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	NFAF3D			96.20 %	96.78 %	93.26 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)

2	SFD			96.17 %	98.97 %	91.13 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

3	VPFNet			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. 2021.
4	Anonymous			96.12 %	98.74 %	91.12 %	0.1 s	GPU @ 2.5 Ghz (Python)

5	Anonymous			96.12 %	99.07 %	91.12 %	n/a s	1 core @ 2.5 Ghz (Python + C/C++)

6	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.
7	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.
8	PE-RCVN			95.94 %	96.90 %	90.98 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

9	CityBrainLab			95.94 %	98.58 %	90.92 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)

10	Anonymous			95.91 %	96.55 %	92.86 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)

11	EA-M-RCNN(BorderAtt)			95.88 %	96.68 %	90.89 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

12	Anonymous			95.86 %	98.93 %	90.98 %	n/a s	1 core @ 2.5 Ghz (C/C++)

13	NFAF3D-light			95.79 %	96.69 %	92.75 %	0.03 s	1 core @ 2.5 Ghz (Python + C/C++)

14	SECOND			95.79 %	96.44 %	90.55 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)

15	DVF-V			95.77 %	96.60 %	90.89 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

16	Fast-CLOCs			95.75 %	96.69 %	90.95 %	0.1 s	GPU @ 2.5 Ghz (Python)

17	DSGN++			95.70 %	98.08 %	88.27 %	0.4 s	1 core @ 2.5 Ghz (C/C++)

18	TBD			95.69 %	96.33 %	93.07 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

19	BANet		code	95.61 %	98.75 %	90.64 %	0.11 s	1 core @ 2.5 Ghz (C/C++)
R. Qian, X. Lai and X. Li: Boundary-Aware 3D Object Detection from Point Clouds. 2021.
20	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.
21	JPVNet			95.52 %	96.41 %	90.72 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

22	Anonymous			95.50 %	98.36 %	90.63 %	0.1s	1 core @ 2.5 Ghz (C/C++)

23	DVF-PV			95.49 %	96.42 %	92.57 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

24	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.
25	ISE-RCNN-PV			95.46 %	96.20 %	92.88 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

26	ISE-RCNN			95.43 %	96.38 %	92.82 %	0.09 s	1 core @ 2.5 Ghz (Python + C/C++)

27	PLNL-3DSSD			95.38 %	96.37 %	90.31 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)

28	SPG_mini			95.32 %	96.23 %	92.68 %	0.09 s	GPU @ 2.5 Ghz (Python)

29	EQ-PVRCNN			95.32 %	98.23 %	92.65 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)

30	anonymous			95.31 %	96.36 %	92.57 %	0.09 s	GPU @ 2.5 Ghz (Python)

31	TBD			95.29 %	96.17 %	90.56 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

32	Anonymous		code	95.28 %	96.30 %	92.69 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

33	DGDNH			95.24 %	98.36 %	92.69 %	0.04 s	1 core @ 2.5 Ghz (C/C++)

34	VoxSeT			95.23 %	96.16 %	90.49 %	0.09 s	1 core @ 2.5 Ghz (C/C++)

35	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.
36	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.
37	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.
38	EPNet++			95.17 %	96.73 %	92.10 %	0.1 s	GPU @ 2.5 Ghz (Python)

39	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.
40	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.
41	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.
42	TBD			95.10 %	96.48 %	92.62 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

43	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.
44	3DIoU++			95.06 %	96.37 %	90.52 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

45	PV-RCNN++			95.05 %	96.08 %	92.42 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

46	PDV			95.00 %	96.07 %	92.44 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

47	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.
48	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.
49	VueronNet		code	94.97 %	97.85 %	89.68 %	0.06 s	1 core @ 2.0 Ghz (Python)

50	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.
51	CSVoxel-RCNN			94.91 %	96.33 %	92.11 %	0.03 s	GPU @ 1.0 Ghz (Python)

52	TBD			94.85 %	97.04 %	92.37 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

53	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.
54	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.
55	E^2-PV-RCNN			94.80 %	95.95 %	92.26 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)

56	SRIF-RCNN			94.79 %	95.63 %	92.35 %	0.0947 s	1 core @ 2.5 Ghz (C/C++)

57	ST-RCNN			94.79 %	98.06 %	92.12 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)

58	ST-RCNN (SNLW-RCNN)		code	94.79 %	98.06 %	92.12 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)

59	TPCG			94.78 %	95.96 %	92.25 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

60	VCRCNN			94.77 %	96.06 %	92.28 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

61	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.
62	SCIR-Net			94.76 %	96.15 %	91.99 %	0.03 s	GPU @ 2.5 Ghz (Python + C/C++)
Q. He, H. Zeng, Y. Zeng and Y. Liu: SCIR-Net: Structured Color Image Representation Based 3D Object Detection Network from Point Clouds. AAAI 2022.
63	MSG-PGNN			94.75 %	95.86 %	92.16 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

64	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.
65	FusionDetv2-v4			94.73 %	95.94 %	92.00 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)

66	Generalized-SIENet			94.72 %	95.76 %	92.19 %	0.08 s	1 core @ 2.5 Ghz (Python)

67	SqueezeRCNN			94.72 %	96.02 %	92.12 %	0.08 s	1 core @ 2.5 Ghz (Python)

68	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.
69	CAT-Det			94.71 %	95.97 %	92.07 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)

70	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.
71	3DIoU_v2			94.70 %	96.15 %	92.37 %	0.2 s	1 core @ 2.5 Ghz (C/C++)

72	HyBrid Feature Det			94.69 %	95.89 %	92.11 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)

73	PC-RGNN			94.68 %	95.80 %	92.20 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)

74	DDet			94.66 %	95.82 %	92.17 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

75	FPV-SSD			94.66 %	96.92 %	91.78 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
76	LZY_RCNN			94.65 %	95.81 %	92.08 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

77	FusionDetv2-v3			94.64 %	96.16 %	91.94 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

78	TransCyclistNet			94.64 %	96.08 %	92.10 %	0.08 s	1 core @ 2.5 Ghz (Python)

79	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.
80	Fast VP-RCNN		code	94.62 %	98.00 %	91.91 %	0.05 s	1 core @ 3.5 Ghz (C/C++)

81	DD3D		code	94.62 %	95.32 %	89.89 %	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) .
82	TransDet3D			94.61 %	95.83 %	92.07 %	0.08 s	1 core @ 2.5 Ghz (Python)

83	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.
84	Point Image Fusion			94.61 %	95.70 %	92.11 %	0.2 s	1 core @ 2.5 Ghz (C/C++)

85	SA-voxel-centernet		code	94.59 %	95.80 %	92.05 %	0.04 s	1 core @ 2.5 Ghz (C/C++)

86	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.
87	WHUT-iou_ssd		code	94.54 %	95.77 %	91.91 %	0.045s	1 core @ 2.5 Ghz (C/C++)

88	sa-voxel-centernet		code	94.53 %	95.88 %	92.00 %	0.04 s	1 core @ 2.5 Ghz (C/C++)

89	anonymous		code	94.53 %	97.51 %	91.80 %	0.05s	1 core @ >3.5 Ghz (python)

90	FPC3D			94.52 %	96.06 %	91.72 %	33 s	1 core @ 2.5 Ghz (C/C++)

91	FPC-RCNN			94.51 %	96.15 %	91.80 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

92	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.
93	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.
94	GNN-RCNN			94.44 %	95.85 %	91.96 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

95	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.
96	SAA-SECOND			94.39 %	95.67 %	91.63 %	38m s	1 core @ 2.5 Ghz (C/C++)

97	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.
98	FusionDetv1			94.23 %	95.84 %	91.80 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

99	FusionDetv2-v2			94.21 %	95.75 %	89.89 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)

100	TBD			94.21 %	95.51 %	91.69 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

101	SARFE			94.18 %	95.74 %	91.57 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

102	FPCR-CNN			94.13 %	95.95 %	91.20 %	0.05 s	1 core @ 2.5 Ghz (Python)

103	SAA-PV-RCNN			94.11 %	95.01 %	92.50 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

104	NV2P-RCNN			94.07 %	97.82 %	91.20 %	0.1 s	GPU @ 2.5 Ghz (Python)

105	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.
106	VueronNet			94.03 %	96.70 %	87.58 %	0.08 s	GPU @ 2.5 Ghz (Python)

107	DGIST MT-CNN			94.00 %	95.25 %	86.76 %	0.09 s	GPU @ 1.0 Ghz (Python)

108	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. arXiv preprint arXiv:2011.01404 2020.
109	SIF			93.95 %	95.51 %	91.57 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
110	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.
111	LPCG-Monoflex			93.86 %	96.90 %	83.94 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

112	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.
113	LIGA-Stereo-old			93.77 %	96.66 %	83.97 %	0.375 s	Titan Xp

114	Associate-3Ddet_v2			93.77 %	96.83 %	88.57 %	0.04 s	1 core @ 2.5 Ghz (Python)

115	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.
116	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.
117	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.
118	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.
119	FusionDetv2-v5			93.61 %	95.33 %	89.22 %	0.05 s	1 core @ 2.5 Ghz (Java + C/C++)

120	KpNet			93.60 %	96.76 %	85.98 %	0.42 s	1 core @ 2.5 Ghz (C/C++)

121	KpNet			93.60 %	96.74 %	85.97 %	42 s	1 core @ 2.5 Ghz (C/C++)

122	AM-SSD			93.58 %	96.78 %	90.61 %	0.04 s	1 core @ 2.5 Ghz (Python)

123	TF3D			93.57 %	96.59 %	88.64 %	0.1 s	2 cores @ 3.0 Ghz (Python)

124	DVF			93.57 %	96.46 %	88.58 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

125	IA-SSD (multi)			93.56 %	96.10 %	90.68 %	0.014 s	1 core @ 2.5 Ghz (C/C++)

126	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.
127	IA-SSD (single)			93.54 %	96.26 %	88.49 %	0.013 s	1 core @ 2.5 Ghz (C/C++)

128	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.
129	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.
130	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.
131	BtcDet			93.47 %	96.23 %	88.55 %	0.09 s	GPU @ 2.5 Ghz (Python + C/C++)

132	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.
133	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.
134	TBD			93.43 %	96.52 %	90.71 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

135	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.
136	Sem-Aug v1		code	93.39 %	96.39 %	90.70 %	0.04 s	GPU @ 3.5 Ghz (Python)

137	TBD			93.38 %	94.17 %	88.39 %	0.01 s	1 core @ 2.5 Ghz (C/C++)

138	3D-CVF at SPA			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.
139	DSASNet			93.33 %	96.55 %	88.47 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)

140	TBD			93.33 %	96.02 %	90.50 %	TBD	GPU @ 2.5 Ghz (Python + C/C++)

141	FS-Net			93.32 %	96.39 %	90.64 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

142	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.
143	VPN			93.30 %	96.19 %	88.30 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)

144	RoIFusion		code	93.30 %	96.30 %	88.22 %	0.22 s	1 core @ 3.0 Ghz (Python)

145	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.
146	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.
147	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.
148	demo			93.21 %	96.16 %	90.23 %	0.04 s	1 core @ 2.5 Ghz (C/C++)

149	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.
150	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.
151	ASCNet			93.17 %	96.09 %	90.43 %	0.09 s	1 core @ 2.5 Ghz (C/C++)

152	VCT			93.17 %	96.31 %	90.52 %	0.2 s	1 core @ 2.5 Ghz (Python)

153	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.
154	Sem-Aug-PointRCNN		code	93.17 %	95.78 %	88.35 %	0.1 s	GPU @ 3.5 Ghz (C/C++)

155	MVOD			93.16 %	96.17 %	92.56 %	0.16 s	1 core @ 2.5 Ghz (C/C++)

156	MBDF-Net			93.15 %	96.26 %	90.25 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

157	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.
158	KAIST-VDCLab			93.08 %	96.27 %	85.66 %	0.04 s	1 core @ 2.5 Ghz (Python)

159	SGNet			93.08 %	96.43 %	90.53 %	0.09 s	GPU @ 2.5 Ghz (Python)

160	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.
161	MSADet			92.95 %	96.18 %	89.95 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
162	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.
163	MBDF-Net-1			92.85 %	95.98 %	89.91 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

164	YF			92.85 %	96.04 %	89.96 %	0.04 s	GPU @ 2.5 Ghz (C/C++)

165	FPGNN			92.83 %	96.26 %	87.69 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

166	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.
167	DPointNet			92.77 %	95.55 %	89.63 %	0.07s	1 core @ 2.5 Ghz (C/C++)

168	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.
169	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.
170	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.
171	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.
172	TBD			92.66 %	95.60 %	90.55 %	0.3 s	1 core @ 2.5 Ghz (C/C++)

173	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.
174	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.
175	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.
176	NV-RCNN			92.51 %	95.86 %	89.95 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

177	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.
178	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.
179	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.
180	3D-VDNet			92.35 %	95.42 %	89.37 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

181	PointRGCN			92.33 %	97.51 %	87.07 %	0.26 s	GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
182	CVFNet			92.27 %	95.61 %	88.75 %	28.1ms	1 core @ 2.5 Ghz (Python)

183	LSNet			92.23 %	96.06 %	87.35 %	0.09 s	GPU @ 2.5 Ghz (Python + C/C++)

184	F-ConvNet		code	92.19 %	95.85 %	80.09 %	0.47 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
185	PFF3D		code	92.15 %	95.37 %	87.54 %	0.05 s	GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
186	SDP+RPN			92.03 %	95.16 %	79.16 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016. S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 2015.
187	RangeDet		code	92.03 %	95.20 %	87.14 %	0.01 s	1 core @ 2.5 Ghz (C/C++)

188	AB3DMOT		code	92.00 %	95.88 %	86.98 %	0.0047s	1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
189	VGCN			91.97 %	94.91 %	89.34 %	0.09 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
190	MMLab-PointRCNN		code	91.90 %	95.92 %	87.11 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
191	MKFFNet			91.88 %	95.29 %	89.21 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

192	AutoAlign			91.87 %	95.15 %	89.21 %	0.1 s	1 core @ 2.5 Ghz (Python)

193	MMLab-PartA^2		code	91.86 %	95.03 %	89.06 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
194	WeakM3D			91.81 %	94.51 %	85.35 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

195	epBRM		code	91.77 %	94.59 %	88.45 %	0.1 s	GPU @ >3.5 Ghz (Python + C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
196	C-GCN			91.73 %	95.64 %	86.37 %	0.147 s	GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
197	ITVD		code	91.73 %	95.85 %	79.31 %	0.3 s	GPU @ 2.5 Ghz (C/C++)
Y. Wei Liu: Improving Tiny Vehicle Detection in Complex Scenes. IEEE International Conference on Multimedia and Expo (ICME) 2018.
198	sscl-20p			91.71 %	97.14 %	88.72 %	0.02 s	1 core @ 2.5 Ghz (Python)

199	SINet+		code	91.67 %	94.17 %	78.60 %	0.3 s	TITAN X GPU
X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
200	SFD-Retina			91.64 %	94.85 %	82.13 %	0.04 s	GPU @ 2.5 Ghz (Python)

201	HS3D		code	91.62 %	95.51 %	86.94 %	0.12 s	1 core @ 2.5 Ghz (Python + C/C++)

202	mono3d			91.60 %	94.60 %	84.86 %	0.03 s	GPU @ 2.5 Ghz (Python)

203	Cascade MS-CNN		code	91.60 %	94.26 %	78.84 %	0.25 s	GPU @ 2.5 Ghz (C/C++)
Z. Cai and N. Vasconcelos: Cascade R-CNN: High Quality Object Detection and Instance Segmentation. arXiv preprint arXiv:1906.09756 2019. Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A unified multi-scale deep convolutional neural network for fast object detection. European conference on computer vision 2016.
204	GFD-Retina			91.59 %	94.55 %	81.80 %	0.07 s	GPU @ 2.5 Ghz (Python)

205	CA3D			91.48 %	95.19 %	82.01 %	0.04 s	1 core @ 2.5 Ghz (C/C++)

206	PointRGBNet			91.48 %	95.40 %	86.50 %	0.08 s	4 cores @ 2.5 Ghz (Python + C/C++)

207	MAFF-Net(DAF-Pillar)			91.46 %	94.38 %	83.89 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
Z. Zhang, Z. Liang, M. Zhang, X. Zhao, Y. Ming, T. Wenming and S. Pu: MAFF-Net: Filter False Positive for 3D Vehicle Detection with Multi-modal Adaptive Feature Fusion. arXiv preprint arXiv:2009.10945 2020.
208	AIMC-RUC			91.45 %	96.94 %	86.28 %	0.11 s	1 core @ 2.5 Ghz (Python)

209	HRI-VoxelFPN			91.44 %	96.65 %	86.18 %	0.02 s	GPU @ 2.5 Ghz (Python + C/C++)
H. Kuang, B. Wang, J. An, M. Zhang and Z. Zhang: Voxel-FPN:multi-scale voxel feature aggregation in 3D object detection from point clouds. sensors 2020.
210	FPC3D_all			91.42 %	95.52 %	86.76 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

211	EgoNet		code	91.39 %	96.18 %	81.33 %	0.1 s	GPU @ 1.5 Ghz (Python)
S. Li, Z. Yan, H. Li and K. Cheng: Exploring intermediate representation for monocular vehicle pose estimation. The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
212	HNet-3DSSD		code	91.35 %	94.91 %	87.47 %	0.05 s	GPU @ 2.5 Ghz (Python)

213	PP-PCdet		code	91.32 %	94.82 %	88.18 %	0.01 s	1 core @ 2.5 Ghz (Python)

214	Contrastive PP		code	91.29 %	96.93 %	88.11 %	0.01 s	1 core @ 2.5 Ghz (Python)

215	Stereo CenterNet			91.27 %	96.61 %	83.50 %	0.04 s	GPU @ 2.5 Ghz (Python)
Y. Shi, Y. Guo, Z. Mi and X. Li: Stereo CenterNet-based 3D object detection for autonomous driving. Neurocomputing 2022.
216	PointPillars		code	91.19 %	94.00 %	88.17 %	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.
217	LTN			91.18 %	94.68 %	81.51 %	0.4 s	GPU @ >3.5 Ghz (Python)
T. Wang, X. He, Y. Cai and G. Xiao: Learning a Layout Transfer Network for Context Aware Object Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
218	WS3D			91.15 %	95.13 %	86.52 %	0.1 s	GPU @ 2.5 Ghz (Python)
Q. Meng, W. Wang, T. Zhou, J. Shen, L. Van Gool and D. Dai: Weakly Supervised 3D Object Detection from Lidar Point Cloud. 2020.
219	KM3D		code	91.07 %	96.44 %	81.19 %	0.03 s	1 core @ 2.5 Ghz (Python)
P. Li: Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training. 2020.
220	3D_att			91.05 %	96.70 %	85.88 %	0.17 s	GPU @ 2.5 Ghz (Python)

221	Geo3D			91.04 %	94.28 %	78.91 %	0.04 s	GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
222	FII-CenterNet			91.03 %	94.48 %	83.00 %	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.
223	Aston-EAS			91.02 %	93.91 %	77.93 %	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.
224	MonoFlex			91.02 %	96.01 %	83.38 %	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.
225	Mix-Teaching-M3D			91.02 %	96.35 %	83.41 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

226	SCSTSV-MonoFlex			90.99 %	96.44 %	81.16 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

227	ARPNET			90.99 %	94.00 %	83.49 %	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.
228	GA-Aug			90.92 %	93.93 %	83.03 %	0.04 s	GPU @ 2.5 Ghz (Python)

229	MonoEF		code	90.88 %	96.32 %	83.27 %	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.
230	PatchNet		code	90.87 %	93.82 %	79.62 %	0.4 s	1 core @ 2.5 Ghz (C/C++)
X. Ma, S. Liu, Z. Xia, H. Zhang, X. Zeng and W. Ouyang: Rethinking Pseudo-LiDAR Representation. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
231	MV3D			90.83 %	96.47 %	78.63 %	0.36 s	GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
232	MonoDistill			90.81 %	95.92 %	81.08 %	0.04 s	1 core @ 2.5 Ghz (Python)

233	monodle		code	90.81 %	93.83 %	80.93 %	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 .
234	3D IoU Loss			90.79 %	95.92 %	85.65 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
D. Zhou, J. Fang, X. Song, C. Guan, J. Yin, Y. Dai and R. Yang: IoU Loss for 2D/3D Object Detection. International Conference on 3D Vision (3DV) 2019.
235	SINet_VGG		code	90.79 %	93.59 %	77.53 %	0.2 s	TITAN X GPU
X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
236	APL-Second			90.70 %	95.82 %	85.51 %	0.05 s	1 core @ 2.5 Ghz (Python)

237	vadin-TBD			90.69 %	95.92 %	80.91 %	0.04 s	1 core @ 2.5 Ghz (Python)

238	TANet		code	90.67 %	93.67 %	85.31 %	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.
239	MonoGround			90.63 %	93.94 %	80.80 %	0.03 s	1 core @ 2.5 Ghz (Python)

240	NF2			90.62 %	94.14 %	81.30 %	0.1 s	GPU @ 2.5 Ghz (Python)

241	MonoEdge			90.62 %	93.52 %	80.91 %	0.05 s	GPU @ 2.5 Ghz (Python)

242	MonoCInIS			90.60 %	96.05 %	82.43 %	0,13 s	GPU @ 2.5 Ghz (C/C++)
J. Heylen, M. De Wolf, B. Dawagne, M. Proesmans, L. Van Gool, W. Abbeloos, H. Abdelkawy and D. Reino: MonoCInIS: Camera Independent Monocular 3D Object Detection using Instance Segmentation. Proceedings of the IEEE/CVF International Conference on Computer Vision 2021.
243	MonoFlex			90.52 %	95.59 %	83.00 %	0.03 s	1 core @ 2.5 Ghz (Python)

244	CG-Stereo			90.38 %	96.31 %	82.80 %	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.
245	MonoEdge-Rotate			90.30 %	93.53 %	80.60 %	0.05 s	GPU @ 2.5 Ghz (Python)

246	SCNet			90.30 %	95.59 %	85.09 %	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.
247	PS-fld			90.27 %	95.75 %	82.32 %	0.25 s	1 core @ 2.5 Ghz (C/C++)

248	Deep3DBox			90.19 %	94.71 %	76.82 %	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.
249	FQNet			90.17 %	94.72 %	76.78 %	0.5 s	1 core @ 2.5 Ghz (Python)
L. Liu, J. Lu, C. Xu, Q. Tian and J. Zhou: Deep Fitting Degree Scoring Network for Monocular 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
250	MonoGeo			90.14 %	95.11 %	80.19 %	0.05 s	1 core @ 2.5 Ghz (Python)

Table as LaTeX | Only published Methods

Pedestrian

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	DGIST MT-CNN			80.78 %	89.66 %	76.52 %	0.09 s	GPU @ 1.0 Ghz (Python)

2	VueronNet			80.75 %	89.91 %	76.56 %	0.08 s	GPU @ 2.5 Ghz (Python)

3	F-PointNet		code	80.13 %	89.83 %	75.05 %	0.17 s	GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
4	NF2			79.59 %	88.28 %	75.47 %	0.1 s	GPU @ 2.5 Ghz (Python)

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	HHA-ACF			77.12 %	85.32 %	72.69 %	0.14 s	1 core @ 2.5 Ghz (C/C++)

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	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.
16	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.
17	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.
18	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.
19	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.
20	DD3D		code	70.79 %	84.61 %	65.69 %	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) .
21	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.
22	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.
23	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.
24	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.
25	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.
26	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.
27	Frustum-PointPillars			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.
28	GFD-Retina			67.39 %	81.79 %	62.24 %	0.07 s	GPU @ 2.5 Ghz (Python)

29	FII-CenterNet			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.
30	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.
31	SFD-Retina			67.17 %	82.48 %	62.02 %	0.04 s	GPU @ 2.5 Ghz (Python)

32	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.
33	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.
34	EQ-PVRCNN			65.01 %	77.19 %	61.95 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)

35	MM-Retina			64.91 %	79.88 %	59.60 %	0.04 s	GPU @ 2.5 Ghz (Python)

36	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.
37	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.
38	PV-RCNN++			63.47 %	72.82 %	60.96 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

39	ADLAB			63.25 %	70.86 %	60.52 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

40	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.
41	PiFeNet			62.68 %	71.97 %	59.77 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

42	Fast-CLOCs			62.57 %	76.22 %	60.13 %	0.1 s	GPU @ 2.5 Ghz (Python)

43	KAIST-VDCLab			62.35 %	79.37 %	57.42 %	0.04 s	1 core @ 2.5 Ghz (Python)

44	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.
45	VCT			62.00 %	71.19 %	58.52 %	0.2 s	1 core @ 2.5 Ghz (Python)

46	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.
47	TBD			61.74 %	70.58 %	59.45 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

48	PE-RCVN			61.64 %	69.49 %	59.55 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

49	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.
50	H^23D R-CNN			61.50 %	72.21 %	57.50 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

51	SAA-PV-RCNN			61.41 %	70.35 %	58.18 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

52	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 .
53	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.
54	EA-M-RCNN(BorderAtt)			61.06 %	73.07 %	56.86 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

55	ISE-RCNN-PV			61.06 %	70.59 %	57.26 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

56	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.
57	GA-Aug			60.78 %	76.78 %	55.00 %	0.04 s	GPU @ 2.5 Ghz (Python)

58	ISE-RCNN			60.70 %	69.41 %	58.49 %	0.09 s	1 core @ 2.5 Ghz (Python + C/C++)

59	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.
60	TBD			60.30 %	70.50 %	57.06 %	0.3 s	1 core @ 2.5 Ghz (C/C++)

61	FS-Net			60.13 %	69.08 %	57.91 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

62	Generalized-SIENet			59.54 %	69.16 %	57.33 %	0.08 s	1 core @ 2.5 Ghz (Python)

63	VPN			59.48 %	70.97 %	55.29 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)

64	AutoAlign			59.48 %	70.17 %	55.37 %	0.1 s	1 core @ 2.5 Ghz (Python)

65	Fast VP-RCNN		code	59.32 %	69.51 %	56.66 %	0.05 s	1 core @ 3.5 Ghz (C/C++)

66	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.
67	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.
68	SARFE			59.07 %	68.25 %	56.74 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

69	anonymous		code	59.04 %	69.62 %	56.45 %	0.05s	1 core @ >3.5 Ghz (python)

70	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.
71	FusionDetv1			58.68 %	68.44 %	56.22 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

72	P2V_PCV1			58.59 %	68.62 %	56.34 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

73	EG_DETR			58.58 %	74.53 %	53.76 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

74	SA-voxel-centernet		code	58.50 %	66.89 %	56.25 %	0.04 s	1 core @ 2.5 Ghz (C/C++)

75	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.
76	SCIR-Net			58.37 %	68.18 %	55.78 %	0.03 s	GPU @ 2.5 Ghz (Python + C/C++)
Q. He, H. Zeng, Y. Zeng and Y. Liu: SCIR-Net: Structured Color Image Representation Based 3D Object Detection Network from Point Clouds. AAAI 2022.
77	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.
78	TPCG			58.17 %	67.39 %	55.55 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

79	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.
80	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.
81	EPNet++			58.10 %	68.58 %	55.58 %	0.1 s	GPU @ 2.5 Ghz (Python)

82	DSGN++			58.09 %	69.70 %	54.45 %	0.4 s	1 core @ 2.5 Ghz (C/C++)

83	WHUT-iou_ssd		code	58.03 %	66.60 %	55.82 %	0.045s	1 core @ 2.5 Ghz (C/C++)

84	E^2-PV-RCNN			58.01 %	67.39 %	55.77 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)

85	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.
86	Point Image Fusion			57.91 %	66.47 %	55.48 %	0.2 s	1 core @ 2.5 Ghz (C/C++)

87	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.
88	TBD			57.81 %	67.54 %	54.30 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

89	sa-voxel-centernet		code	57.79 %	66.03 %	55.61 %	0.04 s	1 core @ 2.5 Ghz (C/C++)

90	FPC-RCNN			57.46 %	66.88 %	55.09 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

91	SGNet			57.36 %	65.93 %	53.82 %	0.09 s	GPU @ 2.5 Ghz (Python)

92	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. arXiv preprint arXiv:2011.01404 2020.
93	PDV			57.34 %	65.94 %	54.21 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

94	MVOD			57.33 %	66.72 %	54.31 %	0.16 s	1 core @ 2.5 Ghz (C/C++)

95	SIF			57.32 %	67.78 %	54.86 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
96	GNN-RCNN			57.32 %	66.78 %	55.77 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

97	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.
98	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.
99	FPCR-CNN			57.14 %	66.59 %	54.43 %	0.05 s	1 core @ 2.5 Ghz (Python)

100	FusionDetv2-v3			56.93 %	65.96 %	54.73 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

101	IA-SSD (single)			56.87 %	66.69 %	54.68 %	0.013 s	1 core @ 2.5 Ghz (C/C++)

102	DDet			56.81 %	65.02 %	54.65 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

103	CAT-Det			56.75 %	67.15 %	53.44 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)

104	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.
105	FusionDetv2-v4			56.60 %	65.80 %	54.55 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)

106	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.
107	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.
108	TBD			56.42 %	65.60 %	53.61 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

109	TBD			56.42 %	65.60 %	53.61 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

110	FusionDetv2-v5			56.40 %	65.17 %	53.89 %	0.05 s	1 core @ 2.5 Ghz (Java + C/C++)

111	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.
112	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.
113	TBD_IOU1			56.02 %	66.44 %	53.62 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

114	SAA-SECOND			55.95 %	65.50 %	52.88 %	38m s	1 core @ 2.5 Ghz (C/C++)

115	TBD_IOU			55.90 %	64.94 %	53.60 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

116	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.
117	VCRCNN			55.66 %	64.59 %	53.53 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

118	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.
119	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.
120	gupnet_se			55.50 %	74.80 %	50.57 %	0.03s	1 core @ 2.5 Ghz (C/C++)

121	ST-RCNN			55.46 %	64.50 %	52.89 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)

122	TBD			55.43 %	65.62 %	51.97 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

123	FusionDetv2-v2			55.32 %	62.74 %	52.43 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)

124	MSADet			55.31 %	66.73 %	52.97 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
125	TBD			55.14 %	63.44 %	53.89 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

126	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.
127	FPC3D_all			55.08 %	64.74 %	52.59 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

128	ANM			55.08 %	74.09 %	48.00 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

129	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.
130	NV-RCNN			54.98 %	64.78 %	51.64 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

131	GAC3D++			54.96 %	74.08 %	50.06 %	0.25 s	1 core @ 2.5 Ghz (C/C++)

132	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.
133	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.
134	DSASNet			54.67 %	64.59 %	51.05 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)

135	tbd			54.44 %	64.96 %	50.57 %	0.04 s	1 core @ 2.5 Ghz (C/C++)

136	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.
137	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.
138	OSE+			54.12 %	68.48 %	49.93 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

139	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.
140	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.
141	MKFFNet			53.64 %	63.25 %	51.07 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

142	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.
143	Mix-Teaching-M3D			53.52 %	67.34 %	47.45 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

144	TBD			53.34 %	64.17 %	51.00 %	TBD	GPU @ 2.5 Ghz (Python + C/C++)

145	ASCNet			53.28 %	62.40 %	50.88 %	0.09 s	1 core @ 2.5 Ghz (C/C++)

146	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.
147	FPV-SSD			53.10 %	62.00 %	50.67 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
148	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.
149	FusionDetv2-baseline			52.96 %	58.96 %	50.94 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)

150	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.
151	demo			52.90 %	63.79 %	49.54 %	0.04 s	1 core @ 2.5 Ghz (C/C++)

152	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.
153	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.
154	VGCN			52.80 %	61.86 %	50.66 %	0.09 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
155	tbd			52.78 %	63.45 %	50.79 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

156	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.
157	NV2P-RCNN			52.52 %	61.80 %	50.24 %	0.1 s	GPU @ 2.5 Ghz (Python)

158	HS3D		code	52.50 %	63.73 %	49.78 %	0.12 s	1 core @ 2.5 Ghz (Python + C/C++)

159	IA-SSD (multi)			52.45 %	65.07 %	50.20 %	0.014 s	1 core @ 2.5 Ghz (C/C++)

160	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.
161	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.
162	SCSTSV-MonoFlex			52.18 %	67.51 %	45.90 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

163	YF			51.82 %	61.37 %	48.59 %	0.04 s	GPU @ 2.5 Ghz (C/C++)

164	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.
165	TBD_BD		code	50.60 %	60.91 %	48.47 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

166	MonoGround			50.35 %	63.96 %	45.75 %	0.03 s	1 core @ 2.5 Ghz (Python)

167	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.
168	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.
169	Y4		code	49.24 %	68.07 %	44.42 %	0.03 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
170	vadin-TBD			48.97 %	63.77 %	44.60 %	0.04 s	1 core @ 2.5 Ghz (Python)

171	MonoFlex			48.64 %	61.96 %	44.17 %	0.03 s	1 core @ 2.5 Ghz (Python)

172	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.
173	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.
174	MonoEdge			48.26 %	61.11 %	42.37 %	0.05 s	GPU @ 2.5 Ghz (Python)

175	RelationNet3D_dla34		code	47.93 %	63.34 %	43.42 %	0.04 s	1 core @ 2.5 Ghz (C/C++)

176	LIGA-Stereo-old			47.75 %	58.61 %	44.39 %	0.375 s	Titan Xp

177	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.
178	MonoEdge-Rotate			47.56 %	62.79 %	43.02 %	0.05 s	GPU @ 2.5 Ghz (Python)

179	BirdNet+		code	47.50 %	54.78 %	45.53 %	0.11 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.
180	Contrastive PP		code	47.05 %	56.47 %	45.04 %	0.01 s	1 core @ 2.5 Ghz (Python)

181	RoIFusion		code	46.81 %	56.26 %	44.58 %	0.22 s	1 core @ 3.0 Ghz (Python)

182	mono3d			46.73 %	58.34 %	41.92 %	0.03 s	GPU @ 2.5 Ghz (Python)

183	deleted			46.14 %	58.97 %	42.48 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)

184	PP-PCdet		code	45.82 %	54.17 %	43.68 %	0.01 s	1 core @ 2.5 Ghz (Python)

185	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.
186	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.
187	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.
188	M3DSSD++		code	44.89 %	58.54 %	40.66 %	0.16s	1 core @ 2.5 Ghz (C/C++)

189	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.
190	MonoGeo			44.63 %	58.49 %	40.41 %	0.05 s	1 core @ 2.5 Ghz (Python)

191	Geo3D			44.42 %	58.74 %	40.13 %	0.04 s	GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
192	LPCG-Monoflex			44.13 %	62.44 %	39.46 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

193	SAIC_ADC_Mono3D		code	43.96 %	59.27 %	39.83 %	50 s	GPU @ 2.5 Ghz (Python)

194	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.
195	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.
196	MonoEF		code	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.
197	MonoLCD			43.71 %	57.69 %	39.60 %	0.04 s	1 core @ 2.5 Ghz (Python)

198	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.
199	MonoDDE			43.36 %	57.80 %	39.00 %	0.04 s	1 core @ 2.5 Ghz (Python)

200	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.
201	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.
202	MonoDTR			42.86 %	59.44 %	38.57 %	0.04 s	1 core @ 2.5 Ghz (C/C++)

203	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.
204	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.
205	MonoCon			42.44 %	56.57 %	36.34 %	0.02 s	GPU @ 2.5 Ghz (Python)

206	RelationNet3D_res18		code	42.25 %	57.61 %	37.93 %	0.04 s	1 core @ 2.5 Ghz (C/C++)

207	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.
208	ICCV			41.88 %	55.14 %	37.55 %	0.04 s	GPU @ 2.5 Ghz (Python)

209	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.
210	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.
211	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 .
212	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.
213	MP-Mono			40.87 %	55.79 %	36.81 %	0.16 s	GPU @ 2.5 Ghz (Python)

214	SwinMono3D			40.54 %	56.73 %	36.18 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

215	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.
216	PS-fld			40.47 %	55.47 %	36.65 %	0.25 s	1 core @ 2.5 Ghz (C/C++)

217	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.
218	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.
219	FusionDetv2-v1			39.60 %	46.77 %	38.75 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)

220	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.
221	Graph-NMS-baseline			39.46 %	52.32 %	35.60 %	47 ms	GPU @ 2.5 Ghz (Python)

222	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.
223	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.
224	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). .
225	Graph-NMS			38.40 %	51.24 %	35.24 %	36 ms	GPU @ 2.5 Ghz (Python)

226	LSVM-MDPM-sv			37.26 %	50.74 %	33.13 %	10 s	4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010. A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout. NIPS 2011.
227	multi-task CNN			37.00 %	49.38 %	33.46 %	25.1 ms	GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
228	Lite-FPN			36.93 %	50.63 %	32.88 %	0.01 s	1 core @ 2.5 Ghz (C/C++)

229	Complexer-YOLO			36.45 %	42.16 %	32.91 %	0.06 s	GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
230	LSVM-MDPM-us		code	35.92 %	48.73 %	31.70 %	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.
231	ZongmuMono3dV2		code	35.86 %	48.61 %	32.10 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

232	COF3D			35.34 %	50.45 %	31.28 %	200 s	1 core @ 2.5 Ghz (Python + C/C++)

233	Aug3D-RPN			34.95 %	47.22 %	30.64 %	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.
234	MonoHMOO			34.74 %	49.26 %	30.37 %	0.2 s	1 core @ 2.5 Ghz (Python + C/C++)

235	PPTrans			34.45 %	45.02 %	30.94 %	0.2 s	GPU @ 2.5 Ghz (Python)

236	CMKD			34.41 %	47.09 %	30.59 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

237	PointRGBNet			33.92 %	44.35 %	30.43 %	0.08 s	4 cores @ 2.5 Ghz (Python + C/C++)

238	PGD-FCOS3D		code	33.67 %	48.30 %	29.76 %	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.
239	ZongmuMono3d		code	33.47 %	45.86 %	29.84 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

240	Vote3D			33.04 %	42.66 %	30.59 %	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.
241	EGFN			32.60 %	44.09 %	29.10 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)

242	SGM3D			32.48 %	45.03 %	28.58 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

243	CaDDN		code	32.42 %	46.35 %	29.98 %	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.
244	DFR-Net			31.84 %	45.20 %	27.94 %	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.
245	K3D			31.71 %	44.40 %	27.90 %	0.3 s	1 core @ 2.5 Ghz (C/C++)

246	OC Stereo		code	30.79 %	43.50 %	28.40 %	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.
247	mBoW			30.26 %	41.52 %	26.34 %	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.
248	BirdNet			30.07 %	36.82 %	28.40 %	0.11 s	Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
249	CDTrack3D		code	29.98 %	42.95 %	27.59 %	0.0106 s	NVIDIA RTX 3090 GPU, i9 10850k CPU

250	SOD			29.47 %	46.61 %	26.97 %	0.1 s	1 core @ 2.5 Ghz (Python)

Table as LaTeX | Only published Methods

Cyclist

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	ISE-RCNN-PV			82.78 %	88.08 %	75.96 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

2	SARFE			82.00 %	90.01 %	75.30 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

3	SGNet			82.00 %	91.55 %	75.30 %	0.09 s	GPU @ 2.5 Ghz (Python)

4	ISE-RCNN			81.95 %	87.68 %	75.17 %	0.09 s	1 core @ 2.5 Ghz (Python + C/C++)

5	NF2			81.75 %	89.67 %	71.43 %	0.1 s	GPU @ 2.5 Ghz (Python)

6	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.
7	anonymous		code	81.52 %	89.31 %	74.71 %	0.05s	1 core @ >3.5 Ghz (python)

8	Fast VP-RCNN		code	81.41 %	89.29 %	74.88 %	0.05 s	1 core @ 3.5 Ghz (C/C++)

9	Point Image Fusion			80.73 %	87.99 %	74.32 %	0.2 s	1 core @ 2.5 Ghz (C/C++)

10	TPCG			80.71 %	87.73 %	74.17 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

11	SAA-PV-RCNN			80.71 %	88.94 %	73.79 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

12	CAT-Det			80.70 %	87.94 %	73.86 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)

13	SPG_mini			80.58 %	87.77 %	74.86 %	0.09 s	GPU @ 2.5 Ghz (Python)

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	FS-Net			80.54 %	87.05 %	73.81 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

16	BtcDet			80.46 %	88.41 %	74.59 %	0.09 s	GPU @ 2.5 Ghz (Python + C/C++)

17	VCRCNN			80.46 %	87.34 %	73.68 %	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			80.37 %	89.07 %	74.20 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)

20	GNN-RCNN			80.35 %	89.53 %	73.85 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

21	Generalized-SIENet			80.07 %	89.23 %	73.76 %	0.08 s	1 core @ 2.5 Ghz (Python)

22	WHUT-iou_ssd		code	79.98 %	87.31 %	74.30 %	0.045s	1 core @ 2.5 Ghz (C/C++)

23	sa-voxel-centernet		code	79.98 %	88.08 %	73.61 %	0.04 s	1 core @ 2.5 Ghz (C/C++)

24	E^2-PV-RCNN			79.94 %	87.22 %	73.58 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)

25	SA-voxel-centernet		code	79.92 %	86.43 %	73.47 %	0.04 s	1 core @ 2.5 Ghz (C/C++)

26	FPC-RCNN			79.89 %	88.62 %	73.18 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

27	PDV			79.84 %	88.76 %	73.04 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

28	DSASNet			79.56 %	87.41 %	73.08 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)

29	DDet			79.47 %	88.65 %	72.64 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

30	M3DeTR		code	79.29 %	87.38 %	72.46 %	n/a s	GPU @ 1.0 Ghz (Python)
T. Guan, J. Wang, S. Lan, R. Chandra, Z. Wu, L. Davis and D. Manocha: M3DeTR: Multi-representation, Multi- scale, Mutual-relation 3D Object Detection with Transformers. 2021.
31	PV-RCNN++			79.22 %	85.76 %	72.35 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

32	TBD			78.83 %	84.96 %	72.18 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

33	HotSpotNet			78.81 %	86.06 %	71.74 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
34	JPVNet			78.73 %	87.42 %	72.45 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

35	IA-SSD (single)			78.71 %	88.99 %	72.03 %	0.013 s	1 core @ 2.5 Ghz (C/C++)

36	PE-RCVN			78.71 %	90.77 %	71.83 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

37	TBD			78.59 %	86.51 %	73.55 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

38	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.
39	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.
40	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.
41	AutoAlign			77.96 %	89.63 %	71.27 %	0.1 s	1 core @ 2.5 Ghz (Python)

42	MVOD			77.92 %	85.92 %	71.24 %	0.16 s	1 core @ 2.5 Ghz (C/C++)

43	ST-RCNN			77.57 %	85.84 %	70.92 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)

44	SAA-SECOND			77.47 %	88.50 %	70.23 %	38m s	1 core @ 2.5 Ghz (C/C++)

45	TBD			77.31 %	87.22 %	70.53 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

46	TBD			77.06 %	90.28 %	70.46 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

47	ASCNet			76.95 %	83.81 %	70.84 %	0.09 s	1 core @ 2.5 Ghz (C/C++)

48	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.
49	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.
50	FusionDetv2-v5			76.60 %	85.95 %	69.75 %	0.05 s	1 core @ 2.5 Ghz (Java + C/C++)

51	FusionDetv2-v3			76.48 %	86.15 %	69.64 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

52	NV2P-RCNN			76.29 %	83.53 %	69.57 %	0.1 s	GPU @ 2.5 Ghz (Python)

53	VCT			76.08 %	86.69 %	69.91 %	0.2 s	1 core @ 2.5 Ghz (Python)

54	FusionDetv2-v4			75.92 %	87.66 %	70.06 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)

55	FPCR-CNN			75.83 %	87.74 %	68.98 %	0.05 s	1 core @ 2.5 Ghz (Python)

56	FPV-SSD			75.33 %	83.30 %	68.10 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
57	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.
58	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.
59	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.
60	Fast-CLOCs			75.07 %	89.73 %	67.93 %	0.1 s	GPU @ 2.5 Ghz (Python)

61	SCIR-Net			74.88 %	84.92 %	68.13 %	0.03 s	GPU @ 2.5 Ghz (Python + C/C++)
Q. He, H. Zeng, Y. Zeng and Y. Liu: SCIR-Net: Structured Color Image Representation Based 3D Object Detection Network from Point Clouds. AAAI 2022.
62	EA-M-RCNN(BorderAtt)			74.85 %	88.69 %	68.01 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

63	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.
64	DGIST MT-CNN			74.77 %	86.95 %	66.05 %	0.09 s	GPU @ 1.0 Ghz (Python)

65	P2V_PCV1			74.57 %	85.38 %	68.37 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

66	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.
67	MKFFNet			74.39 %	85.89 %	68.02 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

68	FusionDetv2-v2			74.38 %	86.62 %	67.96 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)

69	RoIFusion		code	74.27 %	85.15 %	68.29 %	0.22 s	1 core @ 3.0 Ghz (Python)

70	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.
71	MSADet			74.06 %	87.54 %	68.55 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
72	VPN			73.99 %	89.56 %	66.86 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)

73	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.
74	TBD_IOU			73.84 %	87.97 %	66.36 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

75	FusionDetv1			73.69 %	85.39 %	66.94 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

76	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.
77	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. arXiv preprint arXiv:2011.01404 2020.
78	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.
79	TBD_IOU1			73.46 %	87.05 %	66.14 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

80	tbd			73.43 %	84.88 %	65.72 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

81	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.
82	FPC3D_all			73.25 %	84.12 %	66.56 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

83	SIF			73.19 %	85.18 %	65.41 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
84	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.
85	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.
86	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.
87	NV-RCNN			73.07 %	85.64 %	66.60 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

88	HS3D		code	73.02 %	84.59 %	67.13 %	0.12 s	1 core @ 2.5 Ghz (Python + C/C++)

89	demo			72.90 %	87.20 %	66.56 %	0.04 s	1 core @ 2.5 Ghz (C/C++)

90	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.
91	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.
92	FusionDetv2-baseline			72.68 %	82.09 %	66.42 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)

93	VGCN			72.28 %	86.81 %	65.68 %	0.09 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
94	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.
95	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.
96	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.
97	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.
98	IA-SSD (multi)			70.46 %	84.98 %	65.55 %	0.014 s	1 core @ 2.5 Ghz (C/C++)

99	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.
100	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.
101	TBD			70.12 %	81.15 %	63.79 %	0.3 s	1 core @ 2.5 Ghz (C/C++)

102	YF			70.10 %	80.50 %	64.21 %	0.04 s	GPU @ 2.5 Ghz (C/C++)

103	TBD			69.99 %	85.54 %	65.02 %	TBD	GPU @ 2.5 Ghz (Python + C/C++)

104	TBD_BD		code	69.56 %	83.36 %	63.39 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

105	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.
106	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.
107	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.
108	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.
109	EPNet++			68.30 %	80.27 %	63.00 %	0.1 s	GPU @ 2.5 Ghz (Python)

110	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.
111	Contrastive PP		code	67.62 %	79.63 %	60.81 %	0.01 s	1 core @ 2.5 Ghz (Python)

112	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.
113	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.
114	TBD			67.15 %	82.44 %	60.87 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

115	TBD			67.15 %	82.44 %	60.87 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

116	VueronNet			66.92 %	82.71 %	59.01 %	0.08 s	GPU @ 2.5 Ghz (Python)

117	PP-PCdet		code	66.58 %	78.44 %	60.22 %	0.01 s	1 core @ 2.5 Ghz (Python)

118	FII-CenterNet			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.
119	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.
120	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.
121	PointRGBNet			65.98 %	79.87 %	59.75 %	0.08 s	4 cores @ 2.5 Ghz (Python + C/C++)

122	BirdNet+		code	65.40 %	72.96 %	60.23 %	0.11 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.
123	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.
124	PiFeNet			64.39 %	80.02 %	57.46 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

125	tbd			64.31 %	79.83 %	58.14 %	0.04 s	1 core @ 2.5 Ghz (C/C++)

126	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.
127	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.
128	DD3D		code	62.21 %	79.98 %	54.91 %	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) .
129	DSGN++			62.10 %	77.71 %	55.78 %	0.4 s	1 core @ 2.5 Ghz (C/C++)

130	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.
131	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.
132	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.
133	Mix-Teaching-M3D			58.65 %	75.15 %	50.54 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

134	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.
135	SFD-Retina			57.71 %	76.35 %	50.56 %	0.04 s	GPU @ 2.5 Ghz (Python)

136	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.
137	GFD-Retina			56.93 %	74.59 %	49.91 %	0.07 s	GPU @ 2.5 Ghz (Python)

138	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.
139	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.
140	FusionDetv2-v1			56.30 %	66.22 %	52.29 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)

141	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.
142	LIGA-Stereo-old			55.77 %	74.25 %	49.68 %	0.375 s	Titan Xp

143	MM-Retina			54.90 %	69.95 %	48.49 %	0.04 s	GPU @ 2.5 Ghz (Python)

144	mono3d			54.82 %	70.77 %	47.55 %	0.03 s	GPU @ 2.5 Ghz (Python)

145	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.
146	M3DSSD++		code	54.62 %	69.35 %	46.25 %	0.16s	1 core @ 2.5 Ghz (C/C++)

147	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.
148	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.
149	SCSTSV-MonoFlex			54.42 %	75.37 %	46.25 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

150	vadin-TBD			54.12 %	70.14 %	46.16 %	0.04 s	1 core @ 2.5 Ghz (Python)

151	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 .
152	LPCG-Monoflex			53.04 %	72.36 %	46.11 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

153	GA-Aug			52.71 %	67.62 %	46.37 %	0.04 s	GPU @ 2.5 Ghz (Python)

154	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.
155	MonoEdge-Rotate			51.89 %	68.91 %	45.33 %	0.05 s	GPU @ 2.5 Ghz (Python)

156	MonoGround			51.67 %	67.78 %	45.11 %	0.03 s	1 core @ 2.5 Ghz (Python)

157	Geo3D			51.26 %	71.75 %	44.44 %	0.04 s	GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
158	MonoFlex			51.23 %	66.73 %	44.57 %	0.03 s	1 core @ 2.5 Ghz (Python)

159	MonoDDE			51.10 %	70.85 %	44.02 %	0.04 s	1 core @ 2.5 Ghz (Python)

160	MonoGeo			50.48 %	65.42 %	42.48 %	0.05 s	1 core @ 2.5 Ghz (Python)

161	deleted			50.22 %	68.25 %	44.84 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)

162	MonoDTR			49.48 %	64.93 %	42.76 %	0.04 s	1 core @ 2.5 Ghz (C/C++)

163	MonoEdge			49.33 %	65.93 %	42.44 %	0.05 s	GPU @ 2.5 Ghz (Python)

164	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.
165	MP-Mono			48.54 %	69.48 %	41.58 %	0.16 s	GPU @ 2.5 Ghz (Python)

166	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.
167	EG_DETR			48.42 %	67.71 %	42.99 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

168	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.
169	CMKD			47.21 %	66.52 %	41.56 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

170	KAIST-VDCLab			46.65 %	68.60 %	41.79 %	0.04 s	1 core @ 2.5 Ghz (Python)

171	gupnet_se			46.47 %	68.66 %	37.89 %	0.03s	1 core @ 2.5 Ghz (C/C++)

172	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.
173	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.
174	SwinMono3D			45.72 %	67.95 %	38.55 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

175	CMKD			45.69 %	65.71 %	40.11 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

176	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.
177	MonoLCD			44.57 %	65.17 %	39.96 %	0.04 s	1 core @ 2.5 Ghz (Python)

178	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.
179	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.
180	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.
181	MonoCon			42.49 %	59.39 %	35.94 %	0.02 s	GPU @ 2.5 Ghz (Python)

182	SAIC_ADC_Mono3D		code	42.37 %	60.72 %	36.57 %	50 s	GPU @ 2.5 Ghz (Python)

183	GAC3D++			41.87 %	61.03 %	35.78 %	0.25 s	1 core @ 2.5 Ghz (C/C++)

184	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 .
185	MonoEF		code	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.
186	PS-fld			41.13 %	58.13 %	35.90 %	0.25 s	1 core @ 2.5 Ghz (C/C++)

187	SOD			40.95 %	60.07 %	34.02 %	0.1 s	1 core @ 2.5 Ghz (Python)

188	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.
189	PPTrans			39.55 %	48.93 %	33.74 %	0.2 s	GPU @ 2.5 Ghz (Python)

190	RelationNet3D_dla34		code	39.52 %	59.56 %	34.51 %	0.04 s	1 core @ 2.5 Ghz (C/C++)

191	ANM			39.39 %	57.77 %	34.59 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

192	OSE+			39.26 %	58.13 %	34.35 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

193	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.
194	RelationNet3D_res18		code	37.41 %	56.22 %	32.56 %	0.04 s	1 core @ 2.5 Ghz (C/C++)

195	ZongmuMono3dV2		code	37.18 %	49.10 %	32.04 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

196	AEC3D			37.15 %	48.48 %	34.86 %	18 ms	GPU @ 2.5 Ghz (Python)

197	ICCV			36.70 %	53.31 %	31.94 %	0.04 s	GPU @ 2.5 Ghz (Python)

198	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.
199	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.
200	Y4		code	35.92 %	53.50 %	31.89 %	0.03 s	1 core @ 2.5 Ghz (Python)
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201	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.
202	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.
203	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.
204	COF3D			32.97 %	51.77 %	28.26 %	200 s	1 core @ 2.5 Ghz (Python + C/C++)

205	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.
206	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.
207	VN3D			31.81 %	42.58 %	29.09 %	0.02 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
208	ZongmuMono3d		code	31.56 %	44.68 %	27.48 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

209	BEVC			30.39 %	43.61 %	27.46 %	35ms	GPU @ 1.5 Ghz (Python)

210	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.
211	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.
212	SGM3D			27.89 %	42.21 %	24.73 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

213	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.
214	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.
215	K3D			27.29 %	38.82 %	23.86 %	0.3 s	1 core @ 2.5 Ghz (C/C++)

216	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.
217	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.
218	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.
219	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.
220	E2E-DA-Lite (Res18)			25.82 %	42.48 %	21.16 %	0.01 s	GPU @ 2.5 Ghz (Python)

221	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.
222	E2E-DA			24.46 %	39.34 %	19.59 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

223	MonoHMOO			23.59 %	37.41 %	21.20 %	0.2 s	1 core @ 2.5 Ghz (Python + C/C++)

224	Graph-NMS-baseline			23.07 %	35.72 %	20.54 %	47 ms	GPU @ 2.5 Ghz (Python)

225	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.
226	MAOLoss		code	22.58 %	34.37 %	20.49 %	0.05 s	1 core @ 2.5 Ghz (Python)

227	Graph-NMS			22.43 %	34.13 %	19.65 %	36 ms	GPU @ 2.5 Ghz (Python)

228	Lite-FPN			19.17 %	24.40 %	15.68 %	0.01 s	1 core @ 2.5 Ghz (C/C++)

229	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.
230	CDTrack3D		code	16.04 %	23.84 %	13.15 %	0.0106 s	NVIDIA RTX 3090 GPU, i9 10850k CPU

231	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.
232	EGFN			13.45 %	21.13 %	11.72 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)

233	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.
234	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.
235	MM			6.79 %	11.33 %	6.28 %	1 s	1 core @ 2.5 Ghz (C/C++)

236	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.
237	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.

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