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

2	UDeerPEP		code	97.43 %	98.42 %	94.94 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

3	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.
4	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.
5	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.
6	DAF-SSD			96.28 %	96.95 %	88.75 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

7	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.
8	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.
9	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.
10	CASDC			96.13 %	98.66 %	93.26 %	0.1 s	1 core @ 2.5 Ghz (Python)

11	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.
12	ACF-Net			96.06 %	96.68 %	93.36 %	n/a s	1 core @ 2.5 Ghz (C/C++)

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	VirConv-L			95.96 %	98.74 %	93.19 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

18	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.
19	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.
20	PIPC-3Ddet		code	95.86 %	96.80 %	90.92 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

21	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.
22	CDF			95.83 %	96.22 %	90.77 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

23	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.
24	NIV-SSD			95.82 %	98.68 %	90.80 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

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	LightCPC		code	95.76 %	96.56 %	90.79 %	0.02 s	1 core @ 2.5 Ghz (Python + C/C++)

31	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.
32	SoRL			95.74 %	96.79 %	90.83 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

33	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.
34	HRNet++			95.72 %	98.82 %	90.81 %	0.07 s	1 core @ 2.5 Ghz (C/C++)

35	PA-RCNN		code	95.72 %	96.49 %	93.00 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

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

37	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.
38	MMF			95.69 %	96.71 %	90.84 %	1 s	1 core @ 2.5 Ghz (C/C++)

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

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

41	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.
42	CAP			95.62 %	96.66 %	90.76 %	0.07 s	1 core @ 2.5 Ghz (C/C++)

43	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.
44	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.
45	SGFNet			95.60 %	98.46 %	92.74 %	0.09 s	1 core @ 2.5 Ghz (C/C++)

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

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

48	HRNet			95.58 %	96.63 %	92.86 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

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

50	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.
51	FEMV-RCNN		code	95.56 %	96.27 %	90.87 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

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

53	FYI3D			95.56 %	96.40 %	92.92 %	0.1 s	GPU @ 2.5 Ghz (Python)

54	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.
55	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.
56	MPFusion			95.53 %	96.35 %	92.81 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

57	SA-Net			95.52 %	98.29 %	92.69 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
58	GT3D			95.51 %	96.58 %	92.80 %	0.1 s	1 core @ 2.5 Ghz (Python)

59	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.
60	SFD++			95.48 %	98.40 %	92.61 %	0.12 s	1 core @ 2.5 Ghz (Python + C/C++)

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

62	3D-BCM			95.47 %	98.50 %	92.70 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

63	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.
64	DSASA		code	95.40 %	96.14 %	92.46 %	0.05s	GPU @ >3.5 Ghz (Python)

65	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.
66	Multi-Weights			95.39 %	96.38 %	90.60 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)

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

68	PV-DT3D			95.36 %	96.34 %	92.70 %	1.4 s	1 core @ 2.5 Ghz (C/C++)

69	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.
70	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.
71	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.
72	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.
73	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.
74	ATT_SSD			95.27 %	95.87 %	90.56 %	0.01 s	1 core @ 2.5 Ghz (Python)

75	MVENet			95.25 %	96.20 %	92.54 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

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

77	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.
78	LGSL			95.22 %	98.00 %	92.72 %	0.1 s	GPU @ 2.5 Ghz (Python)

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

80	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.
81	VGT-RCNN			95.19 %	96.38 %	92.55 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

82	R^2 R-CNN			95.19 %	96.38 %	92.57 %	0.1 s	1 core @ 2.5 Ghz (Python)

83	BSConv			95.18 %	97.90 %	92.28 %	0.1 s	1 core @ 2.5 Ghz (Java)

84	4cls-center-hirige			95.17 %	98.39 %	85.83 %	0.01 s	1 core @ 2.5 Ghz (Python)

85	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.
86	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.
87	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.
88	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.
89	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.
90	IPS			95.16 %	95.86 %	90.57 %	TBD s	1 core @ 2.5 Ghz (C/C++)

91	GS-FPS		code	95.15 %	95.83 %	92.39 %	TBD s	1 core @ 2.5 Ghz (C/C++)

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

93	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.
94	TEDx			95.14 %	96.15 %	92.45 %	0.01 s	1 core @ 2.5 Ghz (C/C++)

95	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.
96	GS			95.13 %	95.73 %	90.55 %	TBD s	1 core @ 2.5 Ghz (C/C++)

97	VGRCNN++			95.12 %	96.48 %	92.50 %	0.07 s	1 core @ 2.5 Ghz (C/C++)

98	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.
99	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.
100	DTSSD			95.08 %	95.96 %	90.50 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

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

102	VG-RCNN			95.06 %	96.41 %	92.45 %	0.07 s	1 core @ 2.5 Ghz (C/C++)

103	GEO_LOC			95.05 %	95.93 %	90.43 %	TBD s	1 core @ 2.5 Ghz (C/C++)

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

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

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

107	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.
108	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.
109	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.
110	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.
111	L-AUG			94.92 %	95.84 %	92.22 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

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

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

114	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.
115	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.
116	Rnet			94.80 %	95.98 %	92.22 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

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

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

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

120	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.
121	U_PV_V2_ep_100_100			94.76 %	95.76 %	92.18 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

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

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

124	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.
125	DTE3D			94.73 %	96.06 %	91.84 %	0.15s	1 core @ 2.5 Ghz (C/C++)

126	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.
127	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.
128	Anonymous			94.70 %	96.12 %	92.05 %	0.03s

129	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.
130	OA-TSSD			94.68 %	95.43 %	91.91 %	20 s	8 cores @ 2.5 Ghz (C/C++)

131	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.
132	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.
133	U_VOXELRCNN_CAR_V3			94.64 %	96.02 %	91.94 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

134	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.
135	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.
136	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.
137	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.
138	src-hirige			94.46 %	95.92 %	88.91 %	0.01 s	1 core @ 2.5 Ghz (Python)

139	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.
140	HA-PillarNet			94.44 %	95.80 %	92.02 %	0.05 s	1 core @ 2.5 Ghz (Python)

141	DD3D-dequity			94.43 %	95.58 %	90.81 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

142	U_RVRCNN_V2_1			94.42 %	95.76 %	91.75 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

143	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.
144	MSIT-Det			94.39 %	97.21 %	86.85 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)

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

146	multi-center-hirige			94.27 %	96.87 %	88.51 %	0.01 s	1 core @ 2.5 Ghz (Python)

147	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.
148	singal-hirige			94.24 %	95.38 %	87.76 %	0.01 s	1 core @ 2.5 Ghz (Python)

149	SRDL			94.24 %	95.86 %	91.80 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
150	PVRCNN_8369			94.24 %	95.84 %	91.80 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

151	HA-SSD			94.19 %	95.06 %	91.17 %	1 s	1 core @ 2.5 Ghz (C/C++)

152	U_PVRCNN_V2			94.18 %	95.83 %	91.58 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

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

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

155	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.
156	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.
157	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) .
158	U_second_v4_ep_100_8			93.96 %	94.85 %	91.14 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

159	SIF			93.95 %	95.51 %	91.57 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
160	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.
161	3ONet			93.87 %	96.97 %	88.84 %	0.09 s	1 core @ 2.5 Ghz (C/C++)

162	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.
163	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.
164	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.
165	IMLIDAR(base)			93.75 %	96.76 %	88.78 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

166	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.
167	SoRL-V			93.73 %	96.87 %	88.73 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

168	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.
169	IIOU		code	93.72 %	96.45 %	90.91 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)

170	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.
171	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.
172	Anonymous			93.66 %	96.59 %	90.98 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

173	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.
174	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.
175	HPV-RCNN			93.58 %	96.68 %	88.57 %	0.08 s	1 core @ 2.5 Ghz (Python)

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

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

178	multi-ssd-hirige			93.57 %	95.05 %	83.79 %	0.01 s	1 core @ 2.5 Ghz (C/C++)

179	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.
180	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.
181	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.
182	IA-SSDx			93.55 %	96.67 %	90.88 %	0.01 s	1 core @ 2.5 Ghz (C/C++)

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

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

185	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.
186	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.
187	fuf			93.53 %	96.74 %	88.43 %	10 s	1 core @ 2.5 Ghz (C/C++)

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

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

190	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.
191	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.
192	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.
193	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.
194	MOPNet		code	93.47 %	96.57 %	83.62 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

195	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.
196	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.
197	IOUFusion			93.43 %	96.38 %	90.63 %	0.1 s	GPU @ 2.5 Ghz (Python)

198	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.
199	MonoLSS			93.42 %	96.19 %	83.62 %	0.04 s	1 core @ 2.5 Ghz (Python)

200	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.
201	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.
202	IIOU_LDR		code	93.33 %	96.33 %	88.22 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

203	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.
204	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.
205	PVTr			93.32 %	94.71 %	90.82 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

206	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.
207	ROT_S3D			93.30 %	96.28 %	88.20 %	0.1 s	GPU @ 2.5 Ghz (Python)

208	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.
209	MonoInsight			93.29 %	96.21 %	83.59 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

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

211	BAIR			93.26 %	96.33 %	85.49 %	0.04 s	1 core @ 2.5 Ghz (Python)

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

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

214	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.
215	DA-Net			93.22 %	96.59 %	90.71 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

216	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.
217	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.
218	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.
219	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.
220	P2P		code	93.14 %	96.67 %	88.01 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)

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

222	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.
223	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.
224	3DSeMoDLE		code	93.04 %	96.21 %	81.01 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

225	GS-FPS-LT			93.04 %	95.77 %	90.21 %	TBD s	1 core @ 2.5 Ghz (C/C++)

226	GA-RCNN			93.03 %	96.10 %	90.41 %	47ms	1 core @ 2.5 Ghz (C/C++)

227	Mono3DMethod			93.00 %	96.25 %	83.35 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

228	FSFNet			92.99 %	96.36 %	89.99 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

229	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.
230	WA			92.93 %	96.02 %	87.41 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)

231	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.
232	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.
233	PEF		code	92.81 %	96.09 %	87.99 %	N/A s	1 core @ 2.5 Ghz (C/C++)

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

235	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.
236	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.
237	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.
238	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.
239	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.
240	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.
241	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.
242	MMpointpillars			92.57 %	95.47 %	87.45 %	0.05 s	1 core @ 2.5 Ghz (Python)

243	MLAFF			92.54 %	95.39 %	87.77 %	0.39 s	2 cores @ 2.5 Ghz (Python)

244	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.
245	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.
246	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.
247	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.
248	VPNetv2			92.44 %	95.87 %	87.76 %	0.1 s	1 core @ 2.5 Ghz (Python)

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

250	HA PillarNet			92.37 %	95.38 %	87.40 %	0.05 s	1 core @ 2.5 Ghz (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	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.
3	4cls-center-hirige			79.49 %	88.78 %	73.86 %	0.01 s	1 core @ 2.5 Ghz (Python)

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	BAIR			67.95 %	83.26 %	62.81 %	0.04 s	1 core @ 2.5 Ghz (Python)

30	MonoLSS			67.78 %	82.88 %	60.87 %	0.04 s	1 core @ 2.5 Ghz (Python)

31	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.
32	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.
33	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.
34	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.
35	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.
36	VPNetv2			65.49 %	74.73 %	61.31 %	0.1 s	1 core @ 2.5 Ghz (Python)

37	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.
38	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.
39	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.
40	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.
41	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.
42	MPFusion			63.86 %	74.08 %	60.01 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

43	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.
44	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.
45	R^2 R-CNN			63.07 %	72.34 %	59.49 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

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	ACF-Net			60.12 %	71.42 %	55.96 %	n/a s	1 core @ 2.5 Ghz (C/C++)

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

65	3DSeMoDLE		code	59.65 %	78.87 %	54.61 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

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

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

68	DPPFA-Net			59.52 %	67.68 %	56.87 %	0.1 s	1 core @ 2.5 Ghz (Python)

69	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.
70	CZY_PPF_Net			59.26 %	67.81 %	57.04 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

71	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.
72	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.
73	U_RVRCNN_V2_1			58.97 %	68.45 %	56.64 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

74	MonoUNI		code	58.97 %	76.17 %	53.99 %	0.04 s	1 core @ 2.5 Ghz (Python)

75	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.
76	PSMS-Net			58.81 %	70.59 %	56.27 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

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

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

79	SRDL			58.70 %	68.45 %	56.23 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
80	PVRCNN_8369			58.70 %	68.43 %	56.24 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

81	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.
82	casxv1			58.34 %	71.44 %	55.96 %	0.01 s	1 core @ 2.5 Ghz (Python)

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

84	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.
85	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.
86	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.
87	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.
88	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.
89	IA-SSDx			58.01 %	71.00 %	53.95 %	0.01 s	1 core @ 2.5 Ghz (C/C++)

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

91	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.
92	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.
93	Rnet			57.87 %	66.14 %	54.87 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

94	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.
95	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.
96	Anonymous			57.63 %	68.62 %	53.59 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

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

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

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

100	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.
101	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.
102	SIF			57.32 %	67.78 %	54.86 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
103	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.
104	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.
105	u_second_v4_epoch_10			57.25 %	67.10 %	55.01 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

106	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.
107	Anonymous			57.12 %	68.51 %	53.06 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

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

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

110	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.
111	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.
112	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.
113	U_PVRCNN_V2			56.58 %	65.92 %	54.26 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

114	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.
115	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.
116	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.
117	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.
118	U_PV_V2_ep_100_100			56.18 %	64.52 %	54.12 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

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

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

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

122	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.
123	HybridPillars			55.90 %	64.07 %	53.46 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

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

125	DTE3D			55.83 %	66.41 %	52.21 %	0.15s	1 core @ 2.5 Ghz (C/C++)

126	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.
127	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.
128	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.
129	HA-PillarNet			55.60 %	64.75 %	53.23 %	0.05 s	1 core @ 2.5 Ghz (Python)

130	HybridPillars (SSD)			55.54 %	65.55 %	52.96 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

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

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

133	BCA			55.20 %	74.33 %	50.26 %	0.17 s	GPU @ 2.5 Ghz (Python)

134	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.
135	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.
136	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.
137	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.
138	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.
139	MSAW			54.91 %	68.16 %	52.64 %	0.42 s	2 cores @ 2.5 Ghz (Python)

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

141	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.
142	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.
143	L-AUG			54.61 %	65.71 %	51.67 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

144	RealSynthesis-SECOND			54.18 %	63.77 %	50.93 %	0.05 s	1 core @ 2.5 Ghz (Python)

145	GS-FPS-LT			54.14 %	63.45 %	51.67 %	TBD s	1 core @ 2.5 Ghz (C/C++)

146	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.
147	DFSemONet(Baseline)			54.13 %	65.42 %	52.05 %	0.04 s	GPU @ 2.5 Ghz (Python)

148	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.
149	aliii0			54.03 %	68.48 %	48.04 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

150	DTSSD			53.84 %	62.75 %	51.66 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

151	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.
152	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.
153	MonoInsight			53.72 %	67.31 %	47.54 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

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

155	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.
156	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.
157	GS			53.46 %	62.71 %	50.25 %	TBD s	1 core @ 2.5 Ghz (C/C++)

158	IPS			53.38 %	62.38 %	51.15 %	TBD s	1 core @ 2.5 Ghz (C/C++)

159	DA-Net			53.36 %	68.50 %	48.89 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

160	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.
161	PVTr			53.23 %	62.15 %	51.04 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

162	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.
163	Mono3DMethod			53.12 %	69.34 %	46.69 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

164	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.
165	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.
166	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.
167	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.
168	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.
169	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.
170	MVAF-Net(3-classes)			52.37 %	64.19 %	49.14 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

171	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.
172	ACCF			52.28 %	62.54 %	50.16 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

173	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.
174	GS-FPS		code	51.98 %	62.86 %	49.70 %	TBD s	1 core @ 2.5 Ghz (C/C++)

175	ATT_SSD			51.76 %	61.25 %	49.79 %	0.01 s	1 core @ 2.5 Ghz (Python)

176	GEO_LOC			51.57 %	61.00 %	49.42 %	TBD s	1 core @ 2.5 Ghz (C/C++)

177	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.
178	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.
179	OA-TSSD			51.19 %	60.26 %	49.28 %	20 s	8 cores @ 2.5 Ghz (C/C++)

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

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

182	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.
183	LightCPC		code	49.41 %	58.24 %	46.99 %	0.02 s	1 core @ 2.5 Ghz (Python + C/C++)

184	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.
185	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.
186	MMpointpillars			48.95 %	60.01 %	46.12 %	0.05 s	1 core @ 2.5 Ghz (Python)

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

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

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

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

191	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.
192	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.
193	Anonymous			48.51 %	65.02 %	41.77 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

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

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

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

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

198	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.
199	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.
200	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.
201	MVAF-Net(3-classes)			46.87 %	57.07 %	44.08 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

202	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.
203	MMpp			46.09 %	56.10 %	43.62 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

204	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.
205	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.
206	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.
207	3Dv2			45.64 %	59.74 %	41.29 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

208	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.
209	MonoA^2			44.89 %	59.60 %	40.56 %	na s	1 core @ 2.5 Ghz (C/C++)

210	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.
211	P2P		code	44.30 %	55.25 %	42.40 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)

212	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.
213	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.
214	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.
215	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.
216	Yolo5x6_Ghost			43.70 %	59.74 %	39.06 %	0.03 s	GPU @ 2.5 Ghz (Python)

217	Yolo5x6_Ghost			43.70 %	59.74 %	39.06 %	0.03 s	GPU @ 2.5 Ghz (Python)

218	Anonymous			43.65 %	55.61 %	39.54 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

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

220	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.
221	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.
222	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.
223	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.
224	Res			43.10 %	58.82 %	38.56 %	0.03 s	1 core @ 2.5 Ghz (Python)

225	GHos_3d			43.10 %	58.82 %	38.56 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

226	ALI_TRY1			43.10 %	58.82 %	38.56 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

227	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.
228	OccupancyM3D			42.95 %	57.08 %	38.28 %	0.11 s	1 core @ 2.5 Ghz (C/C++)

229	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.
230	Yolov5ObjectDetector			42.74 %	58.46 %	38.32 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

231	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.
232	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.
233	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.
234	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.
235	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.
236	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 .
237	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.
238	fuf			41.42 %	53.95 %	37.48 %	10 s	1 core @ 2.5 Ghz (C/C++)

239	BKDStereo3D		code	41.17 %	55.94 %	34.99 %	0.1 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
240	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.
241	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.
242	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.
243	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.
244	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.
245	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.
246	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.
247	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.
248	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). .
249	UNM3D			37.67 %	51.80 %	33.73 %	na s	1 core @ 2.5 Ghz (C/C++)

250	LSVM-MDPM-sv			37.26 %	50.74 %	33.13 %	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	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.
2	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.
3	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.
4	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.
5	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.
6	IMLIDAR(base)			81.72 %	90.91 %	75.19 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

7	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.
8	PA-RCNN		code	81.41 %	90.11 %	72.90 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

9	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.
10	PV-PMRTNet			81.12 %	89.07 %	74.38 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

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

12	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.
13	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.
14	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.
15	PIPC-3Ddet		code	80.49 %	89.69 %	73.73 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

16	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.
17	Rnet			80.44 %	87.56 %	73.55 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

18	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.
19	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.
20	Anonymous			80.24 %	90.30 %	71.50 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

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

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

23	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.
24	CZY_PPF_Net			79.75 %	87.34 %	73.05 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

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

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

27	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.
28	VPNetv2			79.22 %	89.12 %	70.60 %	0.1 s	1 core @ 2.5 Ghz (Python)

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

30	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.
31	HybridPillars			78.81 %	87.30 %	72.25 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

32	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.
33	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.
34	3ONet			78.51 %	90.18 %	71.38 %	0.09 s	1 core @ 2.5 Ghz (C/C++)

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

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

37	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.
38	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.
39	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.
40	HPV-RCNN			77.94 %	89.26 %	71.06 %	0.08 s	1 core @ 2.5 Ghz (Python)

41	U_RVRCNN_V2_1			77.90 %	86.33 %	71.12 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

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

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

44	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.
45	IA-SSDx			77.58 %	88.94 %	70.80 %	0.01 s	1 core @ 2.5 Ghz (C/C++)

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

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

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

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

50	IPS			77.19 %	89.29 %	70.72 %	TBD s	1 core @ 2.5 Ghz (C/C++)

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

52	HA-PillarNet			77.04 %	84.81 %	71.13 %	0.05 s	1 core @ 2.5 Ghz (Python)

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

54	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.
55	LightCPC		code	76.92 %	87.79 %	70.75 %	0.02 s	1 core @ 2.5 Ghz (Python + C/C++)

56	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.
57	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.
58	U_PVRCNN_V2			76.72 %	86.37 %	70.09 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

59	GS-FPS-LT			76.50 %	86.71 %	70.53 %	TBD s	1 core @ 2.5 Ghz (C/C++)

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

61	ACF-Net			76.15 %	86.92 %	71.33 %	n/a s	1 core @ 2.5 Ghz (C/C++)

62	GS			75.78 %	86.48 %	70.99 %	TBD s	1 core @ 2.5 Ghz (C/C++)

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

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

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

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

67	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.
68	u_second_v4_epoch_10			75.38 %	87.96 %	69.54 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

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

70	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.
71	LGSL			75.29 %	84.01 %	70.60 %	0.1 s	GPU @ 2.5 Ghz (Python)

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

73	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.
74	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.
75	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.
76	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.
77	DTSSD			74.81 %	86.18 %	68.26 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

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

79	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.
80	U_second_v4_ep_100_8			74.75 %	84.43 %	67.71 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

81	ATT_SSD			74.75 %	87.02 %	69.63 %	0.01 s	1 core @ 2.5 Ghz (Python)

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

83	GEO_LOC			74.60 %	86.95 %	69.67 %	TBD s	1 core @ 2.5 Ghz (C/C++)

84	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.
85	HA-SSD			74.45 %	84.37 %	67.85 %	1 s	1 core @ 2.5 Ghz (C/C++)

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

87	4cls-center-hirige			74.15 %	83.63 %	64.69 %	0.01 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	PVRCNN_8369			73.68 %	85.41 %	66.98 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

91	SRDL			73.68 %	85.44 %	66.94 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
92	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.
93	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.
94	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.
95	L-AUG			73.43 %	83.88 %	68.12 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

96	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.
97	HybridPillars (SSD)			73.24 %	82.36 %	67.38 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

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

99	SIF			73.19 %	85.18 %	65.41 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
100	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.
101	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.
102	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.
103	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.
104	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.
105	IOUFusion			72.49 %	86.41 %	65.60 %	0.1 s	GPU @ 2.5 Ghz (Python)

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

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

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

109	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.
110	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.
111	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.
112	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.
113	single-src-hirige		code	70.46 %	80.95 %	60.99 %	0.01 s	GPU @ 2.5 Ghz (Python)

114	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.
115	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.
116	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.
117	fuf			69.67 %	84.99 %	62.91 %	10 s	1 core @ 2.5 Ghz (C/C++)

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

119	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.
120	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.
121	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.
122	ROT_S3D			68.53 %	84.35 %	63.76 %	0.1 s	GPU @ 2.5 Ghz (Python)

123	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.
124	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.
125	DTE3D			68.23 %	82.63 %	61.99 %	0.15s	1 core @ 2.5 Ghz (C/C++)

126	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.
127	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.
128	MSAW			67.22 %	82.14 %	62.13 %	0.42 s	2 cores @ 2.5 Ghz (Python)

129	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.
130	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.
131	MMpp			66.87 %	80.54 %	60.69 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

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

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

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

135	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.
136	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.
137	DD3D-dequity			66.43 %	81.25 %	58.92 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

138	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.
139	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) .
140	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.
141	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.
142	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.
143	MLAFF			64.41 %	80.28 %	58.52 %	0.39 s	2 cores @ 2.5 Ghz (Python)

144	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.
145	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.
146	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.
147	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.
148	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.
149	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.
150	DFSemONet(Baseline)			61.46 %	77.65 %	55.87 %	0.04 s	GPU @ 2.5 Ghz (Python)

151	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.
152	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.
153	MVAF-Net(3-classes)			60.04 %	74.01 %	55.71 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

154	BAIR			59.97 %	76.29 %	51.80 %	0.04 s	1 core @ 2.5 Ghz (Python)

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

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

157	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.
158	MVAF-Net(3-classes)			59.54 %	72.32 %	55.44 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

159	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.
160	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.
161	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.
162	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.
163	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.
164	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.
165	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.
166	Anonymous			55.47 %	74.27 %	47.25 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

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

168	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.
169	MonoLSS			54.63 %	74.54 %	47.98 %	0.04 s	1 core @ 2.5 Ghz (Python)

170	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.
171	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.
172	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.
173	MonoUNI		code	53.71 %	71.68 %	45.26 %	0.04 s	1 core @ 2.5 Ghz (Python)

174	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 .
175	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.
176	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.
177	PS++			52.29 %	72.60 %	46.82 %	0.4 s	1 core @ 2.5 Ghz (C/C++)

178	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.
179	3Dv2			51.59 %	73.71 %	44.86 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

180	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.
181	Mono3DMethod			50.24 %	67.46 %	43.86 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

182	3DSeMoDLE		code	49.71 %	69.51 %	43.11 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

183	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.
184	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.
185	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.
186	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.
187	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.
188	BCA			46.50 %	67.68 %	39.47 %	0.17 s	GPU @ 2.5 Ghz (Python)

189	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.
190	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.
191	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.
192	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.
193	MonoA^2			44.12 %	59.98 %	37.93 %	na s	1 core @ 2.5 Ghz (C/C++)

194	Anonymous			43.40 %	67.49 %	38.66 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

195	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.
196	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.
197	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.
198	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 .
199	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.
200	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.
201	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.
202	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.
203	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.
204	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.
205	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.
206	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.
207	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.
208	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.
209	Yolo5x6_Ghost			35.20 %	50.96 %	31.53 %	0.03 s	GPU @ 2.5 Ghz (Python)

210	Yolo5x6_Ghost			35.20 %	50.96 %	31.53 %	0.03 s	GPU @ 2.5 Ghz (Python)

211	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.
212	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.
213	Res			34.56 %	49.83 %	30.90 %	0.03 s	1 core @ 2.5 Ghz (Python)

214	GHos_3d			34.56 %	49.83 %	30.90 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

215	ALI_TRY1			34.56 %	49.83 %	30.90 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

216	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.
217	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.
218	SVDM-VIEW			31.75 %	47.79 %	27.83 %	1 s	1 core @ 2.5 Ghz (Python)

219	Yolov5ObjectDetector			31.69 %	45.65 %	29.37 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

220	AMNet			31.01 %	45.93 %	27.06 %	0.03 s	GPU @ 1.0 Ghz (Python)

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

222	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.
223	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.
224	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.
225	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.
226	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.
227	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.
228	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.
229	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.
230	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.
231	MonoXiver			26.95 %	39.81 %	23.32 %	0.03s	GPU @ 2.5 Ghz (Python)

232	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.
233	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.
234	MM3D			25.98 %	35.92 %	22.18 %	NA s	1 core @ 2.5 Ghz (C/C++)

235	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.
236	MM3DV2			25.28 %	30.00 %	22.67 %	NA s	1 core @ 2.5 Ghz (C/C++)

237	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.
238	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.
239	UNM3D			21.06 %	26.77 %	18.37 %	na s	1 core @ 2.5 Ghz (C/C++)

240	BEV_GHOST			20.80 %	29.52 %	19.38 %	0.1 s	1 core @ 2.5 Ghz (Python)

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

242	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.
243	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.
244	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.
245	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.
246	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.
247	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.
248	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.
249	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.
250	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