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud. arXiv:1903.09847 2019.
420	MonoPSR		code	7.25 %	10.76 %	5.85 %	0.2 s	GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
421	Decoupled-3D			7.02 %	11.08 %	5.63 %	0.08 s	GPU @ 2.5 Ghz (C/C++)
Y. Cai, B. Li, Z. Jiao, H. Li, X. Zeng and X. Wang: Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation. AAAI 2020.
422	VoxelJones		code	6.35 %	7.39 %	5.80 %	.18 s	1 core @ 2.5 Ghz (Python + C/C++)
M. Motro and J. Ghosh: Vehicular Multi-object Tracking with Persistent Detector Failures. arXiv preprint arXiv:1907.11306 2019.
423	MonoGRNet		code	5.74 %	9.61 %	4.25 %	0.04s	NVIDIA P40
Z. Qin, J. Wang and Y. Lu: MonoGRNet: A Geometric Reasoning Network for 3D Object Localization. The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) 2019.
424	A3DODWTDA (image)		code	5.27 %	6.88 %	4.45 %	0.8 s	GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
425	MonoFENet			5.14 %	8.35 %	4.10 %	0.15 s	1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
426	TLNet (Stereo)		code	4.37 %	7.64 %	3.74 %	0.1 s	1 core @ 2.5 Ghz (Python)
Z. Qin, J. Wang and Y. Lu: Triangulation Learning Network: from Monocular to Stereo 3D Object Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
427	CSoR			4.06 %	5.61 %	3.17 %	3.5 s	4 cores @ >3.5 Ghz (Python + C/C++)
L. Plotkin: PyDriver: Entwicklung eines Frameworks für räumliche Detektion und Klassifikation von Objekten in Fahrzeugumgebung. 2015.
428	Shift R-CNN (mono)		code	3.87 %	6.88 %	2.83 %	0.25 s	GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
429	MVRA + I-FRCNN+			3.27 %	5.19 %	2.49 %	0.18 s	GPU @ 2.5 Ghz (Python)
H. Choi, H. Kang and Y. Hyun: Multi-View Reprojection Architecture for Orientation Estimation. The IEEE International Conference on Computer Vision (ICCV) Workshops 2019.
430	SparVox3D			3.20 %	5.27 %	2.56 %	0.05 s	GPU @ 2.0 Ghz (Python)
E. Balatkan and F. Kıraç: Improving Regression Performance on Monocular 3D Object Detection Using Bin-Mixing and Sparse Voxel Data. 2021 6th International Conference on Computer Science and Engineering (UBMK) 2021.
431	TopNet-UncEst			3.02 %	3.24 %	2.26 %	0.09 s	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
432	GS3D			2.90 %	4.47 %	2.47 %	2 s	1 core @ 2.5 Ghz (C/C++)
B. Li, W. Ouyang, L. Sheng, X. Zeng and X. Wang: GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
433	3D-GCK			2.52 %	3.27 %	2.11 %	24 ms	Tesla V100
N. Gählert, J. Wan, N. Jourdan, J. Finkbeiner, U. Franke and J. Denzler: Single-Shot 3D Detection of Vehicles from Monocular RGB Images via Geometrically Constrained Keypoints in Real-Time. 2020 IEEE Intelligent Vehicles Symposium (IV) 2020.
434	WeakM3D		code	2.26 %	5.03 %	1.63 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
L. Peng, S. Yan, B. Wu, Z. Yang, X. He and D. Cai: WeakM3D: Towards Weakly Supervised Monocular 3D Object Detection. ICLR 2022.
435	ROI-10D			2.02 %	4.32 %	1.46 %	0.2 s	GPU @ 3.5 Ghz (Python)
F. Manhardt, W. Kehl and A. Gaidon: ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape. Computer Vision and Pattern Recognition (CVPR) 2019.
436	CDTrack3D		code	1.92 %	3.20 %	1.63 %	0.0106 s	NVIDIA RTX 3090 GPU, i9 10850k CPU
437	FQNet			1.51 %	2.77 %	1.01 %	0.5 s	1 core @ 2.5 Ghz (Python)
L. Liu, J. Lu, C. Xu, Q. Tian and J. Zhou: Deep Fitting Degree Scoring Network for Monocular 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
438	3D-SSMFCNN		code	1.41 %	1.88 %	1.11 %	0.1 s	GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.
439	test		code	0.03 %	0.01 %	0.03 %	50 s	1 core @ 2.5 Ghz (Python)
440	MonoDET		code	0.01 %	0.03 %	0.01 %	0.04 s	1 core @ 2.5 Ghz (Python)
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	CasA++			49.29 %	56.33 %	46.70 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
2	TED			49.21 %	55.85 %	46.52 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
3	VPFNet		code	48.36 %	54.65 %	44.98 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
C. Wang, H. Chen and L. Fu: VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection. 2021.
4	CAD			47.91 %	55.98 %	44.63 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
5	PV-RCNN++		code	47.19 %	54.29 %	43.49 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
6	CasA			47.09 %	54.04 %	44.56 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
7	EQ-PVRCNN		code	47.02 %	55.84 %	42.94 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, L. Jiang, Y. Sun, B. Schiele and J. Jia: A Unified Query-based Paradigm for Point Cloud Understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.
8	PiFeNet			46.71 %	56.39 %	42.71 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
D. Le, H. Shi, H. Rezatofighi and J. Cai: PiFeNet: Pillar-Feature Network for Real-Time 3D Pedestrian Detection from Point Cloud. 2021.
9	ISE-RCNN			45.66 %	51.44 %	42.43 %	0.09 s	1 core @ 2.5 Ghz (Python + C/C++)
10	CAT-Det			45.44 %	54.26 %	41.94 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Zhang, J. Chen and D. Huang: CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection. CVPR 2022.
11	HotSpotNet			45.37 %	53.10 %	41.47 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
12	PE-RCVN			45.01 %	50.29 %	41.85 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
13	variance_point			44.89 %	53.72 %	41.21 %	0.05 s	1 core @ 2.5 Ghz (Python)
14	VPN			44.56 %	54.13 %	41.73 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
15	EPNet++			44.38 %	52.79 %	41.29 %	0.1 s	GPU @ 2.5 Ghz (Python)
Z. Liu, H. tengteng, B. Li, X. Chen, X. Wang and X. Bai: EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object Detection. arXiv preprint arXiv:2112.11088 2021.
16	TANet		code	44.34 %	53.72 %	40.49 %	0.035s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
17	CFF-tv			44.33 %	52.72 %	41.11 %	1 s	1 core @ 2.5 Ghz (C/C++)
18	TBD			44.32 %	49.37 %	41.24 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
19	3DSSD		code	44.27 %	54.64 %	40.23 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
20	AutoAlign			44.08 %	53.99 %	40.82 %	0.1 s	1 core @ 2.5 Ghz (Python)
21	ISE-RCNN-PV			43.78 %	50.03 %	40.50 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
22	Point-GNN		code	43.77 %	51.92 %	40.14 %	0.6 s	GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
23	VCT			43.65 %	50.27 %	41.43 %	0.2 s	1 core @ 2.5 Ghz (Python)
24	USVLab BSAODet			43.63 %	51.71 %	41.09 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
25	CFF-ep25			43.47 %	51.85 %	40.69 %	1 s	1 core @ 2.5 Ghz (C/C++)
26	FV2P v2			43.47 %	50.64 %	40.09 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
27	F-ConvNet		code	43.38 %	52.16 %	38.80 %	0.47 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
28	MMLab-PartA^2		code	43.35 %	53.10 %	40.06 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
29	FS-Net			43.31 %	49.82 %	40.89 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
30	MMLab PV-RCNN		code	43.29 %	52.17 %	40.29 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
31	FromVoxelToPoint		code	43.28 %	51.80 %	40.71 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to- Point Decoder. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
32	VMVS			43.27 %	53.44 %	39.51 %	0.25 s	GPU @ 2.5 Ghz (Python)
J. Ku, A. Pon, S. Walsh and S. Waslander: Improving 3D object detection for pedestrians with virtual multi-view synthesis orientation estimation. IROS 2019.
33	cff-tv-v2-ep25			43.25 %	51.40 %	40.23 %	1 s	1 core @ 2.5 Ghz (C/C++)
34	P2V-RCNN			43.19 %	50.91 %	40.81 %	0.1 s	2 cores @ 2.5 Ghz (Python)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.
35	KeyFuse2B			43.18 %	51.49 %	40.70 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
36	CF-ctdep-tv-ta			43.11 %	50.40 %	40.51 %	1 s	1 core @ 2.5 Ghz (C/C++)
37	MGAF-3DSSD		code	43.09 %	50.65 %	39.65 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
38	Reprod-Two-Branch			43.07 %	52.07 %	40.40 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
39	SGNet			43.00 %	49.68 %	40.45 %	0.09 s	GPU @ 2.5 Ghz (Python)
40	Frustum-PointPillars		code	42.89 %	51.22 %	39.28 %	0.06 s	4 cores @ 3.0 Ghz (Python)
A. Paigwar, D. Sierra-Gonzalez, \. Erkent and C. Laugier: Frustum-PointPillars: A Multi-Stage Approach for 3D Object Detection using RGB Camera and LiDAR. International Conference on Computer Vision, ICCV, Workshop on Autonomous Vehicle Vision 2021.
41	CFF-tv-v2			42.77 %	51.08 %	40.15 %	1 s	1 core @ 2.5 Ghz (C/C++)
42	Fast-CLOCs			42.72 %	52.10 %	39.08 %	0.1 s	GPU @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: Fast-CLOCs: Fast Camera-LiDAR Object Candidates Fusion for 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022.
43	CF-base-tv			42.66 %	50.01 %	39.76 %	1 s	1 core @ 2.5 Ghz (C/C++)
44	HMFI		code	42.65 %	50.88 %	39.78 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
45	TBD			42.65 %	50.88 %	39.78 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
46	USVLab BSAODet (S)			42.62 %	49.52 %	39.12 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
47	STD		code	42.47 %	53.29 %	38.35 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
48	Anonymous			42.32 %	47.96 %	39.69 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
49	AVOD-FPN		code	42.27 %	50.46 %	39.04 %	0.1 s	Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
50	SemanticVoxels			42.19 %	50.90 %	39.52 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Fei, W. Chen, P. Heidenreich, S. Wirges and C. Stiller: SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentation. MFI 2020.
51	TBD			42.19 %	49.89 %	39.34 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
52	TBD			42.19 %	49.89 %	39.34 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
53	F-PointNet		code	42.15 %	50.53 %	38.08 %	0.17 s	GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
54	TBD			42.05 %	48.66 %	38.94 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
55	3SNet			42.02 %	48.91 %	39.39 %	0.07 s	GPU @ 2.5 Ghz (Python)
56	CF-ctdep-tv			41.98 %	49.14 %	39.03 %	1 s	1 core @ 2.5 Ghz (C/C++)
57	Self-Calib Conv			41.95 %	48.88 %	39.52 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
58	PointPillars		code	41.92 %	51.45 %	38.89 %	16 ms	1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
59	CenterFuse			41.80 %	49.77 %	38.49 %	0.059 sec/frame	2 x V100
60	cp-tv-kp			41.70 %	48.77 %	39.41 %	1 s	1 core @ 2.5 Ghz (C/C++)
61	epBRM		code	41.52 %	49.17 %	39.08 %	0.10 s	1 core @ 2.5 Ghz (C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
62	TCDVF			41.47 %	49.44 %	38.41 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
63	DGT-Det3D			41.40 %	49.06 %	38.43 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
64	tbd			41.10 %	50.56 %	37.49 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
65	IA-SSD (single)		code	41.03 %	47.90 %	37.98 %	0.013 s	1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
66	PointPainting			40.97 %	50.32 %	37.87 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
67	DSA-PV-RCNN		code	40.89 %	46.97 %	38.80 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
68	SARFE			40.79 %	47.29 %	38.25 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
69	IoU-2B			40.62 %	50.33 %	36.94 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
70	MSADet			40.58 %	49.54 %	38.19 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
71	TBD			40.57 %	47.65 %	38.33 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
72	PDV		code	40.56 %	47.80 %	38.46 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
J. Hu, T. Kuai and S. Waslander: Point Density-Aware Voxels for LiDAR 3D Object Detection. CVPR 2022.
73	cp-tv			40.55 %	47.71 %	38.23 %	1 s	1 core @ 2.5 Ghz (C/C++)
74	CM3DV			40.43 %	46.10 %	38.32 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
75	cff-tv-t			40.41 %	49.46 %	37.02 %	1 s	1 core @ 2.5 Ghz (C/C++)
76	SVGA-Net			40.39 %	48.48 %	37.92 %	0.03s	1 core @ 2.5 Ghz (Python + C/C++)
Q. He, Z. Wang, H. Zeng, Y. Zeng and Y. Liu: SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds. AAAI 2022.
77	FusionDetv2-v3			40.38 %	46.86 %	37.41 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
78	P2V_PCV1			40.27 %	45.43 %	38.24 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
79	sa-voxel-centernet		code	40.24 %	46.08 %	38.07 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
80	KeyPoint-IoUHead			40.15 %	47.86 %	37.01 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
81	TBD			40.07 %	46.11 %	37.87 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
82	TPCG			39.97 %	46.35 %	37.66 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
83	M3DeTR		code	39.94 %	45.70 %	37.66 %	n/a s	GPU @ 1.0 Ghz (Python)
T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis and D. Manocha: M3DeTR: Multi-representation, Multi- scale, Mutual-relation 3D Object Detection with Transformers. 2021.
84	cp-tv-kp-io-sc			39.92 %	48.06 %	37.68 %	1 s	1 core @ 2.5 Ghz (C/C++)
85	FusionDetv2-v5			39.91 %	47.50 %	37.39 %	0.05 s	1 core @ 2.5 Ghz (Java + C/C++)
86	DDet			39.87 %	45.82 %	38.00 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
87	CF-cd-io-tv			39.82 %	48.67 %	36.45 %	1 s	1 core @ 2.5 Ghz (C/C++)
88	MVOD			39.82 %	46.22 %	37.56 %	0.16 s	1 core @ 2.5 Ghz (C/C++)
89	FusionDetv2-v4			39.68 %	46.93 %	37.31 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
90	DSASNet			39.65 %	47.14 %	37.05 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
91	VCRCNN			39.64 %	45.19 %	37.55 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
92	AFTD			39.45 %	48.28 %	36.07 %	1 s	1 core @ 2.5 Ghz (Python + C/C++)
93	TBD		code	39.45 %	47.08 %	37.12 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
94	Dune-DCF-e09			39.43 %	47.29 %	36.74 %	1 s	1 core @ 2.5 Ghz (C/C++)
95	LazyTorch-CP-Infer-O			39.43 %	47.38 %	36.96 %	1 s	1 core @ 2.5 Ghz (C/C++)
96	SRDL			39.43 %	47.30 %	36.99 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
97	FusionDetv1			39.42 %	47.30 %	36.97 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
98	AGS-SSD[la]			39.41 %	46.04 %	36.28 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
99	MMLab-PointRCNN		code	39.37 %	47.98 %	36.01 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
100	ST-RCNN			39.36 %	44.96 %	37.09 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
101	LazyTorch-CP-Small-P			39.33 %	47.27 %	36.89 %	1 s	1 core @ 2.5 Ghz (C/C++)
102	FusionDetv2-v2			39.31 %	44.98 %	37.22 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
103	ARPNET			39.31 %	48.32 %	35.93 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
104	CenterPoint (pcdet)			39.28 %	47.25 %	36.78 %	0.051 sec/frame	2 x V100
105	Dune-DCF-e11			39.26 %	47.32 %	36.63 %	1 s	1 core @ 2.5 Ghz (C/C++)
106	ATT_SSD			39.24 %	45.92 %	36.35 %	0.01 s	1 core @ 2.5 Ghz (Python)
107	City-CF-fixed			39.22 %	47.68 %	36.55 %	1 s	1 core @ 2.5 Ghz (C/C++)
108	IA-SSD (multi)		code	39.03 %	46.51 %	35.61 %	0.014 s	1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
109	NV-RCNN			38.75 %	47.05 %	36.52 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
110	SIF			38.74 %	46.23 %	36.06 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
111	CrazyTensor-CP			38.67 %	46.58 %	36.15 %	1 s	1 core @ 2.5 Ghz (Python)
112	SCNet			38.66 %	47.83 %	35.70 %	0.04 s	GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
113	Dune-DCF-e15			38.61 %	46.41 %	36.02 %	1 s	1 core @ 2.5 Ghz (C/C++)
114	Faraway-Frustum		code	38.58 %	46.33 %	35.71 %	0.1 s	GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-frustum: Dealing with lidar sparsity for 3D object detection using fusion. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) 2021.
115	IKT3D			38.58 %	44.18 %	36.56 %	0.05 s	1 core @ 2.5 Ghz (Python)
116	FPV-SSD			38.45 %	45.83 %	36.03 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
117	CF-base-train			38.44 %	45.89 %	35.55 %	1 s	1 core @ 2.5 Ghz (C/C++)
118	TBD			38.27 %	46.35 %	36.39 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
119	City-CF			38.04 %	45.42 %	35.40 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
120	CF-ctdep-train			38.03 %	44.75 %	35.49 %	1 s	1 core @ 2.5 Ghz (C/C++)
121	PVTr			37.58 %	43.99 %	35.48 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
122	DVFENet			37.50 %	43.55 %	35.33 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection. Neurocomputing 2021.
123	MLOD		code	37.47 %	47.58 %	35.07 %	0.12 s	GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
124	S-AT GCN			37.37 %	44.63 %	34.92 %	0.02 s	GPU @ 2.0 Ghz (Python)
L. Wang, C. Wang, X. Zhang, T. Lan and J. Li: S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection. CoRR 2021.
125	HS3D		code	36.86 %	45.62 %	33.67 %	0.12 s	1 core @ 2.5 Ghz (Python + C/C++)
126	XView			36.79 %	42.44 %	34.96 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
127	FusionDetv2-baseline			36.66 %	41.34 %	34.60 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
128	TBD			36.53 %	44.11 %	34.30 %	TBD	GPU @ 2.5 Ghz (Python + C/C++)
129	PFF3D		code	36.07 %	43.93 %	32.86 %	0.05 s	GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
130	NV2P-RCNN			35.98 %	43.18 %	33.88 %	0.1 s	GPU @ 2.5 Ghz (Python)
131	CrazyTensor-CF			35.83 %	43.50 %	33.23 %	1 s	1 core @ 2.5 Ghz (C/C++)
132	BirdNet+		code	35.06 %	41.55 %	32.93 %	0.11 s	Titan Xp (PyTorch)
A. Barrera, J. Beltrán, C. Guindel, J. Iglesias and F. García: BirdNet+: Two-Stage 3D Object Detection in LiDAR through a Sparsity-Invariant Bird’s Eye View. IEEE Access 2021.
133	TBD_BD		code	34.86 %	42.56 %	32.49 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
134	AB3DMOT		code	34.59 %	42.27 %	31.37 %	0.0047s	1 core @ 2.5 Ghz (python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
135	KPP3D		code	32.91 %	41.34 %	30.45 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
136	DSGN++		code	32.74 %	43.05 %	29.54 %	0.2 s	GeForce RTX 2080Ti
Y. Chen, S. Huang, S. Liu, B. Yu and J. Jia: DSGN++: Exploiting Visual-Spatial Relation for Stereo-based 3D Detectors. arXiv preprint arXiv:2204.03039 2022.
137	StereoDistill			32.23 %	44.12 %	28.95 %	0.4 s	1 core @ 2.5 Ghz (Python)
138	PP-PCdet		code	32.04 %	39.23 %	29.79 %	0.01 s	1 core @ 2.5 Ghz (Python)
139	Contrastive PP		code	31.64 %	38.47 %	29.30 %	0.01 s	1 core @ 2.5 Ghz (Python)
140	BirdNet+ (legacy)		code	31.46 %	37.99 %	29.46 %	0.1 s	Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird’s Eye View. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
141	SparsePool		code	30.38 %	37.84 %	26.94 %	0.13 s	8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
142	MMLAB LIGA-Stereo		code	30.00 %	40.46 %	27.07 %	0.4 s	1 core @ 2.5 Ghz (Python + C/C++)
X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
143	DisposalNet			29.77 %	37.21 %	27.62 %	0.2 s	1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
144	SparsePool		code	27.92 %	35.52 %	25.87 %	0.13 s	8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
145	AVOD		code	27.86 %	36.10 %	25.76 %	0.08 s	Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
146	PS++		code	27.45 %	36.89 %	24.01 %	PS++ s	1 core @ 2.5 Ghz (C/C++)
147	CSW3D			26.64 %	33.75 %	23.34 %	0.03 s	4 cores @ 2.5 Ghz (C/C++)
J. Hu, T. Wu, H. Fu, Z. Wang and K. Ding: Cascaded Sliding Window Based Real-Time 3D Region Proposal for Pedestrian Detection. ROBIO 2019.
148	PointRGBNet			26.40 %	34.77 %	24.03 %	0.08 s	4 cores @ 2.5 Ghz (Python + C/C++)
P. Xie Desheng: Real-time Detection of 3D Objects Based on Multi-Sensor Information Fusion. Automotive Engineering 2022.
149	PS		code	26.01 %	35.52 %	23.24 %	PS s	1 core @ 2.5 Ghz (C/C++)
150	Disp R-CNN (velo)		code	25.80 %	37.12 %	22.04 %	0.387 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
151	CZY			25.47 %	32.33 %	23.60 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
152	Disp R-CNN		code	25.40 %	35.75 %	21.79 %	0.387 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
153	FusionDetv2-v1			24.55 %	30.58 %	23.64 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
154	CG-Stereo			24.31 %	33.22 %	20.95 %	0.57 s	GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
155	YOLOStereo3D		code	19.75 %	28.49 %	16.48 %	0.1 s	GPU 1080Ti
Y. Liu, L. Wang and M. Liu: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
156	AEC3D			19.00 %	24.39 %	17.43 %	18 ms	GPU @ 2.5 Ghz (Python)
157	BEVC			17.65 %	23.49 %	15.92 %	35ms	GPU @ 1.5 Ghz (Python)
158	OC Stereo		code	17.58 %	24.48 %	15.60 %	0.35 s	1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
159	BirdNet			17.08 %	22.04 %	15.82 %	0.11 s	Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
160	VN3D			15.69 %	19.56 %	13.17 %	0.02 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
161	DSGN		code	15.55 %	20.53 %	14.15 %	0.67 s	NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
162	Complexer-YOLO			13.96 %	17.60 %	12.70 %	0.06 s	GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
163	Anonymous			11.69 %	17.79 %	10.09 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
164	RT3D-GMP			11.41 %	16.23 %	10.12 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
165	DD3D		code	11.04 %	16.64 %	9.38 %	n/a s	1 core @ 2.5 Ghz (C/C++)
D. Park, R. Ambrus, V. Guizilini, J. Li and A. Gaidon: Is Pseudo-Lidar needed for Monocular 3D Object detection?. IEEE/CVF International Conference on Computer Vision (ICCV) .
166	PS-fld		code	10.82 %	16.95 %	9.26 %	0.25 s	1 core @ 2.5 Ghz (C/C++)
Y. Chen, H. Dai and Y. Ding: Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
167	DEPT			10.81 %	16.28 %	9.15 %	0.03 s	1 core @ 2.5 Ghz (Python)
168	OPA-3D		code	10.49 %	15.65 %	8.80 %	0.04 s	1 core @ 3.5 Ghz (Python)
169	anonymity			10.39 %	16.89 %	9.29 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
170	ESGN			10.27 %	14.05 %	9.02 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
171	MonoDTR			10.18 %	15.33 %	8.61 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
K. Huang, T. Wu, H. Su and W. Hsu: MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer. CVPR 2022.
172	LT-M3OD			9.99 %	14.85 %	8.52 %	0.03 s	1 core @ 2.5 Ghz (Python)
173	GUPNet		code	9.76 %	14.95 %	8.41 %	NA s	1 core @ 2.5 Ghz (Python + C/C++)
Y. Lu, X. Ma, L. Yang, T. Zhang, Y. Liu, Q. Chu, J. Yan and W. Ouyang: Geometry Uncertainty Projection Network for Monocular 3D Object Detection. arXiv preprint arXiv:2107.13774 2021.
174	MonoInsight			9.42 %	14.41 %	7.96 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
175	GPENet		code	9.36 %	14.61 %	7.91 %	0.02 s	GPU @ 2.5 Ghz (Python)
176	Lite-FPN-GUPNet			9.32 %	14.13 %	7.93 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
177	mono3d		code	9.20 %	14.53 %	7.82 %	TBD	TBD
178	ZongmuMono3d		code	9.18 %	14.23 %	7.82 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
179	SGM3D			8.81 %	13.99 %	7.26 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
Z. Zhou, L. Du, X. Ye, Z. Zou, X. Tan, E. Ding, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection. 2021.
180	CMKD*			8.79 %	13.94 %	7.42 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
181	SCSTSV-MonoFlex			8.75 %	13.10 %	7.38 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
182	gupnet_se			8.65 %	13.40 %	7.78 %	0.03s	1 core @ 2.5 Ghz (C/C++)
183	SwinMono3D			8.54 %	12.96 %	7.19 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
184	SARM3D			8.48 %	12.95 %	7.25 %	0.03 s	GPU @ 2.5 Ghz (Python)
185	MonoCon		code	8.41 %	13.10 %	6.94 %	0.02 s	GPU @ 2.5 Ghz (Python)
T. Xianpeng Liu: Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection. AAAI 2022.
186	HBD			8.33 %	13.47 %	6.99 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
187	MonoEdge			8.33 %	12.11 %	7.03 %	0.05 s	GPU @ 2.5 Ghz (Python)
188	GCDR			8.27 %	11.50 %	7.37 %	0.28 s	1 core @ 2.5 Ghz (Python)
189	MonoFlex			8.16 %	11.89 %	6.81 %	0.03 s	1 core @ 2.5 Ghz (Python)
190	CaDDN		code	8.14 %	12.87 %	6.76 %	0.63 s	GPU @ 2.5 Ghz (Python)
C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.
191	MonoGround			7.89 %	12.37 %	7.13 %	0.03 s	1 core @ 2.5 Ghz (Python)
192	HomoLoss(monoflex)		code	7.66 %	11.87 %	6.82 %	0.04 s	1 core @ 2.5 Ghz (Python)
J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homography Loss for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
193	MonoFar			7.62 %	11.21 %	6.47 %	0.04 s	1 core @ 2.5 Ghz (Python)
194	K3D			7.60 %	12.58 %	6.73 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
195	ANM			7.54 %	11.92 %	6.37 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
196	SAIC_ADC_Mono3D		code	7.54 %	12.06 %	6.41 %	50 s	GPU @ 2.5 Ghz (Python)
197	Mix-Teaching			7.47 %	11.67 %	6.61 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
198	M3DGAF			7.42 %	11.68 %	6.65 %	0.07 s	1 core @ 2.5 Ghz (Python)
199	LPCG-Monoflex			7.33 %	10.82 %	6.18 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
200	MonoDDE			7.32 %	11.13 %	6.67 %	0.04 s	1 core @ 2.5 Ghz (Python)
Z. Li, Z. Qu, Y. Zhou, J. Liu, H. Wang and L. Jiang: Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection. CVPR 2022.
201	MonoAug			7.31 %	11.31 %	6.12 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
202	RefinedMPL			7.18 %	11.14 %	5.84 %	0.15 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
203	Shape-Aware			7.16 %	10.40 %	5.93 %	0.05 s	1 core @ 2.5 Ghz (Python)
204	MDSNet			7.09 %	10.68 %	6.06 %	0.07 s	1 core @ 2.5 Ghz (Python)
205	MonoEdge-Rotate			7.02 %	10.47 %	5.84 %	0.05 s	GPU @ 2.5 Ghz (Python)
206	TopNet-HighRes			6.92 %	10.40 %	6.63 %	101ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
207	MonoRUn		code	6.78 %	10.88 %	5.83 %	0.07 s	GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
208	MonoPair			6.68 %	10.02 %	5.53 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
209	mono3d			6.62 %	10.10 %	5.46 %	0.03 s	GPU @ 2.5 Ghz (Python)
210	monodle		code	6.55 %	9.64 %	5.44 %	0.04 s	GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for Monocular 3D Object Detection. CVPR 2021 .
211	MonoAug			6.36 %	9.59 %	5.24 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
212	MonoFlex			6.31 %	9.43 %	5.26 %	0.03 s	GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.
213	E2E-DA			5.95 %	8.79 %	5.72 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
214	MDNet			5.66 %	8.24 %	4.74 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
215	M3DSSD++		code	5.65 %	8.10 %	4.72 %	0.16s	1 core @ 2.5 Ghz (C/C++)
216	MK3D			5.00 %	7.29 %	4.54 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
217	Aug3D-RPN			4.71 %	6.01 %	3.87 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth. 2021.
218	MM			4.70 %	7.81 %	4.01 %	1 s	1 core @ 2.5 Ghz (C/C++)
219	Shift R-CNN (mono)		code	4.66 %	7.95 %	4.16 %	0.25 s	GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
220	Lite-FPN			4.38 %	6.57 %	3.56 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
221	COF3D			4.37 %	6.02 %	3.55 %	200 s	1 core @ 2.5 Ghz (Python + C/C++)
222	MAOLoss		code	4.18 %	5.81 %	3.67 %	0.05 s	1 core @ 2.5 Ghz (Python)
223	MonoPSR		code	4.00 %	6.12 %	3.30 %	0.2 s	GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
224	MP-Mono			3.75 %	5.09 %	3.50 %	0.16 s	GPU @ 2.5 Ghz (Python)
225	Geo3D			3.65 %	5.74 %	3.01 %	0.04 s	GPU @ 2.5 Ghz (Python)
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226	DFR-Net			3.62 %	6.09 %	3.39 %	0.18 s	1080 Ti (Pytorch)
Z. Zou, X. Ye, L. Du, X. Cheng, X. Tan, L. Zhang, J. Feng, X. Xue and E. Ding: The devil is in the task: Exploiting reciprocal appearance-localization features for monocular 3d object detection . ICCV 2021.
227	DDMP-3D			3.55 %	4.93 %	3.01 %	0.18 s	1 core @ 2.5 Ghz (Python)
L. Wang, L. Du, X. Ye, Y. Fu, G. Guo, X. Xue, J. Feng and L. Zhang: Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection. CVPR 2020.
228	E2E-DA-Lite (Res18)			3.51 %	5.82 %	3.42 %	0.01 s	GPU @ 2.5 Ghz (Python)
229	M3D-RPN		code	3.48 %	4.92 %	2.94 %	0.16 s	GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
230	D4LCN		code	3.42 %	4.55 %	2.83 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
231	MoGDE			3.42 %	5.57 %	2.73 %	0.03 s	GPU @ 2.5 Ghz (Python)
232	CMAN			3.41 %	4.62 %	2.87 %	0.15 s	1 core @ 2.5 Ghz (Python)
233	QD-3DT		code	3.37 %	5.53 %	3.02 %	0.03 s	GPU @ 2.5 Ghz (Python)
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.
234	MonoEF			2.79 %	4.27 %	2.21 %	0.03 s	1 core @ 2.5 Ghz (Python)
Y. Zhou, Y. He, H. Zhu, C. Wang, H. Li and Q. Jiang: Monocular 3D Object Detection: An Extrinsic Parameter Free Approach. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
235	RT3DStereo			2.45 %	3.28 %	2.35 %	0.08 s	GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
236	TopNet-UncEst			1.87 %	3.42 %	1.73 %	0.09 s	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
237	PPTrans			1.85 %	2.68 %	1.44 %	0.2 s	GPU @ 2.5 Ghz (Python)
238	SS3D			1.78 %	2.31 %	1.48 %	48 ms	Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
239	PGD-FCOS3D		code	1.49 %	2.28 %	1.38 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
T. Wang, X. Zhu, J. Pang and D. Lin: Probabilistic and Geometric Depth: Detecting Objects in Perspective. Conference on Robot Learning (CoRL) 2021.
240	SparVox3D			1.35 %	1.93 %	1.04 %	0.05 s	GPU @ 2.0 Ghz (Python)
E. Balatkan and F. Kıraç: Improving Regression Performance on Monocular 3D Object Detection Using Bin-Mixing and Sparse Voxel Data. 2021 6th International Conference on Computer Science and Engineering (UBMK) 2021.
241	EM		code	1.18 %	1.09 %	0.80 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
242	CDTrack3D		code	1.01 %	1.48 %	0.69 %	0.0106 s	NVIDIA RTX 3090 GPU, i9 10850k CPU
243	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.