The KITTI Vision Benchmark Suite

Object Detection Evaluation 2012

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

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

We evaluate object detection performance using the PASCAL criteria and object detection and orientation estimation performance using the measure discussed in our CVPR 2012 publication. For cars we require an overlap of 70%, while for pedestrians and cyclists we require an overlap of 50% for a detection. Detections in don't care areas or detections which are smaller than the minimum size do not count as false positive. Difficulties are defined as follows:

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

All methods are ranked based on the moderately difficult results. Note that for the hard evaluation ~2 % of the provided bounding boxes have not been recognized by humans, thereby upper bounding recall at 98 %. Hence, the hard evaluation is only given for reference.
Note 1: On 25.04.2017, we have fixed a bug in the object detection evaluation script. As of now, the submitted detections are filtered based on the min. bounding box height for the respective category which we have been done before only for the ground truth detections, thus leading to false positives for the category "Easy" when bounding boxes of height 25-39 Px were submitted (and to false positives for all categories if bounding boxes smaller than 25 Px were submitted). We like to thank Amy Wu, Matt Wilder, Pekka Jänis and Philippe Vandermersch for their feedback. The last leaderboards right before the changes can be found here!

Note 2: On 08.10.2019, we have followed the suggestions of the Mapillary team in their paper Disentangling Monocular 3D Object Detection and use 40 recall positions instead of the 11 recall positions proposed in the original Pascal VOC benchmark. This results in a more fair comparison of the results, please check their paper. The last leaderboards right before this change can be found here: Object Detection Evaluation, 3D Object Detection Evaluation, Bird's Eye View Evaluation.

Important Policy Update: As more and more non-published work and re-implementations of existing work is submitted to KITTI, we have established a new policy: from now on, only submissions with significant novelty that are leading to a peer-reviewed paper in a conference or journal are allowed. Minor modifications of existing algorithms or student research projects are not allowed. Such work must be evaluated on a split of the training set. To ensure that our policy is adopted, new users must detail their status, describe their work and specify the targeted venue during registration. Furthermore, we will regularly delete all entries that are 6 months old but are still anonymous or do not have a paper associated with them. For conferences, 6 month is enough to determine if a paper has been accepted and to add the bibliography information. For longer review cycles, you need to resubmit your results.

Additional information used by the methods

Stereo: Method uses left and right (stereo) images
Flow: Method uses optical flow (2 temporally adjacent images)
Multiview: Method uses more than 2 temporally adjacent images
Laser Points: Method uses point clouds from Velodyne laser scanner
Additional training data: Use of additional data sources for training (see details)

Car

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	LVP(84.92)			97.84 %	98.70 %	93.07 %	0.04 s	1 core @ 2.5 Ghz (C/C++)

2	HPC-Net			97.59 %	98.61 %	93.01 %	0.18 s	1 core @ 2.5 Ghz (C/C++)

3	UDeerPEP		code	97.43 %	98.42 %	94.94 %	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.
4	VirConv-S		code	97.27 %	98.00 %	94.53 %	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.
5	GraR-VoI		code	96.38 %	96.81 %	91.20 %	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.
6	VirConv-T		code	96.38 %	98.93 %	93.56 %	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.
7	DAF-SSD			96.28 %	96.95 %	88.75 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

8	GraR-Po		code	96.18 %	96.84 %	91.11 %	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.
9	SFD		code	96.17 %	98.97 %	91.13 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
X. Wu, L. Peng, H. Yang, L. Xie, C. Huang, C. Deng, H. Liu and D. Cai: Sparse Fuse Dense: Towards High Quality 3D Detection with Depth Completion. CVPR 2022.
10	VPFNet		code	96.15 %	96.64 %	91.14 %	0.06 s	2 cores @ 2.5 Ghz (Python)
H. Zhu, J. Deng, Y. Zhang, J. Ji, Q. Mao, H. Li and Y. Zhang: VPFNet: Improving 3D Object Detection with Virtual Point based LiDAR and Stereo Data Fusion. IEEE Transactions on Multimedia 2022.
11	CASDC			96.13 %	98.66 %	93.26 %	0.1 s	1 core @ 2.5 Ghz (Python)

12	CLOCs		code	96.07 %	96.77 %	91.11 %	0.1 s	1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection . 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
13	RDIoU		code	96.05 %	98.79 %	91.03 %	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.
14	GraR-Vo		code	96.05 %	96.67 %	93.01 %	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.
15	TED		code	96.03 %	96.64 %	93.35 %	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.
16	CLOCs_PVCas		code	95.96 %	96.76 %	91.08 %	0.1 s	1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection . 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
17	PVT-SSD			95.90 %	96.75 %	90.69 %	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.
18	GraR-Pi		code	95.89 %	98.59 %	92.85 %	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.
19	PIPC-3Ddet		code	95.86 %	96.80 %	90.92 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

20	OcTr			95.84 %	96.48 %	90.99 %	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.
21	CDF			95.83 %	96.22 %	90.77 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

22	3D Dual-Fusion		code	95.82 %	96.54 %	93.11 %	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.
23	NIV-SSD			95.82 %	98.68 %	90.80 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

24	URFormer			95.81 %	98.52 %	93.03 %	0.1 s	1 core @ 2.5 Ghz (Python)

25	GLENet-VR		code	95.81 %	96.85 %	90.91 %	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.
26	POP-RCNN			95.81 %	96.62 %	90.96 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

27	TSSTDet			95.81 %	96.65 %	93.05 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

28	DVF-V			95.77 %	96.60 %	90.89 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
A. Mahmoud, J. Hu and S. Waslander: Dense Voxel Fusion for 3D Object Detection. WACV 2023.
29	HAF-PVP_test			95.76 %	98.86 %	92.95 %	0.09 s	1 core @ 2.5 Ghz (C/C++)

30	Fast-CLOCs			95.75 %	96.69 %	90.95 %	0.1 s	GPU @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: Fast-CLOCs: Fast Camera-LiDAR Object Candidates Fusion for 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022.
31	SoRL			95.74 %	96.79 %	90.83 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

32	3D HANet		code	95.73 %	98.61 %	92.96 %	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.
33	PA-RCNN		code	95.72 %	96.49 %	93.00 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

34	MAK		code	95.71 %	96.68 %	90.86 %	0.03 s	GPU @ 2.5 Ghz (Python)

35	DSGN++		code	95.70 %	98.08 %	88.27 %	0.2 s	GeForce RTX 2080Ti
Y. Chen, S. Huang, S. Liu, B. Yu and J. Jia: DSGN++: Exploiting Visual-Spatial Relation for Stereo-Based 3D Detectors. IEEE Transactions on Pattern Analysis and Machine Intelligence 2022.
36	MMF			95.69 %	96.71 %	90.84 %	1 s	1 core @ 2.5 Ghz (C/C++)

37	MAK_VOXEL_RCNN			95.67 %	98.63 %	92.97 %	0.03 s	1 core @ 2.5 Ghz (Python)

38	LVP			95.67 %	98.48 %	92.88 %	0.04 s	1 core @ 2.5 Ghz (Python)

39	GENet			95.63 %	96.53 %	90.82 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

40	CasA		code	95.62 %	96.52 %	92.86 %	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.
41	BADet		code	95.61 %	98.75 %	90.64 %	0.14 s	1 core @ 2.5 Ghz (C/C++)
R. Qian, X. Lai and X. Li: BADet: Boundary-Aware 3D Object Detection from Point Clouds. Pattern Recognition 2022.
42	SE-SSD		code	95.60 %	96.69 %	90.53 %	0.03 s	1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, L. Jiang and C. Fu: SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud. CVPR 2021.
43	SGFNet			95.60 %	98.46 %	92.74 %	0.09 s	1 core @ 2.5 Ghz (C/C++)

44	FEIF3D			95.59 %	96.44 %	92.95 %	0.1 s	GPU @ 2.5 Ghz (Python)

45	PA-Det3D			95.59 %	96.37 %	90.97 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

46	PSMS-Net			95.58 %	96.70 %	90.76 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

47	VDF			95.58 %	98.58 %	92.72 %	0.03 s	GPU @ 2.5 Ghz (Python)

48	FARP-Net		code	95.57 %	96.11 %	93.07 %	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.
49	FEMV-RCNN		code	95.56 %	96.27 %	90.87 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

50	CAFI-Pillars			95.56 %	96.47 %	90.75 %	30ms	NVIDIA Tesla P40 GPU

51	LoGoNet		code	95.55 %	96.60 %	93.07 %	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.
52	GD-MAE			95.54 %	98.38 %	90.42 %	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.
53	MPFusion			95.53 %	96.35 %	92.81 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

54	SA-Net			95.52 %	98.29 %	92.69 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
55	DVF-PV			95.49 %	96.42 %	92.57 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
A. Mahmoud, J. Hu and S. Waslander: Dense Voxel Fusion for 3D Object Detection. WACV 2023.
56	SFD++			95.48 %	98.40 %	92.61 %	0.12 s	1 core @ 2.5 Ghz (Python + C/C++)

57	SS-3DSSD		code	95.47 %	96.31 %	90.55 %	0.014s	1 core @ 2.5 Ghz (C/C++)

58	3D-BCM			95.47 %	98.50 %	92.70 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

59	SPANet			95.46 %	96.54 %	90.47 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
Y. Ye: SPANet: Spatial and Part-Aware Aggregation Network for 3D Object Detection. Pacific Rim International Conference on Artificial Intelligence 2021.
60	Anonymous			95.44 %	96.41 %	92.85 %	0.1 s	1 core @ 2.5 Ghz (Python)

61	DSASA		code	95.40 %	96.14 %	92.46 %	0.05s	GPU @ >3.5 Ghz (Python)

62	PG-RCNN		code	95.40 %	96.66 %	90.55 %	0.06 s	GPU @ 1.5 Ghz (Python)
I. Koo, I. Lee, S. Kim, H. Kim, W. Jeon and C. Kim: PG-RCNN: Semantic Surface Point Generation for 3D Object Detection. 2023.
63	Multi-Weights			95.39 %	96.38 %	90.60 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)

64	Simi-fusion			95.38 %	98.35 %	92.87 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

65	SASA		code	95.35 %	96.01 %	92.53 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
C. Chen, Z. Chen, J. Zhang and D. Tao: SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object Detection. arXiv preprint arXiv:2201.01976 2022.
66	TED-S Reproduced			95.33 %	98.45 %	92.75 %	0.1 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
67	SPG_mini		code	95.32 %	96.23 %	92.68 %	0.09 s	GPU @ 2.5 Ghz (Python)
Q. Xu, Y. Zhou, W. Wang, C. Qi and D. Anguelov: SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation. Proceedings of the IEEE conference on computer vision and pattern recognition (ICCV) 2021.
68	EQ-PVRCNN		code	95.32 %	98.23 %	92.65 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, L. Jiang, Y. Sun, B. Schiele and J. Jia: A Unified Query-based Paradigm for Point Cloud Understanding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.
69	Focals Conv		code	95.28 %	96.30 %	92.69 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
Y. Chen, Y. Li, X. Zhang, J. Sun and J. Jia: Focal Sparse Convolutional Networks for 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2022.
70	CasA++		code	95.28 %	95.83 %	94.28 %	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.
71	MVENet			95.25 %	96.20 %	92.54 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

72	RPF3D			95.25 %	96.31 %	90.51 %	0.1 s	1 core @ 2.5 Ghz (Python)

73	VoxSeT		code	95.23 %	96.16 %	90.49 %	33 ms	1 core @ 2.5 Ghz (C/C++)
C. He, R. Li, S. Li and L. Zhang: Voxel Set Transformer: A Set-to-Set Approach to 3D Object Detection from Point Clouds. CVPR 2022.
74	LGSL			95.22 %	98.00 %	92.72 %	0.1 s	GPU @ 2.5 Ghz (Python)

75	ACCF			95.22 %	95.65 %	90.57 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

76	PC-CNN-V2			95.20 %	96.06 %	89.37 %	0.5 s	GPU @ 2.5 Ghz (Matlab + C/C++)
X. Du, M. Ang, S. Karaman and D. Rus: A General Pipeline for 3D Detection of Vehicles. 2018 IEEE International Conference on Robotics and Automation (ICRA) 2018.
77	R^2 R-CNN			95.19 %	96.38 %	92.57 %	0.1 s	1 core @ 2.5 Ghz (Python)

78	PR-SSD			95.18 %	97.64 %	92.48 %	0.02 s	GPU @ 2.5 Ghz (Python)

79	VPFNet		code	95.17 %	96.06 %	92.66 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
C. Wang, H. Chen and L. Fu: VPFNet: Voxel-Pixel Fusion Network for Multi-class 3D Object Detection. 2021.
80	F-PointNet		code	95.17 %	95.85 %	85.42 %	0.17 s	GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
81	EPNet++			95.17 %	96.73 %	92.10 %	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.
82	SA-SSD		code	95.16 %	97.92 %	90.15 %	0.04 s	1 core @ 2.5 Ghz (Python)
C. He, H. Zeng, J. Huang, X. Hua and L. Zhang: Structure Aware Single-stage 3D Object Detection from Point Cloud. CVPR 2020.
83	HMFI		code	95.16 %	96.29 %	92.45 %	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.
84	GS-FPS		code	95.15 %	95.83 %	92.39 %	TBD s	1 core @ 2.5 Ghz (C/C++)

85	RBEV-Voxel		code	95.15 %	96.43 %	90.32 %	0.08 s	GPU @ 2.5 Ghz (Python)

86	USVLab BSAODet		code	95.15 %	96.26 %	92.62 %	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.
87	TEDx			95.14 %	96.15 %	92.45 %	0.01 s	1 core @ 2.5 Ghz (C/C++)

88	Pyramid R-CNN			95.13 %	95.88 %	92.62 %	0.07 s	1 core @ 2.5 Ghz (C/C++)
J. Mao, M. Niu, H. Bai, X. Liang, H. Xu and C. Xu: Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection. ICCV 2021.
89	Voxel R-CNN		code	95.11 %	96.49 %	92.45 %	0.04 s	GPU @ 3.0 Ghz (C/C++)
J. Deng, S. Shi, P. Li, W. Zhou, Y. Zhang and H. Li: Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection . AAAI 2021.
90	3DSSD		code	95.10 %	97.69 %	92.18 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
91	DTSSD			95.08 %	95.96 %	90.50 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

92	GF-pointnet			95.08 %	95.93 %	92.36 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

93	Anonymous			95.07 %	96.37 %	92.44 %	0.1 s	GPU @ 2.5 Ghz (Python)

94	RAFDet			95.04 %	95.96 %	92.41 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

95	BSH-Det3D			95.02 %	96.34 %	92.36 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

96	HybridPillars			95.01 %	96.15 %	92.35 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

97	single-src-hirige		code	95.01 %	96.44 %	86.38 %	0.01 s	GPU @ 2.5 Ghz (Python)

98	PDV		code	95.00 %	96.07 %	92.44 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
J. Hu, T. Kuai and S. Waslander: Point Density-Aware Voxels for LiDAR 3D Object Detection. CVPR 2022.
99	MVRA + I-FRCNN+			94.98 %	95.87 %	82.52 %	0.18 s	GPU @ 2.5 Ghz (Python)
H. Choi, H. Kang and Y. Hyun: Multi-View Reprojection Architecture for Orientation Estimation. The IEEE International Conference on Computer Vision (ICCV) Workshops 2019.
100	SIENet		code	94.97 %	96.02 %	92.40 %	0.08 s	1 core @ 2.5 Ghz (Python)
Z. Li, Y. Yao, Z. Quan, W. Yang and J. Xie: SIENet: Spatial Information Enhancement Network for 3D Object Detection from Point Cloud. 2021.
101	VoTr-TSD		code	94.94 %	95.97 %	92.44 %	0.07 s	1 core @ 2.5 Ghz (C/C++)
J. Mao, Y. Xue, M. Niu, H. Bai, J. Feng, X. Liang, H. Xu and C. Xu: Voxel Transformer for 3D Object Detection. ICCV 2021.
102	L-AUG			94.92 %	95.84 %	92.22 %	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.
103	AMVFNet		code	94.87 %	96.12 %	92.33 %	0.04 s	GPU @ 2.5 Ghz (Python)

104	GraphAlign			94.87 %	98.06 %	92.47 %	0.03 s	GPU @ 2.0 Ghz (Python)

105	CZY_PPF_Net			94.86 %	98.07 %	92.22 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

106	M3DeTR		code	94.83 %	97.39 %	92.10 %	n/a s	GPU @ 1.0 Ghz (Python)
T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis and D. Manocha: M3DeTR: Multi-representation, Multi- scale, Mutual-relation 3D Object Detection with Transformers. 2021.
107	StructuralIF			94.81 %	96.14 %	92.12 %	0.02 s	8 cores @ 2.5 Ghz (Python)
J. Pei An: Deep structural information fusion for 3D object detection on LiDAR-camera system. Accepted in CVIU 2021.
108	Rnet			94.80 %	95.98 %	92.22 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

109	Under Blind Review#1			94.79 %	95.63 %	92.35 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

110	U_PV_V2_ep100_80			94.78 %	95.82 %	92.27 %	0... s	1 core @ 2.5 Ghz (Python)

111	DCAF		code	94.78 %	96.31 %	90.20 %	0.12 s	1 core @ 2.5 Ghz (C/C++)

112	XView			94.77 %	95.89 %	92.23 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
113	U_PV_V2_ep_100_100			94.76 %	95.76 %	92.18 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

114	F3D			94.75 %	95.99 %	92.28 %	0.01 s	1 core @ 2.5 Ghz (C/C++)

115	HA-PillarNet			94.75 %	95.91 %	92.16 %	0.05 s	1 core @ 2.5 Ghz (Python)

116	PV-PMRTNet			94.73 %	96.03 %	92.19 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

117	P2V-RCNN			94.73 %	96.03 %	92.34 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.
118	SPG		code	94.71 %	97.80 %	92.19 %	0.09 s	1 core @ 2.5 Ghz (Python + C/C++)
Q. Xu, Y. Zhou, W. Wang, C. Qi and D. Anguelov: SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation. Proceedings of the IEEE conference on computer vision and pattern recognition (ICCV) 2021.
119	CAT-Det			94.71 %	95.97 %	92.07 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Zhang, J. Chen and D. Huang: CAT-Det: Contrastively Augmented Transformer for Multi-modal 3D Object Detection. CVPR 2022.
120	MMLab PV-RCNN		code	94.70 %	98.17 %	92.04 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
121	GeVo			94.69 %	97.31 %	92.10 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

122	OA-TSSD			94.68 %	95.43 %	91.91 %	20 s	8 cores @ 2.5 Ghz (C/C++)

123	OFFNet			94.68 %	96.18 %	92.07 %	0.1 s	GPU @ 2.5 Ghz (Python)

124	SVGA-Net			94.67 %	96.05 %	91.86 %	0.03s	1 core @ 2.5 Ghz (Python + C/C++)
Q. He, Z. Wang, H. Zeng, Y. Zeng and Y. Liu: SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds. AAAI 2022.
125	RangeDet (Official)		code	94.64 %	95.50 %	91.77 %	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.
126	U_VOXELRCNN_CAR_V3			94.64 %	96.02 %	91.94 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

127	DSA-PV-RCNN		code	94.64 %	95.86 %	92.10 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
128	RangeIoUDet			94.61 %	95.74 %	91.98 %	0.02 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, Z. Zhang, M. Zhang, X. Zhao and S. Pu: RangeIoUDet: Range Image Based Real-Time 3D Object Detector Optimized by Intersection Over Union. CVPR 2021.
129	af			94.59 %	95.79 %	92.17 %	1 s	GPU @ 2.5 Ghz (Python)

130	DVFENet			94.57 %	95.35 %	91.77 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection. Neurocomputing 2021.
131	AAMVFNet		code	94.53 %	95.89 %	91.98 %	0.04 s	GPU @ 2.5 Ghz (Python)

132	TuSimple		code	94.47 %	95.12 %	86.45 %	1.6 s	GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016. K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016.
133	EPNet		code	94.44 %	96.15 %	89.99 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
T. Huang, Z. Liu, X. Chen and X. Bai: EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection. ECCV 2020.
134	DD3D-dequity			94.43 %	95.58 %	90.81 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

135	SERCNN			94.42 %	96.33 %	89.96 %	0.1 s	1 core @ 2.5 Ghz (Python)
D. Zhou, J. Fang, X. Song, L. Liu, J. Yin, Y. Dai, H. Li and R. Yang: Joint 3D Instance Segmentation and Object Detection for Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020.
136	MSIT-Det			94.39 %	97.21 %	86.85 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)

137	RealSynthesis-SECOND			94.34 %	95.14 %	91.34 %	0.05 s	1 core @ 2.5 Ghz (Python)

138	UberATG-MMF			94.25 %	97.41 %	89.87 %	0.08 s	GPU @ 2.5 Ghz (Python)
M. Liang, B. Yang, Y. Chen, R. Hu and R. Urtasun: Multi-Task Multi-Sensor Fusion for 3D Object Detection. CVPR 2019.
139	SRDL			94.24 %	95.86 %	91.80 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
140	HA-SSD			94.19 %	95.06 %	91.17 %	1 s	1 core @ 2.5 Ghz (C/C++)

141	u_second_v4_epoch_10			94.13 %	95.33 %	91.36 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

142	STNet			94.10 %	95.75 %	91.51 %	0.60 s	1 core @ 2.5 Ghz (Python)

143	RangeRCNN			94.03 %	95.48 %	91.74 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, M. Zhang, Z. Zhang, X. Zhao and S. Pu: RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representation. arXiv preprint arXiv:2009.00206 2020.
144	Faraway-Frustum		code	93.99 %	95.81 %	91.72 %	0.1 s	GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-frustum: Dealing with lidar sparsity for 3D object detection using fusion. 2021 IEEE International Intelligent Transportation Systems Conference (ITSC) 2021.
145	DD3D		code	93.99 %	94.69 %	89.37 %	n/a s	1 core @ 2.5 Ghz (C/C++)
D. Park, R. Ambrus, V. Guizilini, J. Li and A. Gaidon: Is Pseudo-Lidar needed for Monocular 3D Object detection?. IEEE/CVF International Conference on Computer Vision (ICCV) .
146	U_second_v4_ep_100_8			93.96 %	94.85 %	91.14 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

147	SIF			93.95 %	95.51 %	91.57 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
148	MGAF-3DSSD		code	93.87 %	94.45 %	86.37 %	0.1 s	1 core @ 2.5 Ghz (Python)
J. Li, H. Dai, L. Shao and Y. Ding: Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
149	3ONet			93.87 %	96.97 %	88.84 %	0.09 s	1 core @ 2.5 Ghz (C/C++)

150	LPCG-Monoflex		code	93.86 %	96.90 %	83.94 %	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.
151	MMLAB LIGA-Stereo		code	93.82 %	96.43 %	86.19 %	0.4 s	1 core @ 2.5 Ghz (Python + C/C++)
X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
152	Sem-Aug			93.77 %	96.79 %	88.78 %	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.
153	IMLIDAR(base)			93.75 %	96.76 %	88.78 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

154	Patches - EMP			93.75 %	97.91 %	90.56 %	0.5 s	GPU @ 2.5 Ghz (Python)
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
155	SoRL-V			93.73 %	96.87 %	88.73 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

156	CIA-SSD		code	93.72 %	96.87 %	86.20 %	0.03 s	1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, S. Chen, L. Jiang and C. Fu: CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud. AAAI 2021.
157	IIOU		code	93.72 %	96.45 %	90.91 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)

158	QD-3DT		code	93.66 %	94.26 %	83.63 %	0.03 s	GPU @ 2.5 Ghz (Python)
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.
159	MVAF-Net		code	93.66 %	95.37 %	90.90 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
G. Wang, B. Tian, Y. Zhang, L. Chen, D. Cao and J. Wu: Multi-View Adaptive Fusion Network for 3D Object Detection. arXiv preprint arXiv:2011.00652 2020.
160	Anonymous			93.66 %	96.59 %	90.98 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

161	SSL-PointGNN		code	93.65 %	96.61 %	88.53 %	0.56 s	GPU @ 1.5 Ghz (Python)
E. Erçelik, E. Yurtsever, M. Liu, Z. Yang, H. Zhang, P. Topçam, M. Listl, Y. Çaylı and A. Knoll: 3D Object Detection with a Self-supervised Lidar Scene Flow Backbone. arXiv preprint arXiv:2205.00705 2022.
162	PA3DNet			93.62 %	96.57 %	88.65 %	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.
163	HPV-RCNN			93.58 %	96.68 %	88.57 %	0.08 s	1 core @ 2.5 Ghz (Python)

164	Anonymous			93.57 %	96.59 %	88.64 %	0.1 s	GPU @ 2.5 Ghz (Python)

165	Anonymous			93.57 %	96.66 %	90.87 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

166	IA-SSD (multi)		code	93.56 %	96.10 %	90.68 %	0.014 s	1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
167	MonoLiG		code	93.56 %	96.70 %	83.74 %	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.
168	MonoPair			93.55 %	96.61 %	83.55 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
169	IA-SSDx			93.55 %	96.67 %	90.88 %	0.01 s	1 core @ 2.5 Ghz (C/C++)

170	casx			93.55 %	96.67 %	90.88 %	0.01 s	1 core @ 2.5 Ghz (C/C++)

171	MMAE			93.55 %	96.52 %	90.53 %	0.07 s	1 core @ 2.5 Ghz (C/C++)

172	IA-SSD (single)		code	93.54 %	96.26 %	88.49 %	0.013 s	1 core @ 2.5 Ghz (C/C++)
Y. Zhang, Q. Hu, G. Xu, Y. Ma, J. Wan and Y. Guo: Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds. CVPR 2022.
173	EBM3DOD		code	93.54 %	96.81 %	88.33 %	0.12 s	1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.
174	fuf			93.53 %	96.74 %	88.43 %	10 s	1 core @ 2.5 Ghz (C/C++)

175	casxv1			93.52 %	96.67 %	90.85 %	0.01 s	1 core @ 2.5 Ghz (Python)

176	TADP			93.51 %	96.83 %	88.28 %	0.04 s	GPU @ 2.5 Ghz (Python)

177	Deep MANTA			93.50 %	98.89 %	83.21 %	0.7 s	GPU @ 2.5 Ghz (Python + C/C++)
F. Chabot, M. Chaouch, J. Rabarisoa, C. Teulière and T. Chateau: Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image. CVPR 2017.
178	Point-GNN		code	93.50 %	96.58 %	88.35 %	0.6 s	GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
179	Point-GNN_GT		code	93.49 %	96.58 %	88.35 %	5 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
180	BtcDet		code	93.47 %	96.23 %	88.55 %	0.09 s	GPU @ 2.5 Ghz (Python + C/C++)
Q. Xu, Y. Zhong and U. Neumann: Behind the Curtain: Learning Occluded Shapes for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2022.
181	MOPNet		code	93.47 %	96.57 %	83.62 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

182	Struc info fusion II			93.45 %	96.72 %	88.31 %	0.05 s	GPU @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.
183	EBM3DOD baseline		code	93.45 %	96.72 %	88.25 %	0.05 s	1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.
184	IOUFusion			93.43 %	96.38 %	90.63 %	0.1 s	GPU @ 2.5 Ghz (Python)

185	StereoDistill			93.43 %	97.61 %	87.71 %	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.
186	MonoLSS			93.42 %	96.19 %	83.62 %	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.
187	RRC		code	93.40 %	95.68 %	87.37 %	3.6 s	GPU @ 2.5 Ghz (C/C++)
J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y. Tai and L. Xu: Accurate Single Stage Detector Using Recurrent Rolling Convolution. CVPR 2017.
188	3D-CVF at SPA		code	93.36 %	96.78 %	86.11 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
J. Yoo, Y. Kim, J. Kim and J. Choi: 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection. ECCV 2020.
189	IIOU_LDR		code	93.33 %	96.33 %	88.22 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

190	SNVC		code	93.32 %	96.33 %	85.81 %	1 s	GPU @ 1.0 Ghz (Python)
S. Li, Z. Liu, Z. Shen and K. Cheng: Stereo Neural Vernier Caliper. Proceedings of the AAAI Conference on Artificial Intelligence 2022.
191	DFAF3D			93.32 %	96.58 %	90.24 %	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.
192	PVTr			93.32 %	94.71 %	90.82 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

193	Struc info fusion I			93.31 %	96.59 %	88.23 %	0.05 s	1 core @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: Struc info fusion. Submitted to CVIU 2021.
194	ROT_S3D			93.30 %	96.28 %	88.20 %	0.1 s	GPU @ 2.5 Ghz (Python)

195	CityBrainLab-CT3D		code	93.30 %	96.28 %	90.58 %	0.07 s	1 core @ 2.5 Ghz (Python + C/C++)
H. Sheng, S. Cai, Y. Liu, B. Deng, J. Huang, X. Hua and M. Zhao: Improving 3D Object Detection with Channel- wise Transformer. ICCV 2021.
196	MonoInsight			93.29 %	96.21 %	83.59 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

197	MonoInsight			93.29 %	96.21 %	83.59 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

198	DTSSD			93.25 %	95.85 %	90.42 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

199	HybridPillars (SSD)			93.23 %	95.84 %	88.07 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

200	STD		code	93.22 %	96.14 %	90.53 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
201	DA-Net			93.22 %	96.59 %	90.71 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

202	SARPNET			93.21 %	96.07 %	88.09 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
Y. Ye, H. Chen, C. Zhang, X. Hao and Z. Zhang: SARPNET: Shape Attention Regional Proposal Network for LiDAR-based 3D Object Detection. Neurocomputing 2019.
203	H^23D R-CNN		code	93.20 %	96.20 %	90.55 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
J. Deng, W. Zhou, Y. Zhang and H. Li: From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2021.
204	Fast Point R-CNN			93.18 %	96.13 %	87.68 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, S. Liu, X. Shen and J. Jia: Fast Point R-CNN. Proceedings of the IEEE international conference on computer vision (ICCV) 2019.
205	sensekitti		code	93.17 %	94.79 %	84.38 %	4.5 s	GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.
206	P2P		code	93.14 %	96.67 %	88.01 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)

207	B2PE			93.11 %	95.80 %	90.25 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

208	SJTU-HW			93.11 %	96.30 %	82.21 %	0.85s	GPU @ 1.5 Ghz (Python + C/C++)
S. Zhang, X. Zhao, L. Fang, F. Haiping and S. Haitao: LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR. IEEE International Conference on Image Processing 2018. L. Fang, X. Zhao and S. Zhang: Small-objectness sensitive detection based on shifted single shot detector. Multimedia Tools and Applications 2018.
209	FromVoxelToPoint		code	93.06 %	96.08 %	90.53 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to- Point Decoder. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
210	GA-RCNN			93.03 %	96.10 %	90.41 %	47ms	1 core @ 2.5 Ghz (C/C++)

211	CLOCs_SecCas			92.95 %	95.43 %	89.21 %	0.1 s	1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
212	WA			92.93 %	96.02 %	87.41 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)

213	MonoCD			92.91 %	96.43 %	85.55 %	n/a s	1 core @ 2.5 Ghz (Python)

214	ACDet		code	92.84 %	96.18 %	89.83 %	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.
215	HotSpotNet			92.81 %	96.21 %	89.80 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
216	PEF		code	92.81 %	96.09 %	87.99 %	N/A s	1 core @ 2.5 Ghz (C/C++)

217	MSAW			92.77 %	95.69 %	88.01 %	0.42 s	2 cores @ 2.5 Ghz (Python)

218	SegVoxelNet			92.73 %	96.00 %	87.60 %	0.04 s	1 core @ 2.5 Ghz (Python)
H. Yi, S. Shi, M. Ding, J. Sun, K. Xu, H. Zhou, Z. Wang, S. Li and G. Wang: SegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloud. ICRA 2020.
219	Patches			92.72 %	96.34 %	87.63 %	0.15 s	GPU @ 2.0 Ghz
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
220	Cube R-CNN		code	92.72 %	95.78 %	84.81 %	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.
221	CenterNet3D			92.69 %	95.76 %	89.81 %	0.04 s	GPU @ 1.5 Ghz (Python)
G. Wang, B. Tian, Y. Ai, T. Xu, L. Chen and D. Cao: CenterNet3D:An Anchor free Object Detector for Autonomous Driving. 2020.
222	R-GCN			92.67 %	96.19 %	87.66 %	0.16 s	GPU @ 2.5 Ghz (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
223	PI-RCNN			92.66 %	96.17 %	87.68 %	0.1 s	1 core @ 2.5 Ghz (Python)
L. Xie, C. Xiang, Z. Yu, G. Xu, Z. Yang, D. Cai and X. He: PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module. AAAI 2020 : The Thirty-Fourth AAAI Conference on Artificial Intelligence 2020.
224	PointPainting			92.58 %	98.39 %	89.71 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
225	MMpointpillars			92.57 %	95.47 %	87.45 %	0.05 s	1 core @ 2.5 Ghz (Python)

226	VSAC			92.55 %	96.00 %	87.81 %	0.07 s	1 core @ 1.0 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
227	MLAFF			92.54 %	95.39 %	87.77 %	0.39 s	2 cores @ 2.5 Ghz (Python)

228	DASS			92.53 %	96.23 %	87.75 %	0.09 s	1 core @ 2.0 Ghz (Python)
O. Unal, L. Van Gool and D. Dai: Improving Point Cloud Semantic Segmentation by Learning 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2021.
229	3D IoU-Net			92.47 %	96.31 %	87.67 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, S. Krylov, Y. Ding and L. Shao: 3D IoU-Net: IoU Guided 3D Object Detector for Point Clouds. arXiv preprint arXiv:2004.04962 2020.
230	Associate-3Ddet		code	92.45 %	95.61 %	87.32 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
L. Du, X. Ye, X. Tan, J. Feng, Z. Xu, E. Ding and S. Wen: Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection. The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
231	S-AT GCN			92.44 %	95.06 %	90.78 %	0.02 s	GPU @ 2.0 Ghz (Python)
L. Wang, C. Wang, X. Zhang, T. Lan and J. Li: S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection. CoRR 2021.
232	VPNetv2			92.44 %	95.87 %	87.76 %	0.1 s	1 core @ 2.5 Ghz (Python)

233	CAT2			92.37 %	95.77 %	87.27 %	1 s	1 core @ 2.5 Ghz (C/C++)

234	HA PillarNet			92.37 %	95.38 %	87.40 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

235	PointRGCN			92.33 %	97.51 %	87.07 %	0.26 s	GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
236	Sem-Aug-PointRCNN++			92.32 %	95.65 %	87.62 %	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.
237	OccupancyM3D			92.29 %	95.78 %	84.96 %	0.11 s	1 core @ 2.5 Ghz (C/C++)

238	Harmonic PointPillar		code	92.25 %	95.16 %	89.11 %	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.
239	F-ConvNet		code	92.19 %	95.85 %	80.09 %	0.47 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
240	PFF3D		code	92.15 %	95.37 %	87.54 %	0.05 s	GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
241	PASS-PointPillar			92.09 %	95.20 %	88.73 %	1 s	1 core @ 2.5 Ghz (C/C++)

242	SDP+RPN			92.03 %	95.16 %	79.16 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016. S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 2015.
243	AB3DMOT		code	92.00 %	95.88 %	86.98 %	0.0047s	1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
244	MMpp			91.97 %	95.21 %	87.04 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

245	MMLab-PointRCNN		code	91.90 %	95.92 %	87.11 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
246	MMLab-PartA^2		code	91.86 %	95.03 %	89.06 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
247	Anonymous Submission			91.84 %	95.69 %	87.05 %	1s	1 core @ 2.5 Ghz (Python)

248	WeakM3D		code	91.81 %	94.51 %	85.35 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
L. Peng, S. Yan, B. Wu, Z. Yang, X. He and D. Cai: WeakM3D: Towards Weakly Supervised Monocular 3D Object Detection. ICLR 2022.
249	Voxel-MAE+SECOND		code	91.78 %	93.11 %	89.53 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

250	epBRM		code	91.77 %	94.59 %	88.45 %	0.1 s	GPU @ >3.5 Ghz (Python + C/C++)

Table as LaTeX | Only published Methods

Pedestrian

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	IMOU_ALG			82.77 %	89.73 %	78.73 %	0.01 s	1 core @ 2.5 Ghz (C/C++)

2	LSFM			81.26 %	86.81 %	77.64 %	0.05 s	4 cores @ 2.5 Ghz (Python)

3	F-PointNet		code	80.13 %	89.83 %	75.05 %	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.
4	HHA-TFFEM			78.53 %	87.01 %	74.70 %	0.14 s	GPU @ 2.5 Ghz (Python + C/C++)
F. Tan, Z. Xia, Y. Ma and X. Feng: 3D Sensor Based Pedestrian Detection by Integrating Improved HHA Encoding and Two-Branch Feature Fusion. Remote Sensing 2022.
5	TuSimple		code	78.40 %	88.87 %	73.66 %	1.6 s	GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016. K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016.
6	single-src-hirige		code	76.66 %	87.01 %	71.09 %	0.01 s	GPU @ 2.5 Ghz (Python)

7	RRC		code	76.61 %	85.98 %	71.47 %	3.6 s	GPU @ 2.5 Ghz (C/C++)
J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y. Tai and L. Xu: Accurate Single Stage Detector Using Recurrent Rolling Convolution. CVPR 2017.
8	WSSN			76.42 %	84.91 %	71.86 %	0.37 s	GPU @ >3.5 Ghz (Python + C/C++)
Z. Guo, W. Liao, Y. Xiao, P. Veelaert and W. Philips: Weak Segmentation Supervised Deep Neural Networks for Pedestrian Detection. Pattern Recognition 2021.
9	ECP Faster R-CNN			76.25 %	85.96 %	70.55 %	0.25 s	GPU @ 2.5 Ghz (Python)
M. Braun, S. Krebs, F. Flohr and D. Gavrila: The EuroCity Persons Dataset: A Novel Benchmark for Object Detection. CoRR 2018.
10	Aston-EAS			76.07 %	86.71 %	70.02 %	0.24 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Wei, J. He, Y. Zhou, K. Chen, Z. Tang and Z. Xiong: Enhanced Object Detection With Deep Convolutional Neural Networks for Advanced Driving Assistance. IEEE Transactions on Intelligent Transportation Systems 2019.
11	MHN			75.99 %	87.21 %	69.50 %	0.39 s	GPU @ 2.5 Ghz (Python)
J. Cao, Y. Pang, S. Zhao and X. Li: High-Level Semantic Networks for Multi- Scale Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2019.
12	FFNet		code	75.81 %	87.17 %	69.86 %	1.07 s	GPU @ 1.5 Ghz (Python)
C. Zhao, Y. Qian and M. Yang: Monocular Pedestrian Orientation Estimation Based on Deep 2D-3D Feedforward. Pattern Recognition 2019.
13	SJTU-HW			75.81 %	87.17 %	69.86 %	0.85s	GPU @ 1.5 Ghz (Python + C/C++)
S. Zhang, X. Zhao, L. Fang, F. Haiping and S. Haitao: LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR. IEEE International Conference on Image Processing 2018. L. Fang, X. Zhao and S. Zhang: Small-objectness sensitive detection based on shifted single shot detector. Multimedia Tools and Applications 2018.
14	MS-CNN		code	74.89 %	85.71 %	68.99 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection. ECCV 2016.
15	DD3D		code	73.09 %	85.71 %	68.54 %	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) .
16	F-ConvNet		code	72.91 %	83.63 %	67.18 %	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.
17	GN			72.29 %	82.93 %	65.56 %	1 s	GPU @ 2.5 Ghz (Matlab + C/C++)
S. Jung and K. Hong: Deep network aided by guiding network for pedestrian detection. Pattern Recognition Letters 2017.
18	SubCNN			72.27 %	84.88 %	66.82 %	2 s	GPU @ 3.5 Ghz (Python + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.
19	VMVS			71.82 %	82.80 %	66.85 %	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.
20	DD3D-dequity			71.53 %	85.86 %	66.82 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

21	EOTL		code	71.45 %	84.74 %	64.58 %	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.
22	IVA		code	71.37 %	84.61 %	64.90 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
Y. Zhu, J. Wang, C. Zhao, H. Guo and H. Lu: Scale-adaptive Deconvolutional Regression Network for Pedestrian Detection. ACCV 2016. S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 2015.
23	MM-MRFC			70.76 %	83.79 %	64.81 %	0.05 s	GPU @ 2.5 Ghz (C/C++)
A. Costea, R. Varga and S. Nedevschi: Fast Boosting based Detection using Scale Invariant Multimodal Multiresolution Filtered Features. CVPR 2017.
24	SDP+RPN			70.42 %	82.07 %	65.09 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016. S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 2015.
25	3DOP		code	69.57 %	83.17 %	63.48 %	3s	GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
26	MonoPSR		code	68.56 %	85.60 %	63.34 %	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.
27	DeepStereoOP			68.46 %	83.00 %	63.35 %	3.4 s	GPU @ 3.5 Ghz (Matlab + C/C++)
C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.
28	sensekitti		code	68.41 %	82.72 %	62.72 %	4.5 s	GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.
29	MonoLSS			67.78 %	82.88 %	60.87 %	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.
30	Frustum-PointPillars		code	67.51 %	76.80 %	63.81 %	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.
31	FII-CenterNet		code	67.31 %	81.32 %	61.29 %	0.09 s	GPU @ 2.5 Ghz (Python)
S. Fan, F. Zhu, S. Chen, H. Zhang, B. Tian, Y. Lv and F. Wang: FII-CenterNet: An Anchor-Free Detector With Foreground Attention for Traffic Object Detection. IEEE Transactions on Vehicular Technology 2021.
32	Mono3D		code	67.29 %	80.30 %	62.23 %	4.2 s	GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.
33	Faster R-CNN		code	66.24 %	79.97 %	61.09 %	2 s	GPU @ 3.5 Ghz (Python + C/C++)
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards Real- Time Object Detection with Region Proposal Networks. NIPS 2015.
34	VPFNet		code	65.68 %	75.03 %	61.95 %	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.
35	VPNetv2			65.49 %	74.73 %	61.31 %	0.1 s	1 core @ 2.5 Ghz (Python)

36	EQ-PVRCNN		code	65.01 %	77.19 %	61.95 %	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.
37	CasA++		code	64.94 %	74.41 %	62.35 %	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.
38	TED		code	64.74 %	74.26 %	62.08 %	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.
39	LoGoNet		code	64.55 %	72.47 %	62.24 %	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.
40	SDP+CRC (ft)			64.36 %	79.22 %	59.16 %	0.6 s	GPU @ 2.5 Ghz (C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
41	MPFusion			63.86 %	74.08 %	60.01 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

42	Pose-RCNN			63.54 %	80.07 %	57.02 %	2 s	>8 cores @ 2.5 Ghz (Python)
M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on 2016.
43	USVLab BSAODet		code	63.21 %	72.86 %	59.48 %	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.
44	R^2 R-CNN			63.07 %	72.34 %	59.49 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

45	af			62.87 %	71.50 %	59.22 %	1 s	GPU @ 2.5 Ghz (Python)

46	CFM			62.84 %	74.76 %	56.06 %	<2 s	GPU @ 2.5 Ghz (Matlab + C/C++)
Q. Hu, P. Wang, C. Shen, A. Hengel and F. Porikli: Pushing the Limits of Deep CNNs for Pedestrian Detection. IEEE Transactions on Circuits and Systems for Video Technology 2017.
47	CasA		code	62.73 %	72.65 %	60.12 %	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.
48	Fast-CLOCs			62.57 %	76.22 %	60.13 %	0.1 s	GPU @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: Fast-CLOCs: Fast Camera-LiDAR Object Candidates Fusion for 3D Object Detection. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022.
49	PiFeNet		code	62.35 %	72.74 %	59.29 %	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.
50	RPF3D			62.35 %	72.52 %	59.68 %	0.1 s	1 core @ 2.5 Ghz (Python)

51	HotSpotNet			62.31 %	71.43 %	59.24 %	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.
52	IMLIDAR(base)			61.97 %	72.42 %	58.90 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

53	P2V-RCNN			61.83 %	71.76 %	59.29 %	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.
54	MonoPair			61.57 %	78.81 %	56.51 %	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.
55	monodle		code	61.29 %	78.66 %	56.18 %	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 .
56	HPV-RCNN			61.27 %	74.34 %	58.80 %	0.08 s	1 core @ 2.5 Ghz (Python)

57	RPN+BF		code	61.22 %	77.06 %	55.22 %	0.6 s	GPU @ 2.5 Ghz (Matlab + C/C++)
L. Zhang, L. Lin, X. Liang and K. He: Is Faster R-CNN Doing Well for Pedestrian Detection?. ECCV 2016.
58	PIPC-3Ddet		code	61.15 %	68.23 %	57.53 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

59	Regionlets			60.83 %	73.79 %	54.72 %	1 s	>8 cores @ 2.5 Ghz (C/C++)
X. Wang, M. Yang, S. Zhu and Y. Lin: Regionlets for Generic Object Detection. T-PAMI 2015. W. Zou, X. Wang, M. Sun and Y. Lin: Generic Object Detection with Dense Neural Patterns and Regionlets. British Machine Vision Conference 2014. C. Long, X. Wang, G. Hua, M. Yang and Y. Lin: Accurate Object Detection with Location Relaxation and Regionlets Relocalization. Asian Conference on Computer Vision 2014.
60	3DSSD		code	60.51 %	72.33 %	56.28 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
61	3ONet			60.39 %	71.60 %	56.18 %	0.09 s	1 core @ 2.5 Ghz (C/C++)

62	PA-RCNN		code	60.17 %	69.95 %	56.44 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

63	OFFNet			60.03 %	67.58 %	57.71 %	0.1 s	GPU @ 2.5 Ghz (Python)

64	PV-PMRTNet			59.73 %	68.33 %	57.40 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

65	LGSL			59.58 %	68.54 %	57.34 %	0.1 s	GPU @ 2.5 Ghz (Python)

66	IOUFusion			59.52 %	69.10 %	55.41 %	0.1 s	GPU @ 2.5 Ghz (Python)

67	DPPFA-Net			59.52 %	67.68 %	56.87 %	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.
68	ACDet		code	59.51 %	71.27 %	57.03 %	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.
69	CZY_PPF_Net			59.26 %	67.81 %	57.04 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

70	QD-3DT		code	59.26 %	78.41 %	54.37 %	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.
71	TANet		code	59.07 %	69.90 %	56.44 %	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.
72	MonoUNI		code	58.97 %	76.17 %	53.99 %	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.
73	DSA-PV-RCNN		code	58.81 %	66.93 %	56.57 %	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.
74	PSMS-Net			58.81 %	70.59 %	56.27 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

75	FEMV-RCNN		code	58.78 %	68.31 %	56.39 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

76	MMF			58.74 %	71.95 %	54.56 %	1 s	1 core @ 2.5 Ghz (C/C++)

77	SRDL			58.70 %	68.45 %	56.23 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
78	MMLab PV-RCNN		code	58.37 %	68.88 %	55.38 %	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.
79	casxv1			58.34 %	71.44 %	55.96 %	0.01 s	1 core @ 2.5 Ghz (Python)

80	F3D			58.25 %	67.94 %	55.96 %	0.01 s	1 core @ 2.5 Ghz (C/C++)

81	Point-GNN		code	58.20 %	71.59 %	54.06 %	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.
82	DeepParts			58.15 %	71.47 %	51.92 %	~1 s	GPU @ 2.5 Ghz (Matlab)
Y. Tian, P. Luo, X. Wang and X. Tang: Deep Learning Strong Parts for Pedestrian Detection. ICCV 2015.
83	CompACT-Deep			58.14 %	70.93 %	52.29 %	1 s	1 core @ 2.5 Ghz (Matlab + C/C++)
Z. Cai, M. Saberian and N. Vasconcelos: Learning Complexity-Aware Cascades for Deep Pedestrian Detection. ICCV 2015.
84	EPNet++			58.10 %	68.58 %	55.58 %	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.
85	DSGN++		code	58.09 %	69.70 %	54.45 %	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.
86	IA-SSDx			58.01 %	71.00 %	53.95 %	0.01 s	1 core @ 2.5 Ghz (C/C++)

87	casx			58.01 %	71.00 %	53.95 %	0.01 s	1 core @ 2.5 Ghz (C/C++)

88	MMLab-PartA^2		code	57.96 %	68.78 %	54.01 %	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.
89	SVGA-Net			57.92 %	67.81 %	55.25 %	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.
90	Rnet			57.87 %	66.14 %	54.87 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

91	AVOD-FPN		code	57.87 %	67.95 %	55.23 %	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.
92	DFAF3D			57.65 %	67.45 %	53.89 %	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.
93	Anonymous			57.63 %	68.62 %	53.59 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

94	HA-PillarNet			57.59 %	66.29 %	55.14 %	0.05 s	1 core @ 2.5 Ghz (Python)

95	PA-Det3D			57.55 %	66.80 %	55.18 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

96	U_PV_V2_ep100_80			57.50 %	66.11 %	55.21 %	0... s	1 core @ 2.5 Ghz (Python)

97	POP-RCNN			57.40 %	68.55 %	53.42 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

98	Faraway-Frustum		code	57.35 %	67.88 %	54.42 %	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.
99	PDV		code	57.34 %	65.94 %	54.21 %	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.
100	SIF			57.32 %	67.78 %	54.86 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
101	PG-RCNN		code	57.31 %	67.77 %	54.83 %	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.
102	FromVoxelToPoint		code	57.26 %	68.26 %	54.74 %	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.
103	u_second_v4_epoch_10			57.25 %	67.10 %	55.01 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

104	SemanticVoxels			57.22 %	67.62 %	54.90 %	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.
105	Anonymous			57.12 %	68.51 %	53.06 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

106	IIOU		code	57.05 %	66.36 %	53.06 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)

107	RFA			57.00 %	66.02 %	53.63 %	0.1s	GPU @ 2 Ghz (python)

108	IA-SSD (single)		code	56.87 %	66.69 %	54.68 %	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.
109	CAT-Det			56.75 %	67.15 %	53.44 %	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.
110	FRCNN+Or		code	56.68 %	71.64 %	51.53 %	0.09 s	Titan Xp GPU
C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine 2018. C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features. IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.
111	FilteredICF			56.53 %	69.79 %	50.32 %	~ 2 s	>8 cores @ 2.5 Ghz (Matlab + C/C++)
S. Zhang, R. Benenson and B. Schiele: Filtered Channel Features for Pedestrian Detection. CVPR 2015.
112	ARPNET			56.42 %	69.08 %	52.69 %	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.
113	MonoRUn		code	56.40 %	73.05 %	51.40 %	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.
114	MV-RGBD-RF			56.18 %	72.99 %	49.72 %	4 s	4 cores @ 2.5 Ghz (C/C++)
A. Gonzalez, D. Vazquez, A. Lopez and J. Amores: On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts.. IEEE Trans. on Cybernetics 2016. A. Gonzalez, G. Villalonga, J. Xu, D. Vazquez, J. Amores and A. Lopez: Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection. IEEE Intelligent Vehicles Symposium (IV) 2015.
115	U_PV_V2_ep_100_100			56.18 %	64.52 %	54.12 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

116	MVENet			56.09 %	66.93 %	53.48 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

117	GeVo			56.03 %	65.09 %	53.66 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

118	U_second_v4_ep_100_8			56.03 %	65.94 %	53.94 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

119	HAF-PVP_test			55.96 %	65.29 %	53.25 %	0.09 s	1 core @ 2.5 Ghz (C/C++)

120	HMFI		code	55.96 %	66.20 %	53.24 %	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.
121	HybridPillars			55.90 %	64.07 %	53.46 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

122	STNet			55.90 %	65.63 %	53.70 %	0.60 s	1 core @ 2.5 Ghz (Python)

123	MGAF-3DSSD		code	55.80 %	66.31 %	52.02 %	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.
124	GUPNet		code	55.65 %	74.95 %	48.44 %	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.
125	MLOD		code	55.62 %	68.42 %	51.45 %	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.
126	HybridPillars (SSD)			55.54 %	65.55 %	52.96 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

127	RAFDet			55.41 %	64.91 %	53.19 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

128	B2PE			55.38 %	63.90 %	53.18 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

129	MLAFF			55.22 %	67.96 %	53.15 %	0.39 s	2 cores @ 2.5 Ghz (Python)

130	DEVIANT		code	55.16 %	74.27 %	50.21 %	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.
131	PointPillars		code	55.10 %	65.29 %	52.39 %	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.
132	StereoDistill			55.09 %	69.00 %	50.95 %	0.4 s	1 core @ 2.5 Ghz (Python)
Z. Liu, X. Ye, X. Tan, D. Errui, Y. Zhou and X. Bai: StereoDistill: Pick the Cream from LiDAR for Distilling Stereo-based 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2023.
133	STD		code	55.04 %	68.33 %	50.85 %	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.
134	OPA-3D		code	54.92 %	73.93 %	47.87 %	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.
135	MSAW			54.91 %	68.16 %	52.64 %	0.42 s	2 cores @ 2.5 Ghz (Python)

136	HA-SSD			54.83 %	64.15 %	52.65 %	1 s	1 core @ 2.5 Ghz (C/C++)

137	Vote3Deep			54.80 %	67.99 %	51.17 %	1.5 s	4 cores @ 2.5 Ghz (C/C++)
M. Engelcke, D. Rao, D. Zeng Wang, C. Hay Tong and I. Posner: Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks. ArXiv e-prints 2016.
138	M3DeTR		code	54.78 %	63.15 %	52.49 %	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.
139	L-AUG			54.61 %	65.71 %	51.67 %	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.
140	RealSynthesis-SECOND			54.18 %	63.77 %	50.93 %	0.05 s	1 core @ 2.5 Ghz (Python)

141	epBRM		code	54.13 %	62.90 %	51.95 %	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.
142	DFSemONet(Baseline)			54.13 %	65.42 %	52.05 %	0.04 s	GPU @ 2.5 Ghz (Python)

143	DVFENet			54.13 %	63.54 %	51.79 %	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.
144	DTSSD			53.84 %	62.75 %	51.66 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

145	XView			53.83 %	62.27 %	51.61 %	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.
146	PointPainting			53.76 %	61.86 %	50.61 %	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.
147	MonoInsight			53.72 %	67.31 %	47.54 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

148	MonoInsight			53.72 %	67.31 %	47.54 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

149	PDV2			53.54 %	65.59 %	47.65 %	3.7 s	1 core @ 3.0 Ghz Matlab (C/C++)
J. Shen, X. Zuo, J. Li, W. Yang and H. Ling: A novel pixel neighborhood differential statistic feature for pedestrian and face detection . Pattern Recognition 2017.
150	PR-SSD			53.52 %	62.55 %	50.04 %	0.02 s	GPU @ 2.5 Ghz (Python)

151	Mix-Teaching		code	53.52 %	67.34 %	47.45 %	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.
152	DA-Net			53.36 %	68.50 %	48.89 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

153	AMVFNet		code	53.28 %	62.79 %	49.69 %	0.04 s	GPU @ 2.5 Ghz (Python)

154	Cube R-CNN		code	53.27 %	64.96 %	47.84 %	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.
155	GF-pointnet			53.26 %	62.91 %	50.71 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

156	PVTr			53.23 %	62.15 %	51.04 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

157	TAFT			53.15 %	67.62 %	47.08 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
J. Shen, X. Zuo, W. Yang, D. Prokhorov, X. Mei and H. Ling: Differential Features for Pedestrian Detection: A Taylor Series Perspective. IEEE Transactions on Intelligent Transportation Systems 2018.
158	Disp R-CNN		code	52.98 %	71.79 %	48.20 %	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.
159	Disp R-CNN (velo)		code	52.90 %	71.63 %	48.15 %	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.
160	pAUCEnsT			52.88 %	65.84 %	46.97 %	60 s	1 core @ 2.5 Ghz (Matlab + C/C++)
S. Paisitkriangkrai, C. Shen and A. Hengel: Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning. arXiv 2014.
161	SparVox3D			52.84 %	69.33 %	48.49 %	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.
162	AAMVFNet		code	52.73 %	63.13 %	50.44 %	0.04 s	GPU @ 2.5 Ghz (Python)

163	MonoAIU			52.68 %	71.73 %	45.61 %	0.03 s	GPU @ 2.5 Ghz (Python)

164	PFF3D		code	52.53 %	62.12 %	50.27 %	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.
165	IA-SSD (multi)		code	52.45 %	65.07 %	50.20 %	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.
166	MVAF-Net(3-classes)			52.37 %	64.19 %	49.14 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

167	S-AT GCN			52.30 %	62.01 %	50.10 %	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.
168	ACCF			52.28 %	62.54 %	50.16 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

169	MMLAB LIGA-Stereo		code	52.18 %	65.59 %	49.29 %	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.
170	GS-FPS		code	51.98 %	62.86 %	49.70 %	TBD s	1 core @ 2.5 Ghz (C/C++)

171	HINTED			51.95 %	66.52 %	47.83 %	0.04 s	1 core @ 2.5 Ghz (C/C++)

172	Plane-Constraints			51.57 %	64.64 %	46.98 %	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.
173	Shift R-CNN (mono)		code	51.30 %	70.86 %	46.37 %	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.
174	OA-TSSD			51.19 %	60.26 %	49.28 %	20 s	8 cores @ 2.5 Ghz (C/C++)

175	APDM		code	51.08 %	61.99 %	48.24 %	0.7 s	1 core @ 2.5 Ghz (Python)

176	DTSSD			50.09 %	59.08 %	48.08 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

177	SCNet			49.61 %	60.95 %	46.91 %	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.
178	MMLab-PointRCNN		code	49.41 %	58.93 %	46.44 %	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.
179	HomoLoss(monoflex)		code	48.97 %	63.77 %	44.60 %	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.
180	MMpointpillars			48.95 %	60.01 %	46.12 %	0.05 s	1 core @ 2.5 Ghz (Python)

181	PEF		code	48.93 %	59.21 %	46.66 %	N/A s	1 core @ 2.5 Ghz (C/C++)

182	Voxel-MAE+SECOND		code	48.80 %	57.27 %	46.87 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

183	HA PillarNet			48.78 %	59.59 %	46.03 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

184	PS++			48.73 %	61.33 %	44.66 %	0.4 s	1 core @ 2.5 Ghz (C/C++)

185	ACFD		code	48.63 %	61.62 %	44.15 %	0.2 s	4 cores @ >3.5 Ghz (C/C++)
M. Dimitrievski, P. Veelaert and W. Philips: Semantically aware multilateral filter for depth upsampling in automotive LiDAR point clouds. IEEE Intelligent Vehicles Symposium, IV 2017, Los Angeles, CA, USA, June 11-14, 2017 2017.
186	R-CNN			48.57 %	62.88 %	43.05 %	4 s	GPU @ 3.3 Ghz (C/C++)
J. Hosang, M. Omran, R. Benenson and B. Schiele: Taking a Deeper Look at Pedestrians. arXiv 2015.
187	prcnn_v18_80_100			48.53 %	60.07 %	45.72 %	0.1 s	1 core @ 2.5 Ghz (Python)

188	Anonymous			48.51 %	65.02 %	41.77 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

189	GraphAlign			48.47 %	55.17 %	46.68 %	0.03 s	GPU @ 2.0 Ghz (Python)

190	IIOU_LDR		code	48.40 %	60.07 %	46.41 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

191	ROT_S3D			48.11 %	59.38 %	46.18 %	0.1 s	GPU @ 2.5 Ghz (Python)

192	MonoATT_V2		code	48.07 %	64.25 %	43.73 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

193	MonoTRKD			47.70 %	63.28 %	43.16 %	40 s	1 core @ 2.5 Ghz (Python)

194	MonoLiG		code	47.69 %	62.87 %	43.27 %	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.
195	MonoFlex			47.58 %	62.64 %	43.15 %	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.
196	BirdNet+		code	47.50 %	54.78 %	45.53 %	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.
197	MVAF-Net(3-classes)			46.87 %	57.07 %	44.08 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

198	CMKD		code	46.84 %	61.04 %	42.92 %	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.
199	VSAC			46.33 %	59.10 %	44.07 %	0.07 s	1 core @ 1.0 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
200	MonOAPC			46.31 %	60.93 %	42.05 %	0035 s	1 core @ 2.5 Ghz (Python)

201	MMpp			46.09 %	56.10 %	43.62 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

202	SS3D			45.79 %	61.58 %	41.14 %	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.
203	MonoRCNN++		code	45.76 %	60.29 %	39.39 %	0.07 s	GPU @ 2.5 Ghz (Python)
X. Shi, Z. Chen and T. Kim: Multivariate Probabilistic Monocular 3D Object Detection. WACV 2023.
204	ACF			45.67 %	59.81 %	40.88 %	1 s	1 core @ 3.5 Ghz (Matlab + C/C++)
P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014.
205	3Dv2			45.64 %	59.74 %	41.29 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

206	Fusion-DPM		code	44.99 %	58.93 %	40.19 %	~ 30 s	1 core @ 3.5 Ghz (Matlab + C/C++)
C. Premebida, J. Carreira, J. Batista and U. Nunes: Pedestrian Detection Combining RGB and Dense LIDAR Data. IROS 2014.
207	ACF-MR			44.79 %	58.29 %	39.94 %	0.6 s	1 core @ 3.5 Ghz (C/C++)
R. Rajaram, E. Ohn-Bar and M. Trivedi: Looking at Pedestrians at Different Scales: A Multi-resolution Approach and Evaluations. T-ITS 2016.
208	MonoTRKDv2			44.54 %	59.66 %	40.12 %	40 s	1 core @ 2.5 Ghz (Python)

209	P2P		code	44.30 %	55.25 %	42.40 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)

210	LPCG-Monoflex		code	44.13 %	62.44 %	39.46 %	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.
211	HA-SSVM			43.87 %	58.76 %	38.81 %	21 s	1 core @ >3.5 Ghz (Matlab + C/C++)
J. Xu, S. Ramos, D. Vázquez and A. López: Hierarchical Adaptive Structural SVM for Domain Adaptation. IJCV 2016.
212	AB3DMOT		code	43.86 %	54.55 %	40.99 %	0.0047s	1 core @ 2.5 Ghz (python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
213	MonoEF			43.73 %	58.79 %	39.45 %	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.
214	Anonymous			43.65 %	55.61 %	39.54 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

215	DSC3D			43.64 %	58.46 %	39.31 %	0.31 s	GPU @ 2.5 Ghz (Python)

216	D4LCN		code	43.50 %	59.55 %	37.12 %	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.
217	DMF			43.43 %	52.99 %	41.29 %	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.
218	MonoDDE			43.36 %	57.80 %	39.00 %	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.
219	DPM-VOC+VP			43.26 %	59.21 %	38.12 %	8 s	1 core @ 2.5 Ghz (C/C++)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015.
220	ACF-SC			42.97 %	53.30 %	38.12 %	<0.3 s	1 core @ >3.5 Ghz (Matlab + C/C++)
C. Cadena, A. Dick and I. Reid: A Fast, Modular Scene Understanding System using Context-Aware Object Detection. Robotics and Automation (ICRA), 2015 IEEE International Conference on 2015.
221	OccupancyM3D			42.95 %	57.08 %	38.28 %	0.11 s	1 core @ 2.5 Ghz (C/C++)

222	MonoDTR			42.86 %	59.44 %	38.57 %	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.
223	SquaresICF		code	42.61 %	57.08 %	37.85 %	1 s	GPU @ >3.5 Ghz (C/C++)
R. Benenson, M. Mathias, T. Tuytelaars and L. Gool: Seeking the strongest rigid detector. CVPR 2013.
224	CG-Stereo			42.54 %	54.64 %	38.45 %	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.
225	BirdNet+ (legacy)		code	41.97 %	51.38 %	40.15 %	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.
226	DDMP-3D			41.54 %	56.73 %	35.52 %	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.
227	CSW3D			41.50 %	53.76 %	37.25 %	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.
228	M3D-RPN		code	41.46 %	56.64 %	37.31 %	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 .
229	YOLOStereo3D		code	41.46 %	56.20 %	37.07 %	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.
230	fuf			41.42 %	53.95 %	37.48 %	10 s	1 core @ 2.5 Ghz (C/C++)

231	BKDStereo3D		code	41.17 %	55.94 %	34.99 %	0.1 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
232	CIE			41.04 %	53.27 %	37.73 %	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.
233	SubCat			40.50 %	53.75 %	35.66 %	1.2 s	6 cores @ 2.5 Ghz (Matlab + C/C++)
E. Ohn-Bar and M. Trivedi: Fast and Robust Object Detection Using Visual Subcategories. Computer Vision and Pattern Recognition Workshops Mobile Vision 2014.
234	PS-fld		code	40.47 %	55.47 %	36.65 %	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.
235	DSGN		code	39.93 %	49.28 %	38.13 %	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.
236	RT3D-GMP			39.83 %	55.56 %	35.18 %	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.
237	SparsePool		code	39.59 %	50.81 %	35.91 %	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.
238	SparsePool		code	39.43 %	50.94 %	35.78 %	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.
239	AVOD		code	39.43 %	50.90 %	35.75 %	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.
240	ACF			39.12 %	48.42 %	35.03 %	0.2 s	1 core @ >3.5 Ghz (Matlab + C/C++)
P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014. P. Doll\'ar: Piotr's Image and Video Matlab Toolbox (PMT). .
241	MonoLTKD			38.60 %	54.33 %	34.12 %	0.04 s	1 core @ 2.5 Ghz (C/C++)

242	UNM3D			37.67 %	51.80 %	33.73 %	na s	1 core @ 2.5 Ghz (C/C++)

243	LSVM-MDPM-sv			37.26 %	50.74 %	33.13 %	10 s	4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010. A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout. NIPS 2011.
244	MM3DV2			37.13 %	51.09 %	33.36 %	NA s	1 core @ 2.5 Ghz (C/C++)

245	ODGS			37.12 %	46.01 %	34.56 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

246	BKDStereo3D w/o KD		code	37.02 %	50.58 %	32.92 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
247	multi-task CNN			37.00 %	49.38 %	33.46 %	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.
248	MonoSIM			36.71 %	49.49 %	33.24 %	0.16 s	1 core @ 2.5 Ghz (Python)

249	Complexer-YOLO			36.45 %	42.16 %	32.91 %	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.
250	LSVM-MDPM-us		code	35.92 %	48.73 %	31.70 %	10 s	4 cores @ 3.0 Ghz (C/C++)

Table as LaTeX | Only published Methods

Cyclist

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	OFFNet			86.24 %	89.05 %	81.37 %	0.1 s	GPU @ 2.5 Ghz (Python)

2	TED		code	84.36 %	92.60 %	78.43 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
H. Wu, C. Wen, W. Li, R. Yang and C. Wang: Transformation-Equivariant 3D Object Detection for Autonomous Driving. AAAI 2023.
3	CasA++		code	84.26 %	92.38 %	78.42 %	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.
4	LoGoNet		code	84.00 %	90.14 %	77.97 %	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.
5	CasA		code	83.21 %	92.86 %	77.12 %	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.
6	HMFI		code	81.76 %	89.35 %	74.93 %	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.
7	IMLIDAR(base)			81.72 %	90.91 %	75.19 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

8	RangeIoUDet			81.67 %	90.43 %	74.90 %	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.
9	PA-RCNN		code	81.41 %	90.11 %	72.90 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

10	USVLab BSAODet		code	81.36 %	86.82 %	74.40 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
W. Xiao, Y. Peng, C. Liu, J. Gao, Y. Wu and X. Li: Balanced Sample Assignment and Objective for Single-Model Multi-Class 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2023.
11	PV-PMRTNet			81.12 %	89.07 %	74.38 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

12	HA-PillarNet			81.03 %	89.88 %	75.05 %	0.05 s	1 core @ 2.5 Ghz (Python)

13	U_PV_V2_ep100_80			80.85 %	89.46 %	74.01 %	0... s	1 core @ 2.5 Ghz (Python)

14	CAT-Det			80.70 %	87.94 %	73.86 %	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.
15	SPG_mini		code	80.58 %	87.77 %	74.86 %	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.
16	DSA-PV-RCNN		code	80.57 %	88.65 %	74.81 %	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.
17	PIPC-3Ddet		code	80.49 %	89.69 %	73.73 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

18	BtcDet		code	80.46 %	88.41 %	74.59 %	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.
19	Rnet			80.44 %	87.56 %	73.55 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

20	MMLab PV-RCNN		code	80.42 %	86.62 %	73.64 %	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.
21	EQ-PVRCNN		code	80.37 %	89.07 %	74.20 %	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.
22	Anonymous			80.24 %	90.30 %	71.50 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

23	POP-RCNN			80.20 %	90.41 %	71.46 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

24	PSMS-Net			80.10 %	88.76 %	73.27 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

25	HINTED			80.04 %	86.76 %	73.45 %	0.04 s	1 core @ 2.5 Ghz (C/C++)

26	PDV		code	79.84 %	88.76 %	73.04 %	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.
27	CZY_PPF_Net			79.75 %	87.34 %	73.05 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

28	PEF		code	79.70 %	91.33 %	72.64 %	N/A s	1 core @ 2.5 Ghz (C/C++)

29	U_PV_V2_ep_100_100			79.60 %	86.87 %	73.32 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

30	M3DeTR		code	79.29 %	87.38 %	72.46 %	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.
31	VPNetv2			79.22 %	89.12 %	70.60 %	0.1 s	1 core @ 2.5 Ghz (Python)

32	HAF-PVP_test			79.19 %	89.84 %	71.93 %	0.09 s	1 core @ 2.5 Ghz (C/C++)

33	HotSpotNet			78.81 %	86.06 %	71.74 %	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.
34	HybridPillars			78.81 %	87.30 %	72.25 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

35	IA-SSD (single)		code	78.71 %	88.99 %	72.03 %	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.
36	PG-RCNN		code	78.69 %	88.17 %	72.16 %	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.
37	GeVo			78.68 %	87.50 %	72.46 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

38	3ONet			78.51 %	90.18 %	71.38 %	0.09 s	1 core @ 2.5 Ghz (C/C++)

39	PA-Det3D			78.31 %	87.56 %	71.82 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

40	MMF			78.29 %	89.26 %	71.70 %	1 s	1 core @ 2.5 Ghz (C/C++)

41	MMLab-PartA^2		code	78.29 %	88.90 %	71.19 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
42	F-ConvNet		code	78.05 %	86.75 %	68.12 %	0.47 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
43	PointPainting			78.04 %	87.70 %	69.27 %	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.
44	HPV-RCNN			77.94 %	89.26 %	71.06 %	0.08 s	1 core @ 2.5 Ghz (Python)

45	MPFusion			77.90 %	90.42 %	72.35 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

46	FEMV-RCNN		code	77.83 %	87.12 %	70.66 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

47	DFAF3D			77.74 %	87.20 %	70.77 %	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.
48	IA-SSDx			77.58 %	88.94 %	70.80 %	0.01 s	1 core @ 2.5 Ghz (C/C++)

49	casx			77.58 %	88.94 %	70.80 %	0.01 s	1 core @ 2.5 Ghz (C/C++)

50	PVTr			77.49 %	89.71 %	70.58 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

51	IMOU_ALG			77.46 %	86.30 %	69.79 %	0.01 s	1 core @ 2.5 Ghz (C/C++)

52	RPF3D			77.20 %	89.45 %	70.36 %	0.1 s	1 core @ 2.5 Ghz (Python)

53	GraphAlign			77.15 %	84.72 %	72.34 %	0.03 s	GPU @ 2.0 Ghz (Python)

54	RAFDet			77.00 %	87.23 %	70.90 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

55	Anonymous			76.98 %	88.37 %	70.15 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

56	P2V-RCNN			76.93 %	88.40 %	70.35 %	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.
57	EOTL		code	76.88 %	85.62 %	66.04 %	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.
58	RRC		code	76.81 %	86.81 %	66.59 %	3.6 s	GPU @ 2.5 Ghz (C/C++)
J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y. Tai and L. Xu: Accurate Single Stage Detector Using Recurrent Rolling Convolution. CVPR 2017.
59	af			76.63 %	85.66 %	71.49 %	1 s	GPU @ 2.5 Ghz (Python)

60	MVENet			76.37 %	86.56 %	69.69 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

61	GF-pointnet			76.31 %	85.23 %	69.74 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

62	AAMVFNet		code	76.28 %	85.63 %	69.91 %	0.04 s	GPU @ 2.5 Ghz (Python)

63	AMVFNet		code	75.81 %	84.77 %	69.81 %	0.04 s	GPU @ 2.5 Ghz (Python)

64	F3D			75.75 %	88.38 %	69.02 %	0.01 s	1 core @ 2.5 Ghz (C/C++)

65	DA-Net			75.64 %	86.64 %	71.06 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

66	PR-SSD			75.64 %	85.78 %	70.35 %	0.02 s	GPU @ 2.5 Ghz (Python)

67	DTSSD			75.55 %	87.11 %	69.43 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

68	casxv1			75.52 %	88.33 %	68.68 %	0.01 s	1 core @ 2.5 Ghz (Python)

69	ACDet		code	75.41 %	88.54 %	69.45 %	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.
70	u_second_v4_epoch_10			75.38 %	87.96 %	69.54 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

71	B2PE			75.35 %	86.18 %	70.09 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

72	MS-CNN		code	75.30 %	84.88 %	65.27 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection. ECCV 2016.
73	LGSL			75.29 %	84.01 %	70.60 %	0.1 s	GPU @ 2.5 Ghz (Python)

74	STNet			75.22 %	87.47 %	68.28 %	0.60 s	1 core @ 2.5 Ghz (Python)

75	TuSimple		code	75.22 %	83.68 %	65.22 %	1.6 s	GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016. K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016.
76	SVGA-Net			75.14 %	85.13 %	68.14 %	0.03s	1 core @ 2.5 Ghz (Python + C/C++)
Q. He, Z. Wang, H. Zeng, Y. Zeng and Y. Liu: SVGA-Net: Sparse Voxel-Graph Attention Network for 3D Object Detection from Point Clouds. AAAI 2022.
77	Point-GNN		code	75.08 %	85.75 %	68.69 %	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.
78	Fast-CLOCs			75.07 %	89.73 %	67.93 %	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.
79	DTSSD			74.81 %	86.18 %	68.26 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

80	OA-TSSD			74.79 %	87.90 %	68.52 %	20 s	8 cores @ 2.5 Ghz (C/C++)

81	Deep3DBox			74.78 %	84.36 %	64.05 %	1.5 s	GPU @ 2.5 Ghz (C/C++)
A. Mousavian, D. Anguelov, J. Flynn and J. Kosecka: 3D Bounding Box Estimation Using Deep Learning and Geometry. CVPR 2017.
82	U_second_v4_ep_100_8			74.75 %	84.43 %	67.71 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

83	prcnn_v18_80_100			74.74 %	86.53 %	67.00 %	0.1 s	1 core @ 2.5 Ghz (Python)

84	ACCF			74.65 %	87.37 %	69.59 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

85	VPFNet		code	74.52 %	82.60 %	66.04 %	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.
86	HA-SSD			74.45 %	84.37 %	67.85 %	1 s	1 core @ 2.5 Ghz (C/C++)

87	RealSynthesis-SECOND			74.37 %	87.41 %	66.91 %	0.05 s	1 core @ 2.5 Ghz (Python)

88	3DSSD		code	74.12 %	87.09 %	67.67 %	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.
89	SDP+RPN			73.85 %	82.59 %	64.87 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016. S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 2015.
90	SRDL			73.68 %	85.44 %	66.94 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
91	DVFENet			73.66 %	85.45 %	67.10 %	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.
92	Faraway-Frustum		code	73.63 %	85.43 %	66.64 %	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.
93	sensekitti		code	73.48 %	82.90 %	64.03 %	4.5 s	GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.
94	L-AUG			73.43 %	83.88 %	68.12 %	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.
95	MMLab-PointRCNN		code	73.42 %	86.21 %	66.45 %	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.
96	HybridPillars (SSD)			73.24 %	82.36 %	67.38 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

97	IIOU		code	73.20 %	86.92 %	66.11 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)

98	SIF			73.19 %	85.18 %	65.41 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
99	F-PointNet		code	73.16 %	86.86 %	65.21 %	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.
100	FromVoxelToPoint		code	73.16 %	87.07 %	65.98 %	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.
101	XView			73.16 %	88.02 %	65.37 %	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	S-AT GCN			72.81 %	82.79 %	66.72 %	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.
103	H^23D R-CNN		code	72.73 %	85.50 %	65.81 %	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.
104	IOUFusion			72.49 %	86.41 %	65.60 %	0.1 s	GPU @ 2.5 Ghz (Python)

105	GS-FPS		code	72.17 %	84.60 %	66.52 %	TBD s	1 core @ 2.5 Ghz (C/C++)

106	P2P		code	72.17 %	82.32 %	65.65 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)

107	Voxel-MAE+SECOND		code	72.09 %	82.55 %	65.45 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

108	MonoPSR		code	72.08 %	82.06 %	62.43 %	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.
109	ARPNET			71.95 %	84.96 %	65.21 %	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.
110	SubCNN			71.72 %	79.36 %	62.74 %	2 s	GPU @ 3.5 Ghz (Python + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.
111	STD		code	71.63 %	83.99 %	64.92 %	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.
112	single-src-hirige		code	70.46 %	80.95 %	60.99 %	0.01 s	GPU @ 2.5 Ghz (Python)

113	IA-SSD (multi)		code	70.46 %	84.98 %	65.55 %	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.
114	MGAF-3DSSD		code	70.41 %	86.42 %	63.26 %	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.
115	AB3DMOT		code	70.18 %	82.86 %	63.55 %	0.0047s	1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
116	fuf			69.67 %	84.99 %	62.91 %	10 s	1 core @ 2.5 Ghz (C/C++)

117	IIOU_LDR		code	69.49 %	83.10 %	64.59 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

118	PointPillars		code	68.98 %	83.97 %	62.17 %	16 ms	1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
119	Vote3Deep			68.82 %	78.41 %	62.50 %	1.5 s	4 cores @ 2.5 Ghz (C/C++)
M. Engelcke, D. Rao, D. Zeng Wang, C. Hay Tong and I. Posner: Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks. ArXiv e-prints 2016.
120	3DOP		code	68.71 %	80.52 %	61.07 %	3s	GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
121	ROT_S3D			68.53 %	84.35 %	63.76 %	0.1 s	GPU @ 2.5 Ghz (Python)

122	Pose-RCNN			68.40 %	81.53 %	59.43 %	2 s	>8 cores @ 2.5 Ghz (Python)
M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on 2016.
123	EPNet++			68.30 %	80.27 %	63.00 %	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.
124	TANet		code	68.20 %	82.24 %	62.13 %	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.
125	IVA		code	67.57 %	78.48 %	58.83 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
Y. Zhu, J. Wang, C. Zhao, H. Guo and H. Lu: Scale-adaptive Deconvolutional Regression Network for Pedestrian Detection. ACCV 2016. S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 2015.
126	MSAW			67.22 %	82.14 %	62.13 %	0.42 s	2 cores @ 2.5 Ghz (Python)

127	DeepStereoOP			67.22 %	79.35 %	58.60 %	3.4 s	GPU @ 3.5 Ghz (Matlab + C/C++)
C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.
128	Cube R-CNN		code	66.98 %	81.99 %	58.56 %	0.05 s	GPU @ 2.5 Ghz (Python)
G. Brazil, A. Kumar, J. Straub, N. Ravi, J. Johnson and G. Gkioxari: Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild. CVPR 2023.
129	MMpp			66.87 %	80.54 %	60.69 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

130	MMpointpillars			66.66 %	77.03 %	61.25 %	0.05 s	1 core @ 2.5 Ghz (Python)

131	APDM		code	66.65 %	80.16 %	60.51 %	0.7 s	1 core @ 2.5 Ghz (Python)

132	HA PillarNet			66.63 %	78.41 %	60.09 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

133	FII-CenterNet		code	66.54 %	79.04 %	57.76 %	0.09 s	GPU @ 2.5 Ghz (Python)
S. Fan, F. Zhu, S. Chen, H. Zhang, B. Tian, Y. Lv and F. Wang: FII-CenterNet: An Anchor-Free Detector With Foreground Attention for Traffic Object Detection. IEEE Transactions on Vehicular Technology 2021.
134	epBRM		code	66.51 %	79.65 %	60.31 %	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.
135	DD3D-dequity			66.43 %	81.25 %	58.92 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

136	PFF3D		code	66.25 %	79.44 %	60.11 %	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.
137	DD3D		code	65.98 %	81.13 %	58.86 %	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) .
138	PointRGBNet			65.98 %	79.87 %	59.75 %	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.
139	VSAC			65.90 %	82.03 %	59.18 %	0.07 s	1 core @ 1.0 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
140	BirdNet+		code	65.40 %	72.96 %	60.23 %	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.
141	Mono3D		code	65.15 %	77.19 %	57.88 %	4.2 s	GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.
142	MLAFF			64.41 %	80.28 %	58.52 %	0.39 s	2 cores @ 2.5 Ghz (Python)

143	DMF			63.39 %	74.69 %	56.96 %	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.
144	PiFeNet		code	63.34 %	78.05 %	56.46 %	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.
145	Faster R-CNN		code	62.86 %	72.40 %	54.97 %	2 s	GPU @ 3.5 Ghz (Python + C/C++)
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards Real- Time Object Detection with Region Proposal Networks. NIPS 2015.
146	SCNet			62.50 %	78.48 %	56.34 %	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.
147	DSGN++		code	62.10 %	77.71 %	55.78 %	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.
148	StereoDistill			61.46 %	80.92 %	54.64 %	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.
149	DFSemONet(Baseline)			61.46 %	77.65 %	55.87 %	0.04 s	GPU @ 2.5 Ghz (Python)

150	AVOD-FPN		code	60.79 %	70.38 %	55.37 %	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.
151	SDP+CRC (ft)			60.72 %	75.63 %	53.00 %	0.6 s	GPU @ 2.5 Ghz (C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
152	MVAF-Net(3-classes)			60.04 %	74.01 %	55.71 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

153	MonoInsight			59.86 %	76.21 %	51.17 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

154	MonoInsight			59.86 %	76.21 %	51.17 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

155	Complexer-YOLO			59.78 %	66.94 %	55.63 %	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.
156	MVAF-Net(3-classes)			59.54 %	72.32 %	55.44 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

157	Mix-Teaching		code	58.65 %	75.15 %	50.54 %	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.
158	Regionlets			58.52 %	71.12 %	50.83 %	1 s	>8 cores @ 2.5 Ghz (C/C++)
X. Wang, M. Yang, S. Zhu and Y. Lin: Regionlets for Generic Object Detection. T-PAMI 2015. W. Zou, X. Wang, M. Sun and Y. Lin: Generic Object Detection with Dense Neural Patterns and Regionlets. British Machine Vision Conference 2014. C. Long, X. Wang, G. Hua, M. Yang and Y. Lin: Accurate Object Detection with Location Relaxation and Regionlets Relocalization. Asian Conference on Computer Vision 2014.
159	MonoLiG		code	58.35 %	80.41 %	51.21 %	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.
160	FRCNN+Or		code	57.01 %	70.99 %	50.14 %	0.09 s	Titan Xp GPU
C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine 2018. C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features. IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.
161	QD-3DT		code	56.51 %	75.55 %	49.70 %	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.
162	MonoPair			56.37 %	74.77 %	48.37 %	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.
163	MLOD		code	56.04 %	75.35 %	49.11 %	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.
164	Anonymous			55.47 %	74.27 %	47.25 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

165	MonoATT_V2		code	54.84 %	78.21 %	47.98 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

166	MonoFlex			54.76 %	72.41 %	46.21 %	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.
167	MonoLSS			54.63 %	74.54 %	47.98 %	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.
168	BirdNet+ (legacy)		code	54.61 %	74.97 %	50.29 %	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.
169	MMLAB LIGA-Stereo		code	54.57 %	74.40 %	48.11 %	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.
170	HomoLoss(monoflex)		code	54.12 %	70.14 %	46.16 %	0.04 s	1 core @ 2.5 Ghz (Python)
J. Gu, B. Wu, L. Fan, J. Huang, S. Cao, Z. Xiang and X. Hua: Homography Loss for Monocular 3D Object Detection. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022.
171	MonoUNI		code	53.71 %	71.68 %	45.26 %	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.
172	monodle		code	53.29 %	70.78 %	45.01 %	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 .
173	LPCG-Monoflex		code	53.04 %	72.36 %	46.11 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
L. Peng, F. Liu, Z. Yu, S. Yan, D. Deng, Z. Yang, H. Liu and D. Cai: Lidar Point Cloud Guided Monocular 3D Object Detection. ECCV 2022.
174	AVOD		code	52.60 %	66.45 %	46.39 %	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.
175	PS++			52.29 %	72.60 %	46.82 %	0.4 s	1 core @ 2.5 Ghz (C/C++)

176	CMKD		code	51.76 %	73.18 %	45.37 %	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.
177	MonOAPC			51.68 %	68.18 %	44.08 %	0035 s	1 core @ 2.5 Ghz (Python)

178	3Dv2			51.59 %	73.71 %	44.86 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

179	MonoDDE			51.10 %	70.85 %	44.02 %	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.
180	MonoDTR			49.48 %	64.93 %	42.76 %	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.
181	MonoRUn		code	49.13 %	67.47 %	43.41 %	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.
182	MonoRCNN++		code	48.84 %	67.78 %	42.44 %	0.07 s	GPU @ 2.5 Ghz (Python)
X. Shi, Z. Chen and T. Kim: Multivariate Probabilistic Monocular 3D Object Detection. WACV 2023.
183	CG-Stereo			48.46 %	69.98 %	42.41 %	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.
184	BirdNet			47.64 %	64.91 %	44.59 %	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.
185	MonoAIU			46.87 %	69.33 %	40.57 %	0.03 s	GPU @ 2.5 Ghz (Python)

186	DEVIANT		code	46.42 %	67.71 %	39.44 %	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.
187	Disp R-CNN (velo)		code	46.37 %	63.22 %	40.15 %	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.
188	Disp R-CNN		code	46.37 %	63.24 %	40.15 %	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.
189	SparsePool		code	44.57 %	60.53 %	40.37 %	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.
190	Anonymous			43.40 %	67.49 %	38.66 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

191	Shift R-CNN (mono)		code	42.96 %	63.24 %	38.22 %	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.
192	D4LCN		code	42.86 %	65.29 %	36.29 %	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.
193	GUPNet		code	42.78 %	67.11 %	37.94 %	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.
194	M3D-RPN		code	41.54 %	61.54 %	35.23 %	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 .
195	MonoEF			41.19 %	51.06 %	35.70 %	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.
196	PS-fld		code	41.13 %	58.13 %	35.90 %	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.
197	Plane-Constraints			41.01 %	58.71 %	35.35 %	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.
198	MV-RGBD-RF			40.94 %	51.10 %	34.83 %	4 s	4 cores @ 2.5 Ghz (C/C++)
A. Gonzalez, D. Vazquez, A. Lopez and J. Amores: On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts.. IEEE Trans. on Cybernetics 2016. A. Gonzalez, G. Villalonga, J. Xu, D. Vazquez, J. Amores and A. Lopez: Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection. IEEE Intelligent Vehicles Symposium (IV) 2015.
199	MonoTRKDv2			39.61 %	56.76 %	34.50 %	40 s	1 core @ 2.5 Ghz (Python)

200	MonoTRKD			39.12 %	58.34 %	34.59 %	40 s	1 core @ 2.5 Ghz (Python)

201	MonoLTKD			38.84 %	58.63 %	33.99 %	0.04 s	1 core @ 2.5 Ghz (C/C++)

202	DDMP-3D			38.62 %	58.70 %	34.10 %	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.
203	CMAN			38.36 %	58.12 %	31.79 %	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.
204	OPA-3D		code	38.35 %	55.98 %	33.83 %	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.
205	Aug3D-RPN			36.69 %	51.49 %	30.04 %	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.
206	SparsePool		code	36.26 %	44.21 %	32.57 %	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.
207	SS3D			35.48 %	52.97 %	31.07 %	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.
208	DSGN		code	35.15 %	49.10 %	31.41 %	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.
209	pAUCEnsT			34.90 %	50.51 %	30.35 %	60 s	1 core @ 2.5 Ghz (Matlab + C/C++)
S. Paisitkriangkrai, C. Shen and A. Hengel: Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning. arXiv 2014.
210	TopNet-Retina			31.98 %	47.51 %	29.84 %	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.
211	DFR-Net			31.93 %	48.34 %	27.95 %	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.
212	SVDM-VIEW			31.75 %	47.79 %	27.83 %	1 s	1 core @ 2.5 Ghz (Python)

213	OccupancyM3D			30.22 %	41.76 %	26.32 %	0.11 s	1 core @ 2.5 Ghz (C/C++)

214	CIE			30.10 %	38.03 %	26.99 %	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.
215	MonoNeRD		code	29.89 %	45.35 %	26.49 %	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.
216	OC Stereo		code	28.76 %	43.18 %	24.80 %	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.
217	Vote3D			27.99 %	39.81 %	25.19 %	0.5 s	4 cores @ 2.8 Ghz (C/C++)
D. Wang and I. Posner: Voting for Voting in Online Point Cloud Object Detection. Proceedings of Robotics: Science and Systems 2015.
218	SGM3D		code	27.89 %	42.21 %	24.73 %	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.
219	LSVM-MDPM-us		code	27.81 %	37.66 %	24.83 %	10 s	4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
220	DPM-VOC+VP			27.73 %	41.58 %	24.61 %	8 s	1 core @ 2.5 Ghz (C/C++)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015.
221	RefinedMPL			27.17 %	44.47 %	22.84 %	0.15 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
222	CaDDN		code	27.13 %	40.03 %	23.23 %	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.
223	PGD-FCOS3D		code	26.48 %	44.28 %	23.03 %	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.
224	LSVM-MDPM-sv			26.05 %	35.70 %	23.56 %	10 s	4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010. A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout. NIPS 2011.
225	DPM-C8B1			25.57 %	41.47 %	21.93 %	15 s	4 cores @ 2.5 Ghz (Matlab + C/C++)
J. Yebes, L. Bergasa and M. García-Garrido: Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes. Sensors 2015. J. Yebes, L. Bergasa, R. Arroyo and A. Lázaro: Supervised learning and evaluation of KITTI's cars detector with DPM. IV 2014.
226	MM3DV2			25.28 %	30.00 %	22.67 %	NA s	1 core @ 2.5 Ghz (C/C++)

227	FMF-occlusion-net			23.59 %	37.41 %	21.20 %	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.
228	RT3D-GMP			22.90 %	33.64 %	19.87 %	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.
229	UNM3D			21.06 %	26.77 %	18.37 %	na s	1 core @ 2.5 Ghz (C/C++)

230	MonoSIM			20.28 %	27.90 %	17.23 %	0.16 s	1 core @ 2.5 Ghz (Python)

231	mBoW			17.63 %	26.66 %	16.02 %	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.
232	MonoGhost_Ped_Cycl			17.42 %	24.56 %	15.40 %	0.03 s	1 core @ 2.5 Ghz (Python)

233	MDSNet			16.64 %	28.23 %	14.14 %	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.
234	TopNet-HighRes			13.98 %	22.86 %	14.52 %	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.
235	ESGN			13.45 %	21.13 %	11.72 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
A. Gao, Y. Pang, J. Nie, Z. Shao, J. Cao, Y. Guo and X. Li: ESGN: Efficient Stereo Geometry Network for Fast 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2022.
236	RT3DStereo			12.96 %	19.58 %	11.47 %	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.
237	TopNet-UncEst			12.00 %	18.14 %	11.85 %	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.
238	init			0.13 %	0.08 %	0.07 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

239	YOLOv2		code	0.06 %	0.15 %	0.07 %	0.02 s	GPU @ 3.5 Ghz (C/C++)
J. Redmon, S. Divvala, R. Girshick and A. Farhadi: You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016. J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
240	TopNet-DecayRate			0.04 %	0.00 %	0.04 %	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.
241	DA3D			0.00 %	0.00 %	0.00 %	0.03 s	1 core @ 2.5 Ghz (Python)

Table as LaTeX | Only published Methods

The KITTI Vision Benchmark Suite

A project of Karlsruhe Institute of Technology
and Toyota Technological Institute at Chicago

Object Detection Evaluation 2012

Car

Pedestrian

Cyclist

Object Detection and Orientation Estimation Evaluation

Cars

The KITTI Vision Benchmark Suite

A project of Karlsruhe Institute of Technologyand Toyota Technological Institute at Chicago

Object Detection Evaluation 2012

Car

Pedestrian

Cyclist

Object Detection and Orientation Estimation Evaluation

Cars

A project of Karlsruhe Institute of Technology
and Toyota Technological Institute at Chicago