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	LDRFusion			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	3D-AWARE			84.85 %	91.38 %	80.39 %	0.1 s	1 core @ 2.5 Ghz (Python)
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	MuStD			84.36 %	91.03 %	80.78 %	67 ms	>8 cores @ 2.5 Ghz (Python)
28	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.
29	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.
30	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.
31	LumiNet		code	83.32 %	91.76 %	78.29 %	0.1 s	1 core @ 2.5 Ghz (Python)
32	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.
33	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.
34	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.
35	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.
36	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.
37	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.
38	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.
39	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.
40	R2Pfusion-Det			82.83 %	89.20 %	80.02 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
41	ImagePG			82.78 %	91.31 %	79.87 %	1 s	1 core @ 2.5 Ghz (C/C++)
42	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.
43	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.
44	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.
45	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.
46	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.
47	MPC3DNet			82.52 %	92.19 %	77.55 %	0.05 s	GPU @ 1.5 Ghz (Python)
48	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.
49	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.
50	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.
51	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.
52	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.
53	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.
54	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.
55	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.
56	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.
57	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.
58	SpaA			82.20 %	90.40 %	77.41 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
59	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.
60	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.
61	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.
62	RobusTor3D			82.12 %	90.78 %	79.36 %	... s	1 core @ 2.5 Ghz (C/C++)
63	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.
64	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.
65	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.
66	BFT3D_easy			82.03 %	92.75 %	74.92 %	0.18 s	1 core @ 2.5 Ghz (C/C++)
67	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.
68	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.
69	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.
70	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.
71	CEF		code	81.93 %	88.19 %	77.14 %	0.03 s	1 core @ 2.5 Ghz (Python)
72	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 .
73	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.
74	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.
75	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.
76	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.
77	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.
78	FIRM-Net_SCF+			81.67 %	88.24 %	77.00 %	0.07 s	1 core @ 2.5 Ghz (Python)
79	FIRM-Net-SCF			81.66 %	88.25 %	76.99 %	0.07 s	1 core @ 2.5 Ghz (Python)
80	SFA_IGCL_Focalsconv*		code	81.63 %	90.59 %	77.28 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
81	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.
82	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.
83	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.
84	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.
85	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.
86	FocalsConv*			81.48 %	90.48 %	77.18 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
87	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.
88	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.
89	New_VLGCL		code	81.45 %	90.48 %	77.14 %	0.4 s	1 core @ 2.5 Ghz (Python)
90	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.
91	...		code	81.39 %	90.57 %	76.96 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
92	2025AAAI-SSLfusion		code	81.36 %	90.23 %	76.56 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
93	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.
94	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.
95	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.
96	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.
97	CGML			81.22 %	90.32 %	76.97 %	0.33 s	1 core @ 2.5 Ghz (C/C++)
98	VLGCL_NoText		code	81.21 %	90.35 %	77.00 %	0.3 s	1 core @ 2.5 Ghz (Python)
99	Voxel RCNN-Focal*		code	81.14 %	87.96 %	77.07 %	0.2 s	1 core @ 2.5 Ghz (Python)
100	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.
101	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.
102	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.
103	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.
104	PointVit V2			80.54 %	89.81 %	74.96 %	.006 s	1 core @ 2.5 Ghz (Python + C/C++)
105	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.
106	SRDL			80.38 %	87.73 %	76.27 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
107	Fast-CLOCs			80.35 %	89.10 %	76.99 %	0.1 s	GPU @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: Fast-CLOCs: Fast Camera-LiDAR Object Candidates Fusion for 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022.
108	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.
109	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.
110	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.
111	CAIA_PRO		code	80.16 %	88.52 %	75.05 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
112	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.
113	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.
114	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.
115	PointVit V1			79.93 %	91.16 %	72.51 %	.006 s	1 core @ 2.5 Ghz (Python + C/C++)
116	SIF			79.88 %	86.84 %	75.89 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
117	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.
118	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.
119	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.
120	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.
121	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.
122	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.
123	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.
124	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.
125	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.
126	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.
127	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.
128	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.
129	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.
130	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.
131	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.
132	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.
133	Voxel RCNN*		code	79.01 %	87.56 %	76.16 %	0.07 s	1 core @ 2.5 Ghz (C/C++)
134	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.
135	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.
136	BVIFusion+			78.82 %	87.38 %	75.89 %	0.09 s	1 core @ 2.5 Ghz (Python)
137	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.
138	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.
139	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.
140	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.
141	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.
142	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.
143	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.
144	NoText_VLGCL		code	77.90 %	88.99 %	73.48 %	0.2 s	1 core @ 2.5 Ghz (Python)
145	SVFMamba		code	77.88 %	86.41 %	72.90 %	N/A s	1 core @ 2.5 Ghz (C/C++)
146	HMNet			77.86 %	86.96 %	73.94 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
147	work6_new1			77.65 %	86.02 %	72.39 %	0.5 s	GPU @ 2.5 Ghz (Python)
148	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.
149	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.
150	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.
151	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.
152	CS3D			77.35 %	85.51 %	72.24 %	0.5 s	1 core @ 2.5 Ghz (Python)
153	MSFASA-3DNet			77.21 %	86.06 %	71.89 %	0.03 s	GPU @ 2.5 Ghz (Python)
154	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.
155	dsvd+vx			77.20 %	84.74 %	73.94 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
156	AARMOD			76.90 %	87.70 %	69.62 %	0.1 s	1 core @ 2.5 Ghz (Python)
157	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.
158	geo-pillars			76.78 %	85.97 %	71.77 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
159	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.
160	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.
161	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.
162	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.
163	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.
164	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.
165	T-SSD			76.00 %	86.97 %	69.11 %	0.04	1 core @ 2.0 Ghz (C/C++)
166	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.
167	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.
168	SA V1			75.64 %	82.79 %	71.00 %	0.5 s	GPU @ 2.5 Ghz (Python)
169	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.
170	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.
171	DensePointPillars			75.46 %	84.60 %	68.43 %	0.03 s	GPU @ 2.5 Ghz (Python)
172	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.
173	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.
174	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.
175	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.
176	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.
177	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.
178	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.
179	Fade-kd			74.85 %	83.43 %	68.38 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
180	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.
181	XPillars			74.78 %	83.53 %	69.79 %	0.02 s	GPU @ 2.5 Ghz (Python)
182	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.
183	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.
184	PCNet3D++			74.19 %	84.00 %	69.65 %	0.5 s	GPU @ 3.5 Ghz (Python)
185	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.
186	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.
187	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.
188	M3DNet			73.87 %	83.30 %	68.70 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
189	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.
190	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.
191	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.
192	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.
193	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.
194	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.
195	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.
196	PointPillars_mmdet3d			73.13 %	83.51 %	68.02 %	0.03 s	1 core @ 2.5 Ghz (Python)
197	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.
198	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.
199	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.
200	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.
201	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.
202	DSFNet			70.44 %	80.65 %	65.03 %	0.5 s	GPU @ 2.5 Ghz (Python)
203	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.
204	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.
205	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.
206	CPD++(unsupervised)		code	67.90 %	84.20 %	62.53 %	0.1 s	GPU @ >3.5 Ghz (Python)
207	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.
208	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.
209	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.
210	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.
211	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.
212	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.
213	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.
214	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.
215	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.
216	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.
217	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.
218	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.
219	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.
220	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.
221	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.
222	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.
223	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.
224	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.
225	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.
226	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.
227	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.
228	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.
229	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.
230	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.
231	DDStereo			43.96 %	62.40 %	36.14 %	0.02 s	GPU @ 2.5 Ghz (Python)
232	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.
233	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.
234	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.
235	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.
236	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.
237	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.
238	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.
239	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.
240	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.
241	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.
242	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.
243	test_det			25.75 %	27.73 %	24.39 %	-1 s	1 core @ 2.5 Ghz (C/C++)
244	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.
245	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.
246	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.
247	MDD-M3D-X			21.20 %	30.76 %	18.76 %	0.01 s	1 core @ 2.5 Ghz (Python)
248	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.
249	MonoCoP-Car			21.15 %	29.84 %	18.13 %	0.01 s	GPU @ 2.5 Ghz (Python)
250	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.
251	MonoMH		code	20.88 %	29.12 %	17.93 %	0.04 s	1 core @ 2.5 Ghz (Python)
252	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.
253	M3D			20.35 %	29.26 %	17.40 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
254	SSM3D			20.33 %	29.27 %	17.35 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
255	STLM3D			20.32 %	29.08 %	17.55 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
256	MonoCoP			19.89 %	28.80 %	17.65 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
257	MonoDLGD			19.87 %	29.11 %	17.74 %	0.04 s	GPU @ 2.5 Ghz (Python)
258	M5_3D			19.84 %	30.43 %	17.43 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
259	MonoCLUE			19.70 %	27.94 %	16.69 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
260	AM			19.64 %	27.94 %	16.74 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
261	MonoHPE-Mask			19.59 %	27.07 %	17.36 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
262	MM3D			19.54 %	29.86 %	17.17 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
263	MonoLSPF			19.51 %	27.34 %	17.26 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
264	MonoHPE			19.50 %	27.33 %	17.22 %	0.04 s	1 core @ 2.5 Ghz (Python)
265	MonoGeo		code	19.34 %	27.07 %	16.57 %	0.14 s	GPU @ 2.5 Ghz (Python)
266	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.
267	MonoVQD			19.20 %	28.26 %	16.21 %	0.02 s	1 core @ 2.5 Ghz (Python)
268	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.
269	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.
270	MonoAFKD			19.06 %	26.07 %	16.85 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
271	H3			19.01 %	28.07 %	16.09 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
272	MonoGeo		code	18.96 %	27.37 %	16.28 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
273	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.
274	IDEAL-M3D 60%			18.87 %	27.06 %	16.73 %	0.04 s	1 core @ 2.5 Ghz (Python)
275	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.
276	GATE3D		code	18.85 %	26.07 %	16.76 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
277	MonoCLUE			18.77 %	26.30 %	16.10 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
278	MonoCLUE_all			18.70 %	26.59 %	15.99 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
279	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.
280	DetAny3D		code	18.67 %	26.89 %	15.48 %	0.58 s	1 core @ 2.5 Ghz (Python)
281	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.
282	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.
283	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.
284	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.
285	UniCuboid			18.12 %	26.80 %	15.42 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
286	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.
287	MonoSC			18.03 %	24.57 %	15.80 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
288	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.
289	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.
290	fdaa11			17.65 %	26.09 %	15.54 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
291	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.
292	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.
293	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.
294	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.
295	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.
296	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) .
297	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 .
298	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.
299	Monohan			16.75 %	22.44 %	14.10 %	0.05 s	1 core @ 2.5 Ghz (Python)
300	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.
301	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.
302	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.
303	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.
304	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.
305	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.
306	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.
307	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.
308	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.
309	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.
310	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.
311	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.
312	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.
313	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.
314	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.
315	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.
316	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.
317	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.
318	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.
319	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.
320	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.
321	temp			13.53 %	18.63 %	11.13 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
322	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.
323	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.
324	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.
325	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.
326	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.
327	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.
328	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.
329	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.
330	mdab			12.74 %	18.62 %	11.10 %	0.02 s	1 core @ 2.5 Ghz (Python)
331	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 .
332	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.
333	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.
334	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.
335	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 .
336	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.
337	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.
338	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.
339	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.
340	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.
341	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.
342	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.
343	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.
344	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.
345	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.
346	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.
347	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.
348	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.
349	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.
350	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.
351	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.
352	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.
353	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 .
354	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.
355	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.
356	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.
357	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.
358	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.
359	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.
360	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.
361	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.
362	monospb			7.08 %	9.95 %	6.06 %	0.01 s	1 core @ 2.5 Ghz (Python)
363	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.
364	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.
365	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.
366	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.
367	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.
368	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.
369	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.
370	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.
371	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.
372	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.
373	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.
374	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.
375	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.
376	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.
377	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.
378	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.
379	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.
380	EAEPNet			0.00 %	0.00 %	0.00 %	0.1 s	1 core @ 2.5 Ghz (Python)
381	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	...		code	45.86 %	54.39 %	43.40 %	0.2 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	RobusTor3D			44.97 %	52.08 %	42.68 %	... s	1 core @ 2.5 Ghz (C/C++)
23	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.
24	2025AAAI-SSLfusion		code	44.72 %	52.39 %	42.35 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
25	dsvd+vx			44.54 %	52.67 %	41.78 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
26	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.
27	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.
28	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.
29	R2Pfusion-Det			44.05 %	53.15 %	41.83 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
30	SpaA			44.04 %	50.16 %	41.17 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
31	SFA_IGCL_Focalsconv*		code	44.03 %	51.58 %	40.77 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
32	CEF		code	44.01 %	52.30 %	41.42 %	0.03 s	1 core @ 2.5 Ghz (Python)
33	New_VLGCL		code	43.93 %	51.26 %	41.60 %	0.4 s	1 core @ 2.5 Ghz (Python)
34	CGML			43.86 %	51.04 %	41.66 %	0.33 s	1 core @ 2.5 Ghz (C/C++)
35	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.
36	VLGCL_NoText		code	43.70 %	50.82 %	41.50 %	0.3 s	1 core @ 2.5 Ghz (Python)
37	FIRM-Net_SCF+			43.61 %	51.80 %	41.17 %	0.07 s	1 core @ 2.5 Ghz (Python)
38	FIRM-Net-SCF			43.51 %	51.71 %	41.06 %	0.07 s	1 core @ 2.5 Ghz (Python)
39	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.
40	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.
41	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.
42	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.
43	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.
44	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.
45	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.
46	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.
47	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.
48	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.
49	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.
50	FocalsConv*			42.56 %	50.40 %	40.24 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
51	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.
52	WWW			42.47 %	50.11 %	38.99 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
53	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.
54	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.
55	HMNet			42.16 %	49.90 %	38.86 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
56	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.
57	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.
58	PointPillars		code	41.92 %	51.45 %	38.89 %	16 ms	1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
59	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.
60	Voxel RCNN-Focal*		code	41.71 %	49.70 %	39.45 %	0.2 s	1 core @ 2.5 Ghz (Python)
61	epBRM		code	41.52 %	49.17 %	39.08 %	0.10 s	1 core @ 2.5 Ghz (C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
62	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.
63	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.
64	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.
65	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.
66	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.
67	Voxel RCNN*		code	40.68 %	48.70 %	38.51 %	0.07 s	1 core @ 2.5 Ghz (C/C++)
68	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.
69	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.
70	MPC3DNet			40.31 %	44.88 %	37.53 %	0.05 s	GPU @ 1.5 Ghz (Python)
71	CAIA_PRO		code	40.20 %	47.70 %	37.69 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
72	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.
73	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.
74	NoText_VLGCL		code	39.75 %	46.97 %	36.98 %	0.2 s	1 core @ 2.5 Ghz (Python)
75	XPillars			39.57 %	47.99 %	36.47 %	0.02 s	GPU @ 2.5 Ghz (Python)
76	SRDL			39.43 %	47.30 %	36.99 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
77	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.
78	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.
79	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.
80	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.
81	work6_new1			39.00 %	46.12 %	36.53 %	0.5 s	GPU @ 2.5 Ghz (Python)
82	SIF			38.74 %	46.23 %	36.06 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
83	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.
84	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.
85	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.
86	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.
87	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.
88	PCNet3D++			37.47 %	45.22 %	34.95 %	0.5 s	GPU @ 3.5 Ghz (Python)
89	SA V1			37.38 %	44.09 %	35.19 %	0.5 s	GPU @ 2.5 Ghz (Python)
90	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.
91	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.
92	M3DNet			36.93 %	45.15 %	34.27 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
93	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.
94	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.
95	CS3D			36.44 %	43.92 %	34.27 %	0.5 s	1 core @ 2.5 Ghz (Python)
96	MSFASA-3DNet			36.32 %	42.89 %	34.33 %	0.03 s	GPU @ 2.5 Ghz (Python)
97	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.
98	Ped_Net			35.75 %	43.42 %	33.05 %	0.5 s	GPU @ 2.5 Ghz (Python + C/C++)
99	DensePointPillars			35.38 %	42.76 %	32.63 %	0.03 s	GPU @ 2.5 Ghz (Python)
100	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.
101	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.
102	geo-pillars			34.87 %	42.23 %	32.38 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
103	DSFNet			34.60 %	43.19 %	31.85 %	0.5 s	GPU @ 2.5 Ghz (Python)
104	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.
105	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.
106	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.
107	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.
108	Fade-kd			32.17 %	39.47 %	29.40 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
109	PointPillars_mmdet3d			32.10 %	39.38 %	29.54 %	0.03 s	1 core @ 2.5 Ghz (Python)
110	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.
111	T-SSD			31.36 %	39.36 %	29.14 %	0.04	1 core @ 2.0 Ghz (C/C++)
112	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.
113	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.
114	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.
115	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.
116	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.
117	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.
118	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.
119	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.
120	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.
121	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.
122	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.
123	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.
124	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.
125	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.
126	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.
127	DDStereo			21.92 %	32.14 %	19.43 %	0.02 s	GPU @ 2.5 Ghz (Python)
128	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.
129	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.
130	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.
131	CPD++(unsupervised)		code	15.58 %	17.64 %	14.60 %	0.1 s	GPU @ >3.5 Ghz (Python)
132	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.
133	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.
134	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.
135	MonoMH		code	11.70 %	17.45 %	10.05 %	0.04 s	1 core @ 2.5 Ghz (Python)
136	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.
137	MonoAFKD			11.32 %	17.15 %	9.94 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
138	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.
139	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) .
140	MDD-M3D-X			10.98 %	16.68 %	9.20 %	0.01 s	1 core @ 2.5 Ghz (Python)
141	MonoCoP			10.94 %	16.76 %	9.31 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
142	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.
143	MonoHPE-Mask			10.74 %	15.83 %	9.38 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
144	AM			10.61 %	16.08 %	8.90 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
145	fdaa11			10.58 %	16.26 %	8.96 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
146	MonoGeo		code	10.54 %	16.62 %	9.03 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
147	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.
148	MonoHPE			10.49 %	15.91 %	8.90 %	0.04 s	1 core @ 2.5 Ghz (Python)
149	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.
150	MonoCLUE_all			10.45 %	16.18 %	8.75 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
151	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.
152	MonoCLUE			10.32 %	16.03 %	8.76 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
153	MonoLSPF			10.32 %	15.90 %	8.67 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
154	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.
155	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.
156	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.
157	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.
158	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.
159	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.
160	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.
161	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.
162	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.
163	IDEAL-M3D 60%			8.50 %	13.73 %	7.52 %	0.04 s	1 core @ 2.5 Ghz (Python)
164	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.
165	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.
166	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.
167	mdab			8.22 %	12.88 %	6.91 %	0.02 s	1 core @ 2.5 Ghz (Python)
168	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.
169	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.
170	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.
171	temp			7.61 %	11.83 %	6.29 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
172	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.
173	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.
174	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.
175	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.
176	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.
177	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.
178	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.
179	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.
180	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.
181	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.
182	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 .
183	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.
184	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.
185	UniCuboid			6.05 %	9.03 %	5.08 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
186	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.
187	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.
188	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.
189	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.
190	monospb			4.07 %	5.20 %	3.43 %	0.01 s	1 core @ 2.5 Ghz (Python)
191	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.
192	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.
193	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.
194	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.
195	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 .
196	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.
197	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.
198	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.
199	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.
200	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.
201	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.
202	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.
203	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.
204	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.
205	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.
206	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.
207	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.
208	GATE3D		code	0.15 %	0.12 %	0.15 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
209	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.