The KITTI Vision Benchmark Suite

The object detection and object orientation estimation benchmark consists of 7481 training images and 7518 test images, comprising a total of 80.256 labeled objects. All images are color and saved as png. For evaluation, we compute precision-recall curves for object detection and orientation-similarity-recall curves for joint object detection and orientation estimation. In the latter case not only the object 2D bounding box has to be located correctly, but also the orientation estimate in bird's eye view is evaluated. To rank the methods we compute average precision and average orientation similiarity. We require that all methods use the same parameter set for all test pairs. Our development kit provides details about the data format as well as MATLAB / C++ utility functions for reading and writing the label files.

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

We evaluate object detection performance using the PASCAL criteria and object detection and orientation estimation performance using the measure discussed in our CVPR 2012 publication. For cars we require an overlap of 70%, while for pedestrians and cyclists we require an overlap of 50% for a detection. Detections in don't care areas or detections which are smaller than the minimum size do not count as false positive. 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. Note that for the hard evaluation ~2 % of the provided bounding boxes have not been recognized by humans, thereby upper bounding recall at 98 %. Hence, the hard evaluation is only given for reference.
Note 1: On 25.04.2017, we have fixed a bug in the object detection evaluation script. As of now, the submitted detections are filtered based on the min. bounding box height for the respective category which we have been done before only for the ground truth detections, thus leading to false positives for the category "Easy" when bounding boxes of height 25-39 Px were submitted (and to false positives for all categories if bounding boxes smaller than 25 Px were submitted). We like to thank Amy Wu, Matt Wilder, Pekka Jänis and Philippe Vandermersch for their feedback. The last leaderboards right before the changes can be found here!

Car

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	Anonymous			97.19 %	98.27 %	94.59 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
2	GraR-VoI			96.38 %	96.81 %	91.20 %	0.07 s	1 core @ 2.5 Ghz (Python + C/C++)
3	GraR-Po			96.18 %	96.84 %	91.11 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
4	SFD			96.17 %	98.97 %	91.13 %	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.
5	VPFNet			96.15 %	96.64 %	91.14 %	0.06 s	2 cores @ 2.5 Ghz (Python)
H. Zhu, J. Deng, Y. Zhang, J. Ji, Q. Mao, H. Li and Y. Zhang: VPFNet: Improving 3D Object Detection with Virtual Point based LiDAR and Stereo Data Fusion. 2021.
6	Anonymous			96.12 %	99.07 %	91.12 %	n/a s	1 core @ 2.5 Ghz (Python + C/C++)
7	CLOCs		code	96.07 %	96.77 %	91.11 %	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.
8	GraR-Vo			96.05 %	96.67 %	93.01 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
9	TED			96.03 %	96.64 %	93.35 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
10	ImpDet			96.00 %	96.73 %	90.96 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
11	CLOCs_PVCas		code	95.96 %	96.76 %	91.08 %	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.
12	CityBrainLab			95.96 %	96.59 %	90.94 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
13	PE-RCVN			95.94 %	96.90 %	90.98 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
14	Anonymous			95.91 %	96.55 %	92.86 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
15	PVT-SSD			95.90 %	96.75 %	90.69 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
16	GraR-Pi			95.89 %	98.59 %	92.85 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
17	Anonymous			95.86 %	98.93 %	90.98 %	n/a s	1 core @ 2.5 Ghz (C/C++)
18	GLENet-VR			95.81 %	96.85 %	90.91 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
19	HCPVF			95.80 %	96.62 %	93.03 %	0.07 s	1 core @ 2.5 Ghz (Python + C/C++)
20	Anonymous			95.79 %	96.69 %	92.75 %	0.03 s	1 core @ 2.5 Ghz (Python + C/C++)
21	SECOND			95.79 %	96.44 %	90.55 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
22	DVF-V			95.77 %	96.60 %	90.89 %	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.
23	LIVOX_Det			95.75 %	98.62 %	93.05 %	n/a s	1 core @ 2.5 Ghz (Python + C/C++)
24	Fast-CLOCs			95.75 %	96.69 %	90.95 %	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.
25	DSGN++		code	95.70 %	98.08 %	88.27 %	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.
26	TBD			95.69 %	96.33 %	93.07 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
27	CasA			95.62 %	96.52 %	92.86 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
28	BADet		code	95.61 %	98.75 %	90.64 %	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.
29	SE-SSD		code	95.60 %	96.69 %	90.53 %	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.
30	JPVNet			95.52 %	96.41 %	90.72 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
31	Anonymous			95.50 %	98.36 %	90.63 %	0.1s	1 core @ 2.5 Ghz (C/C++)
32	DVF-PV			95.49 %	96.42 %	92.57 %	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.
33	Anonymous			95.47 %	98.35 %	90.55 %	n/a s	1 core @ 2.5 Ghz (C/C++)
34	SPANet			95.46 %	96.54 %	90.47 %	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.
35	ISE-RCNN-PV			95.46 %	96.20 %	92.88 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
36	LGNet			95.43 %	96.52 %	92.73 %	0.07 s	1 core @ 2.5 Ghz (C/C++)
37	TBD			95.43 %	96.06 %	90.38 %	0.1 s	1 core @ 2.5 Ghz (Python)
38	ISE-RCNN			95.43 %	96.38 %	92.82 %	0.09 s	1 core @ 2.5 Ghz (Python + C/C++)
39	Anonymous			95.41 %	96.49 %	90.68 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
40	GLENet			95.40 %	96.61 %	90.57 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
41	Anonymous			95.40 %	96.62 %	90.56 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
42	PTA-RCNN			95.39 %	96.40 %	92.40 %	0.08 s	1 core @ 2.5 Ghz (Python)
43	SASA		code	95.35 %	96.01 %	92.53 %	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.
44	SPG_mini		code	95.32 %	96.23 %	92.68 %	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.
45	EQ-PVRCNN		code	95.32 %	98.23 %	92.65 %	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			95.31 %	96.36 %	92.57 %	0.09 s	GPU @ 2.5 Ghz (Python)
47	TBD			95.29 %	96.17 %	90.56 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
48	Focals Conv		code	95.28 %	96.30 %	92.69 %	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.
49	CasA++			95.28 %	95.83 %	94.28 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
50	DGDNH			95.24 %	98.36 %	92.69 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
51	VoxSeT		code	95.23 %	96.16 %	90.49 %	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.
52	PC-CNN-V2			95.20 %	96.06 %	89.37 %	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.
53	VPFNet		code	95.17 %	96.06 %	92.66 %	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.
54	F-PointNet		code	95.17 %	95.85 %	85.42 %	0.17 s	GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
55	EPNet++			95.17 %	96.73 %	92.10 %	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.
56	SA-SSD		code	95.16 %	97.92 %	90.15 %	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.
57	HMFI		code	95.16 %	96.29 %	92.45 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
58	Pyramid R-CNN			95.13 %	95.88 %	92.62 %	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.
59	Voxel R-CNN		code	95.11 %	96.49 %	92.45 %	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.
60	3DSSD		code	95.10 %	97.69 %	92.18 %	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.
61	PV-RCNN++		code	95.05 %	96.08 %	92.42 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
62	PDV		code	95.00 %	96.07 %	92.44 %	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.
63	ATT_SSD			94.99 %	96.02 %	92.18 %	0.01 s	1 core @ 2.5 Ghz (Python)
64	USVLab BSAODet			94.99 %	96.22 %	92.30 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
65	MVRA + I-FRCNN+			94.98 %	95.87 %	82.52 %	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.
66	SIENet		code	94.97 %	96.02 %	92.40 %	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	VueronNet		code	94.97 %	97.85 %	89.68 %	0.06 s	1 core @ 2.0 Ghz (Python)
68	VoTr-TSD		code	94.94 %	95.97 %	92.44 %	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.
69	GVNet-V2			94.92 %	96.29 %	92.21 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
70	AGS-SSD[la]			94.90 %	96.18 %	92.07 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
71	TBD		code	94.90 %	95.98 %	92.11 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
72	GVNet		code	94.86 %	96.30 %	92.22 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
73	TBD			94.85 %	97.04 %	92.37 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
74	M3DeTR		code	94.83 %	97.39 %	92.10 %	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.
75	StructuralIF			94.81 %	96.14 %	92.12 %	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.
76	CSVoxel-RCNN			94.81 %	96.23 %	92.09 %	0.03 s	GPU @ 1.0 Ghz (Python)
77	SRIF-RCNN			94.79 %	95.63 %	92.35 %	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.
78	ST-RCNN			94.79 %	98.06 %	92.12 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
79	ST-RCNN (SNLW-RCNN)		code	94.79 %	98.06 %	92.12 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
80	TPCG			94.78 %	95.96 %	92.25 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
81	DCCA			94.77 %	95.75 %	92.27 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
82	VCRCNN			94.77 %	96.06 %	92.28 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
83	XView			94.77 %	95.89 %	92.23 %	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.
84	TBD			94.74 %	95.88 %	91.96 %	0.06 s	GPU @ 2.5 Ghz (Python)
85	P2V-RCNN			94.73 %	96.03 %	92.34 %	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.
86	FusionDetv2-v4			94.73 %	95.94 %	92.00 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
87	SqueezeRCNN			94.72 %	96.02 %	92.12 %	0.08 s	1 core @ 2.5 Ghz (Python)
88	SC-Voxel-RCNN			94.71 %	96.13 %	91.94 %	0.12 s	GPU @ 1.0 Ghz (Python)
89	SPG		code	94.71 %	97.80 %	92.19 %	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.
90	CAT-Det			94.71 %	95.97 %	92.07 %	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.
91	MMLab PV-RCNN		code	94.70 %	98.17 %	92.04 %	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.
92	DKDet			94.68 %	96.03 %	91.92 %	0.03 s	GPU @ 2.5 Ghz (Python + C/C++)
93	SVGA-Net			94.67 %	96.05 %	91.86 %	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.
94	DDet			94.66 %	95.82 %	92.17 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
95	FPV-SSD			94.66 %	96.92 %	91.78 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
96	FusionDetv2-v3			94.64 %	96.16 %	91.94 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
97	DSA-PV-RCNN		code	94.64 %	95.86 %	92.10 %	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.
98	RangeIoUDet			94.61 %	95.74 %	91.98 %	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.
99	USVLab BSAODet (S)			94.60 %	96.10 %	90.03 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
100	CM3DV			94.59 %	95.80 %	92.05 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
101	DVFENet			94.57 %	95.35 %	91.77 %	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.
102	IKT3D			94.57 %	95.77 %	91.97 %	0.05 s	1 core @ 2.5 Ghz (Python)
103	sa-voxel-centernet		code	94.53 %	95.88 %	92.00 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
104	SPVB-SSD			94.49 %	95.80 %	91.90 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
105	TuSimple		code	94.47 %	95.12 %	86.45 %	1.6 s	GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016. K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016.
106	EPNet		code	94.44 %	96.15 %	89.99 %	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.
107	SERCNN			94.42 %	96.33 %	89.96 %	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.
108	TCDVF			94.31 %	95.02 %	91.33 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
109	UberATG-MMF			94.25 %	97.41 %	89.87 %	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.
110	SRDL			94.24 %	95.86 %	91.80 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
111	FusionDetv1			94.23 %	95.84 %	91.80 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
112	TBD			94.22 %	95.00 %	91.35 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
113	FusionDetv2-v2			94.21 %	95.75 %	89.89 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
114	SARFE			94.18 %	95.74 %	91.57 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
115	NV2P-RCNN			94.07 %	97.82 %	91.20 %	0.1 s	GPU @ 2.5 Ghz (Python)
116	DGT-Det3D			94.03 %	95.55 %	91.58 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
117	RangeRCNN			94.03 %	95.48 %	91.74 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, M. Zhang, Z. Zhang, X. Zhao and S. Pu: RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representation. arXiv preprint arXiv:2009.00206 2020.
118	VueronNet			94.03 %	96.70 %	87.58 %	0.07 s	1 core @ 2.5 Ghz (C/C++)
119	DGIST MT-CNN			94.00 %	95.25 %	86.76 %	0.09 s	GPU @ 1.0 Ghz (Python)
120	Faraway-Frustum		code	93.99 %	95.81 %	91.72 %	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.
121	DD3D		code	93.99 %	94.69 %	89.37 %	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) .
122	SIF			93.95 %	95.51 %	91.57 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
123	TBD			93.89 %	94.52 %	86.44 %	0.1 s	1 core @ 2.5 Ghz (Python)
124	MGAF-3DSSD		code	93.87 %	94.45 %	86.37 %	0.1 s	1 core @ 2.5 Ghz (Python)
J. Li, H. Dai, L. Shao and Y. Ding: Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
125	LPCG-Monoflex			93.86 %	96.90 %	83.94 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
126	MMLAB LIGA-Stereo		code	93.82 %	96.43 %	86.19 %	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.
127	DKAnet			93.79 %	95.16 %	89.27 %	0.05 s	1 core @ 2.0 Ghz (Python)
128	Sem-Aug			93.77 %	96.79 %	88.78 %	0.08 s	GPU @ 2.5 Ghz (Python)
129	Associate-3Ddet_v2			93.77 %	96.83 %	88.57 %	0.04 s	1 core @ 2.5 Ghz (Python)
130	CF-ctdep-tv-ta			93.75 %	95.08 %	91.08 %	1 s	1 core @ 2.5 Ghz (C/C++)
131	Patches - EMP			93.75 %	97.91 %	90.56 %	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.
132	CAD			93.73 %	96.84 %	88.74 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
133	CIA-SSD		code	93.72 %	96.87 %	86.20 %	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.
134	Anonymous			93.68 %	95.19 %	91.12 %	1	1 core @ 2.5 Ghz (Python)
135	CF-base-tv			93.68 %	94.86 %	90.87 %	1 s	1 core @ 2.5 Ghz (C/C++)
136	DTFI			93.67 %	95.17 %	91.10 %	0.03 s	1 core @ 2.5 Ghz (Python)
137	QD-3DT		code	93.66 %	94.26 %	83.63 %	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.
138	MVAF-Net		code	93.66 %	95.37 %	90.90 %	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.
139	SSL-PointGNN		code	93.65 %	96.61 %	88.53 %	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.
140	SA3DNet			93.62 %	96.57 %	88.65 %	0.05 s	GPU @ 2.5 Ghz (Python)
141	FusionDetv2-v5			93.61 %	95.33 %	89.22 %	0.05 s	1 core @ 2.5 Ghz (Java + C/C++)
142	KpNet			93.60 %	96.76 %	85.98 %	0.42 s	1 core @ 2.5 Ghz (C/C++)
143	KpNet			93.60 %	96.74 %	85.97 %	42 s	1 core @ 2.5 Ghz (C/C++)
144	TF3D			93.57 %	96.59 %	88.64 %	0.1 s	2 cores @ 3.0 Ghz (Python)
145	DVF			93.57 %	96.46 %	88.58 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
146	IA-SSD (multi)		code	93.56 %	96.10 %	90.68 %	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.
147	MonoPair			93.55 %	96.61 %	83.55 %	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.
148	IA-SSD (single)		code	93.54 %	96.26 %	88.49 %	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.
149	EBM3DOD		code	93.54 %	96.81 %	88.33 %	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.
150	Deep MANTA			93.50 %	98.89 %	83.21 %	0.7 s	GPU @ 2.5 Ghz (Python + C/C++)
F. Chabot, M. Chaouch, J. Rabarisoa, C. Teulière and T. Chateau: Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image. CVPR 2017.
151	Point-GNN		code	93.50 %	96.58 %	88.35 %	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.
152	FV2P v2			93.49 %	94.33 %	90.85 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
153	BtcDet		code	93.47 %	96.23 %	88.55 %	0.09 s	GPU @ 2.5 Ghz (Python + C/C++)
Q. Xu, Y. Zhong and U. Neumann: Behind the Curtain: Learning Occluded Shapes for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2022.
154	Struc info fusion II			93.45 %	96.72 %	88.31 %	0.05 s	GPU @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.
155	TBD			93.45 %	96.35 %	90.35 %	0.03 s	GPU @ 2.5 Ghz (Python)
156	EBM3DOD baseline		code	93.45 %	96.72 %	88.25 %	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.
157	TBD			93.43 %	96.52 %	90.71 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
158	StereoDistill			93.43 %	97.61 %	87.71 %	0.4 s	1 core @ 2.5 Ghz (Python)
159	RRC		code	93.40 %	95.68 %	87.37 %	3.6 s	GPU @ 2.5 Ghz (C/C++)
J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y. Tai and L. Xu: Accurate Single Stage Detector Using Recurrent Rolling Convolution. CVPR 2017.
160	Sem-Aug v1		code	93.39 %	96.39 %	90.70 %	0.04 s	GPU @ 3.5 Ghz (Python)
161	TBD			93.38 %	94.17 %	88.39 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
162	3D-CVF at SPA			93.36 %	96.78 %	86.11 %	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.
163	PVTr			93.36 %	94.54 %	90.82 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
164	Anonymous			93.34 %	96.44 %	83.76 %	40 s	1 core @ 2.5 Ghz (C/C++)
165	DSASNet			93.33 %	96.55 %	88.47 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
166	3SNet			93.33 %	96.40 %	90.65 %	0.07 s	GPU @ 2.5 Ghz (Python)
167	TBD			93.33 %	96.02 %	90.50 %	TBD	GPU @ 2.5 Ghz (Python + C/C++)
168	SNVC		code	93.32 %	96.33 %	85.81 %	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.
169	FS-Net			93.32 %	96.39 %	90.64 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
170	CF-ctdep-tv			93.31 %	94.89 %	90.87 %	1 s	1 core @ 2.5 Ghz (C/C++)
171	Struc info fusion I			93.31 %	96.59 %	88.23 %	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.
172	VPN			93.30 %	96.19 %	88.30 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
173	CityBrainLab-CT3D		code	93.30 %	96.28 %	90.58 %	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.
174	MonoInsight			93.29 %	96.21 %	83.59 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
175	ITCA-SSD		code	93.27 %	96.65 %	88.14 %	0.05 s	1 core @ 2.5 Ghz (Python)
176	Reprod-Two-Branch			93.26 %	94.83 %	90.61 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
177	STD		code	93.22 %	96.14 %	90.53 %	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.
178	SARPNET			93.21 %	96.07 %	88.09 %	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.
179	CFF-ep25			93.20 %	94.81 %	90.76 %	1 s	1 core @ 2.5 Ghz (C/C++)
180	H^23D R-CNN		code	93.20 %	96.20 %	90.55 %	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.
181	Fast Point R-CNN			93.18 %	96.13 %	87.68 %	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.
182	VCT			93.17 %	96.31 %	90.52 %	0.2 s	1 core @ 2.5 Ghz (Python)
183	sensekitti		code	93.17 %	94.79 %	84.38 %	4.5 s	GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.
184	Sem-Aug-PointRCNN		code	93.17 %	95.78 %	88.35 %	0.1 s	GPU @ 3.5 Ghz (C/C++)
185	MVOD			93.16 %	96.17 %	92.56 %	0.16 s	1 core @ 2.5 Ghz (C/C++)
186	SJTU-HW			93.11 %	96.30 %	82.21 %	0.85s	GPU @ 1.5 Ghz (Python + C/C++)
S. Zhang, X. Zhao, L. Fang, F. Haiping and S. Haitao: LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR. IEEE International Conference on Image Processing 2018. L. Fang, X. Zhao and S. Zhang: Small-objectness sensitive detection based on shifted single shot detector. Multimedia Tools and Applications 2018.
187	SGNet			93.08 %	96.43 %	90.53 %	0.09 s	GPU @ 2.5 Ghz (Python)
188	FromVoxelToPoint		code	93.06 %	96.08 %	90.53 %	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.
189	CFF-tv			93.06 %	94.68 %	90.61 %	1 s	1 core @ 2.5 Ghz (C/C++)
190	cp-tv			92.99 %	94.52 %	90.36 %	1 s	1 core @ 2.5 Ghz (C/C++)
191	Anonymous			92.99 %	96.18 %	87.80 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
192	MSADet			92.95 %	96.18 %	89.95 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
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193	CLOCs_SecCas			92.95 %	95.43 %	89.21 %	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.
194	cff-tv-v2-ep25			92.94 %	94.65 %	90.62 %	1 s	1 core @ 2.5 Ghz (C/C++)
195	KPP3D		code	92.93 %	98.38 %	89.93 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
196	mbdf-netv1		code	92.83 %	96.00 %	89.90 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
197	CF-base-train			92.82 %	94.98 %	90.19 %	1 s	1 core @ 2.5 Ghz (C/C++)
198	MDNet			92.82 %	96.14 %	85.37 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
199	HotSpotNet			92.81 %	96.21 %	89.80 %	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.
200	SegVoxelNet			92.73 %	96.00 %	87.60 %	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.
201	Patches			92.72 %	96.34 %	87.63 %	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.
202	CenterNet3D			92.69 %	95.76 %	89.81 %	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.
203	R-GCN			92.67 %	96.19 %	87.66 %	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.
204	PI-RCNN			92.66 %	96.17 %	87.68 %	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.
205	PointPainting			92.58 %	98.39 %	89.71 %	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.
206	DASS			92.53 %	96.23 %	87.75 %	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.
207	NV-RCNN			92.51 %	95.86 %	89.95 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
208	Anonymous			92.51 %	95.88 %	85.35 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
209	3D IoU-Net			92.47 %	96.31 %	87.67 %	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.
210	CSNet8306		code	92.47 %	96.05 %	87.61 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
211	CSNet			92.46 %	95.99 %	89.24 %	0.1 s	1 core @ 2.5 Ghz (Python)
212	Associate-3Ddet		code	92.45 %	95.61 %	87.32 %	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.
213	S-AT GCN			92.44 %	95.06 %	90.78 %	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.
214	LazyTorch-CP-Small-P			92.38 %	94.71 %	90.04 %	1 s	1 core @ 2.5 Ghz (C/C++)
215	3D-VDNet			92.35 %	95.42 %	89.37 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
216	LazyTorch-CP-Infer-O			92.35 %	94.76 %	90.09 %	1 s	1 core @ 2.5 Ghz (C/C++)
217	cp-tv-kp			92.34 %	94.32 %	90.25 %	1 s	1 core @ 2.5 Ghz (C/C++)
218	PointRGCN			92.33 %	97.51 %	87.07 %	0.26 s	GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
219	Sem-Aug-PointRCNN++			92.32 %	95.65 %	87.62 %	0.1 s	8 cores @ 3.0 Ghz (Python)
220	CSNet8299		code	92.31 %	96.14 %	87.54 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
221	PSM_stereo			92.27 %	95.63 %	87.24 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
222	CVFNet			92.27 %	95.61 %	88.75 %	28.1ms	1 core @ 2.5 Ghz (Python)
223	CenterFuse			92.26 %	95.04 %	89.24 %	0.059 sec/frame	2 x V100
224	F-ConvNet		code	92.19 %	95.85 %	80.09 %	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.
225	Self-Calib Conv			92.17 %	94.45 %	90.19 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
226	PFF3D		code	92.15 %	95.37 %	87.54 %	0.05 s	GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
227	AFTD			92.14 %	95.56 %	87.45 %	1 s	1 core @ 2.5 Ghz (Python + C/C++)
228	cp-tv-kp-io-sc			92.11 %	95.02 %	88.97 %	1 s	1 core @ 2.5 Ghz (C/C++)
229	CF-cd-io-tv			92.06 %	94.80 %	88.80 %	1 s	1 core @ 2.5 Ghz (C/C++)
230	SDP+RPN			92.03 %	95.16 %	79.16 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016. S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 2015.
231	RangeDet		code	92.03 %	95.20 %	87.14 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
232	KeyFuse2B			92.01 %	94.57 %	90.56 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
233	AB3DMOT		code	92.00 %	95.88 %	86.98 %	0.0047s	1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
234	CrazyTensor-CP			91.95 %	93.64 %	89.23 %	1 s	1 core @ 2.5 Ghz (Python)
235	CFF-tv-v2			91.91 %	94.72 %	90.57 %	1 s	1 core @ 2.5 Ghz (C/C++)
236	Dune-DCF-e09			91.90 %	94.97 %	88.74 %	1 s	1 core @ 2.5 Ghz (C/C++)
237	MMLab-PointRCNN		code	91.90 %	95.92 %	87.11 %	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.
238	CrazyTensor-CF			91.89 %	94.98 %	88.70 %	1 s	1 core @ 2.5 Ghz (C/C++)
239	AutoAlign			91.87 %	95.15 %	89.21 %	0.1 s	1 core @ 2.5 Ghz (Python)
240	MMLab-PartA^2		code	91.86 %	95.03 %	89.06 %	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.
241	WeakM3D		code	91.81 %	94.51 %	85.35 %	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.
242	KeyPoint-IoUHead			91.79 %	94.65 %	88.61 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
243	epBRM		code	91.77 %	94.59 %	88.45 %	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.
244	TBD			91.75 %	94.53 %	86.54 %	0.1 s	1 core @ 2.5 Ghz (Python)
245	C-GCN			91.73 %	95.64 %	86.37 %	0.147 s	GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
246	ITVD		code	91.73 %	95.85 %	79.31 %	0.3 s	GPU @ 2.5 Ghz (C/C++)
Y. Wei Liu: Improving Tiny Vehicle Detection in Complex Scenes. IEEE International Conference on Multimedia and Expo (ICME) 2018.
247	sscl-20p			91.71 %	97.14 %	88.72 %	0.02 s	1 core @ 2.5 Ghz (Python)
248	Dune-DCF-e11			91.68 %	94.69 %	88.57 %	1 s	1 core @ 2.5 Ghz (C/C++)
249	City-CF-fixed			91.67 %	94.87 %	88.80 %	1 s	1 core @ 2.5 Ghz (C/C++)
250	Dune-DCF-e15			91.67 %	94.72 %	88.53 %	1 s	1 core @ 2.5 Ghz (C/C++)
251	SINet+		code	91.67 %	94.17 %	78.60 %	0.3 s	TITAN X GPU
X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
252	SFD-Retina			91.64 %	94.85 %	82.13 %	0.04 s	GPU @ 2.5 Ghz (Python)
253	CF-ctdep-train			91.64 %	94.81 %	90.16 %	1 s	1 core @ 2.5 Ghz (C/C++)
254	HS3D		code	91.62 %	95.51 %	86.94 %	0.12 s	1 core @ 2.5 Ghz (Python + C/C++)
255	mono3d			91.60 %	94.60 %	84.86 %	0.03 s	GPU @ 2.5 Ghz (Python)
256	Cascade MS-CNN		code	91.60 %	94.26 %	78.84 %	0.25 s	GPU @ 2.5 Ghz (C/C++)
Z. Cai and N. Vasconcelos: Cascade R-CNN: High Quality Object Detection and Instance Segmentation. arXiv preprint arXiv:1906.09756 2019. Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A unified multi-scale deep convolutional neural network for fast object detection. European conference on computer vision 2016.
257	GFD-Retina			91.59 %	94.55 %	81.80 %	0.07 s	GPU @ 2.5 Ghz (Python)
258	CenterPoint (pcdet)			91.58 %	93.99 %	89.15 %	0.051 sec/frame	2 x V100
259	TBD			91.56 %	94.52 %	86.29 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
260	CA3D			91.48 %	95.19 %	82.01 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
261	PointRGBNet			91.48 %	95.40 %	86.50 %	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.
262	MAFF-Net(DAF-Pillar)			91.46 %	94.38 %	83.89 %	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.
263	HRI-VoxelFPN			91.44 %	96.65 %	86.18 %	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.
264	new_stereo			91.39 %	95.01 %	86.77 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
265	EgoNet		code	91.39 %	96.18 %	81.33 %	0.1 s	GPU @ 1.5 Ghz (Python)
S. Li, Z. Yan, H. Li and K. Cheng: Exploring intermediate representation for monocular vehicle pose estimation. The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
266	IoU-2B			91.38 %	94.81 %	85.63 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
267	GPENet		code	91.36 %	94.11 %	83.40 %	0.02 s	GPU @ 2.5 Ghz (Python)
268	HNet-3DSSD		code	91.35 %	94.91 %	87.47 %	0.05 s	GPU @ 2.5 Ghz (Python)
269	City-CF			91.35 %	94.54 %	88.38 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
270	PP-PCdet		code	91.32 %	94.82 %	88.18 %	0.01 s	1 core @ 2.5 Ghz (Python)
271	Contrastive PP		code	91.29 %	96.93 %	88.11 %	0.01 s	1 core @ 2.5 Ghz (Python)
272	Stereo CenterNet			91.27 %	96.61 %	83.50 %	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.
273	PointPillars		code	91.19 %	94.00 %	88.17 %	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.
274	LTN			91.18 %	94.68 %	81.51 %	0.4 s	GPU @ >3.5 Ghz (Python)
T. Wang, X. He, Y. Cai and G. Xiao: Learning a Layout Transfer Network for Context Aware Object Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
275	WS3D			91.15 %	95.13 %	86.52 %	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.
276	KM3D		code	91.07 %	96.44 %	81.19 %	0.03 s	1 core @ 2.5 Ghz (Python)
P. Li: Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training. 2020.
277	PS++		code	91.06 %	96.23 %	83.39 %	PS++ s	1 core @ 2.5 Ghz (C/C++)
278	3D_att			91.05 %	96.70 %	85.88 %	0.17 s	GPU @ 2.5 Ghz (Python)
279	Geo3D			91.04 %	94.28 %	78.91 %	0.04 s	GPU @ 2.5 Ghz (Python)
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280	DID-M3D			91.04 %	94.29 %	81.31 %	0.04 s	1 core @ 2.5 Ghz (Python)
281	FII-CenterNet		code	91.03 %	94.48 %	83.00 %	0.09 s	GPU @ 2.5 Ghz (Python)
S. Fan, F. Zhu, S. Chen, H. Zhang, B. Tian, Y. Lv and F. Wang: FII-CenterNet: An Anchor-Free Detector With Foreground Attention for Traffic Object Detection. IEEE Transactions on Vehicular Technology 2021.
282	Aston-EAS			91.02 %	93.91 %	77.93 %	0.24 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Wei, J. He, Y. Zhou, K. Chen, Z. Tang and Z. Xiong: Enhanced Object Detection With Deep Convolutional Neural Networks for Advanced Driving Assistance. IEEE Transactions on Intelligent Transportation Systems 2019.
283	MonoFlex			91.02 %	96.01 %	83.38 %	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.
284	Mix-Teaching			91.02 %	96.35 %	83.41 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
285	SCSTSV-MonoFlex			90.99 %	96.44 %	81.16 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
286	ARPNET			90.99 %	94.00 %	83.49 %	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.
287	cff-tv-t			90.98 %	94.68 %	84.39 %	1 s	1 core @ 2.5 Ghz (C/C++)
288	monopd		code	90.93 %	96.44 %	83.36 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
289	DDS		code	90.93 %	96.44 %	83.36 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
290	VMDet			90.92 %	96.39 %	81.14 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
291	MoGDE			90.91 %	96.47 %	83.66 %	0.03 s	GPU @ 2.5 Ghz (Python)
292	MonoEF			90.88 %	96.32 %	83.27 %	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.
293	PatchNet		code	90.87 %	93.82 %	79.62 %	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.
294	PS		code	90.85 %	96.20 %	83.08 %	PS s	1 core @ 2.5 Ghz (C/C++)
295	MV3D			90.83 %	96.47 %	78.63 %	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.
296	MonoDistill			90.81 %	95.92 %	81.08 %	0.04 s	1 core @ 2.5 Ghz (Python)
297	monodle		code	90.81 %	93.83 %	80.93 %	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 .
298	3D IoU Loss			90.79 %	95.92 %	85.65 %	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.
299	SINet_VGG		code	90.79 %	93.59 %	77.53 %	0.2 s	TITAN X GPU
X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
300	Anonymous			90.78 %	96.42 %	83.51 %	40 s	1 core @ 2.5 Ghz (C/C++)
301	HomoLoss(monoflex)		code	90.69 %	95.92 %	80.91 %	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.
302	TANet		code	90.67 %	93.67 %	85.31 %	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.
303	MF			90.66 %	93.57 %	86.15 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
304	MonoGround			90.63 %	93.94 %	80.80 %	0.03 s	1 core @ 2.5 Ghz (Python)
305	MonoEdge			90.62 %	93.52 %	80.91 %	0.05 s	GPU @ 2.5 Ghz (Python)
306	MonoCInIS			90.60 %	96.05 %	82.43 %	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.
307	MonoFlex			90.52 %	95.59 %	83.00 %	0.03 s	1 core @ 2.5 Ghz (Python)
308	CG-Stereo			90.38 %	96.31 %	82.80 %	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.
309	MonoEdge-Rotate			90.30 %	93.53 %	80.60 %	0.05 s	GPU @ 2.5 Ghz (Python)
310	SCNet			90.30 %	95.59 %	85.09 %	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.
311	CMKD*			90.28 %	95.14 %	83.91 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
312	PS-fld		code	90.27 %	95.75 %	82.32 %	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.
313	MonoAug			90.24 %	95.59 %	80.47 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
314	Deep3DBox			90.19 %	94.71 %	76.82 %	1.5 s	GPU @ 2.5 Ghz (C/C++)
A. Mousavian, D. Anguelov, J. Flynn and J. Kosecka: 3D Bounding Box Estimation Using Deep Learning and Geometry. CVPR 2017.
315	FQNet			90.17 %	94.72 %	76.78 %	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.
316	DeepStereoOP			90.06 %	95.15 %	79.91 %	3.4 s	GPU @ 3.5 Ghz (Matlab + C/C++)
C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.
317	variance_point			90.01 %	95.79 %	87.50 %	0.05 s	1 core @ 2.5 Ghz (Python)
318	SubCNN			89.98 %	94.26 %	79.78 %	2 s	GPU @ 3.5 Ghz (Python + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.
319	MLOD		code	89.97 %	94.88 %	84.98 %	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.
320	GPP		code	89.96 %	94.02 %	81.13 %	0.23 s	GPU @ 1.5 Ghz (Python + C/C++)
A. Rangesh and M. Trivedi: Ground plane polling for 6dof pose estimation of objects on the road. IEEE Transactions on Intelligent Vehicles 2020.
321	AVOD		code	89.88 %	95.17 %	82.83 %	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.
322	SINet_PVA		code	89.86 %	92.72 %	76.47 %	0.11 s	TITAN X GPU
X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
323	Digging_M3D			89.77 %	93.73 %	79.68 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
324	TBD_BD		code	89.73 %	94.18 %	86.84 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
325	3DOP		code	89.55 %	92.96 %	79.38 %	3s	GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
326	IAFA			89.46 %	93.08 %	79.83 %	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.
327	Mono3D		code	89.37 %	94.52 %	79.15 %	4.2 s	GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.
328	4d-MSCNN		code	89.37 %	92.40 %	77.00 %	0.3 min	GPU @ 3.0 Ghz (Matlab + C/C++)
P. Ferraz, B. Oliveira, F. Ferreira, C. Silva Martins and others: Three-stage RGBD architecture for vehicle and pedestrian detection using convolutional neural networks and stereo vision. IET Intelligent Transport Systems 2020.
329	MonoDDE			89.19 %	96.76 %	81.60 %	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.
330	PPTrans			89.17 %	95.17 %	81.77 %	0.2 s	GPU @ 2.5 Ghz (Python)
331	FusionDetv2-v1			89.10 %	94.82 %	84.28 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
332	M3DSSD++		code	89.06 %	94.94 %	77.17 %	0.16s	1 core @ 2.5 Ghz (C/C++)
333	MonoEdge-RCNN			88.97 %	94.19 %	74.23 %	0.05 s	1 core @ 2.5 Ghz (Python)
334	AVOD-FPN		code	88.92 %	94.70 %	84.13 %	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.
335	Keypoint-3D			88.87 %	93.31 %	76.10 %	14 s	1 core @ 2.5 Ghz (C/C++)
336	Shape-Aware			88.85 %	94.26 %	81.33 %	0.05 s	1 core @ 2.5 Ghz (Python)
337	PCT		code	88.78 %	96.45 %	78.85 %	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.
338	OPA-3D		code	88.77 %	96.50 %	76.55 %	0.04 s	1 core @ 3.5 Ghz (Python)
339	FusionDetv2-baseline			88.76 %	94.28 %	85.65 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
340	Lite-FPN-GUPNet			88.76 %	96.45 %	76.54 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
341	AM3D			88.71 %	92.55 %	77.78 %	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.
342	MS-CNN		code	88.68 %	93.87 %	76.11 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection. ECCV 2016.
343	MM-Retina			88.63 %	93.74 %	78.56 %	0.04 s	GPU @ 2.5 Ghz (Python)
344	MonoPSR		code	88.50 %	93.63 %	73.36 %	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.
345	Shift R-CNN (mono)		code	88.48 %	94.07 %	78.34 %	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.
346	RCD			88.46 %	92.52 %	83.73 %	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.
347	MM-MRFC			88.46 %	95.54 %	78.14 %	0.05 s	GPU @ 2.5 Ghz (C/C++)
A. Costea, R. Varga and S. Nedevschi: Fast Boosting based Detection using Scale Invariant Multimodal Multiresolution Filtered Features. CVPR 2017.
348	MonoDTR			88.41 %	93.90 %	76.20 %	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.
349	anonymity			88.41 %	95.28 %	81.68 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
350	anonymity			88.40 %	95.03 %	81.67 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
351	MonoFar			88.33 %	93.74 %	78.59 %	0.04 s	1 core @ 2.5 Ghz (Python)
352	3DBN			88.29 %	93.74 %	80.74 %	0.13s	1080Ti (Python+C/C++)
X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.
353	MonoCon		code	88.22 %	93.59 %	76.18 %	0.02 s	GPU @ 2.5 Ghz (Python)
T. Xianpeng Liu: Learning Auxiliary Monocular Contexts Helps Monocular 3D Object Detection. AAAI 2022.
354	MonoCInIS			88.16 %	96.22 %	75.72 %	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.
355	DEPT			88.00 %	96.45 %	78.40 %	0.03 s	1 core @ 2.5 Ghz (Python)
356	EW		code	87.94 %	92.22 %	78.32 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
357	MonoRUn		code	87.91 %	95.48 %	78.10 %	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.
358	SAIC_ADC_Mono3D		code	87.78 %	95.21 %	80.14 %	50 s	GPU @ 2.5 Ghz (Python)
359	MonoAug			87.76 %	93.65 %	77.91 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
360	CZY			87.55 %	94.63 %	82.97 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
361	SMOKE		code	87.51 %	93.21 %	77.66 %	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.
362	zongmuDistill			87.47 %	95.44 %	79.97 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
363	CDN		code	87.19 %	95.85 %	79.43 %	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.
364	EG_DETR			87.10 %	93.04 %	79.05 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
365	mono3d		code	87.07 %	93.28 %	77.72 %	TBD	TBD
366	RTM3D		code	86.93 %	91.82 %	77.41 %	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.
367	MonoRCNN		code	86.78 %	91.98 %	66.97 %	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.
368	Anonymous		code	86.77 %	94.34 %	71.93 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
369	BirdNet+		code	86.73 %	92.61 %	81.80 %	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.
370	UPF_3D			86.61 %	96.12 %	79.53 %	0.29 s	1 core @ 2.5 Ghz (Python)
371	MP-Mono			86.54 %	90.66 %	65.72 %	0.16 s	GPU @ 2.5 Ghz (Python)
372	MK3D			86.48 %	94.40 %	74.21 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
373	ANM			86.45 %	94.32 %	76.54 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
374	GUPNet		code	86.45 %	94.15 %	74.18 %	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.
375	GCDR			86.45 %	94.15 %	74.18 %	0.28 s	1 core @ 2.5 Ghz (Python)
376	DSGN		code	86.43 %	95.53 %	78.75 %	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.
377	gupnet_se			86.33 %	94.27 %	74.08 %	0.03s	1 core @ 2.5 Ghz (C/C++)
378	OBMO_GUPNet			86.27 %	96.49 %	76.47 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
379	MonoDETR		code	86.17 %	93.99 %	76.19 %	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.
380	TBD			86.03 %	91.50 %	78.52 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
381	Stereo R-CNN		code	85.98 %	93.98 %	71.25 %	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.
382	StereoFENet			85.70 %	91.48 %	77.62 %	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.
383	City-ICN			85.70 %	93.91 %	73.41 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
384	Anonymous			85.65 %	92.64 %	78.83 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
385	DisposalNet			85.49 %	89.50 %	82.52 %	0.2 s	1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
386	LT-M3OD			85.42 %	93.99 %	75.77 %	0.03 s	1 core @ 2.5 Ghz (Python)
387	ResNet-RRC_Car			85.33 %	91.45 %	74.27 %	0.06 s	GPU @ 1.5 Ghz (Python + C/C++)
H. Jeon and others: High-Speed Car Detection Using ResNet- Based Recurrent Rolling Convolution. Proceedings of the IEEE conference on systems, man, and cybernetics 2018.
388	SparseLiDAR_fusion			85.26 %	93.60 %	73.11 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
389	AEC3D			85.22 %	90.74 %	80.82 %	18 ms	GPU @ 2.5 Ghz (Python)
390	PL++ (SDN+GDC)		code	85.15 %	94.95 %	77.78 %	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.
391	M3D-RPN		code	85.08 %	89.04 %	69.26 %	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 .
392	CDN-PL++			85.01 %	94.66 %	77.60 %	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.
393	M3DGAF			85.01 %	93.33 %	77.55 %	0.07 s	1 core @ 2.5 Ghz (Python)
394	SDP+CRC (ft)			85.00 %	92.06 %	71.71 %	0.6 s	GPU @ 2.5 Ghz (C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
395	SS3D			84.92 %	92.72 %	70.35 %	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.
396	VN3D			84.80 %	90.73 %	78.41 %	0.02 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
397	ZongmuMono3d		code	84.64 %	93.06 %	75.29 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
398	MonoFENet			84.63 %	91.68 %	76.71 %	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.
399	DLE		code	84.45 %	94.66 %	62.10 %	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.
400	MV3D (LIDAR)			84.39 %	93.08 %	79.27 %	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.
401	Complexer-YOLO			84.16 %	91.92 %	79.62 %	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.
402	ZoomNet		code	83.92 %	94.22 %	69.00 %	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.
403	CMAN			83.74 %	89.74 %	65.35 %	0.15 s	1 core @ 2.5 Ghz (Python)
404	D4LCN		code	83.67 %	90.34 %	65.33 %	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.
405	Faster R-CNN		code	83.16 %	88.97 %	72.62 %	2 s	GPU @ 3.5 Ghz (Python + C/C++)
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards Real- Time Object Detection with Region Proposal Networks. NIPS 2015.
406	ppt			83.13 %	85.27 %	77.48 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
407	SGM3D			83.05 %	93.66 %	73.35 %	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.
408	Pseudo-LiDAR++		code	82.90 %	94.46 %	75.45 %	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.
409	Disp R-CNN		code	82.86 %	93.64 %	68.33 %	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.
410	BS3D			82.72 %	95.35 %	70.01 %	22 ms	Titan Xp
N. Gählert, J. Wan, M. Weber, J. Zöllner, U. Franke and J. Denzler: Beyond Bounding Boxes: Using Bounding Shapes for Real-Time 3D Vehicle Detection from Monocular RGB Images. 2019 IEEE Intelligent Vehicles Symposium (IV) 2019.
411	CrazyTensor-ICN			82.70 %	90.79 %	70.61 %	1 s	1 core @ 2.5 Ghz (C/C++)
412	Disp R-CNN (velo)		code	82.64 %	93.45 %	70.45 %	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.
413	HBD			82.64 %	93.13 %	75.14 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
414	HomoLoss(imvoxelnet)		code	82.54 %	92.81 %	72.80 %	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.
415	YOLOStereo3D		code	82.15 %	94.81 %	62.17 %	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.
416	Ground-Aware		code	82.05 %	92.33 %	62.08 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
Y. Liu, Y. Yuan and M. Liu: Ground-aware Monocular 3D Object Detection for Autonomous Driving. IEEE Robotics and Automation Letters 2021.
417	FRCNN+Or		code	82.00 %	92.91 %	68.79 %	0.09 s	Titan Xp GPU
C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine 2018. C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features. IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.
418	Anonymous			81.71 %	96.77 %	69.38 %	40 s	1 core @ 2.5 Ghz (C/C++)
419	SwinMono3D			81.71 %	91.99 %	61.78 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
420	DDMP-3D			81.70 %	91.15 %	63.12 %	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.
421	SD3DOD			81.64 %	92.60 %	75.97 %	0.04 s	GPU @ 2.5 Ghz (Python)
422	A3DODWTDA (image)		code	81.25 %	78.96 %	70.56 %	0.8 s	GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
423	none			81.07 %	91.14 %	68.85 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
424	ART			81.03 %	90.97 %	74.44 %	20ms s	1 core @ 2.5 Ghz (C/C++)
425	RefineNet			81.01 %	91.91 %	65.67 %	0.20 s	GPU @ 2.5 Ghz (Matlab + C++)
R. Rajaram, E. Bar and M. Trivedi: RefineNet: Refining Object Detectors for Autonomous Driving. IEEE Transactions on Intelligent Vehicles 2016. R. Rajaram, E. Bar and M. Trivedi: RefineNet: Iterative Refinement for Accurate Object Localization. Intelligent Transportation Systems Conference 2016.
426	K3D			80.86 %	93.58 %	71.18 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
427	CaDDN		code	80.73 %	93.61 %	71.09 %	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.
428	ESGN			80.58 %	93.07 %	70.68 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
429	PGD-FCOS3D		code	80.58 %	92.04 %	69.67 %	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.
430	FD			80.38 %	92.08 %	75.65 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
431	GrooMeD-NMS		code	80.28 %	90.14 %	63.78 %	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.
432	3D-GCK			80.19 %	89.55 %	68.08 %	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.
433	COF3D			80.16 %	87.85 %	61.97 %	200 s	1 core @ 2.5 Ghz (Python + C/C++)
434	EM		code	80.05 %	87.00 %	66.77 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
435	Lite-FPN			79.65 %	87.04 %	65.56 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
436	YoloMono3D		code	79.63 %	92.37 %	59.69 %	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.
437	E2E-DA			79.40 %	92.12 %	69.52 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
438	A3DODWTDA		code	79.15 %	82.98 %	68.30 %	0.08 s	GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
439	ImVoxelNet		code	79.09 %	89.80 %	69.45 %	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.
440	DFR-Net			78.81 %	90.13 %	60.40 %	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.
441	spLBP			78.66 %	81.66 %	61.69 %	1.5 s	8 cores @ 2.5 Ghz (Matlab + C/C++)
Q. Hu, S. Paisitkriangkrai, C. Shen, A. Hengel and F. Porikli: Fast Detection of Multiple Objects in Traffic Scenes With a Common Detection Framework. IEEE Trans. Intelligent Transportation Systems 2016.
442	E2E-DA-Lite (Res18)			78.21 %	90.79 %	66.16 %	0.01 s	GPU @ 2.5 Ghz (Python)
443	3D-SSMFCNN		code	78.19 %	77.92 %	69.19 %	0.1 s	GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.
444	MonoGRNet		code	77.94 %	88.65 %	63.31 %	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.
445	Aug3D-RPN			77.88 %	85.57 %	61.16 %	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.
446	AutoShape		code	77.66 %	86.51 %	64.40 %	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.
447	Reinspect		code	77.48 %	90.27 %	66.73 %	2s	1 core @ 2.5 Ghz (C/C++)
R. Stewart, M. Andriluka and A. Ng: End-to-End People Detection in Crowded Scenes. CVPR 2016.
448	SARM3D			77.41 %	90.20 %	68.24 %	0.03 s	GPU @ 2.5 Ghz (Python)
449	multi-task CNN			77.18 %	86.12 %	68.09 %	25.1 ms	GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
450	Regionlets			76.99 %	88.75 %	60.49 %	1 s	>8 cores @ 2.5 Ghz (C/C++)
X. Wang, M. Yang, S. Zhu and Y. Lin: Regionlets for Generic Object Detection. T-PAMI 2015. W. Zou, X. Wang, M. Sun and Y. Lin: Generic Object Detection with Dense Neural Patterns and Regionlets. British Machine Vision Conference 2014. C. Long, X. Wang, G. Hua, M. Yang and Y. Lin: Accurate Object Detection with Location Relaxation and Regionlets Relocalization. Asian Conference on Computer Vision 2014.
451	3DVP		code	76.98 %	84.95 %	65.78 %	40 s	8 cores @ 3.5 Ghz (Matlab + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Data-Driven 3D Voxel Patterns for Object Category Recognition. IEEE Conference on Computer Vision and Pattern Recognition 2015.
452	Mobile Stereo R-CNN			76.73 %	90.08 %	62.23 %	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.
453	SubCat		code	76.36 %	84.10 %	60.56 %	0.7 s	6 cores @ 3.5 Ghz (Matlab + C/C++)
E. Ohn-Bar and M. Trivedi: Learning to Detect Vehicles by Clustering Appearance Patterns. T-ITS 2015.
454	GS3D			76.35 %	86.23 %	62.67 %	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.
455	AOG		code	76.24 %	86.08 %	61.51 %	3 s	4 cores @ 2.5 Ghz (Matlab)
T. Wu, B. Li and S. Zhu: Learning And-Or Models to Represent Context and Occlusion for Car Detection and Viewpoint Estimation. TPAMI 2016. B. Li, T. Wu and S. Zhu: Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model. ECCV 2014.
456	Pose-RCNN			75.83 %	89.59 %	64.06 %	2 s	>8 cores @ 2.5 Ghz (Python)
M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on 2016.
457	3D FCN			74.65 %	86.74 %	67.85 %	>5 s	1 core @ 2.5 Ghz (C/C++)
B. Li: 3D Fully Convolutional Network for Vehicle Detection in Point Cloud. IROS 2017.
458	OC Stereo		code	74.60 %	87.39 %	62.56 %	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.
459	MAOLoss		code	73.79 %	89.31 %	63.59 %	0.05 s	1 core @ 2.5 Ghz (Python)
460	Kinematic3D		code	71.73 %	89.67 %	54.97 %	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 .
461	AOG-View			71.26 %	85.01 %	55.73 %	3 s	1 core @ 2.5 Ghz (Matlab, C/C++)
B. Li, T. Wu and S. Zhu: Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model. ECCV 2014.
462	BEVC			70.93 %	79.97 %	64.46 %	35ms	GPU @ 1.5 Ghz (Python)
463	GAC3D			70.73 %	83.30 %	52.23 %	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.
464	MV-RGBD-RF			70.70 %	77.89 %	57.41 %	4 s	4 cores @ 2.5 Ghz (C/C++)
A. Gonzalez, D. Vazquez, A. Lopez and J. Amores: On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts.. IEEE Trans. on Cybernetics 2016. A. Gonzalez, G. Villalonga, J. Xu, D. Vazquez, J. Amores and A. Lopez: Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection. IEEE Intelligent Vehicles Symposium (IV) 2015.
465	Vote3Deep			70.30 %	78.95 %	63.12 %	1.5 s	4 cores @ 2.5 Ghz (C/C++)
M. Engelcke, D. Rao, D. Zeng Wang, C. Hay Tong and I. Posner: Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks. ArXiv e-prints 2016.
466	ROI-10D			70.16 %	76.56 %	61.15 %	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.
467	RetinaMono			69.83 %	74.54 %	60.95 %	0.02 s	1 core @ 2.5 Ghz (Python)
468	BirdNet+ (legacy)		code	68.05 %	92.10 %	65.61 %	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.
469	Decoupled-3D			67.92 %	87.78 %	54.53 %	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.
470	SparVox3D			67.88 %	83.76 %	52.56 %	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.
471	Pseudo-Lidar		code	67.79 %	85.40 %	58.50 %	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.
472	OC-DPM			67.06 %	79.07 %	52.61 %	10 s	8 cores @ 2.5 Ghz (Matlab)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Occlusion Patterns for Object Class Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2013.
473	DPM-VOC+VP			66.72 %	82.15 %	49.01 %	8 s	1 core @ 2.5 Ghz (C/C++)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015.
474	BdCost48LDCF		code	66.63 %	81.38 %	52.20 %	0.5 s	8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
475	MM			65.65 %	88.80 %	56.53 %	1 s	1 core @ 2.5 Ghz (C/C++)
476	RefinedMPL			65.24 %	88.29 %	53.20 %	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.
477	MDPM-un-BB			64.06 %	79.74 %	49.07 %	60 s	4 core @ 2.5 Ghz (MATLAB)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
478	TLNet (Stereo)		code	63.53 %	76.92 %	54.58 %	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.
479	PDV-Subcat			63.24 %	78.27 %	47.67 %	7 s	1 core @ 2.5 Ghz (C/C++)
J. Shen, X. Zuo, J. Li, W. Yang and H. Ling: A novel pixel neighborhood differential statistic feature for pedestrian and face detection . Pattern Recognition 2017.
480	MDSNet			62.74 %	85.94 %	50.27 %	0.07 s	1 core @ 2.5 Ghz (Python)
481	MODet			62.54 %	66.06 %	60.04 %	0.05 s	GTX1080Ti
Y. Zhang, Z. Xiang, C. Qiao and S. Chen: Accurate and Real-Time Object Detection Based on Bird's Eye View on 3D Point Clouds. 2019 International Conference on 3D Vision (3DV) 2019.
482	Graph-NMS			61.93 %	78.55 %	53.72 %	36 ms	GPU @ 2.5 Ghz (Python)
483	SubCat48LDCF		code	61.16 %	78.86 %	44.69 %	0.5 s	8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
484	DPM-C8B1			60.21 %	75.24 %	44.73 %	15 s	4 cores @ 2.5 Ghz (Matlab + C/C++)
J. Yebes, L. Bergasa and M. García-Garrido: Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes. Sensors 2015. J. Yebes, L. Bergasa, R. Arroyo and A. Lázaro: Supervised learning and evaluation of KITTI's cars detector with DPM. IV 2014.
485	SAMME48LDCF		code	58.38 %	77.47 %	44.43 %	0.5 s	8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
486	LSVM-MDPM-sv			58.36 %	71.11 %	43.22 %	10 s	4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010. A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout. NIPS 2011.
487	BirdNet			57.12 %	79.30 %	55.16 %	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.
488	ACF-SC			56.60 %	69.90 %	43.61 %	<0.3 s	1 core @ >3.5 Ghz (Matlab + C/C++)
C. Cadena, A. Dick and I. Reid: A Fast, Modular Scene Understanding System using Context-Aware Object Detection. Robotics and Automation (ICRA), 2015 IEEE International Conference on 2015.
489	LSVM-MDPM-us		code	55.95 %	68.94 %	41.45 %	10 s	4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
490	VMDet_boost			54.16 %	71.33 %	47.50 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
491	ACF			54.09 %	63.05 %	41.81 %	0.2 s	1 core @ >3.5 Ghz (Matlab + C/C++)
P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014. P. Doll\'ar: Piotr's Image and Video Matlab Toolbox (PMT). .
492	Mono3D_PLiDAR		code	53.36 %	80.85 %	44.80 %	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.
493	Graph-NMS-baseline			52.92 %	76.21 %	43.38 %	47 ms	GPU @ 2.5 Ghz (Python)
494	RT3D-GMP			51.95 %	62.41 %	39.14 %	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.
495	VeloFCN			51.82 %	70.53 %	45.70 %	1 s	GPU @ 2.5 Ghz (Python + C/C++)
B. Li, T. Zhang and T. Xia: Vehicle Detection from 3D Lidar Using Fully Convolutional Network. RSS 2016 .
496	Vote3D			45.94 %	54.38 %	40.48 %	0.5 s	4 cores @ 2.8 Ghz (C/C++)
D. Wang and I. Posner: Voting for Voting in Online Point Cloud Object Detection. Proceedings of Robotics: Science and Systems 2015.
497	TopNet-HighRes			45.85 %	58.04 %	41.11 %	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.
498	RT3DStereo			45.81 %	56.53 %	37.63 %	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.
499	Multimodal Detection		code	45.46 %	63.91 %	37.25 %	0.06 s	GPU @ 3.5 Ghz (Matlab + C/C++)
A. Asvadi, L. Garrote, C. Premebida, P. Peixoto and U. Nunes: Multimodal vehicle detection: fusing 3D- LIDAR and color camera data. Pattern Recognition Letters 2017.
500	CDTrack3D		code	44.58 %	53.11 %	37.41 %	0.0106 s	NVIDIA RTX 3090 GPU, i9 10850k CPU
501	RT3D			39.69 %	50.33 %	40.04 %	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.
502	VoxelJones		code	36.31 %	43.89 %	34.16 %	.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.
503	CSoR		code	21.66 %	31.52 %	17.99 %	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.
504	mBoW			21.59 %	35.22 %	16.89 %	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.
505	DepthCN		code	21.18 %	37.45 %	16.08 %	2.3 s	GPU @ 3.5 Ghz (Matlab)
A. Asvadi, L. Garrote, C. Premebida, P. Peixoto and U. Nunes: DepthCN: vehicle detection using 3D- LIDAR and convnet. IEEE ITSC 2017.
506	YOLOv2		code	14.31 %	26.74 %	10.94 %	0.02 s	GPU @ 3.5 Ghz (C/C++)
J. Redmon, S. Divvala, R. Girshick and A. Farhadi: You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016. J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
507	TopNet-UncEst			6.24 %	7.24 %	5.42 %	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.
508	MonoDET		code	5.80 %	6.41 %	5.13 %	0.04 s	1 core @ 2.5 Ghz (Python)
509	TopNet-Retina			5.00 %	6.82 %	4.52 %	52ms	GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
510	test		code	4.63 %	1.85 %	4.96 %	50 s	1 core @ 2.5 Ghz (Python)
511	TopNet-DecayRate			0.01 %	0.00 %	0.01 %	92 ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
512	LaserNet			0.00 %	0.00 %	0.00 %	12 ms	GPU @ 2.5 Ghz (C/C++)
G. Meyer, A. Laddha, E. Kee, C. Vallespi-Gonzalez and C. Wellington: LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
513	Neighbor-Vote			0.00 %	0.00 %	0.00 %	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.