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.
• Koray Koca (TUM) has released conversion scripts to export LIDAR data to Tensorflow records.

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	VirConv-S		code	87.20 %	92.48 %	82.45 %	0.09 s	1 core @ 2.5 Ghz (C/C++)
H. Wu, C. Wen, S. Shi and C. Wang: Virtual Sparse Convolution for Multimodal 3D Object Detection. CVPR 2023.
2	UDeerPEP		code	86.72 %	91.77 %	82.57 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
Z. Dong, H. Ji, X. Huang, W. Zhang, X. Zhan and J. Chen: PeP: a Point enhanced Painting method for unified point cloud tasks. 2023.
3	VirConv-T		code	86.25 %	92.54 %	81.24 %	0.09 s	1 core @ 2.5 Ghz (C/C++)
H. Wu, C. Wen, S. Shi and C. Wang: Virtual Sparse Convolution for Multimodal 3D Object Detection. CVPR 2023.
4	ViKIENet-R			86.04 %	91.20 %	81.18 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
Z. Yu, B. Qiu and A. Khong: ViKIENet: Towards Efficient 3D Object Detection with Virtual Key Instance Enhanced Network. CVPR 2025.
5	BFT3D			85.65 %	92.32 %	78.88 %	0.15 s	1 core @ 2.5 Ghz (C/C++)
6	MPCF		code	85.50 %	92.46 %	80.69 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
P. Gao and P. Zhang: MPCF: Multi-Phase Consolidated Fusion for Multi-Modal 3D Object Detection with Pseudo Point Cloud. 2024.
7	TSSTDet			85.47 %	91.84 %	80.65 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
H. Hoang, D. Bui and M. Yoo: TSSTDet: Transformation-Based 3-D Object Detection via a Spatial Shape Transformer. IEEE Sensors Journal 2024.
8	LPRFusion			85.47 %	91.92 %	80.43 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
9	3ONet			85.47 %	92.03 %	78.64 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
H. Hoang and M. Yoo: 3ONet: 3-D Detector for Occluded Object Under Obstructed Conditions. IEEE Sensors Journal 2023.
10	mm3d			85.45 %	92.09 %	80.68 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
11	mat3D			85.36 %	92.01 %	80.58 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
12	WWW			85.34 %	92.25 %	80.35 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
13	TED		code	85.28 %	91.61 %	80.68 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
H. Wu, C. Wen, W. Li, R. Yang and C. Wang: Transformation-Equivariant 3D Object Detection for Autonomous Driving. AAAI 2023.
14	SQD++			85.14 %	92.12 %	80.14 %	0.08 s	GPU @ >3.5 Ghz (Python)
15	None			85.14 %	92.12 %	80.14 %	0.05	1 core @ 2.5 Ghz (C/C++)
16	LoGoNet		code	85.06 %	91.80 %	80.74 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
X. Li, T. Ma, Y. Hou, B. Shi, Y. Yang, Y. Liu, X. Wu, Q. Chen, Y. Li, Y. Qiao and others: LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross-Modal Fusion. CVPR 2023.
17	TRTConv-L			85.04 %	91.90 %	80.38 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
18	ViKIENet			84.96 %	91.79 %	80.20 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
Z. Yu, B. Qiu and A. Khong: ViKIENet: Towards Efficient 3D Object Detection with Virtual Key Instance Enhanced Network. CVPR 2025.
19	SCDA-Net			84.94 %	91.61 %	80.61 %	- s	1 core @ 2.5 Ghz (C/C++)
20	LVP			84.92 %	91.37 %	80.07 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
Y. Chen, G. Cai, Z. Song, Z. Liu, B. Zeng, J. Li and Z. Wang: LVP: Leverage Virtual Points in Multi- modal Early Fusion for 3D Object Detection. IEEE Transactions on Geoscience and Remote Sensing 2024.
21	BVPConv-T			84.83 %	91.59 %	80.38 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
22	P3GMF			84.82 %	90.99 %	81.57 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
23	TRTConv-T			84.80 %	91.74 %	80.22 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
24	SFD		code	84.76 %	91.73 %	77.92 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
X. Wu, L. Peng, H. Yang, L. Xie, C. Huang, C. Deng, H. Liu and D. Cai: Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completion. CVPR 2022.
25	RM3D			84.72 %	90.86 %	81.17 %	0.18 s	1 core @ 2.5 Ghz (C/C++)
26	ACFNet			84.67 %	90.80 %	80.14 %	0.11 s	1 core @ 2.5 Ghz (C/C++)
Y. Tian, X. Zhang, X. Wang, J. Xu, J. Wang, R. Ai, W. Gu and W. Ding: ACF-Net: Asymmetric Cascade Fusion for 3D Detection With LiDAR Point Clouds and Images. IEEE Transactions on Intelligent Vehicles 2023.
27	SCEMF			84.66 %	91.19 %	81.45 %	1 s	1 core @ 2.5 Ghz (C/C++)
28	BVPConv-L			84.40 %	91.38 %	80.07 %	0.01 s	1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
29	MuStD			84.36 %	91.03 %	80.78 %	67 ms	>8 cores @ 2.5 Ghz (Python)
30	3D HANet		code	84.18 %	90.79 %	77.57 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
Q. Xia, Y. Chen, G. Cai, G. Chen, D. Xie, J. Su and Z. Wang: 3D HANet: A Flexible 3D Heatmap Auxiliary Network for Object Detection. IEEE Transactions on Geoscience and Remote Sensing 2023.
31	CasA++		code	84.04 %	90.68 %	79.69 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.
32	L-AUG			83.84 %	90.53 %	79.10 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
T. Cortinhal, I. Gouigah and E. Aksoy: Semantics-aware LiDAR-Only Pseudo Point Cloud Generation for 3D Object Detection. 2023.
33	LumiNet		code	83.32 %	91.76 %	78.29 %	0.1 s	1 core @ 2.5 Ghz (Python)
34	GraR-VoI		code	83.27 %	91.89 %	77.78 %	0.07 s	1 core @ 2.5 Ghz (Python + C/C++)
H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph. ECCV 2022.
35	GLENet-VR		code	83.23 %	91.67 %	78.43 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Zhang, Z. Zhu, J. Hou and Y. Yuan: GLENet: Boosting 3D object detectors with generative label uncertainty estimation. International Journal of Computer Vision 2023. Y. Zhang, J. Hou and Y. Yuan: A Comprehensive Study of the Robustness for LiDAR-based 3D Object Detectors against Adversarial Attacks. International Journal of Computer Vision 2023.
36	VPFNet		code	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. IEEE Transactions on Multimedia 2022.
37	GraR-Po		code	83.18 %	91.79 %	77.98 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph. ECCV 2022.
38	CasA		code	83.06 %	91.58 %	80.08 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.
39	UPIDet		code	82.97 %	89.13 %	80.05 %	0.11 s	1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Zhang, J. Hou, Y. Yuan and G. Xing: Unleash the Potential of Image Branch for Cross-modal 3D Object Detection. Thirty-seventh Conference on Neural Information Processing Systems 2023.
40	MLF-DET			82.89 %	91.18 %	77.89 %	0.09 s	1 core @ 2.5 Ghz (C/C++)
Z. Lin, Y. Shen, S. Zhou, S. Chen and N. Zheng: MLF-DET: Multi-Level Fusion for Cross- Modal 3D Object Detection. International Conference on Artificial Neural Networks 2023.
41	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.
42	R2Pfusion-Det			82.83 %	89.20 %	80.02 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
43	ImagePG			82.78 %	91.31 %	79.87 %	1 s	1 core @ 2.5 Ghz (C/C++)
44	GraR-Vo		code	82.77 %	91.29 %	77.20 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph. ECCV 2022.
45	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.
46	OcTr			82.64 %	90.88 %	77.77 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
C. Zhou, Y. Zhang, J. Chen and D. Huang: OcTr: Octree-based Transformer for 3D Object Detection. CVPR 2023.
47	PA3DNet			82.57 %	90.49 %	77.88 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
M. Wang, L. Zhao and Y. Yue: PA3DNet: 3-D Vehicle Detection with Pseudo Shape Segmentation and Adaptive Camera- LiDAR Fusion. IEEE Transactions on Industrial Informatics 2023.
48	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.
49	MPC3DNet			82.52 %	92.19 %	77.55 %	0.05 s	GPU @ 1.5 Ghz (Python)
50	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. WACV 2023.
51	GraR-Pi		code	82.42 %	90.94 %	77.00 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
H. Yang, Z. Liu, X. Wu, W. Wang, W. Qian, X. He and D. Cai: Graph R-CNN: Towards Accurate 3D Object Detection with Semantic-Decorated Local Graph. ECCV 2022.
52	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. WACV 2023.
53	3D Dual-Fusion		code	82.40 %	91.01 %	79.39 %	0.1 s	1 core @ 2.5 Ghz (Python)
Y. Kim, K. Park, M. Kim, D. Kum and J. Choi: 3D Dual-Fusion: Dual-Domain Dual-Query Camera-LiDAR Fusion for 3D Object Detection. arXiv preprint arXiv:2211.13529 2022.
54	DPFusion		code	82.35 %	90.98 %	77.26 %	0.07 s	1 core @ 2.5 Ghz (C/C++)
Y. Mo, Y. Wu, J. Zhao, Y. Hu, J. Wang and J. Yan: Enhancing LiDAR Point Features with Foundation Model Priors for 3D Object Detection. ITSC 2025.
55	RDIoU		code	82.30 %	90.65 %	77.26 %	0.03 s	1 core @ 2.5 Ghz (Python + C/C++)
H. Sheng, S. Cai, N. Zhao, B. Deng, J. Huang, X. Hua, M. Zhao and G. Lee: Rethinking IoU-based Optimization for Single- stage 3D Object Detection. ECCV 2022.
56	PVT-SSD			82.29 %	90.65 %	76.85 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
H. Yang, W. Wang, M. Chen, B. Lin, T. He, H. Chen, X. He and W. Ouyang: PVT-SSD: Single-Stage 3D Object Detector with Point-Voxel Transformer. CVPR 2023.
57	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.
58	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.
59	GraphAlign(ICCV2023)		code	82.23 %	90.90 %	79.67 %	0.03 s	GPU @ 2.0 Ghz (Python)
Z. Song, H. Wei, L. Bai, L. Yang and C. Jia: GraphAlign: Enhancing accurate feature alignment by graph matching for multi-modal 3D object detection. Proceedings of the IEEE/CVF International Conference on Computer Vision 2023.
60	SpaA			82.20 %	90.40 %	77.41 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
61	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.
62	PG-RCNN		code	82.13 %	89.38 %	77.33 %	0.06 s	GPU @ 1.5 Ghz (Python)
I. Koo, I. Lee, S. Kim, H. Kim, W. Jeon and C. Kim: PG-RCNN: Semantic Surface Point Generation for 3D Object Detection. 2023.
63	focal			82.13 %	90.60 %	79.51 %	100 s	1 core @ 2.5 Ghz (Python)
64	GEFPN			82.13 %	90.60 %	79.51 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
65	GeVo			82.13 %	90.60 %	79.51 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
66	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.
67	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.
68	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.
69	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.
70	BFT3D_easy			82.03 %	92.75 %	74.92 %	0.18 s	1 core @ 2.5 Ghz (C/C++)
71	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.
72	EPNet++			81.96 %	91.37 %	76.71 %	0.1 s	GPU @ 2.5 Ghz (Python)
Z. Liu, T. Huang, B. Li, X. Chen, X. Wang and X. Bai: EPNet++: Cascade Bi-Directional Fusion for Multi-Modal 3D Object Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 2022.
73	USVLab BSAODet		code	81.95 %	88.66 %	77.40 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
W. Xiao, Y. Peng, C. Liu, J. Gao, Y. Wu and X. Li: Balanced Sample Assignment and Objective for Single-Model Multi-Class 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2023.
74	HMFI		code	81.93 %	88.90 %	77.30 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
X. Li, B. Shi, Y. Hou, X. Wu, T. Ma, Y. Li and L. He: Homogeneous Multi-modal Feature Fusion and Interaction for 3D Object Detection. ECCV 2022.
75	CEF		code	81.93 %	88.19 %	77.14 %	0.03 s	1 core @ 2.5 Ghz (Python)
76	AFFN-G			81.92 %	90.57 %	79.24 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
77	RagNet3D		code	81.91 %	88.74 %	77.45 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
J. Chen, Y. Han, Z. Yan, J. Qian, J. Li and J. Yang: Ragnet3d: Learning Distinguishable Representation for Pooled Grids in 3d Object Detection. Available at SSRN 4979473 .
78	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.
79	SQD		code	81.82 %	91.58 %	79.07 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
Y. Mo, Y. Wu, J. Zhao, Z. Hou, W. Huang, Y. Hu, J. Wang and J. Yan: Sparse Query Dense: Enhancing 3D Object Detection with Pseudo Points. ACM MM Oral 2024.
80	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.
81	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.
82	HA-PillarNet			81.72 %	90.86 %	77.32 %	0.05 s	1 core @ 2.5 Ghz (Python)
83	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.
84	FIRM-Net_SCF+			81.67 %	88.24 %	77.00 %	0.07 s	1 core @ 2.5 Ghz (Python)
85	FIRM-Net-SCF			81.66 %	88.25 %	76.99 %	0.07 s	1 core @ 2.5 Ghz (Python)
86	SFA_IGCL_Focalsconv*		code	81.63 %	90.59 %	77.28 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
87	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.
88	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.
89	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.
90	test			81.58 %	90.04 %	76.53 %	0.04 s	GPU @ 1.5 Ghz (Python + C/C++)
91	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.
92	FARP-Net		code	81.53 %	88.36 %	78.98 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
T. Xie, L. Wang, K. Wang, R. Li, X. Zhang, H. Zhang, L. Yang, H. Liu and J. Li: FARP-Net: Local-Global Feature Aggregation and Relation-Aware Proposals for 3D Object Detection. IEEE Transactions on Multimedia 2023.
93	FocalsConv*			81.48 %	90.48 %	77.18 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
94	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.
95	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.
96	New_VLGCL		code	81.45 %	90.48 %	77.14 %	0.4 s	1 core @ 2.5 Ghz (Python)
97	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.
98	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.
99	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.
100	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.
101	PASS-PV-RCNN-Plus			81.28 %	87.65 %	76.79 %	1 s	1 core @ 2.5 Ghz (Python)
Anonymous: Leveraging Anchor-based LiDAR 3D Object Detection via Point Assisted Sample Selection. will submit to computer vision conference/journal 2024.
102	CGML			81.22 %	90.32 %	76.97 %	0.33 s	1 core @ 2.5 Ghz (C/C++)
103	VLGCL_NoText		code	81.21 %	90.35 %	77.00 %	0.3 s	1 core @ 2.5 Ghz (Python)
104	AFFN-Ga			81.17 %	88.36 %	76.89 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
105	Voxel RCNN-Focal*		code	81.14 %	87.96 %	77.07 %	0.2 s	1 core @ 2.5 Ghz (Python)
106	AFFN			81.06 %	89.65 %	76.67 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
107	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. C. Wang, H. Chen, Y. Chen, P. Hsiao and L. Fu: VoPiFNet: Voxel-Pixel Fusion Network for Multi-Class 3D Object Detection. IEEE Transactions on Intelligent Transportation Systems 2024.
108	Sem-Aug			80.77 %	89.41 %	75.90 %	0.1 s	GPU @ 2.5 Ghz (Python)
L. Zhao, M. Wang and Y. Yue: Sem-Aug: Improving Camera-LiDAR Feature Fusion With Semantic Augmentation for 3D Vehicle Detection. IEEE Robotics and Automation Letters 2022.
109	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.
110	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.
111	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.
112	SRDL			80.38 %	87.73 %	76.27 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
113	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.
114	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.
115	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.
116	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.
117	CAIA_PRO		code	80.16 %	88.52 %	75.05 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
118	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.
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	3D-CVF at SPA		code	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.
121	SIF			79.88 %	86.84 %	75.89 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
122	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.
123	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.
124	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.
125	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.
126	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.
127	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.
128	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.
129	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.
130	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.
131	DFAF3D			79.37 %	88.59 %	72.21 %	0.05 s	1 core @ 2.5 Ghz (Python)
Q. Tang, X. Bai, J. Guo, B. Pan and W. Jiang: DFAF3D: A dual-feature-aware anchor-free single-stage 3D detector for point clouds. Image and Vision Computing 2023.
132	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.
133	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.
134	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.
135	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.
136	GD-MAE			79.03 %	88.14 %	73.55 %	0.07 s	1 core @ 2.5 Ghz (Python + C/C++)
H. Yang, T. He, J. Liu, H. Chen, B. Wu, B. Lin, X. He and W. Ouyang: GD-MAE: Generative Decoder for MAE Pre- training on LiDAR Point Clouds. CVPR 2023.
137	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.
138	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.
139	ACDet		code	78.85 %	88.47 %	73.86 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
J. Xu, G. Wang, X. Zhang and G. Wan: ACDet: Attentive Cross-view Fusion for LiDAR-based 3D Object Detection. 3DV 2022.
140	BVIFusion+			78.82 %	87.38 %	75.89 %	0.09 s	1 core @ 2.5 Ghz (Python)
141	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.
142	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.
143	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.
144	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.
145	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.
146	Sem-Aug-PointRCNN++			78.06 %	86.69 %	73.85 %	0.1 s	8 cores @ 3.0 Ghz (Python)
L. Zhao, M. Wang and Y. Yue: Sem-Aug: Improving Camera-LiDAR Feature Fusion With Semantic Augmentation for 3D Vehicle Detection. IEEE Robotics and Automation Letters 2022.
147	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.
148	NoText_VLGCL		code	77.90 %	88.99 %	73.48 %	0.2 s	1 core @ 2.5 Ghz (Python)
149	SVFMamba		code	77.88 %	86.41 %	72.90 %	N/A s	1 core @ 2.5 Ghz (C/C++)
150	HMNet			77.86 %	86.96 %	73.94 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
151	work6_new1			77.65 %	86.02 %	72.39 %	0.5 s	GPU @ 2.5 Ghz (Python)
152	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.
153	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.
154	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.
155	RangeDet (Official)		code	77.36 %	85.41 %	72.60 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
L. Fan, X. Xiong, F. Wang, N. Wang and Z. Zhang: RangeDet: In Defense of Range View for LiDAR-Based 3D Object Detection. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
156	CS3D			77.35 %	85.51 %	72.24 %	0.5 s	1 core @ 2.5 Ghz (Python)
157	MSFASA-3DNet			77.21 %	86.06 %	71.89 %	0.5 s	GPU @ 2.5 Ghz (Python)
158	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.
159	dsvd+vx			77.20 %	84.74 %	73.94 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
160	AARMOD			76.90 %	87.70 %	69.62 %	0.1 s	1 core @ 2.5 Ghz (Python)
161	EAEPNet			76.89 %	87.62 %	71.90 %	0.1 s	1 core @ 2.5 Ghz (Python)
162	SeSame-point		code	76.83 %	85.25 %	71.60 %	N/A s	TITAN RTX @ 1.35 Ghz (Python)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
163	geo-pillars			76.78 %	85.97 %	71.77 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
164	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.
165	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.
166	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.
167	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.
168	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.
169	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.
170	T-SSD			76.00 %	86.97 %	69.11 %	0.04	1 core @ 2.0 Ghz (C/C++)
171	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.
172	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.
173	R50_SACINet			75.67 %	86.37 %	70.73 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
174	SA V1			75.64 %	82.79 %	71.00 %	0.5 s	GPU @ 2.5 Ghz (Python)
175	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.
176	L_SACINet			75.61 %	84.36 %	70.46 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
177	Fade 3D		code	75.57 %	85.85 %	70.47 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
W. Ye, Q. Xia, H. Wu, Z. Dong, R. Zhong, C. Wang and C. Wen: Fade3D: Fast and Deployable 3D Object Detection for Autonomous Driving. IEEE Transactions on Intelligent Transportation Systems 2025.
178	DensePointPillars			75.46 %	84.60 %	68.43 %	0.03 s	GPU @ 2.5 Ghz (Python)
179	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.
180	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.
181	PL++: PV-RCNN++			75.23 %	86.60 %	70.34 %	0.342 s	RTX 4060Ti (Python)
X. Gong, X. Huang, S. Chen and B. Zhang: Enhancing 3D Detection Accuracy in Autonomous Driving through Pseudo-LiDAR Augmentation and Downsampling. 2024 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML) 2024.
182	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.
183	SeSame-voxel		code	75.05 %	81.51 %	70.53 %	N/A s	TITAN RTX @ 1.35 Ghz (Python)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
184	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.
185	PASS-PointPillar			74.85 %	84.72 %	69.05 %	1 s	1 core @ 2.5 Ghz (C/C++)
Anonymous: Leveraging Anchor-based LiDAR 3D Object Detection via Point Assisted Sample Selection. will submit to computer vision conference/journal 2024.
186	Fade-kd			74.85 %	83.43 %	68.38 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
187	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.
188	XPillars			74.78 %	83.53 %	69.79 %	0.02 s	GPU @ 2.5 Ghz (Python)
189	mmFUSION		code	74.38 %	85.24 %	69.43 %	1s	1 core @ 2.5 Ghz (Python)
J. Ahmad and A. Del Bue: mmFUSION: Multimodal Fusion for 3D Objects Detection. arXiv preprint arXiv:2311.04058 2023.
190	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.
191	PCNet3D++			74.19 %	84.00 %	69.65 %	0.5 s	GPU @ 3.5 Ghz (Python)
192	HINTED		code	74.13 %	84.00 %	67.03 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
Q. Xia, W. Ye, H. Wu, S. Zhao, L. Xing, X. Huang, J. Deng, X. Li, C. Wen and C. Wang: HINTED: Hard Instance Enhanced Detector with Mixed-Density Feature Fusion for Sparsely- Supervised 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024.
193	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.
194	Harmonic PointPillar		code	73.96 %	82.26 %	69.21 %	0.01 s	1 core @ 2.5 Ghz (Python)
H. Zhang, J. Mekala, Z. Nain, J. Park and H. Jung: 3D Harmonic Loss: Towards Task-consistent and Time-friendly 3D Object Detection for V2X Orchestration. will submit to IEEE Transactions on Vehicular Technology 2022.
195	M3DNet			73.87 %	83.30 %	68.70 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
196	SeSame-pillar		code	73.85 %	83.88 %	68.65 %	N/A s	TITAN RTX @ 1.35 Ghz (Python)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
197	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.
198	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.
199	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.
200	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.
201	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.
202	SeSame-pillar w/scor		code	73.15 %	82.32 %	66.64 %	N/A s	1 core @ 2.5 Ghz (C/C++)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
203	PointPillars_mmdet3d			73.13 %	83.51 %	68.02 %	0.03 s	1 core @ 2.5 Ghz (Python)
204	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.
205	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.
206	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.
207	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.
208	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.
209	DSFNet			70.44 %	80.65 %	65.03 %	0.5 s	GPU @ 2.5 Ghz (Python)
210	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.
211	EOTL		code	69.13 %	79.97 %	58.57 %	TBD s	1 core @ 2.5 Ghz (Python + C/C++)
R. Yang, Z. Yan, T. Yang, Y. Wang and Y. Ruichek: Efficient Online Transfer Learning for Road Participants Detection in Autonomous Driving. IEEE Sensors Journal 2023.
212	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.
213	CPD++(unsupervised)		code	67.90 %	84.20 %	62.53 %	0.1 s	GPU @ >3.5 Ghz (Python)
214	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.
215	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. IEEE Transactions on Pattern Analysis and Machine Intelligence 2022.
216	DMF			67.33 %	77.55 %	62.44 %	0.2 s	1 core @ 2.5 Ghz (Python + C/C++)
X. J. Chen and W. Xu: Disparity-Based Multiscale Fusion Network for Transportation Detection. IEEE Transactions on Intelligent Transportation Systems 2022.
217	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.
218	StereoDistill			66.39 %	81.66 %	57.39 %	0.4 s	1 core @ 2.5 Ghz (Python)
Z. Liu, X. Ye, X. Tan, D. Errui, Y. Zhou and X. Bai: StereoDistill: Pick the Cream from LiDAR for Distilling Stereo-based 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2023.
219	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.
220	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.
221	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.
222	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.
223	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.
224	SeSame-point w/score		code	56.92 %	74.30 %	48.14 %	N/A s	1 core @ 1.5 Ghz (Python)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
225	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.
226	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.
227	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.
228	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.
229	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.
230	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.
231	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.
232	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.
233	SeSame-voxel w/score		code	47.14 %	61.57 %	41.06 %	N/A s	GPU @ 1.5 Ghz (Python)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
234	CPD(unsupervised)		code	47.04 %	68.57 %	44.13 %	0.1 s	GPU @ >3.5 Ghz (Python + C/C++)
H. Wu, S. Zhao, X. Huang, C. Wen, X. Li and C. Wang: Commonsense Prototype for Outdoor Unsupervised 3D Object Detection. CVPR 2024.
235	ESGN			46.39 %	65.80 %	38.42 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
A. Gao, Y. Pang, J. Nie, Z. Shao, J. Cao, Y. Guo and X. Li: ESGN: Efficient Stereo Geometry Network for Fast 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2022.
236	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.
237	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.
238	DDStereo			43.96 %	62.40 %	36.14 %	0.02 s	GPU @ 2.5 Ghz (Python)
239	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.
240	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.
241	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.
242	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.
243	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.
244	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.
245	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.
246	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.
247	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.
248	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.
249	DA3D+KM3D+v2-99		code	26.80 %	34.72 %	23.05 %	0.120s	GPU @ 2.5 Ghz (Python)
Y. Jia, J. Wang, H. Pan and W. Sun: Enhancing Monocular 3-D Object Detection Through Data Augmentation Strategies. IEEE Transactions on Instrumentation and Measurement 2024.
250	test_det			25.75 %	27.73 %	24.39 %	-1 s	1 core @ 2.5 Ghz (C/C++)
251	CIE + DM3D			25.02 %	35.96 %	21.47 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
Ananimities: Consistency of Implicit and Explicit Features Matters for Monocular 3D Object Detection. arXiv preprint arXiv:2207.07933 2022.
252	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.
253	DA3D+KM3D		code	22.08 %	30.83 %	19.20 %	0.02 s	GPU @ 2.5 Ghz (Python)
Y. Jia, J. Wang, H. Pan and W. Sun: Enhancing Monocular 3-D Object Detection Through Data Augmentation Strategies. IEEE Transactions on Instrumentation and Measurement 2024.
254	MDD-M3D-X			21.20 %	30.76 %	18.76 %	0.01 s	1 core @ 2.5 Ghz (Python)
255	MonoDTF			21.19 %	32.02 %	18.80 %	0.1 s	1 core @ 2.5 Ghz (Python)
Anonymities: Revisiting Monocular 3D Object Detection from Scene-Level Depth Retargeting to Instance- Level Spatial Refinement. arXiv preprint arXiv:2412.19165 2024.
256	MonoCoP-Car			21.15 %	29.84 %	18.13 %	0.01 s	GPU @ 2.5 Ghz (Python)
257	CIE			20.95 %	31.55 %	17.83 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
Anonymities: Consistency of Implicit and Explicit Features Matters for Monocular 3D Object Detection. arXiv preprint arXiv:2207.07933 2022.
258	MonoMH		code	20.88 %	29.12 %	17.93 %	0.04 s	1 core @ 2.5 Ghz (Python)
259	DA3D		code	20.47 %	27.76 %	17.89 %	0.03 s	1 core @ 2.5 Ghz (Python)
Y. Jia, J. Wang, H. Pan and W. Sun: Enhancing Monocular 3-D Object Detection Through Data Augmentation Strategies. IEEE Transactions on Instrumentation and Measurement 2024.
260	M3D			20.35 %	29.26 %	17.40 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
261	SSM3D			20.33 %	29.27 %	17.35 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
262	STLM3D			20.32 %	29.08 %	17.55 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
263	zqd			20.19 %	32.74 %	17.04 %	0.1 s	1 core @ 2.5 Ghz (Python)
264	MonoCoP			19.89 %	28.80 %	17.65 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
265	M5_3D			19.84 %	30.43 %	17.43 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
266	AM			19.64 %	27.94 %	16.74 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
267	MonoHPE-Mask			19.59 %	27.07 %	17.36 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
268	MM3D			19.54 %	29.86 %	17.17 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
269	MonoLSPF			19.51 %	27.34 %	17.26 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
270	MonoGeo		code	19.34 %	27.07 %	16.57 %	0.14 s	GPU @ 2.5 Ghz (Python)
271	MonoHPE			19.32 %	26.78 %	16.62 %	0.04 s	1 core @ 2.5 Ghz (Python)
272	AMNet+DDAD15M		code	19.26 %	26.26 %	17.05 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
H. Pan, Y. Jia, J. Wang and W. Sun: MonoAMNet: Three-Stage Real-Time Monocular 3D Object Detection With Adaptive Methods. IEEE Transactions on Intelligent Transportation Systems 2025.
273	MonoQ			19.20 %	28.26 %	16.21 %	0.02 s	1 core @ 2.5 Ghz (Python)
274	MonoLSS			19.15 %	26.11 %	16.94 %	0.04 s	1 core @ 2.5 Ghz (Python)
Z. Li, J. Jia and Y. Shi: MonoLSS: Learnable Sample Selection For Monocular 3D Detection. International Conference on 3D Vision 2024.
275	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.
276	MonoAFKD			19.06 %	26.07 %	16.85 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
277	H3			19.01 %	28.07 %	16.09 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
278	zqd_test2			19.00 %	31.56 %	16.47 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
279	NeurOCS			18.94 %	29.89 %	15.90 %	0.1 s	GPU @ 2.5 Ghz (Python)
Z. Min, B. Zhuang, S. Schulter, B. Liu, E. Dunn and M. Chandraker: NeurOCS: Neural NOCS Supervision for Monocular 3D Object Localization. CVPR 2023.
280	IDEAL-M3D 60%			18.87 %	27.06 %	16.73 %	0.04 s	1 core @ 2.5 Ghz (Python)
281	MonoLiG		code	18.86 %	24.90 %	16.79 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
A. Hekimoglu, M. Schmidt and A. Ramiro: Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learning. 2023.
282	GATE3D		code	18.85 %	26.07 %	16.76 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
283	CMKD		code	18.69 %	28.55 %	16.77 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
Y. Hong, H. Dai and Y. Ding: Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection. ECCV 2022.
284	DetAny3D		code	18.67 %	26.89 %	15.48 %	0.58 s	1 core @ 2.5 Ghz (Python)
285	Mix-Teaching		code	18.54 %	26.89 %	15.79 %	30 s	1 core @ 2.5 Ghz (C/C++)
L. Yang, X. Zhang, L. Wang, M. Zhu, C. Zhang and J. Li: Mix-Teaching: A Simple, Unified and Effective Semi-Supervised Learning Framework for Monocular 3D Object Detection. ArXiv 2022.
286	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.
287	AMNet		code	18.36 %	26.09 %	15.86 %	0.03 s	GPU @ 1.0 Ghz (Python)
H. Pan, Y. Jia, J. Wang and W. Sun: MonoAMNet: Three-Stage Real-Time Monocular 3D Object Detection With Adaptive Methods. IEEE Transactions on Intelligent Transportation Systems 2025.
288	PS-SVDM			18.13 %	29.22 %	15.35 %	1 s	1 core @ 2.5 Ghz (Python)
Y. Shi: SVDM: Single-View Diffusion Model for Pseudo-Stereo 3D Object Detection. arXiv preprint arXiv:2307.02270 2023.
289	UniCuboid			18.12 %	26.80 %	15.42 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
290	MonoSample (DID-M3D)		code	18.05 %	28.63 %	15.19 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
J. Qiao, B. Liu, J. Yang, B. Wang, S. Xiu, X. Du and X. Nie: MonoSample: Synthetic 3D Data Augmentation Method in Monocular 3D Object Detection. IEEE Robotics and Automation Letters 2024.
291	MonoSC			18.03 %	24.57 %	15.80 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
292	LPCG-Monoflex		code	17.80 %	25.56 %	15.38 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
L. Peng, F. Liu, Z. Yu, S. Yan, D. Deng, Z. Yang, H. Liu and D. Cai: Lidar Point Cloud Guided Monocular 3D Object Detection. ECCV 2022.
293	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.
294	MonoSKD		code	17.35 %	28.43 %	15.01 %	0.04 s	1 core @ 2.5 Ghz (Python)
S. Wang and J. Zheng: MonoSKD: General Distillation Framework for Monocular 3D Object Detection via Spearman Correlation Coefficient. ECAI 2023.
295	zqd_test			17.27 %	26.84 %	14.91 %	0.2 s	1 core @ 2.5 Ghz (Python)
296	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.
297	MonoNeRD		code	17.13 %	22.75 %	15.63 %	na s	1 core @ 2.5 Ghz (Python)
J. Xu, L. Peng, H. Cheng, H. Li, W. Qian, K. Li, W. Wang and D. Cai: MonoNeRD: NeRF-like Representations for Monocular 3D Object Detection. ICCV 2023.
298	OPA-3D		code	17.05 %	24.60 %	14.25 %	0.04 s	1 core @ 3.5 Ghz (Python)
Y. Su, Y. Di, G. Zhai, F. Manhardt, J. Rambach, B. Busam, D. Stricker and F. Tombari: OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object Detection. IEEE Robotics and Automation Letters 2023.
299	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.
300	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) .
301	ADD		code	16.81 %	25.61 %	13.79 %	0.1 s	1 core @ 2.5 Ghz (Python)
Z. Wu, Y. Wu, J. Pu, X. Li and X. Wang: Attention-based Depth Distillation with 3D-Aware Positional Encoding for Monocular 3D Object Detection. AAAI2023 .
302	MonOri		code	16.77 %	25.20 %	14.45 %	0.03 s	4 cores @ 2.5 Ghz (Python)
H. Yao, P. Han, J. Chen, Z. Wang, Y. Qiu, X. Wang, Y. wang, X. Chai, C. Cao and W. Jin: MonOri: Orientation-Guided PnP for Monocular 3-D Object Detection. IEEE Transactions on Neural Networks and Learning Systems 2025.
303	Monohan			16.75 %	22.44 %	14.10 %	0.05 s	1 core @ 2.5 Ghz (Python)
304	MonoUNI		code	16.73 %	24.75 %	13.49 %	0.04 s	1 core @ 2.5 Ghz (Python)
J. Jia, Z. Li and Y. Shi: MonoUNI: A Unified Vehicle and Infrastructure-side Monocular 3D Object Detection Network with Sufficient Depth Clues. Thirty-seventh Conference on Neural Information Processing Systems 2023.
305	MonoCD		code	16.59 %	25.53 %	14.53 %	n/a s	1 core @ 2.5 Ghz (Python)
L. Yan, P. Yan, S. Xiong, X. Xiang and Y. Tan: MonoCD: Monocular 3D Object Detection with Complementary Depths. CVPR 2024.
306	DID-M3D		code	16.29 %	24.40 %	13.75 %	0.04 s	1 core @ 2.5 Ghz (Python)
L. Peng, X. Wu, Z. Yang, H. Liu and D. Cai: DID-M3D: Decoupling Instance Depth for Monocular 3D Object Detection. ECCV 2022.
307	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.
308	MonoFRD			16.24 %	21.11 %	14.97 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
Z. Gong, Y. Zhao, F. Zhang, G. Gui, B. Chen, L. Yu, H. Wang, C. Yang and W. Gui: Color intuitive feature guided depth-height fusion and volume rendering for monocular 3D object detection. IEEE Transactions on Intelligent Vehicles(Major Revison) 2024.
309	DCD		code	15.90 %	23.81 %	13.21 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
Y. Li, Y. Chen, J. He and Z. Zhang: Densely Constrained Depth Estimator for Monocular 3D Object Detection. European Conference on Computer Vision 2022.
310	LLW			15.40 %	26.90 %	12.48 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
311	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.
312	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.
313	Cube R-CNN		code	15.01 %	23.59 %	12.56 %	0.05 s	GPU @ 2.5 Ghz (Python)
G. Brazil, A. Kumar, J. Straub, N. Ravi, J. Johnson and G. Gkioxari: Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild. CVPR 2023.
314	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.
315	SGM3D		code	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, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection. RA-L 2022.
316	MonoDSSMs-A			14.55 %	21.47 %	11.78 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
K. Vu, T. Tran and D. Nguyen: MonoDSSMs: Efficient Monocular 3D Object Detection with Depth-Aware State Space Models. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
317	MDSNet			14.46 %	24.30 %	11.12 %	0.05 s	1 core @ 2.5 Ghz (Python)
Z. Xie, Y. Song, J. Wu, Z. Li, C. Song and Z. Xu: MDS-Net: Multi-Scale Depth Stratification 3D Object Detection from Monocular Images. Sensors 2022.
318	DEVIANT		code	14.46 %	21.88 %	11.89 %	0.04 s	1 GPU (Python)
A. Kumar, G. Brazil, E. Corona, A. Parchami and X. Liu: DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection. European Conference on Computer Vision (ECCV) 2022.
319	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.
320	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.
321	MonoDSSMs-M			14.15 %	19.80 %	11.56 %	0.02 s	1 core @ 2.5 Ghz (Python + C/C++)
K. Vu, T. Tran and D. Nguyen: MonoDSSMs: Efficient Monocular 3D Object Detection with Depth-Aware State Space Models. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
322	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.
323	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.
324	MonoRCNN++		code	13.72 %	20.08 %	11.34 %	0.07 s	GPU @ 2.5 Ghz (Python)
X. Shi, Z. Chen and T. Kim: Multivariate Probabilistic Monocular 3D Object Detection. WACV 2023.
325	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.
326	temp			13.53 %	18.63 %	11.13 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
327	PS-SVDM			13.49 %	20.83 %	11.18 %	1 s	1 core @ 2.5 Ghz (Python)
Y. Shi: SVDM: Single-View Diffusion Model for Pseudo-Stereo 3D Object Detection. arXiv preprint arXiv:2307.02270 2023.
328	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.
329	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.
330	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.
331	FMF-occlusion-net			13.12 %	20.28 %	9.56 %	0.16 s	1 core @ 2.5 Ghz (Python + C/C++)
H. Liu, H. Liu, Y. Wang, F. Sun and W. Huang: Fine-grained Multi-level Fusion for Anti- occlusion Monocular 3D Object Detection. IEEE Transactions on Image Processing 2022.
332	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.
333	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.
334	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.
335	mdab			12.74 %	18.62 %	11.10 %	0.02 s	1 core @ 2.5 Ghz (Python)
336	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 .
337	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.
338	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.
339	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.
340	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 .
341	YoloMono3D		code	12.06 %	18.28 %	8.42 %	0.05 s	GPU @ 2.5 Ghz (Python)
Y. Liu, L. Wang and L. Ming: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
342	IAFA			12.01 %	17.81 %	10.61 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
D. Zhou, X. Song, Y. Dai, J. Yin, F. Lu, M. Liao, J. Fang and L. Zhang: IAFA: Instance-Aware Feature Aggregation for 3D Object Detection from a Single Image. Proceedings of the Asian Conference on Computer Vision 2020.
343	MonOAPC			12.00 %	18.77 %	9.75 %	0035 s	1 core @ 2.5 Ghz (Python)
H. Yao, J. Chen, Z. Wang, X. Wang, P. Han, X. Chai and Y. Qiu: Occlusion-Aware Plane-Constraints for Monocular 3D Object Detection. IEEE Transactions on Intelligent Transportation Systems 2023.
344	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.
345	CMAN			11.87 %	17.77 %	9.16 %	0.15 s	1 core @ 2.5 Ghz (Python)
C. Yuanzhouhan Cao: CMAN: Leaning Global Structure Correlation for Monocular 3D Object Detection. IEEE Trans. Intell. Transport. Syst. 2022.
346	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.
347	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.
348	SAKD-MR-Res18			11.65 %	18.38 %	9.42 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
349	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.
350	BEVHeight++		code	11.26 %	16.69 %	9.03 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
L. Yang, T. Tang, J. Li, P. Chen, K. Yuan, L. Wang, Y. Huang, X. Zhang and K. Yu: Bevheight++: Toward robust visual centric 3d object detection. arXiv preprint arXiv:2309.16179 2023.
351	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.
352	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.
353	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.
354	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.
355	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.
356	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.
357	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.
358	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.
359	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 .
360	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.
361	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.
362	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.
363	Plane-Constraints		code	7.88 %	11.29 %	6.48 %	0.05 s	4 cores @ 3.0 Ghz (Python)
H. Yao, J. Chen, Z. Wang, X. Wang, X. Chai, Y. Qiu and P. Han: Vertex points are not enough: Monocular 3D object detection via intra-and inter-plane constraints. Neural Networks 2023.
364	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.
365	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.
366	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.
367	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.
368	monospb			7.08 %	9.95 %	6.06 %	0.01 s	1 core @ 2.5 Ghz (Python)
369	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.
370	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.
371	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.
372	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.
373	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.
374	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.
375	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.
376	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.
377	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.
378	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.
379	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.
380	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.
381	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.
382	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.
383	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.
384	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.
385	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.
386	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++		code	49.29 %	56.33 %	46.70 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.
2	TED		code	49.21 %	55.85 %	46.52 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
H. Wu, C. Wen, W. Li, R. Yang and C. Wang: Transformation-Equivariant 3D Object Detection for Autonomous Driving. AAAI 2023.
3	UPIDet		code	48.77 %	55.59 %	46.12 %	0.11 s	1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Zhang, J. Hou, Y. Yuan and G. Xing: Unleash the Potential of Image Branch for Cross-modal 3D Object Detection. Thirty-seventh Conference on Neural Information Processing Systems 2023.
4	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. C. Wang, H. Chen, Y. Chen, P. Hsiao and L. Fu: VoPiFNet: Voxel-Pixel Fusion Network for Multi-Class 3D Object Detection. IEEE Transactions on Intelligent Transportation Systems 2024.
5	LoGoNet		code	47.43 %	53.07 %	45.22 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
X. Li, T. Ma, Y. Hou, B. Shi, Y. Yang, Y. Liu, X. Wu, Q. Chen, Y. Li, Y. Qiao and others: LoGoNet: Towards Accurate 3D Object Detection with Local-to-Global Cross-Modal Fusion. CVPR 2023.
6	CasA		code	47.09 %	54.04 %	44.56 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
H. Wu, J. Deng, C. Wen, X. Li and C. Wang: CasA: A Cascade Attention Network for 3D Object Detection from LiDAR point clouds. IEEE Transactions on Geoscience and Remote Sensing 2022.
7	ImagePG			47.02 %	54.81 %	44.49 %	1 s	1 core @ 2.5 Ghz (C/C++)
8	EQ-PVRCNN		code	47.02 %	55.84 %	42.94 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, L. Jiang, Y. Sun, B. Schiele and J. Jia: A Unified Query-based Paradigm for Point Cloud Understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.
9	PiFeNet		code	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: Accurate and Real-time 3D Pedestrian Detection Using an Efficient Attentive Pillar Network. IEEE Robotics and Automation Letters 2022.
10	USVLab BSAODet		code	46.50 %	52.69 %	43.10 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
W. Xiao, Y. Peng, C. Liu, J. Gao, Y. Wu and X. Li: Balanced Sample Assignment and Objective for Single-Model Multi-Class 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2023.
11	ACFNet			46.36 %	54.62 %	42.57 %	0.11 s	1 core @ 2.5 Ghz (C/C++)
Y. Tian, X. Zhang, X. Wang, J. Xu, J. Wang, R. Ai, W. Gu and W. Ding: ACF-Net: Asymmetric Cascade Fusion for 3D Detection With LiDAR Point Clouds and Images. IEEE Transactions on Intelligent Vehicles 2023.
12	DPPFA-Net			46.14 %	53.58 %	42.59 %	0.1 s	1 core @ 2.5 Ghz (Python)
J. Wang, X. Kong, H. Nishikawa, Q. Lian and H. Tomiyama: Dynamic Point-Pixel Feature Alignment for Multi-modal 3D Object Detection. IEEE Internet of Things Journal 2023.
13	PillarHist			45.85 %	55.79 %	42.15 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
14	SVFMamba		code	45.60 %	55.13 %	42.94 %	N/A s	1 core @ 2.5 Ghz (C/C++)
15	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.
16	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.
17	MLF-DET			45.29 %	50.86 %	42.05 %	0.09 s	1 core @ 2.5 Ghz (C/C++)
Z. Lin, Y. Shen, S. Zhou, S. Chen and N. Zheng: MLF-DET: Multi-Level Fusion for Cross- Modal 3D Object Detection. International Conference on Artificial Neural Networks 2023.
18	BVIFusion+			45.29 %	51.70 %	41.90 %	0.09 s	1 core @ 2.5 Ghz (Python)
19	LumiNet		code	45.26 %	53.54 %	41.55 %	0.1 s	1 core @ 2.5 Ghz (Python)
20	vsis-PHNet			45.26 %	55.60 %	42.53 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
21	PHNetp			45.26 %	55.60 %	42.53 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
22	ACDet		code	44.79 %	53.41 %	41.96 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
J. Xu, G. Wang, X. Zhang and G. Wan: ACDet: Attentive Cross-view Fusion for LiDAR-based 3D Object Detection. 3DV 2022.
23	AFFN-G			44.63 %	52.89 %	42.36 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
24	GEFPN			44.63 %	52.89 %	42.36 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
25	GeVo			44.63 %	52.89 %	42.36 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
26	dsvd+vx			44.54 %	52.67 %	41.78 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
27	EPNet++			44.38 %	52.79 %	41.29 %	0.1 s	GPU @ 2.5 Ghz (Python)
Z. Liu, T. Huang, B. Li, X. Chen, X. Wang and X. Bai: EPNet++: Cascade Bi-Directional Fusion for Multi-Modal 3D Object Detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 2022.
28	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.
29	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.
30	R2Pfusion-Det			44.05 %	53.15 %	41.83 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
31	SpaA			44.04 %	50.16 %	41.17 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
32	SFA_IGCL_Focalsconv*		code	44.03 %	51.58 %	40.77 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
33	CEF		code	44.01 %	52.30 %	41.42 %	0.03 s	1 core @ 2.5 Ghz (Python)
34	HA-PillarNet			43.98 %	51.83 %	41.61 %	0.05 s	1 core @ 2.5 Ghz (Python)
35	New_VLGCL		code	43.93 %	51.26 %	41.60 %	0.4 s	1 core @ 2.5 Ghz (Python)
36	CGML			43.86 %	51.04 %	41.66 %	0.33 s	1 core @ 2.5 Ghz (C/C++)
37	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.
38	VLGCL_NoText		code	43.70 %	50.82 %	41.50 %	0.3 s	1 core @ 2.5 Ghz (Python)
39	FIRM-Net_SCF+			43.61 %	51.80 %	41.17 %	0.07 s	1 core @ 2.5 Ghz (Python)
40	FIRM-Net-SCF			43.51 %	51.71 %	41.06 %	0.07 s	1 core @ 2.5 Ghz (Python)
41	3ONet			43.45 %	52.81 %	39.74 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
H. Hoang and M. Yoo: 3ONet: 3-D Detector for Occluded Object Under Obstructed Conditions. IEEE Sensors Journal 2023.
42	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.
43	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.
44	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.
45	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.
46	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.
47	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.
48	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.
49	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.
50	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.
51	HMFI		code	42.65 %	50.88 %	39.78 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
X. Li, B. Shi, Y. Hou, X. Wu, T. Ma, Y. Li and L. He: Homogeneous Multi-modal Feature Fusion and Interaction for 3D Object Detection. ECCV 2022.
52	FocalsConv*			42.56 %	50.40 %	40.24 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
53	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.
54	WWW			42.47 %	50.11 %	38.99 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
55	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.
56	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.
57	HMNet			42.16 %	49.90 %	38.86 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
58	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.
59	PASS-PV-RCNN-Plus			41.95 %	47.66 %	38.90 %	1 s	1 core @ 2.5 Ghz (Python)
Anonymous: Leveraging Anchor-based LiDAR 3D Object Detection via Point Assisted Sample Selection. will submit to computer vision conference/journal 2024.
60	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.
61	DPFusion		code	41.85 %	49.04 %	38.29 %	0.07 s	1 core @ 2.5 Ghz (C/C++)
Y. Mo, Y. Wu, J. Zhao, Y. Hu, J. Wang and J. Yan: Enhancing LiDAR Point Features with Foundation Model Priors for 3D Object Detection. ITSC 2025.
62	Voxel RCNN-Focal*		code	41.71 %	49.70 %	39.45 %	0.2 s	1 core @ 2.5 Ghz (Python)
63	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.
64	PG-RCNN		code	41.04 %	47.99 %	38.71 %	0.06 s	GPU @ 1.5 Ghz (Python)
I. Koo, I. Lee, S. Kim, H. Kim, W. Jeon and C. Kim: PG-RCNN: Semantic Surface Point Generation for 3D Object Detection. 2023.
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	DFAF3D			40.99 %	47.58 %	37.65 %	0.05 s	1 core @ 2.5 Ghz (Python)
Q. Tang, X. Bai, J. Guo, B. Pan and W. Jiang: DFAF3D: A dual-feature-aware anchor-free single-stage 3D detector for point clouds. Image and Vision Computing 2023.
67	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.
68	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.
69	AFFN			40.85 %	46.71 %	38.54 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
70	L_SACINet			40.57 %	48.99 %	37.13 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
71	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.
72	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.
73	MPC3DNet			40.31 %	44.88 %	37.53 %	0.05 s	GPU @ 1.5 Ghz (Python)
74	test			40.27 %	45.91 %	38.03 %	0.04 s	GPU @ 1.5 Ghz (Python + C/C++)
75	CAIA_PRO		code	40.20 %	47.70 %	37.69 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
76	EOTL		code	40.11 %	48.65 %	35.99 %	TBD s	1 core @ 2.5 Ghz (Python + C/C++)
R. Yang, Z. Yan, T. Yang, Y. Wang and Y. Ruichek: Efficient Online Transfer Learning for Road Participants Detection in Autonomous Driving. IEEE Sensors Journal 2023.
77	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.
78	NoText_VLGCL		code	39.75 %	46.97 %	36.98 %	0.2 s	1 core @ 2.5 Ghz (Python)
79	AFFN-Ga			39.69 %	45.38 %	37.56 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
80	XPillars			39.57 %	47.99 %	36.47 %	0.02 s	GPU @ 2.5 Ghz (Python)
81	SRDL			39.43 %	47.30 %	36.99 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
82	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.
83	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.
84	L-AUG			39.07 %	46.76 %	35.74 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
T. Cortinhal, I. Gouigah and E. Aksoy: Semantics-aware LiDAR-Only Pseudo Point Cloud Generation for 3D Object Detection. 2023.
85	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.
86	work6_new1			39.00 %	46.12 %	36.53 %	0.5 s	GPU @ 2.5 Ghz (Python)
87	R50_SACINet			38.96 %	47.05 %	36.55 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
88	SIF			38.74 %	46.23 %	36.06 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
89	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.
90	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.
91	HINTED		code	37.75 %	47.33 %	34.10 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
Q. Xia, W. Ye, H. Wu, S. Zhao, L. Xing, X. Huang, J. Deng, X. Li, C. Wen and C. Wang: HINTED: Hard Instance Enhanced Detector with Mixed-Density Feature Fusion for Sparsely- Supervised 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2024.
92	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.
93	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.
94	PCNet3D++			37.47 %	45.22 %	34.95 %	0.5 s	GPU @ 3.5 Ghz (Python)
95	SA V1			37.38 %	44.09 %	35.19 %	0.5 s	GPU @ 2.5 Ghz (Python)
96	SeSame-voxel		code	37.37 %	46.53 %	33.56 %	N/A s	TITAN RTX @ 1.35 Ghz (Python)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
97	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.
98	M3DNet			36.93 %	45.15 %	34.27 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
99	GraphAlign(ICCV2023)		code	36.89 %	41.38 %	34.95 %	0.03 s	GPU @ 2.0 Ghz (Python)
Z. Song, H. Wei, L. Bai, L. Yang and C. Jia: GraphAlign: Enhancing accurate feature alignment by graph matching for multi-modal 3D object detection. Proceedings of the IEEE/CVF International Conference on Computer Vision 2023.
100	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.
101	CS3D			36.44 %	43.92 %	34.27 %	0.5 s	1 core @ 2.5 Ghz (Python)
102	MSFASA-3DNet			36.32 %	42.89 %	34.33 %	0.5 s	GPU @ 2.5 Ghz (Python)
103	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.
104	focal			35.64 %	41.12 %	33.95 %	100 s	1 core @ 2.5 Ghz (Python)
105	DensePointPillars			35.38 %	42.76 %	32.63 %	0.03 s	GPU @ 2.5 Ghz (Python)
106	SeSame-point		code	35.34 %	42.29 %	33.02 %	N/A s	TITAN RTX @ 1.35 Ghz (Python)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
107	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.
108	geo-pillars			34.87 %	42.23 %	32.38 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
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109	DSFNet			34.60 %	43.19 %	31.85 %	0.5 s	GPU @ 2.5 Ghz (Python)
110	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.
111	PL++: PV-RCNN++			33.89 %	41.53 %	31.42 %	0.342 s	RTX 4060Ti (Python)
X. Gong, X. Huang, S. Chen and B. Zhang: Enhancing 3D Detection Accuracy in Autonomous Driving through Pseudo-LiDAR Augmentation and Downsampling. 2024 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML) 2024.
112	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. IEEE Transactions on Pattern Analysis and Machine Intelligence 2022.
113	StereoDistill			32.23 %	44.12 %	28.95 %	0.4 s	1 core @ 2.5 Ghz (Python)
Z. Liu, X. Ye, X. Tan, D. Errui, Y. Zhou and X. Bai: StereoDistill: Pick the Cream from LiDAR for Distilling Stereo-based 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2023.
114	Fade-kd			32.17 %	39.47 %	29.40 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
115	PointPillars_mmdet3d			32.10 %	39.38 %	29.54 %	0.03 s	1 core @ 2.5 Ghz (Python)
116	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.
117	T-SSD			31.36 %	39.36 %	29.14 %	0.04	1 core @ 2.0 Ghz (C/C++)
118	SeSame-pillar		code	31.00 %	37.61 %	28.86 %	N/A s	TITAN RTX @ 1.35 Ghz (Python)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
119	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.
120	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.
121	DMF			29.77 %	37.21 %	27.62 %	0.2 s	1 core @ 2.5 Ghz (Python + C/C++)
X. J. Chen and W. Xu: Disparity-Based Multiscale Fusion Network for Transportation Detection. IEEE Transactions on Intelligent Transportation Systems 2022.
122	SeSame-voxel w/score		code	28.26 %	34.14 %	26.15 %	N/A s	GPU @ 1.5 Ghz (Python)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
123	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.
124	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.
125	SeSame-pillar w/scor		code	27.23 %	33.87 %	25.27 %	N/A s	1 core @ 2.5 Ghz (C/C++)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
126	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.
127	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.
128	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.
129	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.
130	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.
131	Fade 3D		code	24.06 %	30.25 %	21.84 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
W. Ye, Q. Xia, H. Wu, Z. Dong, R. Zhong, C. Wang and C. Wen: Fade3D: Fast and Deployable 3D Object Detection for Autonomous Driving. IEEE Transactions on Intelligent Transportation Systems 2025.
132	SeSame-point w/score		code	23.33 %	31.13 %	20.07 %	N/A s	1 core @ 1.5 Ghz (Python)
H. O, C. Yang and K. Huh: SeSame: Simple, Easy 3D Object Detection with Point-Wise Semantics. Proceedings of the Asian Conference on Computer Vision (ACCV) 2024.
133	DDStereo			21.92 %	32.14 %	19.43 %	0.02 s	GPU @ 2.5 Ghz (Python)
134	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.
135	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.
136	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.
137	CPD++(unsupervised)		code	15.58 %	17.64 %	14.60 %	0.1 s	GPU @ >3.5 Ghz (Python)
138	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.
139	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.
140	MonOri		code	12.76 %	18.97 %	11.00 %	0.03 s	4 cores @ 2.5 Ghz (Python)
H. Yao, P. Han, J. Chen, Z. Wang, Y. Qiu, X. Wang, Y. wang, X. Chai, C. Cao and W. Jin: MonOri: Orientation-Guided PnP for Monocular 3-D Object Detection. IEEE Transactions on Neural Networks and Learning Systems 2025.
141	MonoMH		code	11.70 %	17.45 %	10.05 %	0.04 s	1 core @ 2.5 Ghz (Python)
142	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.
143	MonoAFKD			11.32 %	17.15 %	9.94 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
144	MonoLSS			11.27 %	17.09 %	10.00 %	0.04 s	1 core @ 2.5 Ghz (Python)
Z. Li, J. Jia and Y. Shi: MonoLSS: Learnable Sample Selection For Monocular 3D Detection. International Conference on 3D Vision 2024.
145	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) .
146	MDD-M3D-X			10.98 %	16.68 %	9.20 %	0.01 s	1 core @ 2.5 Ghz (Python)
147	MonoCoP			10.94 %	16.76 %	9.31 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
148	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.
149	MonoHPE			10.75 %	17.04 %	9.10 %	0.04 s	1 core @ 2.5 Ghz (Python)
150	MonoHPE-Mask			10.74 %	15.83 %	9.38 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
151	AM			10.61 %	16.08 %	8.90 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
152	CIE			10.53 %	16.19 %	8.97 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
Anonymities: Consistency of Implicit and Explicit Features Matters for Monocular 3D Object Detection. arXiv preprint arXiv:2207.07933 2022.
153	OPA-3D		code	10.49 %	15.65 %	8.80 %	0.04 s	1 core @ 3.5 Ghz (Python)
Y. Su, Y. Di, G. Zhai, F. Manhardt, J. Rambach, B. Busam, D. Stricker and F. Tombari: OPA-3D: Occlusion-Aware Pixel-Wise Aggregation for Monocular 3D Object Detection. IEEE Robotics and Automation Letters 2023.
154	MonoUNI		code	10.34 %	15.78 %	8.74 %	0.04 s	1 core @ 2.5 Ghz (Python)
J. Jia, Z. Li and Y. Shi: MonoUNI: A Unified Vehicle and Infrastructure-side Monocular 3D Object Detection Network with Sufficient Depth Clues. Thirty-seventh Conference on Neural Information Processing Systems 2023.
155	MonoLSPF			10.32 %	15.90 %	8.67 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
156	ESGN			10.27 %	14.05 %	9.02 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
A. Gao, Y. Pang, J. Nie, Z. Shao, J. Cao, Y. Guo and X. Li: ESGN: Efficient Stereo Geometry Network for Fast 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2022.
157	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.
158	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.
159	MonoFRD			8.88 %	13.86 %	7.53 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
Z. Gong, Y. Zhao, F. Zhang, G. Gui, B. Chen, L. Yu, H. Wang, C. Yang and W. Gui: Color intuitive feature guided depth-height fusion and volume rendering for monocular 3D object detection. IEEE Transactions on Intelligent Vehicles(Major Revison) 2024.
160	SGM3D		code	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, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection. RA-L 2022.
161	CMKD		code	8.79 %	13.94 %	7.42 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
Y. Hong, H. Dai and Y. Ding: Cross-Modality Knowledge Distillation Network for Monocular 3D Object Detection. ECCV 2022.
162	AMNet+DDAD15M		code	8.67 %	13.18 %	7.43 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
H. Pan, Y. Jia, J. Wang and W. Sun: MonoAMNet: Three-Stage Real-Time Monocular 3D Object Detection With Adaptive Methods. IEEE Transactions on Intelligent Transportation Systems 2025.
163	CPD(unsupervised)		code	8.66 %	10.87 %	7.83 %	0.1 s	GPU @ >3.5 Ghz (Python + C/C++)
H. Wu, S. Zhao, X. Huang, C. Wen, X. Li and C. Wang: Commonsense Prototype for Outdoor Unsupervised 3D Object Detection. CVPR 2024.
164	DEVIANT		code	8.65 %	13.43 %	7.69 %	0.04 s	1 GPU (Python)
A. Kumar, G. Brazil, E. Corona, A. Parchami and X. Liu: DEVIANT: Depth EquiVarIAnt NeTwork for Monocular 3D Object Detection. European Conference on Computer Vision (ECCV) 2022.
165	IDEAL-M3D 60%			8.50 %	13.73 %	7.52 %	0.04 s	1 core @ 2.5 Ghz (Python)
166	AMNet		code	8.39 %	12.79 %	7.07 %	0.03 s	GPU @ 1.0 Ghz (Python)
H. Pan, Y. Jia, J. Wang and W. Sun: MonoAMNet: Three-Stage Real-Time Monocular 3D Object Detection With Adaptive Methods. IEEE Transactions on Intelligent Transportation Systems 2025.
167	PS-SVDM			8.33 %	12.93 %	7.20 %	1 s	1 core @ 2.5 Ghz (Python)
Y. Shi: SVDM: Single-View Diffusion Model for Pseudo-Stereo 3D Object Detection. arXiv preprint arXiv:2307.02270 2023.
168	MonoNeRD		code	8.26 %	13.20 %	7.02 %	na s	1 core @ 2.5 Ghz (Python)
J. Xu, L. Peng, H. Cheng, H. Li, W. Qian, K. Li, W. Wang and D. Cai: MonoNeRD: NeRF-like Representations for Monocular 3D Object Detection. ICCV 2023.
169	mdab			8.22 %	12.88 %	6.91 %	0.02 s	1 core @ 2.5 Ghz (Python)
170	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.
171	SAKD-MR-Res18			8.06 %	12.62 %	6.78 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
172	LLW			7.96 %	12.99 %	6.86 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
173	MonoRCNN++		code	7.90 %	12.26 %	6.62 %	0.07 s	GPU @ 2.5 Ghz (Python)
X. Shi, Z. Chen and T. Kim: Multivariate Probabilistic Monocular 3D Object Detection. WACV 2023.
174	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.
175	temp			7.61 %	11.83 %	6.29 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
176	Mix-Teaching		code	7.47 %	11.67 %	6.61 %	30 s	1 core @ 2.5 Ghz (C/C++)
L. Yang, X. Zhang, L. Wang, M. Zhu, C. Zhang and J. Li: Mix-Teaching: A Simple, Unified and Effective Semi-Supervised Learning Framework for Monocular 3D Object Detection. ArXiv 2022.
177	LPCG-Monoflex		code	7.33 %	10.82 %	6.18 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
L. Peng, F. Liu, Z. Yu, S. Yan, D. Deng, Z. Yang, H. Liu and D. Cai: Lidar Point Cloud Guided Monocular 3D Object Detection. ECCV 2022.
178	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.
179	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.
180	MDSNet			7.09 %	10.68 %	6.06 %	0.05 s	1 core @ 2.5 Ghz (Python)
Z. Xie, Y. Song, J. Wu, Z. Li, C. Song and Z. Xu: MDS-Net: Multi-Scale Depth Stratification 3D Object Detection from Monocular Images. Sensors 2022.
181	Cube R-CNN		code	6.95 %	11.17 %	5.87 %	0.05 s	GPU @ 2.5 Ghz (Python)
G. Brazil, A. Kumar, J. Straub, N. Ravi, J. Johnson and G. Gkioxari: Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild. CVPR 2023.
182	PS-SVDM			6.93 %	11.16 %	5.96 %	1 s	1 core @ 2.5 Ghz (Python)
Y. Shi: SVDM: Single-View Diffusion Model for Pseudo-Stereo 3D Object Detection. arXiv preprint arXiv:2307.02270 2023.
183	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.
184	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.
185	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.
186	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 .
187	DA3D+KM3D+v2-99		code	6.32 %	9.38 %	5.54 %	0.120s	GPU @ 2.5 Ghz (Python)
Y. Jia, J. Wang, H. Pan and W. Sun: Enhancing Monocular 3-D Object Detection Through Data Augmentation Strategies. IEEE Transactions on Instrumentation and Measurement 2024.
188	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.
189	UniCuboid			6.05 %	9.03 %	5.08 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
190	MonOAPC			5.87 %	8.75 %	4.84 %	0035 s	1 core @ 2.5 Ghz (Python)
H. Yao, J. Chen, Z. Wang, X. Wang, P. Han, X. Chai and Y. Qiu: Occlusion-Aware Plane-Constraints for Monocular 3D Object Detection. IEEE Transactions on Intelligent Transportation Systems 2023.
191	FMF-occlusion-net			5.23 %	7.62 %	4.28 %	0.16 s	1 core @ 2.5 Ghz (Python + C/C++)
H. Liu, H. Liu, Y. Wang, F. Sun and W. Huang: Fine-grained Multi-level Fusion for Anti- occlusion Monocular 3D Object Detection. IEEE Transactions on Image Processing 2022.
192	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.
193	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.
194	monospb			4.07 %	5.20 %	3.43 %	0.01 s	1 core @ 2.5 Ghz (Python)
195	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.
196	DA3D+KM3D		code	3.64 %	5.60 %	3.10 %	0.02 s	GPU @ 2.5 Ghz (Python)
Y. Jia, J. Wang, H. Pan and W. Sun: Enhancing Monocular 3-D Object Detection Through Data Augmentation Strategies. IEEE Transactions on Instrumentation and Measurement 2024.
197	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.
198	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.
199	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 .
200	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.
201	CMAN			3.41 %	4.62 %	2.87 %	0.15 s	1 core @ 2.5 Ghz (Python)
C. Yuanzhouhan Cao: CMAN: Leaning Global Structure Correlation for Monocular 3D Object Detection. IEEE Trans. Intell. Transport. Syst. 2022.
202	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.
203	DA3D		code	2.95 %	4.62 %	2.58 %	0.03 s	1 core @ 2.5 Ghz (Python)
Y. Jia, J. Wang, H. Pan and W. Sun: Enhancing Monocular 3-D Object Detection Through Data Augmentation Strategies. IEEE Transactions on Instrumentation and Measurement 2024.
204	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.
205	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.
206	MonoLiG		code	1.94 %	2.89 %	1.91 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
A. Hekimoglu, M. Schmidt and A. Ramiro: Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learning. 2023.
207	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.
208	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.
209	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.
210	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.
211	Plane-Constraints		code	1.09 %	1.73 %	1.04 %	0.05 s	4 cores @ 3.0 Ghz (Python)
H. Yao, J. Chen, Z. Wang, X. Wang, X. Chai, Y. Qiu and P. Han: Vertex points are not enough: Monocular 3D object detection via intra-and inter-plane constraints. Neural Networks 2023.
212	GATE3D		code	0.15 %	0.12 %	0.15 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
213	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.