The KITTI Vision Benchmark Suite

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

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

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

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

All methods are ranked based on the moderately difficult results.

Car

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	VirConv-S		code	93.52 %	95.99 %	90.38 %	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	93.40 %	95.34 %	89.07 %	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	92.65 %	96.11 %	89.69 %	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	WWW			92.26 %	95.96 %	89.21 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
5	GraR-Po		code	92.12 %	95.79 %	87.11 %	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.
6	TSSTDet			92.11 %	95.80 %	89.23 %	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.
7	LPRFusion			92.09 %	95.58 %	87.11 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
8	MPCF		code	92.07 %	95.92 %	87.29 %	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.
9	TED		code	92.05 %	95.44 %	87.30 %	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.
10	TRTConv-L			92.04 %	95.55 %	87.23 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
11	BFT3D			92.04 %	95.88 %	85.24 %	0.15 s	1 core @ 2.5 Ghz (C/C++)
12	SQD++			92.03 %	95.72 %	88.92 %	0.08 s	GPU @ >3.5 Ghz (Python)
13	None			92.03 %	95.72 %	88.92 %	0.05	1 core @ 2.5 Ghz (C/C++)
14	mm3d			92.01 %	95.66 %	87.20 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
15	ViKIENet			91.87 %	95.69 %	88.99 %	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.
16	VPFNet		code	91.86 %	93.02 %	86.94 %	0.06 s	2 cores @ 2.5 Ghz (Python)
H. Zhu, J. Deng, Y. Zhang, J. Ji, Q. Mao, H. Li and Y. Zhang: VPFNet: Improving 3D Object Detection with Virtual Point based LiDAR and Stereo Data Fusion. IEEE Transactions on Multimedia 2022.
17	SFD		code	91.85 %	95.64 %	86.83 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
X. Wu, L. Peng, H. Yang, L. Xie, C. Huang, C. Deng, H. Liu and D. Cai: Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completion. CVPR 2022.
18	mat3D			91.85 %	95.49 %	87.07 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
19	SE-SSD		code	91.84 %	95.68 %	86.72 %	0.03 s	1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, L. Jiang and C. Fu: SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud. CVPR 2021.
20	LVP			91.80 %	95.49 %	88.91 %	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	ACFNet			91.78 %	92.91 %	87.06 %	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.
22	BVPConv-T			91.75 %	95.24 %	89.15 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
23	GraR-Vo		code	91.72 %	95.27 %	86.51 %	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.
24	TRTConv-T			91.70 %	95.63 %	89.00 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
25	PVT-SSD			91.63 %	95.23 %	86.43 %	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.
26	SCDA-Net			91.61 %	95.45 %	89.16 %	- s	1 core @ 2.5 Ghz (C/C++)
27	SPANet			91.59 %	95.59 %	86.53 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
Y. Ye: SPANet: Spatial and Part-Aware Aggregation Network for 3D Object Detection. Pacific Rim International Conference on Artificial Intelligence 2021.
28	ViKIENet-R			91.56 %	94.87 %	88.55 %	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.
29	RM3D			91.55 %	94.77 %	88.65 %	0.18 s	1 core @ 2.5 Ghz (C/C++)
30	CasA		code	91.54 %	95.19 %	86.82 %	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.
31	LoGoNet		code	91.52 %	95.48 %	87.09 %	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.
32	GraR-Pi		code	91.52 %	95.06 %	86.42 %	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.
33	BVPConv-L			91.49 %	95.27 %	88.93 %	0.01 s	1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
34	SCEMF			91.46 %	94.76 %	88.77 %	1 s	1 core @ 2.5 Ghz (C/C++)
35	UPIDet		code	91.36 %	92.96 %	86.80 %	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.
36	BADet		code	91.32 %	95.23 %	86.48 %	0.14 s	1 core @ 2.5 Ghz (C/C++)
R. Qian, X. Lai and X. Li: BADet: Boundary-Aware 3D Object Detection from Point Clouds. Pattern Recognition 2022.
37	CasA++		code	91.22 %	94.57 %	88.43 %	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.
38	MuStD			91.13 %	94.62 %	88.28 %	67 ms	>8 cores @ 2.5 Ghz (Python)
39	3D HANet		code	91.13 %	94.33 %	86.33 %	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.
40	SpaA			91.05 %	94.69 %	86.56 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
41	MPC3DNet			91.03 %	95.56 %	86.36 %	0.05 s	GPU @ 1.5 Ghz (Python)
42	SA-SSD		code	91.03 %	95.03 %	85.96 %	0.04 s	1 core @ 2.5 Ghz (Python)
C. He, H. Zeng, J. Huang, X. Hua and L. Zhang: Structure Aware Single-stage 3D Object Detection from Point Cloud. CVPR 2020.
43	L-AUG			91.00 %	94.52 %	88.08 %	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.
44	c2f			90.89 %	92.31 %	86.25 %	1 s	1 core @ 2.5 Ghz (C/C++)
45	3D Dual-Fusion		code	90.86 %	93.08 %	86.44 %	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.
46	focal			90.74 %	92.58 %	88.36 %	100 s	1 core @ 2.5 Ghz (Python)
47	GEFPN			90.74 %	92.58 %	88.36 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
48	GeVo			90.74 %	92.58 %	88.36 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
49	GraphAlign(ICCV2023)		code	90.73 %	94.46 %	88.34 %	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.
50	MMLab PV-RCNN		code	90.65 %	94.98 %	86.14 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
51	SQD		code	90.63 %	95.44 %	88.04 %	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.
52	AFFN-G			90.61 %	94.46 %	88.12 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
53	VPFNet		code	90.52 %	93.94 %	86.25 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
C. Wang, H. Chen and L. Fu: VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection. 2021. 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.
54	ECA			90.50 %	93.87 %	85.94 %	0.08 s	GPU @ 1.5 Ghz (Python)
55	PDV		code	90.48 %	94.56 %	86.23 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
J. Hu, T. Kuai and S. Waslander: Point Density-Aware Voxels for LiDAR 3D Object Detection. CVPR 2022.
56	test			90.39 %	94.58 %	85.69 %	0.04 s	GPU @ 1.5 Ghz (Python + C/C++)
57	M3DeTR		code	90.37 %	94.41 %	85.98 %	n/a s	GPU @ 1.0 Ghz (Python)
T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis and D. Manocha: M3DeTR: Multi-representation, Multi- scale, Mutual-relation 3D Object Detection with Transformers. 2021.
58	VoTr-TSD		code	90.34 %	94.03 %	86.14 %	0.07 s	1 core @ 2.5 Ghz (C/C++)
J. Mao, Y. Xue, M. Niu, H. Bai, J. Feng, X. Liang, H. Xu and C. Xu: Voxel Transformer for 3D Object Detection. ICCV 2021.
59	AFFN			90.33 %	94.29 %	85.99 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
60	DSA-PV-RCNN		code	90.13 %	92.42 %	85.93 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
61	LumiNet		code	90.13 %	95.79 %	85.06 %	0.1 s	1 core @ 2.5 Ghz (Python)
62	XView			90.12 %	92.27 %	85.94 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
63	GraR-VoI		code	90.10 %	95.69 %	86.85 %	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.
64	HA-PillarNet			90.07 %	92.73 %	85.98 %	0.05 s	1 core @ 2.5 Ghz (Python)
65	CAT-Det			90.07 %	92.59 %	85.82 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Zhang, J. Chen and D. Huang: CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection. CVPR 2022.
66	3ONet			90.07 %	95.87 %	85.09 %	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.
67	New_VLGCL		code	89.98 %	94.00 %	85.98 %	0.4 s	1 core @ 2.5 Ghz (Python)
68	SVGA-Net			89.88 %	92.07 %	85.59 %	0.03s	1 core @ 2.5 Ghz (Python + C/C++)
Q. He, Z. Wang, H. Zeng, Y. Zeng and Y. Liu: SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds. AAAI 2022.
69	EBM3DOD		code	89.86 %	95.64 %	84.56 %	0.12 s	1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.
70	CIA-SSD		code	89.84 %	93.74 %	82.39 %	0.03 s	1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, S. Chen, L. Jiang and C. Fu: CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud. AAAI 2021.
71	MLF-DET			89.82 %	93.38 %	84.78 %	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.
72	CLOCs_PVCas		code	89.80 %	93.05 %	86.57 %	0.1 s	1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection . 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
73	Voxel RCNN-Focal*		code	89.76 %	92.06 %	86.01 %	0.2 s	1 core @ 2.5 Ghz (Python)
74	GLENet-VR		code	89.76 %	93.48 %	84.89 %	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.
75	RDIoU		code	89.75 %	94.90 %	84.67 %	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.
76	EBM3DOD baseline		code	89.63 %	95.44 %	84.34 %	0.05 s	1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.
77	ImagePG			89.56 %	95.17 %	86.90 %	1 s	1 core @ 2.5 Ghz (C/C++)
78	3D-CVF at SPA		code	89.56 %	93.52 %	82.45 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
J. Yoo, Y. Kim, J. Kim and J. Choi: 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection. ECCV 2020.
79	OcTr			89.56 %	93.08 %	86.74 %	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.
80	VLGCL_NoText		code	89.55 %	92.22 %	85.79 %	0.3 s	1 core @ 2.5 Ghz (Python)
81	Struc info fusion II			89.54 %	95.26 %	82.31 %	0.05 s	GPU @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.
82	SASA		code	89.51 %	92.87 %	86.35 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
C. Chen, Z. Chen, J. Zhang and D. Tao: SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object Detection. arXiv preprint arXiv:2201.01976 2022.
83	Fast-CLOCs			89.49 %	93.03 %	86.40 %	0.1 s	GPU @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: Fast-CLOCs: Fast Camera-LiDAR Object Candidates Fusion for 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022.
84	IA-SSD (single)		code	89.48 %	93.14 %	84.42 %	0.013 s	1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
85	CLOCs		code	89.48 %	92.91 %	86.42 %	0.1 s	1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection . 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
86	DPFusion		code	89.47 %	95.20 %	86.49 %	0.07 s	1 core @ 2.5 Ghz (C/C++)
87	PA3DNet			89.46 %	93.11 %	84.60 %	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.
88	PG-RCNN		code	89.46 %	93.39 %	86.54 %	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.
89	DFAF3D			89.45 %	93.14 %	84.22 %	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.
90	DVF-V			89.42 %	93.12 %	86.50 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
A. Mahmoud, J. Hu and S. Waslander: Dense Voxel Fusion for 3D Object Detection. WACV 2023.
91	CGML			89.41 %	92.20 %	85.77 %	0.33 s	1 core @ 2.5 Ghz (C/C++)
92	Struc info fusion I			89.38 %	94.91 %	84.29 %	0.05 s	1 core @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.
93	R2Pfusion-Det			89.37 %	92.96 %	86.70 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
94	BtcDet		code	89.34 %	92.81 %	84.55 %	0.09 s	GPU @ 2.5 Ghz (Python + C/C++)
Q. Xu, Y. Zhong and U. Neumann: Behind the Curtain: Learning Occluded Shapes for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2022.
95	IA-SSD (multi)		code	89.33 %	92.79 %	84.35 %	0.014 s	1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
96	CAIA_PRO		code	89.27 %	92.84 %	84.26 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
97	ACDet		code	89.21 %	92.87 %	85.80 %	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.
98	DVF-PV			89.20 %	93.08 %	86.28 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
A. Mahmoud, J. Hu and S. Waslander: Dense Voxel Fusion for 3D Object Detection. WACV 2023.
99	FIRM-Net_SCF+			89.20 %	92.57 %	86.35 %	0.07 s	1 core @ 2.5 Ghz (Python)
100	STD		code	89.19 %	94.74 %	86.42 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
101	FIRM-Net-SCF			89.19 %	92.56 %	86.34 %	0.07 s	1 core @ 2.5 Ghz (Python)
102	Point-GNN		code	89.17 %	93.11 %	83.90 %	0.6 s	GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
103	HMFI		code	89.17 %	93.04 %	86.37 %	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.
104	SSL-PointGNN		code	89.16 %	92.92 %	83.99 %	0.56 s	GPU @ 1.5 Ghz (Python)
E. Erçelik, E. Yurtsever, M. Liu, Z. Yang, H. Zhang, P. Topçam, M. Listl, Y. Çaylı and A. Knoll: 3D Object Detection with a Self-supervised Lidar Scene Flow Backbone. arXiv preprint arXiv:2205.00705 2022.
105	CEF		code	89.16 %	92.51 %	84.27 %	0.03 s	1 core @ 2.5 Ghz (Python)
106	SPG_mini		code	89.12 %	92.80 %	86.27 %	0.09 s	GPU @ 2.5 Ghz (Python)
Q. Xu, Y. Zhou, W. Wang, C. Qi and D. Anguelov: SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation. Proceedings of the IEEE conference on computer vision and pattern recognition (ICCV) 2021.
107	EQ-PVRCNN		code	89.09 %	94.55 %	86.42 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, L. Jiang, Y. Sun, B. Schiele and J. Jia: A Unified Query-based Paradigm for Point Cloud Understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.
108	VoxSeT		code	89.07 %	92.70 %	86.29 %	33 ms	1 core @ 2.5 Ghz (C/C++)
C. He, R. Li, S. Li and L. Zhang: Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds. CVPR 2022.
109	3DSSD		code	89.02 %	92.66 %	85.86 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
110	RagNet3D		code	89.01 %	92.87 %	86.36 %	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 .
111	EPNet++			89.00 %	95.41 %	85.73 %	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.
112	Focals Conv		code	89.00 %	92.67 %	86.33 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
Y. Chen, Y. Li, X. Zhang, J. Sun and J. Jia: Focal Sparse Convolutional Networks for 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.
113	USVLab BSAODet		code	88.90 %	92.66 %	86.23 %	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.
114	H^23D R-CNN		code	88.87 %	92.85 %	86.07 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
J. Deng, W. Zhou, Y. Zhang and H. Li: From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2021.
115	Pyramid R-CNN			88.84 %	92.19 %	86.21 %	0.07 s	1 core @ 2.5 Ghz (C/C++)
J. Mao, M. Niu, H. Bai, X. Liang, H. Xu and C. Xu: Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection. ICCV 2021.
116	CityBrainLab-CT3D		code	88.83 %	92.36 %	84.07 %	0.07 s	1 core @ 2.5 Ghz (Python + C/C++)
H. Sheng, S. Cai, Y. Liu, B. Deng, J. Huang, X. Hua and M. Zhao: Improving 3D Object Detection with Channel- wise Transformer. ICCV 2021.
117	Voxel R-CNN		code	88.83 %	94.85 %	86.13 %	0.04 s	GPU @ 3.0 Ghz (C/C++)
J. Deng, S. Shi, P. Li, W. Zhou, Y. Zhang and H. Li: Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection . AAAI 2021.
118	HVNet			88.82 %	92.83 %	83.38 %	0.03 s	GPU @ 2.0 Ghz (Python)
M. Ye, S. Xu and T. Cao: HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection. CVPR 2020.
119	GD-MAE			88.82 %	94.22 %	83.54 %	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.
120	dsvd+vx			88.81 %	90.54 %	84.62 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
121	SPG		code	88.70 %	94.33 %	85.98 %	0.09 s	1 core @ 2.5 Ghz (Python + C/C++)
Q. Xu, Y. Zhou, W. Wang, C. Qi and D. Anguelov: SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation. Proceedings of the IEEE conference on computer vision and pattern recognition (ICCV) 2021.
122	SIENet		code	88.65 %	92.38 %	86.03 %	0.08 s	1 core @ 2.5 Ghz (Python)
Z. Li, Y. Yao, Z. Quan, W. Yang and J. Xie: SIENet: Spatial Information Enhancement Network for 3D Object Detection from Point Cloud. 2021.
123	P2V-RCNN			88.63 %	92.72 %	86.14 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.
124	FromVoxelToPoint		code	88.61 %	92.23 %	86.11 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to- Point Decoder. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
125	RangeIoUDet			88.59 %	92.28 %	85.83 %	0.02 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, Z. Zhang, M. Zhang, X. Zhao and S. Pu: RangeIoUDet: Range Image Based Real-Time 3D Object Detector Optimized by Intersection Over Union. CVPR 2021.
126	EPNet		code	88.47 %	94.22 %	83.69 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
T. Huang, Z. Liu, X. Chen and X. Bai: EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection. ECCV 2020.
127	CenterNet3D			88.46 %	91.80 %	83.62 %	0.04 s	GPU @ 1.5 Ghz (Python)
G. Wang, B. Tian, Y. Ai, T. Xu, L. Chen and D. Cao: CenterNet3D:An Anchor free Object Detector for Autonomous Driving. 2020.
128	FARP-Net		code	88.45 %	91.20 %	86.01 %	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.
129	BVIFusion+			88.42 %	92.02 %	85.73 %	0.09 s	1 core @ 2.5 Ghz (Python)
130	AFFN-Ga			88.41 %	92.49 %	85.89 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
131	RangeRCNN			88.40 %	92.15 %	85.74 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, M. Zhang, Z. Zhang, X. Zhao and S. Pu: RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representation. arXiv preprint arXiv:2009.00206 2020.
132	second_iou_baseline			88.40 %	92.12 %	85.54 %	0.03 s	1 core @ 2.5 Ghz (Python)
133	Patches			88.39 %	92.72 %	83.19 %	0.15 s	GPU @ 2.0 Ghz
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
134	3D IoU-Net			88.38 %	94.76 %	81.93 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, S. Krylov, Y. Ding and L. Shao: 3D IoU-Net: IoU Guided 3D Object Detector for Point Clouds. arXiv preprint arXiv:2004.04962 2020.
135	StructuralIF			88.38 %	91.78 %	85.67 %	0.02 s	8 cores @ 2.5 Ghz (Python)
J. Pei An: Deep structural information fusion for 3D object detection on LiDAR-camera system. Accepted in CVIU 2021.
136	PASS-PV-RCNN-Plus			88.37 %	92.17 %	85.75 %	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.
137	SFA_IGCL_Focalsconv*		code	88.35 %	92.51 %	86.00 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
138	BFT3D_easy			88.26 %	94.19 %	80.99 %	0.18 s	1 core @ 2.5 Ghz (C/C++)
139	Fade-kd			88.25 %	92.16 %	83.34 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
140	CLOCs_SecCas			88.23 %	91.16 %	82.63 %	0.1 s	1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
141	UberATG-MMF			88.21 %	93.67 %	81.99 %	0.08 s	GPU @ 2.5 Ghz (Python)
M. Liang*, B. Yang*, Y. Chen, R. Hu and R. Urtasun: Multi-Task Multi-Sensor Fusion for 3D Object Detection. CVPR 2019.
142	Patches - EMP			88.17 %	94.49 %	84.75 %	0.5 s	GPU @ 2.5 Ghz (Python)
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
143	SRDL			88.17 %	92.01 %	85.43 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
144	FocalsConv*			88.13 %	92.10 %	85.83 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
145	PointPainting			88.11 %	92.45 %	83.36 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
146	SERCNN			88.10 %	94.11 %	83.43 %	0.1 s	1 core @ 2.5 Ghz (Python)
D. Zhou, J. Fang, X. Song, L. Liu, J. Yin, Y. Dai, H. Li and R. Yang: Joint 3D Instance Segmentation and Object Detection for Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020.
147	Associate-3Ddet		code	88.09 %	91.40 %	82.96 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
L. Du, X. Ye, X. Tan, J. Feng, Z. Xu, E. Ding and S. Wen: Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection. The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
148	HotSpotNet			88.09 %	94.06 %	83.24 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
149	Faraway-Frustum		code	88.08 %	91.90 %	85.35 %	0.1 s	GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-frustum: Dealing with lidar sparsity for 3D object detection using fusion. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) 2021.
150	SVFMamba		code	88.06 %	91.61 %	84.96 %	N/A s	1 core @ 2.5 Ghz (C/C++)
151	UberATG-HDNET			87.98 %	93.13 %	81.23 %	0.05 s	GPU @ 2.5 Ghz (Python)
B. Yang, M. Liang and R. Urtasun: HDNET: Exploiting HD Maps for 3D Object Detection. 2nd Conference on Robot Learning (CoRL) 2018.
152	work6_new1			87.93 %	91.28 %	84.58 %	0.5 s	GPU @ 2.5 Ghz (Python)
153	Fade 3D		code	87.85 %	93.53 %	82.75 %	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.
154	HMNet			87.85 %	91.89 %	84.65 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
155	Fast Point R-CNN			87.84 %	90.87 %	80.52 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, S. Liu, X. Shen and J. Jia: Fast Point R-CNN. Proceedings of the IEEE international conference on computer vision (ICCV) 2019.
156	MMLab-PartA^2		code	87.79 %	91.70 %	84.61 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
157	SIF			87.76 %	91.44 %	85.15 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
158	MVAF-Net		code	87.73 %	91.95 %	85.00 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
G. Wang, B. Tian, Y. Zhang, L. Chen, D. Cao and J. Wu: Multi-View Adaptive Fusion Network for 3D Object Detection. arXiv preprint arXiv:2011.00652 2020.
159	NoText_VLGCL		code	87.71 %	93.84 %	85.21 %	0.2 s	1 core @ 2.5 Ghz (Python)
160	MSFASA-3DNet			87.70 %	91.35 %	84.38 %	0.5 s	GPU @ 2.5 Ghz (Python)
161	geo-pillars			87.68 %	91.50 %	84.06 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
162	DVFENet			87.68 %	90.93 %	84.60 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection. Neurocomputing 2021.
163	S-AT GCN			87.68 %	90.85 %	84.20 %	0.02 s	GPU @ 2.0 Ghz (Python)
L. Wang, C. Wang, X. Zhang, T. Lan and J. Li: S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection. CoRR 2021.
164	RangeDet (Official)		code	87.67 %	90.93 %	82.92 %	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.
165	XPillars			87.63 %	90.82 %	82.78 %	0.02 s	GPU @ 2.5 Ghz (Python)
166	MODet			87.56 %	90.80 %	82.69 %	0.05 s	GTX1080Ti
Y. Zhang, Z. Xiang, C. Qiao and S. Chen: Accurate and Real-Time Object Detection Based on Bird's Eye View on 3D Point Clouds. 2019 International Conference on 3D Vision (3DV) 2019.
167	AB3DMOT		code	87.53 %	91.99 %	81.03 %	0.0047s	1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
168	DSFNet			87.52 %	90.95 %	82.11 %	0.5 s	GPU @ 2.5 Ghz (Python)
169	CS3D			87.51 %	90.76 %	84.22 %	0.5 s	1 core @ 2.5 Ghz (Python)
170	SeSame-point		code	87.49 %	90.84 %	83.77 %	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.
171	PointRGCN			87.49 %	91.63 %	80.73 %	0.26 s	GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
172	MGAF-3DSSD		code	87.47 %	92.70 %	82.19 %	0.1 s	1 core @ 2.5 Ghz (Python)
J. Li, H. Dai, L. Shao and Y. Ding: Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
173	PC-CNN-V2			87.40 %	91.19 %	79.35 %	0.5 s	GPU @ 2.5 Ghz (Matlab + C/C++)
X. Du, M. Ang, S. Karaman and D. Rus: A General Pipeline for 3D Detection of Vehicles. 2018 IEEE International Conference on Robotics and Automation (ICRA) 2018.
174	MMLab-PointRCNN		code	87.39 %	92.13 %	82.72 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
175	Sem-Aug			87.37 %	93.35 %	82.43 %	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.
176	MAFF-Net(DAF-Pillar)			87.34 %	90.79 %	77.66 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
Z. Zhang, Z. Liang, M. Zhang, X. Zhao, Y. Ming, T. Wenming and S. Pu: MAFF-Net: Filter False Positive for 3D Vehicle Detection with Multi-modal Adaptive Feature Fusion. arXiv preprint arXiv:2009.10945 2020.
177	Harmonic PointPillar		code	87.28 %	90.89 %	82.54 %	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.
178	PASS-PointPillar			87.23 %	91.07 %	81.98 %	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	HRI-VoxelFPN			87.21 %	92.75 %	79.82 %	0.02 s	GPU @ 2.5 Ghz (Python + C/C++)
H. Kuang, B. Wang, J. An, M. Zhang and Z. Zhang: Voxel-FPN:multi-scale voxel feature aggregation in 3D object detection from point clouds. sensors 2020.
180	PCNet3D++			87.17 %	90.13 %	83.10 %	0.5 s	GPU @ 3.5 Ghz (Python)
181	epBRM		code	87.13 %	90.70 %	81.92 %	0.1 s	GPU @ >3.5 Ghz (Python + C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
182	AARMOD			87.02 %	92.59 %	79.64 %	0.1 s	1 core @ 2.5 Ghz (Python)
183	M3DNet			86.97 %	90.27 %	81.95 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
184	SARPNET			86.92 %	92.21 %	81.68 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
Y. Ye, H. Chen, C. Zhang, X. Hao and Z. Zhang: SARPNET: Shape Attention Regional Proposal Network for LiDAR-based 3D Object Detection. Neurocomputing 2019.
185	SA V1			86.91 %	89.49 %	83.82 %	0.5 s	GPU @ 2.5 Ghz (Python)
186	SeSame-pillar		code	86.88 %	90.61 %	81.93 %	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.
187	ARPNET			86.81 %	90.06 %	79.41 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
188	C-GCN			86.78 %	91.11 %	80.09 %	0.147 s	GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
189	PointPillars		code	86.56 %	90.07 %	82.81 %	16 ms	1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
190	TANet		code	86.54 %	91.58 %	81.19 %	0.035s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
191	PCNet3D			86.54 %	90.09 %	81.43 %	0.05 s	GPU @ 3.5 Ghz (Python)
192	T-SSD			86.50 %	92.50 %	81.30 %	0.04	1 core @ 2.0 Ghz (C/C++)
193	SCNet			86.48 %	90.07 %	81.30 %	0.04 s	GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
194	EAEPNet			86.43 %	92.74 %	81.40 %	0.1 s	1 core @ 2.5 Ghz (Python)
195	SegVoxelNet			86.37 %	91.62 %	83.04 %	0.04 s	1 core @ 2.5 Ghz (Python)
H. Yi, S. Shi, M. Ding, J. Sun, K. Xu, H. Zhou, Z. Wang, S. Li and G. Wang: SegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloud. ICRA 2020.
196	DensePointPillars			86.31 %	92.13 %	81.12 %	0.03 s	GPU @ 2.5 Ghz (Python)
197	3D IoU Loss			86.22 %	91.36 %	81.20 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
D. Zhou, J. Fang, X. Song, C. Guan, J. Yin, Y. Dai and R. Yang: IoU Loss for 2D/3D Object Detection. International Conference on 3D Vision (3DV) 2019.
198	SeSame-pillar w/scor		code	86.11 %	90.43 %	81.38 %	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.
199	R50_SACINet			86.10 %	91.70 %	83.15 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
200	R-GCN			86.05 %	91.91 %	81.05 %	0.16 s	GPU @ 2.5 Ghz (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
201	UberATG-PIXOR++			86.01 %	93.28 %	80.11 %	0.035 s	GPU @ 2.5 Ghz (Python)
B. Yang, M. Liang and R. Urtasun: HDNET: Exploiting HD Maps for 3D Object Detection. 2nd Conference on Robot Learning (CoRL) 2018.
202	HINTED		code	86.01 %	90.61 %	79.29 %	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.
203	L_SACINet			85.99 %	91.21 %	81.05 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
204	PointPillars_mmdet3d			85.96 %	91.25 %	80.82 %	0.03 s	1 core @ 2.5 Ghz (Python)
205	PL++: PV-RCNN++			85.89 %	91.76 %	81.29 %	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.
206	Sem-Aug-PointRCNN++			85.88 %	91.68 %	83.37 %	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.
207	DASS			85.85 %	91.74 %	80.97 %	0.09 s	1 core @ 2.0 Ghz (Python)
O. Unal, L. Van Gool and D. Dai: Improving Point Cloud Semantic Segmentation by Learning 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2021.
208	F-ConvNet		code	85.84 %	91.51 %	76.11 %	0.47 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
209	SecAtten			85.84 %	91.32 %	82.57 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
210	PI-RCNN			85.81 %	91.44 %	81.00 %	0.1 s	1 core @ 2.5 Ghz (Python)
L. Xie, C. Xiang, Z. Yu, G. Xu, Z. Yang, D. Cai and X. He: PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module. AAAI 2020 : The Thirty-Fourth AAAI Conference on Artificial Intelligence 2020.
211	PointRGBNet			85.73 %	91.39 %	80.68 %	0.08 s	4 cores @ 2.5 Ghz (Python + C/C++)
P. Xie Desheng: Real-time Detection of 3D Objects Based on Multi-Sensor Information Fusion. Automotive Engineering 2022.
212	SeSame-voxel		code	85.62 %	89.86 %	80.95 %	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.
213	UberATG-ContFuse			85.35 %	94.07 %	75.88 %	0.06 s	GPU @ 2.5 Ghz (Python)
M. Liang, B. Yang, S. Wang and R. Urtasun: Deep Continuous Fusion for Multi-Sensor 3D Object Detection. ECCV 2018.
214	PFF3D		code	85.08 %	89.61 %	80.42 %	0.05 s	GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
215	AVOD		code	84.95 %	89.75 %	78.32 %	0.08 s	Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
216	WS3D			84.93 %	90.96 %	77.96 %	0.1 s	GPU @ 2.5 Ghz (Python)
Q. Meng, W. Wang, T. Zhou, J. Shen, L. Van Gool and D. Dai: Weakly Supervised 3D Object Detection from Lidar Point Cloud. 2020.
217	AVOD-FPN		code	84.82 %	90.99 %	79.62 %	0.1 s	Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
218	F-PointNet		code	84.67 %	91.17 %	74.77 %	0.17 s	GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
219	AEPF			84.63 %	89.99 %	80.02 %	0.05 s	GPU @ 2.5 Ghz (Python)
220	mmFUSION		code	84.60 %	90.35 %	79.82 %	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.
221	3DBN			83.94 %	89.66 %	76.50 %	0.13s	1080Ti (Python+C/C++)
X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.
222	EOTL		code	83.14 %	89.10 %	71.41 %	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.
223	MLOD		code	82.68 %	90.25 %	77.97 %	0.12 s	GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
224	BirdNet+		code	81.85 %	87.43 %	75.36 %	0.11 s	Titan Xp (PyTorch)
A. Barrera, J. Beltrán, C. Guindel, J. Iglesias and F. García: BirdNet+: Two-Stage 3D Object Detection in LiDAR through a Sparsity-Invariant Bird’s Eye View. IEEE Access 2021.
225	CPD++(unsupervised)		code	81.62 %	90.22 %	76.56 %	0.1 s	GPU @ >3.5 Ghz (Python)
226	DMF			80.29 %	84.64 %	76.05 %	0.2 s	1 core @ 2.5 Ghz (Python + C/C++)
X. J. Chen and W. Xu: Disparity-Based Multiscale Fusion Network for Transportation Detection. IEEE Transactions on Intelligent Transportation Systems 2022.
227	UberATG-PIXOR			80.01 %	83.97 %	74.31 %	0.035 s	TITAN Xp (Python)
B. Yang, W. Luo and R. Urtasun: PIXOR: Real-time 3D Object Detection from Point Clouds. CVPR 2018.
228	MV3D (LIDAR)			78.98 %	86.49 %	72.23 %	0.24 s	GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
229	DSGN++		code	78.94 %	88.55 %	69.74 %	0.2 s	GeForce RTX 2080Ti
Y. Chen, S. Huang, S. Liu, B. Yu and J. Jia: DSGN++: Exploiting Visual-Spatial Relation for Stereo-Based 3D Detectors. IEEE Transactions on Pattern Analysis and Machine Intelligence 2022.
230	MV3D			78.93 %	86.62 %	69.80 %	0.36 s	GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
231	StereoDistill			78.59 %	89.03 %	69.34 %	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.
232	MMLAB LIGA-Stereo		code	76.78 %	88.15 %	67.40 %	0.4 s	1 core @ 2.5 Ghz (Python + C/C++)
X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
233	RCD			75.83 %	82.26 %	69.61 %	0.1 s	GPU @ 2.5 Ghz (Python)
A. Bewley, P. Sun, T. Mensink, D. Anguelov and C. Sminchisescu: Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection. Conference on Robot Learning (CoRL) 2020.
234	LaserNet			74.52 %	79.19 %	68.45 %	12 ms	GPU @ 2.5 Ghz (C/C++)
G. Meyer, A. Laddha, E. Kee, C. Vallespi-Gonzalez and C. Wellington: LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
235	PL++ (SDN+GDC)		code	73.80 %	84.61 %	65.59 %	0.6 s	GPU @ 2.5 Ghz (C/C++)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.
236	SNVC		code	73.61 %	86.88 %	64.49 %	1 s	GPU @ 1.0 Ghz (Python)
S. Li, Z. Liu, Z. Shen and K. Cheng: Stereo Neural Vernier Caliper. Proceedings of the AAAI Conference on Artificial Intelligence 2022.
237	A3DODWTDA		code	73.26 %	79.58 %	62.77 %	0.08 s	GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
238	Complexer-YOLO			68.96 %	77.24 %	64.95 %	0.06 s	GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
239	TopNet-Retina			68.16 %	80.16 %	63.43 %	52ms	GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
240	SeSame-point w/score		code	67.18 %	83.44 %	57.68 %	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.
241	CG-Stereo			66.44 %	85.29 %	58.95 %	0.57 s	GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
242	PLUME			66.27 %	82.97 %	56.70 %	0.15 s	GPU @ 2.5 Ghz (Python)
Y. Wang, B. Yang, R. Hu, M. Liang and R. Urtasun: PLUME: Efficient 3D Object Detection from Stereo Images. IROS 2021.
243	CDN		code	66.24 %	83.32 %	57.65 %	0.6 s	GPU @ 2.5 Ghz (Python)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems (NeurIPS) 2020.
244	DSGN		code	65.05 %	82.90 %	56.60 %	0.67 s	NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
245	CPD(unsupervised)		code	64.77 %	81.68 %	61.60 %	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.
246	TopNet-DecayRate			64.60 %	79.74 %	58.04 %	92 ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
247	SeSame-voxel w/score		code	63.36 %	71.98 %	57.52 %	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.
248	BirdNet+ (legacy)		code	63.33 %	84.80 %	61.23 %	0.1 s	Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird’s Eye View. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
249	3D FCN			61.67 %	70.62 %	55.61 %	>5 s	1 core @ 2.5 Ghz (C/C++)
B. Li: 3D Fully Convolutional Network for Vehicle Detection in Point Cloud. IROS 2017.
250	CDN-PL++			61.04 %	81.27 %	52.84 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems 2020.
251	BirdNet			59.83 %	84.17 %	57.35 %	0.11 s	Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
252	TopNet-UncEst			59.67 %	72.05 %	51.67 %	0.09 s	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
253	RT3D-GMP			59.00 %	69.14 %	45.49 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
254	Disp R-CNN (velo)		code	58.62 %	79.76 %	47.73 %	0.387 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
255	ESGN			58.12 %	78.10 %	49.28 %	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.
256	Pseudo-LiDAR++		code	58.01 %	78.31 %	51.25 %	0.4 s	GPU @ 2.5 Ghz (Python)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.
257	Disp R-CNN		code	57.98 %	79.61 %	47.09 %	0.387 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
258	ZoomNet		code	54.91 %	72.94 %	44.14 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
L. Z. Xu: ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2020.
259	VoxelJones		code	53.96 %	66.21 %	47.66 %	.18 s	1 core @ 2.5 Ghz (Python + C/C++)
M. Motro and J. Ghosh: Vehicular Multi-object Tracking with Persistent Detector Failures. arXiv preprint arXiv:1907.11306 2019.
260	TopNet-HighRes			53.05 %	67.84 %	46.99 %	101ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
261	OC Stereo		code	51.47 %	68.89 %	42.97 %	0.35 s	1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
262	YOLOStereo3D		code	50.28 %	76.10 %	36.86 %	0.1 s	GPU 1080Ti
Y. Liu, L. Wang and M. Liu: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
263	RT3DStereo			46.82 %	58.81 %	38.38 %	0.08 s	GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
264	Pseudo-Lidar		code	45.00 %	67.30 %	38.40 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Wang, W. Chao, D. Garg, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
265	RT3D			44.00 %	56.44 %	42.34 %	0.09 s	GPU @ 1.8Ghz
Y. Zeng, Y. Hu, S. Liu, J. Ye, Y. Han, X. Li and N. Sun: RT3D: Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving. IEEE Robotics and Automation Letters 2018.
266	Stereo CenterNet			42.12 %	62.97 %	35.37 %	0.04 s	GPU @ 2.5 Ghz (Python)
Y. Shi, Y. Guo, Z. Mi and X. Li: Stereo CenterNet-based 3D object detection for autonomous driving. Neurocomputing 2022.
267	Stereo R-CNN		code	41.31 %	61.92 %	33.42 %	0.3 s	GPU @ 2.5 Ghz (Python)
P. Li, X. Chen and S. Shen: Stereo R-CNN based 3D Object Detection for Autonomous Driving. CVPR 2019.
268	DA3D+KM3D+v2-99		code	34.88 %	44.27 %	30.29 %	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.
269	test_det			33.51 %	35.50 %	30.93 %	-1 s	1 core @ 2.5 Ghz (C/C++)
270	CIE + DM3D			33.13 %	46.17 %	28.80 %	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.
271	StereoFENet			32.96 %	49.29 %	25.90 %	0.15 s	1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
272	MonoDTF			28.92 %	42.67 %	25.89 %	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.
273	Mobile Stereo R-CNN			28.78 %	44.51 %	22.30 %	1.8 s	NVIDIA Jetson TX2
M. Hussein, M. Khalil and B. Abdullah: 3D Object Detection using Mobile Stereo R- CNN on Nvidia Jetson TX2. International Conference on Advanced Engineering, Technology and Applications (ICAETA) 2021.
274	DA3D+KM3D		code	28.71 %	39.50 %	25.20 %	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.
275	CIE			28.50 %	41.41 %	23.88 %	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.
276	MonoMH		code	28.06 %	37.85 %	24.53 %	0.04 s	1 core @ 2.5 Ghz (Python)
277	M3D			27.60 %	38.62 %	24.09 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
278	STLM3D			27.53 %	37.93 %	24.22 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
279	zqd			27.11 %	41.72 %	23.36 %	0.1 s	1 core @ 2.5 Ghz (Python)
280	MonoCoP-Car			27.04 %	38.30 %	23.59 %	0.01 s	GPU @ 2.5 Ghz (Python)
281	DA3D		code	26.92 %	36.83 %	23.41 %	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.
282	MonoLiG		code	26.83 %	35.73 %	24.24 %	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.
283	MonoGLS			26.72 %	37.13 %	23.10 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
284	MonoCoP			26.60 %	37.58 %	23.15 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
285	MM3D			26.58 %	38.80 %	23.07 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
286	MDD-M3D-X			26.57 %	38.33 %	23.74 %	0.01 s	1 core @ 2.5 Ghz (Python)
287	SSM3D			26.48 %	38.25 %	23.01 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
288	M5_3D			26.48 %	38.60 %	22.95 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
289	MonoHPE			26.42 %	36.17 %	23.15 %	0.04 s	1 core @ 2.5 Ghz (Python)
290	zqd_test2			26.21 %	41.36 %	22.64 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
291	MonoHPE-Mask			26.11 %	35.43 %	22.83 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
292	AM			26.05 %	36.38 %	22.87 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
293	MonOri		code	26.00 %	37.86 %	23.10 %	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.
294	MonoLSS			25.95 %	34.89 %	22.59 %	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.
295	H3			25.89 %	35.59 %	22.54 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
296	DetAny3D		code	25.83 %	36.71 %	21.94 %	0.58 s	1 core @ 2.5 Ghz (Python)
297	MonoAFKD			25.83 %	34.57 %	22.47 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
298	CMKD		code	25.82 %	38.98 %	22.80 %	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.
299	IDEAL-M3D 60%			25.44 %	35.33 %	22.25 %	0.04 s	1 core @ 2.5 Ghz (Python)
300	AMNet+DDAD15M		code	25.40 %	34.68 %	22.85 %	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.
301	UniCuboid			25.25 %	35.64 %	22.09 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
302	GATE3D		code	25.06 %	33.94 %	22.04 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
303	BEVHeight++		code	24.90 %	37.51 %	20.93 %	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.
304	AMNet		code	24.84 %	34.71 %	22.14 %	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.
305	MonoQ			24.82 %	35.77 %	21.37 %	0.02 s	1 core @ 2.5 Ghz (Python)
306	PS-SVDM			24.82 %	38.18 %	20.89 %	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.
307	LPCG-Monoflex		code	24.81 %	35.96 %	21.86 %	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.
308	Monohan			24.80 %	32.49 %	21.70 %	0.05 s	1 core @ 2.5 Ghz (Python)
309	MonoSC			24.74 %	32.93 %	21.38 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
310	NeurOCS			24.49 %	37.27 %	20.89 %	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.
311	Mix-Teaching		code	24.23 %	35.74 %	20.80 %	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.
312	MonoSKD		code	24.08 %	37.12 %	20.37 %	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.
313	MonoSample (DID-M3D)		code	23.94 %	37.64 %	20.46 %	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.
314	PS-fld		code	23.76 %	32.64 %	20.64 %	0.25 s	1 core @ 2.5 Ghz (C/C++)
Y. Chen, H. Dai and Y. Ding: Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
315	ADD		code	23.58 %	35.20 %	20.08 %	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 .
316	MonoNeRD		code	23.46 %	31.13 %	20.97 %	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.
317	MonoDDE			23.46 %	33.58 %	20.37 %	0.04 s	1 core @ 2.5 Ghz (Python)
Z. Li, Z. Qu, Y. Zhou, J. Liu, H. Wang and L. Jiang: Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection. CVPR 2022.
318	DD3D		code	23.41 %	32.35 %	20.42 %	n/a s	1 core @ 2.5 Ghz (C/C++)
D. Park, R. Ambrus, V. Guizilini, J. Li and A. Gaidon: Is Pseudo-Lidar needed for Monocular 3D Object detection?. IEEE/CVF International Conference on Computer Vision (ICCV) .
319	MonoUNI		code	23.05 %	33.28 %	19.39 %	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.
320	zqd_test			23.00 %	35.01 %	20.99 %	0.2 s	1 core @ 2.5 Ghz (Python)
321	LLW			22.86 %	38.51 %	19.26 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
322	MonoCD		code	22.81 %	33.41 %	19.57 %	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.
323	MonoFRD			22.77 %	29.65 %	20.41 %	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.
324	DID-M3D		code	22.76 %	32.95 %	19.83 %	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.
325	OPA-3D		code	22.53 %	33.54 %	19.22 %	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.
326	DCD		code	21.50 %	32.55 %	18.25 %	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.
327	MonoDETR		code	21.45 %	32.20 %	18.68 %	0.04 s	1 core @ 2.5 Ghz (Python)
R. Zhang, H. Qiu, T. Wang, X. Xu, Z. Guo, Y. Qiao, P. Gao and H. Li: MonoDETR: Depth-aware Transformer for Monocular 3D Object Detection. arXiv preprint arXiv:2203.13310 2022.
328	SGM3D		code	21.37 %	31.49 %	18.43 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
Z. Zhou, L. Du, X. Ye, Z. Zou, X. Tan, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection. RA-L 2022.
329	Cube R-CNN		code	21.20 %	31.70 %	18.43 %	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.
330	GUPNet		code	21.19 %	30.29 %	18.20 %	NA s	1 core @ 2.5 Ghz (Python + C/C++)
Y. Lu, X. Ma, L. Yang, T. Zhang, Y. Liu, Q. Chu, J. Yan and W. Ouyang: Geometry Uncertainty Projection Network for Monocular 3D Object Detection. arXiv preprint arXiv:2107.13774 2021.
331	HomoLoss(monoflex)		code	20.68 %	29.60 %	17.81 %	0.04 s	1 core @ 2.5 Ghz (Python)
J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homography Loss for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
332	DEVIANT		code	20.44 %	29.65 %	17.43 %	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.
333	MonoDTR			20.38 %	28.59 %	17.14 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
K. Huang, T. Wu, H. Su and W. Hsu: MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer. CVPR 2022.
334	MDSNet			20.14 %	32.81 %	15.77 %	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.
335	AutoShape		code	20.08 %	30.66 %	15.95 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
Z. Liu, D. Zhou, F. Lu, J. Fang and L. Zhang: AutoShape: Real-Time Shape-Aware Monocular 3D Object Detection. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.
336	temp			20.05 %	28.51 %	17.11 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
337	MonoFlex			19.75 %	28.23 %	16.89 %	0.03 s	GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.
338	MonoEF			19.70 %	29.03 %	17.26 %	0.03 s	1 core @ 2.5 Ghz (Python)
Y. Zhou, Y. He, H. Zhu, C. Wang, H. Li and Q. Jiang: Monocular 3D Object Detection: An Extrinsic Parameter Free Approach. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
339	MonOAPC			19.67 %	28.91 %	16.99 %	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.
340	MonoDSSMs-M			19.59 %	28.29 %	16.34 %	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.
341	MonoDSSMs-A			19.54 %	28.84 %	16.30 %	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.
342	HomoLoss(imvoxelnet)		code	19.25 %	29.18 %	16.21 %	0.20 s	1 core @ 2.5 Ghz (Python)
J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homogrpahy Loss for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
343	DFR-Net			19.17 %	28.17 %	14.84 %	0.18 s	1080 Ti (Pytorch)
Z. Zou, X. Ye, L. Du, X. Cheng, X. Tan, L. Zhang, J. Feng, X. Xue and E. Ding: The devil is in the task: Exploiting reciprocal appearance-localization features for monocular 3d object detection . ICCV 2021.
344	PS-SVDM			19.07 %	28.52 %	16.30 %	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.
345	DLE		code	19.05 %	31.09 %	14.13 %	0.06 s	NVIDIA Tesla V100
C. Liu, S. Gu, L. Gool and R. Timofte: Deep Line Encoding for Monocular 3D Object Detection and Depth Prediction. Proceedings of the British Machine Vision Conference (BMVC) 2021.
346	PCT		code	19.03 %	29.65 %	15.92 %	0.045 s	1 core @ 2.5 Ghz (C/C++)
L. Wang, L. Zhang, Y. Zhu, Z. Zhang, T. He, M. Li and X. Xue: Progressive Coordinate Transforms for Monocular 3D Object Detection. NeurIPS 2021.
347	CaDDN		code	18.91 %	27.94 %	17.19 %	0.63 s	GPU @ 2.5 Ghz (Python)
C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.
348	monodle		code	18.89 %	24.79 %	16.00 %	0.04 s	GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for Monocular 3D Object Detection. CVPR 2021 .
349	Neighbor-Vote			18.65 %	27.39 %	16.54 %	0.1 s	GPU @ 2.5 Ghz (Python)
X. Chu, J. Deng, Y. Li, Z. Yuan, Y. Zhang, J. Ji and Y. Zhang: Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance Voting. ACM MM 2021.
350	MonoRCNN++		code	18.62 %	27.20 %	15.69 %	0.07 s	GPU @ 2.5 Ghz (Python)
X. Shi, Z. Chen and T. Kim: Multivariate Probabilistic Monocular 3D Object Detection. WACV 2023.
351	GrooMeD-NMS		code	18.27 %	26.19 %	14.05 %	0.12 s	1 core @ 2.5 Ghz (Python)
A. Kumar, G. Brazil and X. Liu: GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection. CVPR 2021.
352	MonoRCNN		code	18.11 %	25.48 %	14.10 %	0.07 s	GPU @ 2.5 Ghz (Python)
X. Shi, Q. Ye, X. Chen, C. Chen, Z. Chen and T. Kim: Geometry-based Distance Decomposition for Monocular 3D Object Detection. ICCV 2021.
353	Ground-Aware		code	17.98 %	29.81 %	13.08 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
Y. Liu, Y. Yuan and M. Liu: Ground-aware Monocular 3D Object Detection for Autonomous Driving. IEEE Robotics and Automation Letters 2021.
354	Aug3D-RPN			17.89 %	26.00 %	14.18 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth. 2021.
355	DDMP-3D			17.89 %	28.08 %	13.44 %	0.18 s	1 core @ 2.5 Ghz (Python)
L. Wang, L. Du, X. Ye, Y. Fu, G. Guo, X. Xue, J. Feng and L. Zhang: Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection. CVPR 2020.
356	IAFA			17.88 %	25.88 %	15.35 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
D. Zhou, X. Song, Y. Dai, J. Yin, F. Lu, M. Liao, J. Fang and L. Zhang: IAFA: Instance-Aware Feature Aggregation for 3D Object Detection from a Single Image. Proceedings of the Asian Conference on Computer Vision 2020.
357	mdab			17.74 %	26.42 %	15.71 %	0.02 s	1 core @ 2.5 Ghz (Python)
358	FMF-occlusion-net			17.60 %	27.39 %	13.25 %	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.
359	RefinedMPL			17.60 %	28.08 %	13.95 %	0.15 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
360	Kinematic3D		code	17.52 %	26.69 %	13.10 %	0.12 s	1 core @ 1.5 Ghz (C/C++)
G. Brazil, G. Pons-Moll, X. Liu and B. Schiele: Kinematic 3D Object Detection in Monocular Video. ECCV 2020 .
361	MonoRUn		code	17.34 %	27.94 %	15.24 %	0.07 s	GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
362	AM3D			17.32 %	25.03 %	14.91 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
X. Ma, Z. Wang, H. Li, P. Zhang, W. Ouyang and X. Fan: Accurate Monocular Object Detection via Color- Embedded 3D Reconstruction for Autonomous Driving. Proceedings of the IEEE international Conference on Computer Vision (ICCV) 2019.
363	YoloMono3D		code	17.15 %	26.79 %	12.56 %	0.05 s	GPU @ 2.5 Ghz (Python)
Y. Liu, L. Wang and L. Ming: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
364	CMAN			17.04 %	25.89 %	12.88 %	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.
365	GAC3D			16.93 %	25.80 %	12.50 %	0.25 s	1 core @ 2.5 Ghz (Python)
M. Bui, D. Ngo, H. Pham and D. Nguyen: GAC3D: improving monocular 3D object detection with ground-guide model and adaptive convolution. 2021.
366	PatchNet		code	16.86 %	22.97 %	14.97 %	0.4 s	1 core @ 2.5 Ghz (C/C++)
X. Ma, S. Liu, Z. Xia, H. Zhang, X. Zeng and W. Ouyang: Rethinking Pseudo-LiDAR Representation. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
367	SAKD-MR-Res18			16.56 %	26.48 %	13.67 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
368	PGD-FCOS3D		code	16.51 %	26.89 %	13.49 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
T. Wang, X. Zhu, J. Pang and D. Lin: Probabilistic and Geometric Depth: Detecting Objects in Perspective. Conference on Robot Learning (CoRL) 2021.
369	ImVoxelNet		code	16.37 %	25.19 %	13.58 %	0.2 s	GPU @ 2.5 Ghz (Python)
D. Rukhovich, A. Vorontsova and A. Konushin: ImVoxelNet: Image to Voxels Projection for Monocular and Multi-View General-Purpose 3D Object Detection. arXiv preprint arXiv:2106.01178 2021.
370	KM3D		code	16.20 %	23.44 %	14.47 %	0.03 s	1 core @ 2.5 Ghz (Python)
P. Li: Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training. 2020.
371	D4LCN		code	16.02 %	22.51 %	12.55 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
372	MonoPair			14.83 %	19.28 %	12.89 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
373	Decoupled-3D			14.82 %	23.16 %	11.25 %	0.08 s	GPU @ 2.5 Ghz (C/C++)
Y. Cai, B. Li, Z. Jiao, H. Li, X. Zeng and X. Wang: Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation. AAAI 2020.
374	QD-3DT		code	14.71 %	20.16 %	12.76 %	0.03 s	GPU @ 2.5 Ghz (Python)
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.
375	SMOKE		code	14.49 %	20.83 %	12.75 %	0.03 s	GPU @ 2.5 Ghz (Python)
Z. Liu, Z. Wu and R. Tóth: SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation. 2020.
376	RTM3D		code	14.20 %	19.17 %	11.99 %	0.05 s	GPU @ 1.0 Ghz (Python)
P. Li, H. Zhao, P. Liu and F. Cao: RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving. 2020.
377	Mono3D_PLiDAR		code	13.92 %	21.27 %	11.25 %	0.1 s	NVIDIA GeForce 1080 (pytorch)
X. Weng and K. Kitani: Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud. arXiv:1903.09847 2019.
378	M3D-RPN		code	13.67 %	21.02 %	10.23 %	0.16 s	GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
379	CSoR			13.07 %	18.67 %	10.34 %	3.5 s	4 cores @ >3.5 Ghz (Python + C/C++)
L. Plotkin: PyDriver: Entwicklung eines Frameworks für räumliche Detektion und Klassifikation von Objekten in Fahrzeugumgebung. 2015.
380	MonoPSR		code	12.58 %	18.33 %	9.91 %	0.2 s	GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
381	Plane-Constraints		code	12.06 %	17.31 %	10.05 %	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.
382	MonoCInIS			11.64 %	22.28 %	9.95 %	0,13 s	GPU @ 2.5 Ghz (C/C++)
J. Heylen, M. De Wolf, B. Dawagne, M. Proesmans, L. Van Gool, W. Abbeloos, H. Abdelkawy and D. Reino: MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.
383	SS3D			11.52 %	16.33 %	9.93 %	48 ms	Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
384	MonoGRNet		code	11.17 %	18.19 %	8.73 %	0.04s	NVIDIA P40
Z. Qin, J. Wang and Y. Lu: MonoGRNet: A Geometric Reasoning Network for 3D Object Localization. The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) 2019.
385	MonoFENet			11.03 %	17.03 %	9.05 %	0.15 s	1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
386	MonoCInIS			10.96 %	20.42 %	9.23 %	0,14 s	GPU @ 2.5 Ghz (Python)
J. Heylen, M. De Wolf, B. Dawagne, M. Proesmans, L. Van Gool, W. Abbeloos, H. Abdelkawy and D. Reino: MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.
387	monospb			10.83 %	14.92 %	9.55 %	0.01 s	1 core @ 2.5 Ghz (Python)
388	A3DODWTDA (image)		code	8.66 %	10.37 %	7.06 %	0.8 s	GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
389	TLNet (Stereo)		code	7.69 %	13.71 %	6.73 %	0.1 s	1 core @ 2.5 Ghz (Python)
Z. Qin, J. Wang and Y. Lu: Triangulation Learning Network: from Monocular to Stereo 3D Object Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
390	Shift R-CNN (mono)		code	6.82 %	11.84 %	5.27 %	0.25 s	GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
391	SparVox3D			6.39 %	10.20 %	5.06 %	0.05 s	GPU @ 2.0 Ghz (Python)
E. Balatkan and F. Kıraç: Improving Regression Performance on Monocular 3D Object Detection Using Bin-Mixing and Sparse Voxel Data. 2021 6th International Conference on Computer Science and Engineering (UBMK) 2021.
392	GS3D			6.08 %	8.41 %	4.94 %	2 s	1 core @ 2.5 Ghz (C/C++)
B. Li, W. Ouyang, L. Sheng, X. Zeng and X. Wang: GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
393	MVRA + I-FRCNN+			5.84 %	9.05 %	4.50 %	0.18 s	GPU @ 2.5 Ghz (Python)
H. Choi, H. Kang and Y. Hyun: Multi-View Reprojection Architecture for Orientation Estimation. The IEEE International Conference on Computer Vision (ICCV) Workshops 2019.
394	WeakM3D		code	5.66 %	11.82 %	4.08 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
L. Peng, S. Yan, B. Wu, Z. Yang, X. He and D. Cai: WeakM3D: Towards Weakly Supervised Monocular 3D Object Detection. ICLR 2022.
395	ROI-10D			4.91 %	9.78 %	3.74 %	0.2 s	GPU @ 3.5 Ghz (Python)
F. Manhardt, W. Kehl and A. Gaidon: ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape. Computer Vision and Pattern Recognition (CVPR) 2019.
396	3D-GCK			4.57 %	5.79 %	3.64 %	24 ms	Tesla V100
N. Gählert, J. Wan, N. Jourdan, J. Finkbeiner, U. Franke and J. Denzler: Single-Shot 3D Detection of Vehicles from Monocular RGB Images via Geometrically Constrained Keypoints in Real-Time. 2020 IEEE Intelligent Vehicles Symposium (IV) 2020.
397	FQNet			3.23 %	5.40 %	2.46 %	0.5 s	1 core @ 2.5 Ghz (Python)
L. Liu, J. Lu, C. Xu, Q. Tian and J. Zhou: Deep Fitting Degree Scoring Network for Monocular 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
398	3D-SSMFCNN		code	2.63 %	3.20 %	2.40 %	0.1 s	GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.
399	VeloFCN			0.14 %	0.02 %	0.21 %	1 s	GPU @ 2.5 Ghz (Python + C/C++)
B. Li, T. Zhang and T. Xia: Vehicle Detection from 3D Lidar Using Fully Convolutional Network. RSS 2016 .
400	multi-task CNN			0.00 %	0.00 %	0.00 %	25.1 ms	GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
401	mBoW			0.00 %	0.00 %	0.00 %	10 s	1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.

Pedestrian

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	PiFeNet		code	53.92 %	63.25 %	50.53 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
D. Le, H. Shi, H. Rezatofighi and J. Cai: Accurate and Real-time 3D Pedestrian Detection Using an Efficient Attentive Pillar Network. IEEE Robotics and Automation Letters 2022.
2	CasA++		code	53.84 %	60.14 %	51.35 %	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.
3	TED		code	53.48 %	60.13 %	50.89 %	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.
4	UPIDet		code	53.32 %	58.91 %	50.82 %	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.
5	EQ-PVRCNN		code	52.81 %	61.73 %	49.87 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, L. Jiang, Y. Sun, B. Schiele and J. Jia: A Unified Query-based Paradigm for Point Cloud Understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.
6	PillarHist			52.77 %	62.52 %	49.29 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
7	VPFNet		code	52.41 %	60.07 %	50.28 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
C. Wang, H. Chen and L. Fu: VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection. 2021. 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.
8	Frustum-PointPillars		code	52.23 %	60.98 %	48.30 %	0.06 s	4 cores @ 3.0 Ghz (Python)
A. Paigwar, D. Sierra-Gonzalez, \. Erkent and C. Laugier: Frustum-PointPillars: A Multi-Stage Approach for 3D Object Detection using RGB Camera and LiDAR. International Conference on Computer Vision, ICCV, Workshop on Autonomous Vehicle Vision 2021.
9	LoGoNet		code	52.06 %	58.24 %	49.87 %	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.
10	ImagePG			51.77 %	58.37 %	48.23 %	1 s	1 core @ 2.5 Ghz (C/C++)
11	TANet		code	51.38 %	60.85 %	47.54 %	0.035s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
12	CasA		code	51.37 %	57.95 %	49.08 %	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.
13	AFFN-G			51.34 %	58.94 %	49.21 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
14	GEFPN			51.34 %	58.94 %	49.21 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
15	GeVo			51.34 %	58.94 %	49.21 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
16	MLF-DET			50.88 %	56.45 %	47.60 %	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.
17	BVIFusion+			50.78 %	57.56 %	47.31 %	0.09 s	1 core @ 2.5 Ghz (Python)
18	SVFMamba		code	50.68 %	60.47 %	48.07 %	N/A s	1 core @ 2.5 Ghz (C/C++)
19	MMLab PV-RCNN		code	50.57 %	59.86 %	46.74 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
20	DPPFA-Net			50.55 %	57.02 %	47.25 %	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.
21	HotSpotNet			50.53 %	57.39 %	46.65 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
22	LumiNet		code	50.44 %	57.64 %	46.74 %	0.1 s	1 core @ 2.5 Ghz (Python)
23	VMVS			50.34 %	60.34 %	46.45 %	0.25 s	GPU @ 2.5 Ghz (Python)
J. Ku, A. Pon, S. Walsh and S. Waslander: Improving 3D object detection for pedestrians with virtual multi-view synthesis orientation estimation. IROS 2019.
24	AVOD-FPN		code	50.32 %	58.49 %	46.98 %	0.1 s	Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
25	vsis-PHNet			50.23 %	59.41 %	47.62 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
26	PHNetp			50.23 %	59.41 %	47.62 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
27	CGML			50.20 %	56.57 %	48.12 %	0.33 s	1 core @ 2.5 Ghz (C/C++)
28	HA-PillarNet			50.06 %	57.38 %	47.76 %	0.05 s	1 core @ 2.5 Ghz (Python)
29	3DSSD		code	49.94 %	60.54 %	45.73 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
30	PointPainting			49.93 %	58.70 %	46.29 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
31	SemanticVoxels			49.93 %	58.91 %	47.31 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Fei, W. Chen, P. Heidenreich, S. Wirges and C. Stiller: SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentation. MFI 2020.
32	SFA_IGCL_Focalsconv*		code	49.82 %	57.49 %	47.56 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
33	ACDet		code	49.82 %	58.35 %	47.17 %	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.
34	MMLab-PartA^2		code	49.81 %	59.04 %	45.92 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
35	USVLab BSAODet		code	49.75 %	56.05 %	47.59 %	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.
36	ACFNet			49.74 %	58.07 %	47.27 %	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.
37	New_VLGCL		code	49.68 %	56.96 %	47.38 %	0.4 s	1 core @ 2.5 Ghz (Python)
38	dsvd+vx			49.68 %	58.10 %	47.07 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
39	F-PointNet		code	49.57 %	57.13 %	45.48 %	0.17 s	GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
40	VLGCL_NoText		code	49.31 %	56.49 %	47.20 %	0.3 s	1 core @ 2.5 Ghz (Python)
41	CEF		code	49.16 %	57.58 %	45.27 %	0.03 s	1 core @ 2.5 Ghz (Python)
42	R2Pfusion-Det			49.02 %	56.91 %	45.38 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
43	FocalsConv*			48.98 %	55.61 %	45.63 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
44	F-ConvNet		code	48.96 %	57.04 %	44.33 %	0.47 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
45	HVNet			48.86 %	54.84 %	46.33 %	0.03 s	GPU @ 2.0 Ghz (Python)
M. Ye, S. Xu and T. Cao: HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection. CVPR 2020.
46	CAT-Det			48.78 %	57.13 %	45.56 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Zhang, J. Chen and D. Huang: CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection. CVPR 2022.
47	STD		code	48.72 %	60.02 %	44.55 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
48	PointPillars		code	48.64 %	57.60 %	45.78 %	16 ms	1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
49	FIRM-Net_SCF+			48.61 %	55.81 %	46.25 %	0.07 s	1 core @ 2.5 Ghz (Python)
50	EPNet++			48.47 %	56.24 %	45.73 %	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.
51	FIRM-Net-SCF			48.47 %	55.70 %	46.08 %	0.07 s	1 core @ 2.5 Ghz (Python)
52	MGAF-3DSSD		code	48.46 %	56.09 %	44.90 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
53	Fast-CLOCs			48.27 %	57.19 %	44.55 %	0.1 s	GPU @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: Fast-CLOCs: Fast Camera-LiDAR Object Candidates Fusion for 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022.
54	FromVoxelToPoint		code	48.15 %	56.54 %	45.63 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to- Point Decoder. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
55	EOTL		code	47.80 %	56.52 %	43.36 %	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.
56	HMFI		code	47.77 %	55.61 %	45.17 %	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.
57	SpaA			47.67 %	53.74 %	45.55 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
58	P2V-RCNN			47.36 %	54.15 %	45.10 %	0.1 s	2 cores @ 2.5 Ghz (Python)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.
59	Voxel RCNN-Focal*		code	47.35 %	54.62 %	44.42 %	0.2 s	1 core @ 2.5 Ghz (Python)
60	Point-GNN		code	47.07 %	55.36 %	44.61 %	0.6 s	GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
61	3ONet			47.05 %	56.76 %	44.62 %	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.
62	SCNet			46.73 %	56.87 %	42.74 %	0.04 s	GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
63	HMNet			46.69 %	54.73 %	43.46 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
64	PASS-PV-RCNN-Plus			46.36 %	51.47 %	44.10 %	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.
65	MMLab-PointRCNN		code	46.13 %	54.77 %	42.84 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
66	ARPNET			45.92 %	55.48 %	42.54 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
67	WWW			45.86 %	53.53 %	43.63 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
68	DSA-PV-RCNN		code	45.82 %	52.03 %	43.81 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
69	DPFusion		code	45.78 %	53.33 %	43.53 %	0.07 s	1 core @ 2.5 Ghz (C/C++)
70	SVGA-Net			45.68 %	53.09 %	43.30 %	0.03s	1 core @ 2.5 Ghz (Python + C/C++)
Q. He, Z. Wang, H. Zeng, Y. Zeng and Y. Liu: SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds. AAAI 2022.
71	epBRM		code	45.49 %	52.48 %	42.75 %	0.10 s	1 core @ 2.5 Ghz (C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
72	PG-RCNN		code	45.48 %	51.63 %	43.30 %	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.
73	PDV		code	45.45 %	51.95 %	43.33 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
J. Hu, T. Kuai and S. Waslander: Point Density-Aware Voxels for LiDAR 3D Object Detection. CVPR 2022.
74	MLOD		code	45.40 %	55.09 %	41.42 %	0.12 s	GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
75	AFFN			45.26 %	50.50 %	43.21 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
76	CAIA_PRO		code	45.16 %	51.68 %	42.69 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
77	L_SACINet			45.13 %	55.35 %	42.62 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
78	IA-SSD (single)		code	45.07 %	52.73 %	42.75 %	0.013 s	1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
79	DFAF3D			45.01 %	52.86 %	42.73 %	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.
80	MPC3DNet			44.89 %	49.33 %	43.01 %	0.05 s	GPU @ 1.5 Ghz (Python)
81	AFFN-Ga			44.85 %	50.57 %	42.78 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
82	SRDL			44.84 %	52.42 %	42.56 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
83	M3DeTR		code	44.78 %	50.63 %	42.57 %	n/a s	GPU @ 1.0 Ghz (Python)
T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis and D. Manocha: M3DeTR: Multi-representation, Multi- scale, Mutual-relation 3D Object Detection with Transformers. 2021.
84	test			44.57 %	49.95 %	42.55 %	0.04 s	GPU @ 1.5 Ghz (Python + C/C++)
85	SIF			44.28 %	52.05 %	42.03 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
86	work6_new1			44.24 %	51.79 %	41.96 %	0.5 s	GPU @ 2.5 Ghz (Python)
87	NoText_VLGCL		code	44.18 %	51.36 %	41.91 %	0.2 s	1 core @ 2.5 Ghz (Python)
88	R50_SACINet			44.17 %	52.52 %	41.84 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
89	DVFENet			44.12 %	50.98 %	41.62 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection. Neurocomputing 2021.
90	Faraway-Frustum		code	43.85 %	52.15 %	41.68 %	0.1 s	GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-frustum: Dealing with lidar sparsity for 3D object detection using fusion. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) 2021.
91	S-AT GCN			43.43 %	50.63 %	41.58 %	0.02 s	GPU @ 2.0 Ghz (Python)
L. Wang, C. Wang, X. Zhang, T. Lan and J. Li: S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection. CoRR 2021.
92	XPillars			43.10 %	51.95 %	40.57 %	0.02 s	GPU @ 2.5 Ghz (Python)
93	SecAtten			42.89 %	51.42 %	40.28 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
94	BirdNet+		code	42.87 %	48.90 %	40.59 %	0.11 s	Titan Xp (PyTorch)
A. Barrera, J. Beltrán, C. Guindel, J. Iglesias and F. García: BirdNet+: Two-Stage 3D Object Detection in LiDAR through a Sparsity-Invariant Bird’s Eye View. IEEE Access 2021.
95	L-AUG			42.84 %	50.32 %	40.29 %	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.
96	AEPF			42.84 %	50.61 %	40.74 %	0.05 s	GPU @ 2.5 Ghz (Python)
97	PCNet3D++			42.81 %	50.62 %	40.36 %	0.5 s	GPU @ 3.5 Ghz (Python)
98	DSFNet			42.68 %	51.31 %	39.65 %	0.5 s	GPU @ 2.5 Ghz (Python)
99	IA-SSD (multi)		code	42.61 %	51.76 %	40.51 %	0.014 s	1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
100	SA V1			42.45 %	49.18 %	40.32 %	0.5 s	GPU @ 2.5 Ghz (Python)
101	XView			42.42 %	47.24 %	39.96 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
102	PCNet3D			42.19 %	50.02 %	39.69 %	0.05 s	GPU @ 3.5 Ghz (Python)
103	CS3D			42.05 %	49.61 %	39.90 %	0.5 s	1 core @ 2.5 Ghz (Python)
104	GraphAlign(ICCV2023)		code	41.95 %	46.61 %	40.05 %	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.
105	MSFASA-3DNet			41.94 %	48.10 %	40.05 %	0.5 s	GPU @ 2.5 Ghz (Python)
106	M3DNet			41.84 %	50.30 %	39.49 %	0.5 s	1 core @ 2.5 Ghz (C/C++)
107	SeSame-voxel		code	41.59 %	50.12 %	37.79 %	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.
108	HINTED		code	41.55 %	53.09 %	39.18 %	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.
109	SeSame-point		code	41.22 %	48.25 %	39.18 %	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.
110	PFF3D		code	40.94 %	48.74 %	38.54 %	0.05 s	GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
111	geo-pillars			40.91 %	47.95 %	38.49 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
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112	focal			40.56 %	46.08 %	38.76 %	100 s	1 core @ 2.5 Ghz (Python)
113	DensePointPillars			40.31 %	47.80 %	37.49 %	0.03 s	GPU @ 2.5 Ghz (Python)
114	Fade-kd			39.38 %	47.19 %	36.42 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
115	DSGN++		code	38.92 %	50.26 %	35.12 %	0.2 s	GeForce RTX 2080Ti
Y. Chen, S. Huang, S. Liu, B. Yu and J. Jia: DSGN++: Exploiting Visual-Spatial Relation for Stereo-Based 3D Detectors. IEEE Transactions on Pattern Analysis and Machine Intelligence 2022.
116	AB3DMOT		code	38.79 %	47.51 %	35.85 %	0.0047s	1 core @ 2.5 Ghz (python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
117	BirdNet+ (legacy)		code	38.28 %	45.53 %	35.37 %	0.1 s	Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird’s Eye View. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
118	PointPillars_mmdet3d			38.07 %	45.74 %	35.76 %	0.03 s	1 core @ 2.5 Ghz (Python)
119	PL++: PV-RCNN++			37.96 %	44.88 %	35.48 %	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.
120	CSW3D			37.96 %	49.27 %	33.83 %	0.03 s	4 cores @ 2.5 Ghz (C/C++)
J. Hu, T. Wu, H. Fu, Z. Wang and K. Ding: Cascaded Sliding Window Based Real-Time 3D Region Proposal for Pedestrian Detection. ROBIO 2019.
121	StereoDistill			37.75 %	50.79 %	34.28 %	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.
122	SeSame-pillar		code	37.31 %	44.21 %	35.17 %	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.
123	T-SSD			35.17 %	43.33 %	33.02 %	0.04	1 core @ 2.0 Ghz (C/C++)
124	DMF			34.92 %	42.08 %	32.69 %	0.2 s	1 core @ 2.5 Ghz (Python + C/C++)
X. J. Chen and W. Xu: Disparity-Based Multiscale Fusion Network for Transportation Detection. IEEE Transactions on Intelligent Transportation Systems 2022.
125	SparsePool		code	34.15 %	43.33 %	31.78 %	0.13 s	8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
126	MMLAB LIGA-Stereo		code	34.13 %	44.71 %	30.42 %	0.4 s	1 core @ 2.5 Ghz (Python + C/C++)
X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
127	SeSame-voxel w/score		code	33.76 %	39.42 %	31.31 %	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.
128	AVOD		code	33.57 %	42.58 %	30.14 %	0.08 s	Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
129	SparsePool		code	33.22 %	41.55 %	29.66 %	0.13 s	8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
130	SeSame-pillar w/scor		code	32.78 %	39.11 %	30.87 %	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.
131	CG-Stereo			29.56 %	39.24 %	25.87 %	0.57 s	GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
132	PointRGBNet			29.32 %	38.07 %	26.94 %	0.08 s	4 cores @ 2.5 Ghz (Python + C/C++)
P. Xie Desheng: Real-time Detection of 3D Objects Based on Multi-Sensor Information Fusion. Automotive Engineering 2022.
133	Fade 3D		code	29.18 %	36.14 %	27.42 %	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.
134	Disp R-CNN		code	29.12 %	42.72 %	25.09 %	0.387 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
135	Disp R-CNN (velo)		code	28.34 %	40.21 %	24.46 %	0.387 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
136	SeSame-point w/score		code	25.79 %	33.98 %	22.50 %	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.
137	CPD++(unsupervised)		code	24.43 %	28.36 %	23.22 %	0.1 s	GPU @ >3.5 Ghz (Python)
138	BirdNet			23.06 %	28.20 %	21.65 %	0.11 s	Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
139	OC Stereo		code	20.80 %	29.79 %	18.62 %	0.35 s	1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
140	YOLOStereo3D		code	20.76 %	31.01 %	18.41 %	0.1 s	GPU 1080Ti
Y. Liu, L. Wang and M. Liu: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
141	DSGN		code	20.75 %	26.61 %	18.86 %	0.67 s	NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
142	Complexer-YOLO			18.26 %	21.42 %	17.06 %	0.06 s	GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
143	TopNet-Retina			14.57 %	18.04 %	12.48 %	52ms	GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
144	MonOri		code	14.35 %	20.86 %	12.45 %	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.
145	RT3D-GMP			14.22 %	19.92 %	12.83 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
146	TopNet-HighRes			13.50 %	19.43 %	11.93 %	101ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
147	ESGN			13.03 %	17.94 %	11.54 %	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.
148	MonoMH		code	12.70 %	18.63 %	11.06 %	0.04 s	1 core @ 2.5 Ghz (Python)
149	MDD-M3D-X			12.53 %	19.10 %	10.78 %	0.01 s	1 core @ 2.5 Ghz (Python)
150	DD3D		code	12.51 %	18.58 %	10.65 %	n/a s	1 core @ 2.5 Ghz (C/C++)
D. Park, R. Ambrus, V. Guizilini, J. Li and A. Gaidon: Is Pseudo-Lidar needed for Monocular 3D Object detection?. IEEE/CVF International Conference on Computer Vision (ICCV) .
151	MonoLSS			12.34 %	18.40 %	10.54 %	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.
152	MonoAFKD			12.30 %	18.37 %	10.47 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
153	PS-fld		code	12.23 %	19.03 %	10.53 %	0.25 s	1 core @ 2.5 Ghz (C/C++)
Y. Chen, H. Dai and Y. Ding: Pseudo-Stereo for Monocular 3D Object Detection in Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
154	MonoHPE			11.96 %	17.89 %	10.14 %	0.04 s	1 core @ 2.5 Ghz (Python)
155	CIE			11.94 %	17.90 %	10.34 %	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.
156	MonoHPE-Mask			11.91 %	17.30 %	10.09 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
157	AM			11.87 %	17.55 %	10.21 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
158	MonoCoP			11.80 %	17.69 %	10.22 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
159	CPD(unsupervised)		code	11.09 %	13.48 %	10.19 %	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.
160	OPA-3D		code	11.01 %	17.14 %	9.94 %	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.
161	MonoUNI		code	10.90 %	16.54 %	9.17 %	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.
162	MonoGLS			10.79 %	16.46 %	9.67 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
163	IDEAL-M3D 60%			10.65 %	16.74 %	8.83 %	0.04 s	1 core @ 2.5 Ghz (Python)
164	MonoDTR			10.59 %	16.66 %	9.00 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
K. Huang, T. Wu, H. Su and W. Hsu: MonoDTR: Monocular 3D Object Detection with Depth-Aware Transformer. CVPR 2022.
165	MonoFRD			10.38 %	15.68 %	8.79 %	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.
166	GUPNet		code	10.37 %	15.62 %	8.79 %	NA s	1 core @ 2.5 Ghz (Python + C/C++)
Y. Lu, X. Ma, L. Yang, T. Zhang, Y. Liu, Q. Chu, J. Yan and W. Ouyang: Geometry Uncertainty Projection Network for Monocular 3D Object Detection. arXiv preprint arXiv:2107.13774 2021.
167	CMKD		code	10.28 %	16.03 %	8.85 %	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.
168	DEVIANT		code	9.77 %	14.49 %	8.28 %	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.
169	PS-SVDM			9.75 %	15.03 %	8.37 %	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.
170	MonoNeRD		code	9.66 %	15.27 %	8.28 %	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.
171	LLW			9.42 %	15.02 %	7.98 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
172	CaDDN		code	9.41 %	14.72 %	8.17 %	0.63 s	GPU @ 2.5 Ghz (Python)
C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.
173	SGM3D		code	9.39 %	15.39 %	8.61 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
Z. Zhou, L. Du, X. Ye, Z. Zou, X. Tan, L. Zhang, X. Xue and J. Feng: SGM3D: Stereo Guided Monocular 3D Object Detection. RA-L 2022.
174	AMNet+DDAD15M		code	9.30 %	14.10 %	8.02 %	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.
175	AMNet		code	9.30 %	14.02 %	7.93 %	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.
176	MonoRCNN++		code	9.04 %	13.45 %	7.74 %	0.07 s	GPU @ 2.5 Ghz (Python)
X. Shi, Z. Chen and T. Kim: Multivariate Probabilistic Monocular 3D Object Detection. WACV 2023.
177	HomoLoss(monoflex)		code	8.81 %	13.26 %	7.41 %	0.04 s	1 core @ 2.5 Ghz (Python)
J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homography Loss for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
178	mdab			8.79 %	13.79 %	7.99 %	0.02 s	1 core @ 2.5 Ghz (Python)
179	SAKD-MR-Res18			8.60 %	13.52 %	7.18 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
180	temp			8.46 %	12.62 %	7.24 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
181	MonoDDE			8.41 %	12.38 %	7.16 %	0.04 s	1 core @ 2.5 Ghz (Python)
Z. Li, Z. Qu, Y. Zhou, J. Liu, H. Wang and L. Jiang: Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection. CVPR 2022.
182	Mix-Teaching		code	8.40 %	12.34 %	7.06 %	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.
183	MDSNet			8.18 %	12.05 %	7.03 %	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.
184	PS-SVDM			8.11 %	12.70 %	6.84 %	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.
185	LPCG-Monoflex		code	7.92 %	12.11 %	6.61 %	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.
186	RefinedMPL			7.92 %	13.09 %	7.25 %	0.15 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
187	Cube R-CNN		code	7.65 %	11.67 %	6.60 %	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.
188	MonoRUn		code	7.59 %	11.70 %	6.34 %	0.07 s	GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
189	MonoFlex			7.36 %	10.36 %	6.29 %	0.03 s	GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.
190	DA3D+KM3D+v2-99		code	7.06 %	10.32 %	6.10 %	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.
191	MonoPair			7.04 %	10.99 %	6.29 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
192	monodle		code	6.96 %	10.73 %	6.20 %	0.04 s	GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for Monocular 3D Object Detection. CVPR 2021 .
193	MonOAPC			6.82 %	9.62 %	5.78 %	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.
194	UniCuboid			6.75 %	10.26 %	5.61 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
195	TopNet-DecayRate			6.59 %	8.78 %	6.25 %	92 ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
196	Shift R-CNN (mono)		code	5.66 %	8.58 %	4.49 %	0.25 s	GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
197	FMF-occlusion-net			5.62 %	8.69 %	5.25 %	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.
198	Aug3D-RPN			5.22 %	7.14 %	4.21 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth. 2021.
199	TopNet-UncEst			4.60 %	6.88 %	3.79 %	0.09 s	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
200	MonoPSR		code	4.56 %	7.24 %	4.11 %	0.2 s	GPU @ 3.5 Ghz (Python)
J. Ku*, A. Pon* and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
201	DFR-Net			4.52 %	6.66 %	3.71 %	0.18 s	1080 Ti (Pytorch)
Z. Zou, X. Ye, L. Du, X. Cheng, X. Tan, L. Zhang, J. Feng, X. Xue and E. Ding: The devil is in the task: Exploiting reciprocal appearance-localization features for monocular 3d object detection . ICCV 2021.
202	monospb			4.50 %	6.15 %	3.76 %	0.01 s	1 core @ 2.5 Ghz (Python)
203	QD-3DT		code	4.23 %	6.62 %	3.39 %	0.03 s	GPU @ 2.5 Ghz (Python)
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.
204	DA3D+KM3D		code	4.05 %	5.94 %	3.55 %	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.
205	M3D-RPN		code	4.05 %	5.65 %	3.29 %	0.16 s	GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
206	DDMP-3D			4.02 %	5.53 %	3.36 %	0.18 s	1 core @ 2.5 Ghz (Python)
L. Wang, L. Du, X. Ye, Y. Fu, G. Guo, X. Xue, J. Feng and L. Zhang: Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection. CVPR 2020.
207	CMAN			3.96 %	5.24 %	3.18 %	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.
208	D4LCN		code	3.86 %	5.06 %	3.59 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
209	RT3DStereo			3.65 %	4.72 %	3.00 %	0.08 s	GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
210	DA3D		code	3.27 %	4.93 %	2.74 %	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.
211	MonoEF			3.05 %	4.61 %	2.85 %	0.03 s	1 core @ 2.5 Ghz (Python)
Y. Zhou, Y. He, H. Zhu, C. Wang, H. Li and Q. Jiang: Monocular 3D Object Detection: An Extrinsic Parameter Free Approach. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
212	MonoLiG		code	2.72 %	3.74 %	2.55 %	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.
213	SS3D			2.09 %	2.48 %	1.61 %	48 ms	Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
214	SparVox3D			2.05 %	2.90 %	1.69 %	0.05 s	GPU @ 2.0 Ghz (Python)
E. Balatkan and F. Kıraç: Improving Regression Performance on Monocular 3D Object Detection Using Bin-Mixing and Sparse Voxel Data. 2021 6th International Conference on Computer Science and Engineering (UBMK) 2021.
215	PGD-FCOS3D		code	1.88 %	2.82 %	1.54 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
T. Wang, X. Zhu, J. Pang and D. Lin: Probabilistic and Geometric Depth: Detecting Objects in Perspective. Conference on Robot Learning (CoRL) 2021.
216	Plane-Constraints		code	1.16 %	1.87 %	1.13 %	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.
217	GATE3D		code	0.17 %	0.15 %	0.17 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
218	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.