The KITTI Vision Benchmark Suite

3D Object Detection Evaluation 2017

The 3D object detection benchmark consists of 7481 training images and 7518 test images as well as the corresponding point clouds, comprising a total of 80.256 labeled objects. For evaluation, we compute precision-recall curves. To rank the methods we compute average precision. We require that all methods use the same parameter set for all test pairs. Our development kit provides details about the data format as well as MATLAB / C++ utility functions for reading and writing the label files.

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

We evaluate 3D object detection performance using the PASCAL criteria also used for 2D object detection. Far objects are thus filtered based on their bounding box height in the image plane. As only objects also appearing on the image plane are labeled, objects in don't car areas do not count as false positives. We note that the evaluation does not take care of ignoring detections that are not visible on the image plane — these detections might give rise to false positives. For cars we require an 3D bounding box overlap of 70%, while for pedestrians and cyclists we require a 3D bounding box overlap of 50%. Difficulties are defined as follows:

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

All methods are ranked based on the moderately difficult results.

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	BorderAtt			82.33 %	87.77 %	77.37 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

2	HUAWEI Octopus			82.13 %	88.26 %	77.41 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

3	ADLAB			82.08 %	90.92 %	77.36 %	0.08 s	1 core @ >3.5 Ghz (C/C++)

4	RangeRCNN-LV			81.85 %	88.76 %	77.18 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

5	PVGNet			81.81 %	89.94 %	77.09 %	0.05 s	1 core @ >3.5 Ghz (C/C++)

6	Voxel R-CNN			81.62 %	90.90 %	77.06 %	0.04 s	GPU @ 3.0 Ghz (C/C++)

7	IC-PVRCNN			81.57 %	88.60 %	77.09 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

8	Deformable PV-RCNN		code	81.46 %	88.25 %	76.96 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya and K. Czarnecki: Deformable PV-RCNN: Improving 3D Object Detection with Learned Deformations. ECCV 2020 Perception for Autonomous Driving Workshop.
9	MMLab PV-RCNN		code	81.43 %	90.25 %	76.82 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
10	PC-RGNN			81.38 %	87.94 %	76.88 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)

11	RangeRCNN			81.33 %	88.47 %	77.09 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, M. Zhang, Z. Zhang, X. Zhao and S. Pu: RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representation. arXiv preprint arXiv:2009.00206 2020.
12	ReFineNet			81.24 %	87.70 %	76.77 %	0.08 s	1 core @ 2.5 Ghz (Python)

13	HyBrid Feature Det			81.16 %	87.99 %	76.81 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)

14	OneCoLab SicNet V2			81.07 %	88.02 %	76.76 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

15	Associate-3Ddet_v2			80.77 %	91.53 %	75.23 %	0.04 s	1 core @ 2.5 Ghz (Python)

16	CIA-SSD v2			80.71 %	89.61 %	75.06 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

17	CLOCs_PVCas			80.67 %	88.94 %	77.15 %	0.1 s	1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection . 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
18	AIMC-RUC			80.63 %	89.90 %	75.32 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

19	OAP			80.63 %	89.18 %	73.04 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

20	HRI-MSP-L			80.62 %	87.61 %	76.29 %	0.07 s	1 core @ 2.5 Ghz (C/C++)

21	IC-SECOND			80.61 %	88.25 %	75.83 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

22	CVIS-DF3D_v2			80.48 %	87.20 %	76.01 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

23	SVGA-Net			80.38 %	87.73 %	76.27 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)

24	SPANet			80.34 %	91.05 %	74.89 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

25	CVRS_PF			80.33 %	88.04 %	75.21 %	0.09 s	1 core @ 2.5 Ghz (C/C++)

26	CIA-SSD			80.28 %	89.59 %	72.87 %	0.03 s	1 core @ 2.5 Ghz (Python + C/C++)

27	Baseline of CA RCNN			80.28 %	87.45 %	76.21 %	0.1 s	GPU @ 2.5 Ghz (Python)

28	CVIS-DF3D			80.28 %	87.45 %	76.21 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

29	CA-RCNN			80.18 %	87.43 %	76.21 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

30	CBi-GNN			80.18 %	91.50 %	74.76 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

31	deprecated			80.16 %	89.48 %	72.75 %	deprecated	deprecated

32	3D-CVF at SPA			80.05 %	89.20 %	73.11 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
J. Yoo, Y. Kim, J. Kim and J. Choi: 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection. ECCV 2020.
33	CN			79.89 %	90.55 %	76.31 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)

34	VAL			79.87 %	89.35 %	70.27 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

35	SA-SSD		code	79.79 %	88.75 %	74.16 %	0.04 s	1 core @ 2.5 Ghz (Python)
C. He, H. Zeng, J. Huang, X. Hua and L. Zhang: Structure Aware Single-stage 3D Object Detection from Point Cloud. CVPR 2020.
36	CJJ			79.72 %	88.98 %	74.71 %	0.04 s	1 core @ 2.5 Ghz (C/C++)

37	STD		code	79.71 %	87.95 %	75.09 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
38	ISF			79.71 %	89.13 %	74.78 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

39	AF_V1			79.68 %	88.16 %	72.39 %	0.1 s	1 core @ 2.5 Ghz (Python)

40	FCY			79.67 %	89.19 %	74.35 %	0.02 s	GPU @ 2.5 Ghz (Python)

41	scssd-normal(0.3)			79.59 %	88.97 %	72.51 %	0.05 s	GPU @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: SCSSD for multi sensor fusion 3d object detection method. Submitted to Information Science 2020.
42	3DSSD		code	79.57 %	88.36 %	74.55 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
43	PointRes			79.55 %	88.73 %	74.17 %	0.013 s	1 core @ 2.5 Ghz (Python + C/C++)

44	Cas-SSD			79.50 %	88.73 %	72.46 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

45	scssd-normal(0.4)			79.49 %	88.70 %	74.25 %	0.05 s	1 core @ 2.5 Ghz (Python)
P. An, J. Liang, J. Ma, K. Yu and B. Fang: SCSSD for multi sensor fusion 3d object detection method. Submitted to Information Science 2020.
46	Point-GNN		code	79.47 %	88.33 %	72.29 %	0.6 s	GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
47	PP-3D			79.47 %	88.33 %	72.29 %	0.1 s	1 core @ 2.5 Ghz (Python)

48	nonet			79.42 %	88.28 %	75.77 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

49	RoIFusion		code	79.41 %	88.43 %	72.58 %	0.22 s	1 core @ 3.0 Ghz (Python)

50	EPNet		code	79.28 %	89.81 %	74.59 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
T. Huang, Z. Liu, X. Chen and X. Bai: EPNet: Enhancing Point Features with Image Semantics for 3D Object Detection. ECCV 2020.
51	AP-RCNN			79.27 %	87.65 %	76.43 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

52	MGACNet			79.18 %	86.20 %	74.58 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

53	D3D			79.15 %	87.07 %	73.79 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

54	NLK-ALL		code	79.13 %	87.23 %	74.30 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)

55	3D IoU-Net			79.03 %	87.96 %	72.78 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, S. Krylov, Y. Ding and L. Shao: 3D IoU-Net: IoU Guided 3D Object Detector for Point Clouds. arXiv preprint arXiv:2004.04962 2020.
56	Noah CV Lab - SSL			78.99 %	85.50 %	71.75 %	0.1 s	GPU @ 2.5 Ghz (Python)

57	SERCNN			78.96 %	87.74 %	74.30 %	0.1 s	1 core @ 2.5 Ghz (Python)
D. Zhou, J. Fang, X. Song, L. Liu, J. Yin, Y. Dai, H. Li and R. Yang: Joint 3D Instance Segmentation and Object Detection for Autonomous Driving. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020.
58	deprecated			78.83 %	87.89 %	73.52 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

59	FLID			78.78 %	86.73 %	71.24 %	0.04 s	GPU @ 2.5 Ghz (Python)

60	ISF-v2			78.67 %	87.54 %	74.03 %	0.04 s	1 core @ 2.5 Ghz (Python)

61	OneCoLab SicNet			78.65 %	87.41 %	74.16 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

62	PVF-NET			78.58 %	87.05 %	71.68 %	0.1 s	1 core @ 2.5 Ghz (Python)

63	BLPNet_V2			78.57 %	87.10 %	71.67 %	0.04 s	1 core @ 2.5 Ghz (Python)

64	Discrete-PointDet			78.51 %	88.53 %	71.29 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

65	MMLab-PartA^2		code	78.49 %	87.81 %	73.51 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
66	SRDL			78.49 %	86.75 %	74.04 %	0.15 s	GPU @ 2.5 Ghz (Python + C/C++)

67	F-3DNet			78.48 %	85.48 %	71.62 %	0.5 s	GPU @ 2.5 Ghz (Python)

68	CLOCs_SecCas			78.45 %	86.38 %	72.45 %	0.1 s	1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection. 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
69	Patches - EMP			78.41 %	89.84 %	73.15 %	0.5 s	GPU @ 2.5 Ghz (Python)
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
70	LZY_RCNN			78.41 %	85.38 %	74.04 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

71	deprecated			78.32 %	89.34 %	71.21 %	0.06 s	GPU @ >3.5 Ghz (Python)

72	HotSpotNet			78.31 %	87.60 %	73.34 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
73	cvMax			78.28 %	86.60 %	71.60 %	0.04 s	GPU @ >3.5 Ghz (Python)

74	KNN-GCNN			78.26 %	86.37 %	71.14 %	0.4 s	1 core @ 2.5 Ghz (Python + C/C++)

75	Chovy			78.02 %	86.86 %	73.20 %	0.04 s	GPU @ 2.5 Ghz (Python)

76	deprecated			77.97 %	86.76 %	73.00 %	0.04 s	GPU @ 2.5 Ghz (Python)

77	deprecated			77.97 %	86.53 %	67.90 %	0.05 s	1 core @ 2.5 Ghz (Python)

78	CenterNet3D			77.90 %	86.20 %	73.03 %	0.04 s	GPU @ 1.5 Ghz (Python)
G. Wang, B. Tian, Y. Ai, T. Xu, L. Chen and D. Cao: CenterNet3D:An Anchor free Object Detector for Autonomous Driving. 2020.
79	V3D			77.87 %	86.58 %	72.52 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

80	tbd		code	77.72 %	86.09 %	72.53 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

81	PPBA			77.65 %	84.16 %	71.21 %	NA s	GPU @ 2.5 Ghz (Python)

82	TBU			77.65 %	84.16 %	71.21 %	NA s	GPU @ 2.5 Ghz (Python)

83	LZnet			77.51 %	85.58 %	72.48 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)

84	UberATG-MMF			77.43 %	88.40 %	70.22 %	0.08 s	GPU @ 2.5 Ghz (Python)
M. Liang, B. Yang, Y. Chen, R. Hu and R. Urtasun: Multi-Task Multi-Sensor Fusion for 3D Object Detection. CVPR 2019.
85	Associate-3Ddet		code	77.40 %	85.99 %	70.53 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
L. Du, X. Ye, X. Tan, J. Feng, Z. Xu, E. Ding and S. Wen: Associate-3Ddet: Perceptual-to-Conceptual Association for 3D Point Cloud Object Detection. The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
86	Fast Point R-CNN			77.40 %	85.29 %	70.24 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, S. Liu, X. Shen and J. Jia: Fast Point R-CNN. Proceedings of the IEEE international conference on computer vision (ICCV) 2019.
87	deprecated			77.31 %	86.44 %	70.91 %	-	-

88	Dccnet			77.22 %	86.67 %	69.97 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

89	Patches			77.20 %	88.67 %	71.82 %	0.15 s	GPU @ 2.0 Ghz
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
90	deprecated			77.17 %	86.27 %	70.83 %	0.05 s	GPU @ >3.5 Ghz (Python)

91	DEFT			77.15 %	86.34 %	70.76 %	1 s	GPU @ 2.5 Ghz (Python)

92	VAR			77.08 %	84.92 %	72.21 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

93	CU-PointRCNN			76.87 %	86.55 %	73.17 %	0.1 s	GPU @ 1.5 Ghz (Python + C/C++)

94	HRI-VoxelFPN			76.70 %	85.64 %	69.44 %	0.02 s	GPU @ 2.5 Ghz (Python + C/C++)
H. Kuang, B. Wang, J. An, M. Zhang and Z. Zhang: Voxel-FPN:multi-scale voxel feature aggregation in 3D object detection from point clouds. sensors 2020.
95	CLOCs_PointCas			76.68 %	87.50 %	71.21 %	0.1 s	GPU @ 2.5 Ghz (Python)

96	SARPNET			76.64 %	85.63 %	71.31 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
Y. Ye, H. Chen, C. Zhang, X. Hao and Z. Zhang: SARPNET: Shape Attention Regional Proposal Network for LiDAR-based 3D Object Detection. Neurocomputing 2019.
97	TBD			76.57 %	85.33 %	72.05 %	0.05 s	GPU @ 2.5 Ghz (Python)

98	IGRP+			76.54 %	86.90 %	71.77 %	0.18 s	1 core @ 2.5 Ghz (Python + C/C++)

99	3D IoU Loss			76.50 %	86.16 %	71.39 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
D. Zhou, J. Fang, X. Song, C. Guan, J. Yin, Y. Dai and R. Yang: IoU Loss for 2D/3D Object Detection. International Conference on 3D Vision (3DV) 2019.
100	F-ConvNet		code	76.39 %	87.36 %	66.69 %	0.47 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
101	PiP			76.24 %	85.30 %	70.45 %	0.033 s	1 core @ 2.5 Ghz (Python)

102	VICNet			76.18 %	85.21 %	70.60 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

103	SegVoxelNet			76.13 %	86.04 %	70.76 %	0.04 s	1 core @ 2.5 Ghz (Python)
H. Yi, S. Shi, M. Ding, J. Sun, K. Xu, H. Zhou, Z. Wang, S. Li and G. Wang: SegVoxelNet: Exploring Semantic Context and Depth-aware Features for 3D Vehicle Detection from Point Cloud. ICRA 2020.
104	NLK-3D			76.08 %	84.47 %	70.93 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)

105	TANet		code	75.94 %	84.39 %	68.82 %	0.035s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
106	IGRP			75.90 %	86.27 %	69.31 %	0.18 s	1 core @ 2.5 Ghz (Python + C/C++)

107	MVX-Net++			75.86 %	85.99 %	70.70 %	0.15 s	1 core @ 2.5 Ghz (C/C++)

108	PointCSE			75.82 %	86.46 %	70.47 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

109	PointRGCN			75.73 %	85.97 %	70.60 %	0.26 s	GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
110	IE-PointRCNN			75.67 %	86.26 %	70.47 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

111	MMLab-PointRCNN		code	75.64 %	86.96 %	70.70 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
112	AB3DMOT		code	75.43 %	86.10 %	68.88 %	0.0047s	1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
113	PPFNet		code	75.43 %	85.91 %	68.88 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

114	MDA			75.39 %	83.72 %	71.98 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

115	HR-SECOND		code	75.32 %	84.78 %	68.70 %	0.11 s	1 core @ 2.5 Ghz (Python + C/C++)

116	R-GCN			75.26 %	83.42 %	68.73 %	0.16 s	GPU @ 2.5 Ghz (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
117	epBRM		code	75.15 %	85.00 %	69.84 %	0.1 s	GPU @ >3.5 Ghz (Python + C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
118	MuRF			75.11 %	84.81 %	69.99 %	0.05 s	GPU @ 1.5 Ghz (Python + C/C++)

119	3DBN_2			75.06 %	84.90 %	72.10 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
120	MAFF-Net(DAF-Pillar)			75.04 %	85.52 %	67.61 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
Z. Zhang, Z. Liang, M. Zhang, X. Zhao, Y. Ming, T. Wenming and S. Pu: MAFF-Net: Filter False Positive for 3D Vehicle Detection with Multi-modal Adaptive Feature Fusion. arXiv preprint arXiv:2009.10945 2020.
121	PBASN		code	75.02 %	83.16 %	69.72 %	NA s	GPU @ 2.5 Ghz (Python)

122	PI-RCNN			74.82 %	84.37 %	70.03 %	0.1 s	1 core @ 2.5 Ghz (Python)
L. Xie, C. Xiang, Z. Yu, G. Xu, Z. Yang, D. Cai and X. He: PI-RCNN: An Efficient Multi-sensor 3D Object Detector with Point-based Attentive Cont-conv Fusion Module. AAAI 2020 : The Thirty-Fourth AAAI Conference on Artificial Intelligence 2020.
123	EPENet			74.72 %	85.19 %	70.05 %	0.04 s	1 core @ 2.5 Ghz (C/C++)

124	Pointpillar_TV			74.55 %	83.08 %	69.13 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

125	CentrNet-FG			74.47 %	83.67 %	69.94 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

126	RethinkDet3D			74.35 %	82.81 %	67.90 %	0.15 s	1 core @ 2.5 Ghz (Python)

127	PointPillars		code	74.31 %	82.58 %	68.99 %	16 ms	1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
128	Bit			74.30 %	82.67 %	68.73 %	0.11 s	1 core @ 2.5 Ghz (C/C++)

129	Prune			74.28 %	85.03 %	67.16 %	0.11 s	1 core @ 2.5 Ghz (C/C++)

130	autoRUC			74.08 %	84.54 %	67.04 %	0.12 s	1 core @ 2.5 Ghz (C/C++)

131	Simple3D Net			74.06 %	83.06 %	69.17 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

132	ARPNET			74.04 %	84.69 %	68.64 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
133	PointPiallars_SECA			73.99 %	82.62 %	69.98 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

134	VOXEL_FPN_HR			73.98 %	85.33 %	68.67 %	0.12 s	8 cores @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
135	tt		code	73.92 %	84.14 %	69.15 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

136	autonet			73.83 %	82.66 %	67.93 %	0.12 s	1 core @ 2.5 Ghz (C/C++)

137	PC-CNN-V2			73.79 %	85.57 %	65.65 %	0.5 s	GPU @ 2.5 Ghz (Matlab + C/C++)
X. Du, M. Ang, S. Karaman and D. Rus: A General Pipeline for 3D Detection of Vehicles. 2018 IEEE International Conference on Robotics and Automation (ICRA) 2018.
138	C-GCN			73.62 %	83.49 %	67.01 %	0.147 s	GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
139	baseline			73.55 %	82.92 %	67.42 %	0.12 s	1 core @ 2.5 Ghz (C/C++)

140	3DBN			73.53 %	83.77 %	66.23 %	0.13s	1080Ti (Python+C/C++)
X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.
141	BVVF			73.34 %	80.19 %	67.34 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

142	SCNet			73.17 %	83.34 %	67.93 %	0.04 s	GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
143	TBD			73.02 %	82.74 %	67.97 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)

144	DPointNet			73.02 %	79.25 %	68.53 %	0.09 s	1 core @ 2.5 Ghz (C/C++)

145	PFF3D			72.93 %	81.11 %	67.24 %	0.05 s	GPU @ 3.0 Ghz (Python + C/C++)

146	RUC			72.65 %	80.76 %	68.74 %	0.12 s	1 core @ 2.5 Ghz (C/C++)

147	AVOD-FPN		code	71.76 %	83.07 %	65.73 %	0.1 s	Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
148	PointPainting			71.70 %	82.11 %	67.08 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
149	RUC		code	71.40 %	80.98 %	65.98 %	0.12 s	1 core @ 2.5 Ghz (C/C++)

150	RUC		code	71.32 %	81.07 %	64.69 %	0.12 s	1 core @ 2.5 Ghz (C/C++)

151	WS3D			70.59 %	80.99 %	64.23 %	0.1 s	GPU @ 2.5 Ghz (Python)
Q. Meng, W. Wang, T. Zhou, J. Shen, L. Van Gool and D. Dai: Weakly Supervised 3D Object Detection from Lidar Point Cloud. 2020.
152	F-PointNet		code	69.79 %	82.19 %	60.59 %	0.17 s	GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
153	RuiRUC			69.32 %	81.45 %	57.64 %	0.12 s	1 core @ 2.5 Ghz (Python)

154	UberATG-ContFuse			68.78 %	83.68 %	61.67 %	0.06 s	GPU @ 2.5 Ghz (Python)
M. Liang, B. Yang, S. Wang and R. Urtasun: Deep Continuous Fusion for Multi-Sensor 3D Object Detection. ECCV 2018.
155	MLOD		code	67.76 %	77.24 %	62.05 %	0.12 s	GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
156	AVOD		code	66.47 %	76.39 %	60.23 %	0.08 s	Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
157	seivl			66.40 %	77.00 %	63.48 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

158	DAMNET		code	65.52 %	76.25 %	59.54 %	1 s	1 core @ 2.5 Ghz (C/C++)

159	voxelrcnn			64.77 %	73.60 %	60.05 %	15 s	1 core @ 2.5 Ghz (C/C++)

160	MV3D			63.63 %	74.97 %	54.00 %	0.36 s	GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
161	RCD			60.56 %	70.54 %	55.58 %	0.1 s	GPU @ 2.5 Ghz (Python)

162	ANM			59.07 %	74.99 %	47.64 %	0.12 s	1 core @ 2.5 Ghz (Python)

163	A3DODWTDA		code	56.82 %	62.84 %	48.12 %	0.08 s	GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
164	PL++ (SDN+GDC)		code	54.88 %	68.38 %	49.16 %	0.6 s	GPU @ 2.5 Ghz (C/C++)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.
165	MV3D (LIDAR)			54.54 %	68.35 %	49.16 %	0.24 s	GPU @ 2.5 Ghz (Python + C/C++)
X. Chen, H. Ma, J. Wan, B. Li and T. Xia: Multi-View 3D Object Detection Network for Autonomous Driving. CVPR 2017.
166	CDN			54.22 %	74.52 %	46.36 %	0.6 s	GPU @ 2.5 Ghz (Python)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems (NeurIPS) 2020.
167	tiny-stereo-v2			54.18 %	75.05 %	47.16 %	0.4 s	1 core @ 2.5 Ghz (Python + C/C++)

168	CG-Stereo			53.58 %	74.39 %	46.50 %	0.57 s	GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
169	tiny-stereo-v1			52.99 %	74.27 %	45.24 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)

170	DSGN		code	52.18 %	73.50 %	45.14 %	0.67 s	NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
171	SF			51.92 %	58.88 %	44.59 %	0.5 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
172	BirdNet+		code	51.85 %	70.14 %	50.03 %	0.1 s	Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird's Eye View. arXiv:2003.04188 [cs.CV] 2020.
173	Complexer-YOLO			47.34 %	55.93 %	42.60 %	0.06 s	GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
174	CDN-PL++			44.86 %	64.31 %	38.11 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. arXiv preprint arXiv:2007.03085 2020.
175	Pseudo-LiDAR E2E			43.92 %	64.75 %	38.14 %	0.4 s	GPU @ 2.5 Ghz (Python)

176	PB3D			43.27 %	64.78 %	37.13 %	0.42 s	1 core @ 2.5 Ghz (C/C++)

177	Pseudo-LiDAR++		code	42.43 %	61.11 %	36.99 %	0.4 s	GPU @ 2.5 Ghz (Python)
Y. You, Y. Wang, W. Chao, D. Garg, G. Pleiss, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving. International Conference on Learning Representations 2020.
178	Stereo3D			41.25 %	65.68 %	30.42 %	0.1 s	GPU 1080Ti

179	Disp R-CNN (velo)		code	39.36 %	59.61 %	32.01 %	0.42 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.
180	ZoomNet		code	38.64 %	55.98 %	30.97 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
L. Z. Xu: ZoomNet: Part-Aware Adaptive Zooming Neural Network for 3D Object Detection. Proceedings of the AAAI Conference on Artificial Intelligence 2020.
181	Disp R-CNN		code	37.93 %	58.55 %	31.95 %	0.42 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.
182	OC Stereo		code	37.60 %	55.15 %	30.25 %	0.35 s	1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
183	RTS3D			37.38 %	58.51 %	31.12 %	0.03 s	GPU @ 2.5 Ghz (Python)

184	Pseudo-Lidar		code	34.05 %	54.53 %	28.25 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Wang, W. Chao, D. Garg, B. Hariharan, M. Campbell and K. Weinberger: Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
185	m-prcnn			31.21 %	53.96 %	24.52 %	0.43 s	1 core @ 2.5 Ghz (Python)

186	Stereo R-CNN		code	30.23 %	47.58 %	23.72 %	0.3 s	GPU @ 2.5 Ghz (Python)
P. Li, X. Chen and S. Shen: Stereo R-CNN based 3D Object Detection for Autonomous Driving. CVPR 2019.
187	IDA-3D			29.32 %	45.09 %	23.13 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)

188	BirdNet			27.26 %	40.99 %	25.32 %	0.11 s	Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
189	RT3D-GMP			23.83 %	32.44 %	17.91 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)

190	RT3DStereo			23.28 %	29.90 %	18.96 %	0.08 s	GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
191	ASOD			22.37 %	38.42 %	17.01 %	0.28 s	GPU @ 2.5 Ghz (Python)

192	RT3D			19.14 %	23.74 %	18.86 %	0.09 s	GPU @ 1.8Ghz
Y. Zeng, Y. Hu, S. Liu, J. Ye, Y. Han, X. Li and N. Sun: RT3D: Real-Time 3-D Vehicle Detection in LiDAR Point Cloud for Autonomous Driving. IEEE Robotics and Automation Letters 2018.
193	StereoFENet			18.41 %	29.14 %	14.20 %	0.15 s	1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
194	ITS-MDPL			14.21 %	23.81 %	12.11 %	0.16 s	GPU @ 2.5 Ghz (Python)

195	PSMD			13.57 %	21.37 %	10.89 %	0.1 s	GPU @ 2.5 Ghz (Python)

196	I2BEV			13.41 %	19.17 %	11.46 %	0.63 s	GPU @ 2.5 Ghz (Python)

197	deprecated			13.30 %	14.81 %	11.04 %		1 core @ 2.5 Ghz (C/C++)

198	Det3D			13.26 %	24.00 %	9.94 %	0.5 s	1 core @ 2.5 Ghz (C/C++)

199	S3D			12.75 %	14.58 %	10.72 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

200	Kinematic3D		code	12.72 %	19.07 %	9.17 %	0.12 s	1 core @ 1.5 Ghz (C/C++)
G. Brazil, G. Pons-Moll, X. Liu and B. Schiele: Kinematic 3D Object Detection in Monocular Video. ECCV 2020 .
201	MTMono3d			12.44 %	18.54 %	10.09 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

202	DP3D			12.24 %	18.84 %	8.96 %	0.07 s	GPU @ 1.5 Ghz (Python + C/C++)

203	vxnet			12.21 %	16.00 %	9.32 %	1 s	1 core @ 2.5 Ghz (C/C++)

204	YoloMono3D			12.06 %	18.28 %	8.42 %	0.05 s	GPU @ 2.5 Ghz (Python)

205	IAFA			12.01 %	17.81 %	10.61 %	0.04 s	1 core @ 2.5 Ghz (C/C++)

206	D4LCN		code	11.72 %	16.65 %	9.51 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
207	MP-Mono			11.65 %	16.78 %	9.01 %	0.16 s	GPU @ 2.5 Ghz (Python)

208	MCA			11.63 %	18.46 %	10.24 %	0.04 s	1 core @ 2.5 Ghz (C/C++)

209	PG-MonoNet			11.51 %	15.91 %	9.01 %	0.19 s	GPU @ 2.5 Ghz (Python)

210	DA-3Ddet			11.50 %	16.77 %	8.93 %	0.4 s	GPU @ 2.5 Ghz (Python)

211	NL_M3D			11.46 %	17.54 %	8.98 %	0.2 s	1 core @ 2.5 Ghz (Python)

212	SSL-RTM3D			11.45 %	16.73 %	9.92 %	0.03 s	1 core @ 2.5 Ghz (Python)

213	IMA			11.34 %	16.24 %	9.44 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

214	CDI3D			11.32 %	15.70 %	9.26 %	0.03 s	GPU @ 2.5 Ghz (Python)

215	LAPNet			11.29 %	18.02 %	8.50 %	0.03 s	1 core @ 2.5 Ghz (Python)

216	DP3D			11.22 %	17.27 %	8.54 %	0.05 s	GPU @ 1.5 Ghz (Python + C/C++)

217	LNET			11.21 %	12.79 %	9.94 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

218	RefinedMPL			11.14 %	18.09 %	8.94 %	0.15 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
219	UM3D_TUM			11.13 %	15.30 %	9.31 %	0.05 s	1 core @ 2.5 Ghz (Python)

220	PatchNet		code	11.12 %	15.68 %	10.17 %	0.4 s	1 core @ 2.5 Ghz (C/C++)
X. Ma, S. Liu, Z. Xia, H. Zhang, X. Zeng and W. Ouyang: Rethinking Pseudo-LiDAR Representation. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
221	AM3D			10.74 %	16.50 %	9.52 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
X. Ma, Z. Wang, H. Li, P. Zhang, W. Ouyang and X. Fan: Accurate Monocular Object Detection via Color- Embedded 3D Reconstruction for Autonomous Driving. Proceedings of the IEEE international Conference on Computer Vision (ICCV) 2019.
222	OCM3D			10.44 %	17.48 %	7.87 %	0.5 s	1 core @ 2.5 Ghz (C/C++)

223	RTM3D		code	10.34 %	14.41 %	8.77 %	0.05 s	GPU @ 1.0 Ghz (Python)
P. Li, H. Zhao, P. Liu and F. Cao: RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving. 2020.
224	MA			10.21 %	14.90 %	8.78 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

225	MonoPair			9.99 %	13.04 %	8.65 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
226	SMOKE		code	9.76 %	14.03 %	7.84 %	0.03 s	GPU @ 2.5 Ghz (Python)
Z. Liu, Z. Wu and R. Tóth: SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation. 2020.
227	M3D-RPN		code	9.71 %	14.76 %	7.42 %	0.16 s	GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
228	SS3D_HW			9.70 %	14.74 %	7.22 %	0.4 s	GPU @ 2.5 Ghz (Python)

229	Center3D			9.31 %	12.01 %	8.06 %	0.05 s	GPU @ 3.5 Ghz (Python)

230	TopNet-HighRes			9.28 %	12.67 %	7.95 %	101ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
231	Mono3CN			9.17 %	12.73 %	7.91 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

232	LCD3D			9.04 %	13.77 %	7.23 %	0.03 s	GPU @ 2.5 Ghz (Python)

233	RAR-Net			8.73 %	13.70 %	6.92 %	0.5 s	1 core @ 2.5 Ghz (C/C++)

234	SSL-RTM3D Res18			8.39 %	12.65 %	7.12 %	0.02 s	GPU @ 2.5 Ghz (Python)

235	SS3D			7.68 %	10.78 %	6.51 %	48 ms	Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
236	anonymous			7.66 %	15.21 %	6.24 %	1 s	1 core @ 2.5 Ghz (C/C++)

237	Mono3D_PLiDAR		code	7.50 %	10.76 %	6.10 %	0.1 s	NVIDIA GeForce 1080 (pytorch)
X. Weng and K. Kitani: Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud. arXiv:1903.09847 2019.
238	MonoPSR		code	7.25 %	10.76 %	5.85 %	0.2 s	GPU @ 3.5 Ghz (Python)
J. Ku, A. Pon and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
239	Decoupled-3D			7.02 %	11.08 %	5.63 %	0.08 s	GPU @ 2.5 Ghz (C/C++)
Y. Cai, B. Li, Z. Jiao, H. Li, X. Zeng and X. Wang: Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation. AAAI 2020.
240	anonymous			6.77 %	13.18 %	5.63 %	1 s	1 core @ 2.5 Ghz (C/C++)

241	VoxelJones		code	6.35 %	7.39 %	5.80 %	.18 s	1 core @ 2.5 Ghz (Python + C/C++)
M. Motro and J. Ghosh: Vehicular Multi-object Tracking with Persistent Detector Failures. arXiv preprint arXiv:1907.11306 2019.
242	MonoGRNet		code	5.74 %	9.61 %	4.25 %	0.04s	NVIDIA P40
Z. Qin, J. Wang and Y. Lu: MonoGRNet: A Geometric Reasoning Network for 3D Object Localization. The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) 2019.
243	A3DODWTDA (image)		code	5.27 %	6.88 %	4.45 %	0.8 s	GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
244	MonoFENet			5.14 %	8.35 %	4.10 %	0.15 s	1 core @ 3.5 Ghz (Python)
W. Bao, B. Xu and Z. Chen: MonoFENet: Monocular 3D Object Detection with Feature Enhancement Networks. IEEE Transactions on Image Processing 2019.
245	OACV			4.77 %	8.13 %	3.78 %	0.23 s	GPU @ 2.5 Ghz (Python)

246	TLNet (Stereo)		code	4.37 %	7.64 %	3.74 %	0.1 s	1 core @ 2.5 Ghz (Python)
Z. Qin, J. Wang and Y. Lu: Triangulation Learning Network: from Monocular to Stereo 3D Object Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
247	AACL			4.18 %	5.62 %	3.34 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

248	CSoR			4.06 %	5.61 %	3.17 %	3.5 s	4 cores @ >3.5 Ghz (Python + C/C++)
L. Plotkin: PyDriver: Entwicklung eines Frameworks für räumliche Detektion und Klassifikation von Objekten in Fahrzeugumgebung. 2015.
249	Shift R-CNN (mono)		code	3.87 %	6.88 %	2.83 %	0.25 s	GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
250	MVRA + I-FRCNN+			3.27 %	5.19 %	2.49 %	0.18 s	GPU @ 2.5 Ghz (Python)

Table as LaTeX | Only published Methods

Pedestrian

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	HotSpotNet			45.37 %	53.10 %	41.47 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
2	Noah CV Lab - SSL			45.23 %	52.85 %	41.28 %	0.1 s	GPU @ 2.5 Ghz (Python)

3	VICNet			44.80 %	54.00 %	41.11 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

4	TANet		code	44.34 %	53.72 %	40.49 %	0.035s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
5	3DSSD		code	44.27 %	54.64 %	40.23 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
6	PPBA			44.08 %	52.65 %	41.54 %	NA s	GPU @ 2.5 Ghz (Python)

7	CentrNet-FG			44.02 %	53.51 %	40.53 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

8	Point-GNN		code	43.77 %	51.92 %	40.14 %	0.6 s	GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
9	PP-3D			43.77 %	51.92 %	40.14 %	0.1 s	1 core @ 2.5 Ghz (Python)

10	MVX-Net++			43.73 %	50.90 %	39.96 %	0.15 s	1 core @ 2.5 Ghz (C/C++)

11	KNN-GCNN			43.57 %	51.82 %	40.02 %	0.4 s	1 core @ 2.5 Ghz (Python + C/C++)

12	F-ConvNet		code	43.38 %	52.16 %	38.80 %	0.47 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
13	MMLab-PartA^2		code	43.35 %	53.10 %	40.06 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
14	MMLab PV-RCNN		code	43.29 %	52.17 %	40.29 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
S. Shi, C. Guo, L. Jiang, Z. Wang, J. Shi, X. Wang and H. Li: PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection. CVPR 2020.
15	VMVS			43.27 %	53.44 %	39.51 %	0.25 s	GPU @ 2.5 Ghz (Python)
J. Ku, A. Pon, S. Walsh and S. Waslander: Improving 3D object detection for pedestrians with virtual multi-view synthesis orientation estimation. IROS 2019.
16	RethinkDet3D			43.25 %	53.13 %	40.58 %	0.15 s	1 core @ 2.5 Ghz (Python)

17	STD		code	42.47 %	53.29 %	38.35 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu, X. Shen and J. Jia: STD: Sparse-to-Dense 3D Object Detector for Point Cloud. ICCV 2019.
18	AVOD-FPN		code	42.27 %	50.46 %	39.04 %	0.1 s	Titan X (Pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
19	SemanticVoxels			42.19 %	50.90 %	39.52 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Fei, W. Chen, P. Heidenreich, S. Wirges and C. Stiller: SemanticVoxels: Sequential Fusion for 3D Pedestrian Detection using LiDAR Point Cloud and Semantic Segmentation. MFI 2020.
20	F-PointNet		code	42.15 %	50.53 %	38.08 %	0.17 s	GPU @ 3.0 Ghz (Python)
C. Qi, W. Liu, C. Wu, H. Su and L. Guibas: Frustum PointNets for 3D Object Detection from RGB-D Data. arXiv preprint arXiv:1711.08488 2017.
21	PointPillars		code	41.92 %	51.45 %	38.89 %	16 ms	1080ti GPU and Intel i7 CPU
A. Lang, S. Vora, H. Caesar, L. Zhou, J. Yang and O. Beijbom: PointPillars: Fast Encoders for Object Detection from Point Clouds. CVPR 2019.
22	epBRM		code	41.52 %	49.17 %	39.08 %	0.10 s	1 core @ 2.5 Ghz (C/C++)
K. Shin: Improving a Quality of 3D Object Detection by Spatial Transformation Mechanism. arXiv preprint arXiv:1910.04853 2019.
23	TBU			41.16 %	49.33 %	38.84 %	NA s	GPU @ 2.5 Ghz (Python)

24	PiP			41.01 %	49.01 %	37.90 %	0.033 s	1 core @ 2.5 Ghz (Python)

25	PointPainting			40.97 %	50.32 %	37.87 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Vora, A. Lang, B. Helou and O. Beijbom: PointPainting: Sequential Fusion for 3D Object Detection. CVPR 2020.
26	Deformable PV-RCNN		code	40.89 %	46.97 %	38.80 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya and K. Czarnecki: Deformable PV-RCNN: Improving 3D Object Detection with Learned Deformations. ECCV 2020 Perception for Autonomous Driving Workshop.
27	Simple3D Net			40.20 %	48.41 %	37.50 %	0.02 s	GPU @ 2.5 Ghz (Python)

28	PPFNet		code	40.11 %	48.36 %	37.00 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

29	AP-RCNN			39.53 %	47.63 %	36.44 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

30	CA-RCNN			39.47 %	46.93 %	36.99 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

31	IC-PVRCNN			39.46 %	45.19 %	37.16 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

32	SVGA-Net			39.43 %	47.30 %	36.99 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)

33	Baseline of CA RCNN			39.42 %	47.30 %	36.97 %	0.1 s	GPU @ 2.5 Ghz (Python)

34	CVIS-DF3D			39.42 %	47.30 %	36.97 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

35	MMLab-PointRCNN		code	39.37 %	47.98 %	36.01 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, X. Wang and H. Li: Pointrcnn: 3d object proposal generation and detection from point cloud. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
36	ARPNET			39.31 %	48.32 %	35.93 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Ye, C. Zhang and X. Hao: ARPNET: attention region proposal network for 3D object detection. Science China Information Sciences 2019.
37	SCNet			38.66 %	47.83 %	35.70 %	0.04 s	GPU @ 3.0 Ghz (Python)
Z. Wang, H. Fu, L. Wang, L. Xiao and B. Dai: SCNet: Subdivision Coding Network for Object Detection Based on 3D Point Cloud. IEEE Access 2019.
38	CVIS-DF3D_v2			38.31 %	45.10 %	36.15 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

39	3DBN_2			38.23 %	46.79 %	35.57 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
40	IGRP+			38.05 %	46.26 %	34.53 %	0.18 s	1 core @ 2.5 Ghz (Python + C/C++)

41	MGACNet			37.50 %	43.55 %	35.33 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

42	MLOD		code	37.47 %	47.58 %	35.07 %	0.12 s	GPU @ 1.5 Ghz (Python)
J. Deng and K. Czarnecki: MLOD: A multi-view 3D object detection based on robust feature fusion method. arXiv preprint arXiv:1909.04163 2019.
43	TBD			37.37 %	43.60 %	34.36 %	0.05 s	GPU @ 2.5 Ghz (Python)

44	IC-SECOND			37.18 %	43.82 %	35.35 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

45	VOXEL_FPN_HR			37.01 %	46.32 %	34.67 %	0.12 s	8 cores @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
46	PFF3D			36.07 %	43.93 %	32.86 %	0.05 s	GPU @ 3.0 Ghz (Python + C/C++)

47	NLK-3D			35.86 %	45.17 %	32.24 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)

48	HR-SECOND		code	35.52 %	45.31 %	33.14 %	0.11 s	1 core @ 2.5 Ghz (Python + C/C++)

49	SRDL			35.28 %	42.66 %	33.26 %	0.15 s	GPU @ 2.5 Ghz (Python + C/C++)

50	deprecated			35.21 %	41.32 %	33.32 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

51	AB3DMOT		code	34.59 %	42.27 %	31.37 %	0.0047s	1 core @ 2.5 Ghz (python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
52	PBASN		code	34.48 %	41.28 %	32.24 %	NA s	GPU @ 2.5 Ghz (Python)

53	NLK-ALL		code	34.46 %	44.30 %	30.83 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)

54	LZnet			34.15 %	41.26 %	32.03 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)

55	DAMNET		code	33.66 %	43.32 %	30.12 %	1 s	1 core @ 2.5 Ghz (C/C++)

56	BirdNet+		code	31.46 %	37.99 %	29.46 %	0.1 s	Titan Xp (PyTorch)
A. Barrera, C. Guindel, J. Beltrán and F. García: BirdNet+: End-to-End 3D Object Detection in LiDAR Bird's Eye View. arXiv:2003.04188 [cs.CV] 2020.
57	Pointpillar_TV			30.79 %	38.56 %	28.57 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

58	SparsePool		code	30.38 %	37.84 %	26.94 %	0.13 s	8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
59	FCY			29.38 %	37.28 %	26.19 %	0.02 s	GPU @ 2.5 Ghz (Python)

60	SparsePool		code	27.92 %	35.52 %	25.87 %	0.13 s	8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
61	AVOD		code	27.86 %	36.10 %	25.76 %	0.08 s	Titan X (pascal)
J. Ku, M. Mozifian, J. Lee, A. Harakeh and S. Waslander: Joint 3D Proposal Generation and Object Detection from View Aggregation. IROS 2018.
62	CSW3D			26.64 %	33.75 %	23.34 %	0.03 s	4 cores @ 2.5 Ghz (C/C++)
J. Hu, T. Wu, H. Fu, Z. Wang and K. Ding: Cascaded Sliding Window Based Real-Time 3D Region Proposal for Pedestrian Detection. ROBIO 2019.
63	SF			24.84 %	31.61 %	21.96 %	0.5 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
64	CG-Stereo			24.31 %	33.22 %	20.95 %	0.57 s	GeForce RTX 2080 Ti
C. Li, J. Ku and S. Waslander: Confidence Guided Stereo 3D Object Detection with Split Depth Estimation. IROS 2020.
65	Disp R-CNN (velo)		code	21.98 %	30.98 %	18.68 %	0.42 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.
66	Disp R-CNN		code	21.98 %	31.05 %	18.67 %	0.42 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.
67	PB3D			20.65 %	28.68 %	17.65 %	0.42 s	1 core @ 2.5 Ghz (C/C++)

68	Stereo3D			19.75 %	28.49 %	16.48 %	0.1 s	GPU 1080Ti

69	OC Stereo		code	17.58 %	24.48 %	15.60 %	0.35 s	1 core @ 2.5 Ghz (Python + C/C++)
A. Pon, J. Ku, C. Li and S. Waslander: Object-Centric Stereo Matching for 3D Object Detection. ICRA 2020.
70	BirdNet			17.08 %	22.04 %	15.82 %	0.11 s	Titan Xp (Caffe)
J. Beltrán, C. Guindel, F. Moreno, D. Cruzado, F. García and A. Escalera: BirdNet: A 3D Object Detection Framework from LiDAR Information. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
71	DSGN		code	15.55 %	20.53 %	14.15 %	0.67 s	NVIDIA Tesla V100
Y. Chen, S. Liu, X. Shen and J. Jia: DSGN: Deep Stereo Geometry Network for 3D Object Detection. CVPR 2020.
72	Complexer-YOLO			13.96 %	17.60 %	12.70 %	0.06 s	GPU @ 3.5 Ghz (C/C++)
M. Simon, K. Amende, A. Kraus, J. Honer, T. Samann, H. Kaulbersch, S. Milz and H. Michael Gross: Complexer-YOLO: Real-Time 3D Object Detection and Tracking on Semantic Point Clouds. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2019.
73	I2BEV			8.14 %	12.87 %	6.76 %	0.63 s	GPU @ 2.5 Ghz (Python)

74	RefinedMPL			7.18 %	11.14 %	5.84 %	0.15 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
75	TopNet-HighRes			6.92 %	10.40 %	6.63 %	101ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
76	MonoPair			6.68 %	10.02 %	5.53 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
Y. Chen, L. Tai, K. Sun and M. Li: MonoPair: Monocular 3D Object Detection Using Pairwise Spatial Relationships. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
77	vxnet			5.82 %	7.91 %	4.82 %	1 s	1 core @ 2.5 Ghz (C/C++)

78	SS3D_HW			5.00 %	7.77 %	4.03 %	0.4 s	GPU @ 2.5 Ghz (Python)

79	Shift R-CNN (mono)		code	4.66 %	7.95 %	4.16 %	0.25 s	GPU @ 1.5 Ghz (Python)
A. Naiden, V. Paunescu, G. Kim, B. Jeon and M. Leordeanu: Shift R-CNN: Deep Monocular 3D Object Detection With Closed-form Geometric Constraints. ICIP 2019.
80	PG-MonoNet			4.50 %	5.76 %	3.93 %	0.19 s	GPU @ 2.5 Ghz (Python)

81	CDI3D			4.03 %	5.64 %	3.29 %	0.03 s	GPU @ 2.5 Ghz (Python)

82	MonoPSR		code	4.00 %	6.12 %	3.30 %	0.2 s	GPU @ 3.5 Ghz (Python)
J. Ku, A. Pon and S. Waslander: Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction. CVPR 2019.
83	NL_M3D			3.87 %	5.16 %	3.08 %	0.2 s	1 core @ 2.5 Ghz (Python)

84	MP-Mono			3.79 %	5.30 %	3.15 %	0.16 s	GPU @ 2.5 Ghz (Python)

85	DP3D			3.54 %	4.75 %	2.88 %	0.05 s	GPU @ 1.5 Ghz (Python + C/C++)

86	M3D-RPN		code	3.48 %	4.92 %	2.94 %	0.16 s	GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
87	Mono3CN			3.44 %	5.13 %	3.00 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

88	Center3D			3.43 %	4.86 %	2.78 %	0.05 s	GPU @ 3.5 Ghz (Python)

89	RT3D-GMP			3.42 %	4.51 %	2.77 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)

90	D4LCN		code	3.42 %	4.55 %	2.83 %	0.2 s	GPU @ 2.5 Ghz (Python + C/C++)
M. Ding, Y. Huo, H. Yi, Z. Wang, J. Shi, Z. Lu and P. Luo: Learning Depth-Guided Convolutions for Monocular 3D Object Detection. CVPR 2020.
91	DP3D			3.37 %	4.77 %	2.77 %	0.07 s	GPU @ 1.5 Ghz (Python + C/C++)

92	LAPNet			3.16 %	4.41 %	2.70 %	0.03 s	1 core @ 2.5 Ghz (Python)

93	RT3DStereo			2.45 %	3.28 %	2.35 %	0.08 s	GPU @ 2.5 Ghz (C/C++)
H. Königshof, N. Salscheider and C. Stiller: Realtime 3D Object Detection for Automated Driving Using Stereo Vision and Semantic Information. Proc. IEEE Intl. Conf. Intelligent Transportation Systems 2019.
94	MTMono3d			2.05 %	2.40 %	1.68 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

95	TopNet-UncEst			1.87 %	3.42 %	1.73 %	0.09 s	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, M. Braun, M. Lauer and C. Stiller: Capturing Object Detection Uncertainty in Multi-Layer Grid Maps. 2019.
96	SS3D			1.78 %	2.31 %	1.48 %	48 ms	Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
97	UM3D_TUM			1.74 %	3.49 %	1.74 %	0.05 s	1 core @ 2.5 Ghz (Python)

98	UDI-mono3D			1.45 %	2.18 %	1.12 %	0.05 s	1 core @ 2.5 Ghz (Python)

99	UDI-mono3D			1.01 %	1.81 %	0.99 %	0.05 s	1 core @ 2.5 Ghz (Python)

100	SparVox3D			0.25 %	0.35 %	0.25 %	0.05 s	GPU @ 2.0 Ghz (Python)

101	PVNet			0.00 %	0.00 %	0.00 %	0,1 s	1 core @ 2.5 Ghz (Python)

102	mBoW			0.00 %	0.00 %	0.00 %	10 s	1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.

Table as LaTeX | Only published Methods

Cyclist

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	HRI-MSP-L			71.86 %	87.77 %	63.57 %	0.07 s	1 core @ 2.5 Ghz (C/C++)

2	Noah CV Lab - SSL			71.53 %	84.24 %	62.20 %	0.1 s	GPU @ 2.5 Ghz (Python)

3	Deformable PV-RCNN		code	68.54 %	82.19 %	61.33 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya and K. Czarnecki: Deformable PV-RCNN: Improving 3D Object Detection with Learned Deformations. ECCV 2020 Perception for Autonomous Driving Workshop.
4	HotSpotNet			65.95 %	82.59 %	59.00 %	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.
5	TBD			65.64 %	82.29 %	57.98 %	0.05 s	GPU @ 2.5 Ghz (Python)

6	F-ConvNet		code	65.07 %	81.98 %	56.54 %	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.
7	IC-PVRCNN			64.99 %	81.47 %	58.62 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

8	3DSSD		code	64.10 %	82.48 %	56.90 %	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.
9	PointPainting			63.78 %	77.63 %	55.89 %	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.
10	MMLab PV-RCNN		code	63.71 %	78.60 %	57.65 %	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.
11	IC-SECOND			63.65 %	78.29 %	57.27 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

12	NLK-ALL		code	63.65 %	79.94 %	57.28 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)

13	MMLab-PartA^2		code	63.52 %	79.17 %	56.93 %	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.
14	Point-GNN		code	63.48 %	78.60 %	57.08 %	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.
15	PP-3D			63.48 %	78.60 %	57.08 %	0.1 s	1 core @ 2.5 Ghz (Python)

16	CVIS-DF3D_v2			63.05 %	77.46 %	55.43 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

17	KNN-GCNN			62.91 %	80.24 %	56.49 %	0.4 s	1 core @ 2.5 Ghz (Python + C/C++)

18	deprecated			62.16 %	75.45 %	56.00 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

19	CA-RCNN			62.04 %	77.06 %	55.09 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

20	SRDL			62.03 %	80.66 %	55.73 %	0.15 s	GPU @ 2.5 Ghz (Python + C/C++)

21	SVGA-Net			62.02 %	77.35 %	55.52 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)

22	Baseline of CA RCNN			62.02 %	77.33 %	55.52 %	0.1 s	GPU @ 2.5 Ghz (Python)

23	CVIS-DF3D			62.02 %	77.33 %	55.52 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

24	MGACNet			62.00 %	78.73 %	55.18 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

25	PPBA			61.99 %	79.42 %	55.34 %	NA s	GPU @ 2.5 Ghz (Python)

26	TBU			61.99 %	79.42 %	55.34 %	NA s	GPU @ 2.5 Ghz (Python)

27	VOXEL_FPN_HR			61.91 %	78.29 %	55.54 %	0.12 s	8 cores @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
28	STD		code	61.59 %	78.69 %	55.30 %	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.
29	RethinkDet3D			61.10 %	79.31 %	54.47 %	0.15 s	1 core @ 2.5 Ghz (Python)

30	MVX-Net++			61.03 %	76.07 %	53.41 %	0.15 s	1 core @ 2.5 Ghz (C/C++)

31	3DBN_2			60.88 %	78.10 %	54.10 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
32	HR-SECOND		code	60.82 %	75.83 %	53.67 %	0.11 s	1 core @ 2.5 Ghz (Python + C/C++)

33	AB3DMOT		code	60.30 %	75.42 %	53.81 %	0.0047s	1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
34	LZnet			60.05 %	76.08 %	54.37 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)

35	VICNet			59.99 %	78.75 %	52.37 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

36	AP-RCNN			59.92 %	77.88 %	53.48 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

37	FCY			59.54 %	76.30 %	52.29 %	0.02 s	GPU @ 2.5 Ghz (Python)

38	PiP			59.54 %	75.43 %	53.37 %	0.033 s	1 core @ 2.5 Ghz (Python)

39	TANet		code	59.44 %	75.70 %	52.53 %	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.
40	PBASN		code	59.43 %	76.80 %	52.77 %	NA s	GPU @ 2.5 Ghz (Python)

41	NLK-3D			59.30 %	76.45 %	51.82 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)

42	MMLab-PointRCNN		code	58.82 %	74.96 %	52.53 %	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.
43	PointPillars		code	58.65 %	77.10 %	51.92 %	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.
44	ARPNET			58.20 %	74.21 %	52.13 %	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.
45	epBRM		code	56.13 %	72.08 %	49.91 %	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.
46	F-PointNet		code	56.12 %	72.27 %	49.01 %	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.
47	CentrNet-FG			55.54 %	72.07 %	49.03 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

48	Pointpillar_TV			54.69 %	71.61 %	48.22 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

49	Simple3D Net			54.49 %	70.79 %	48.21 %	0.02 s	GPU @ 2.5 Ghz (Python)

50	IGRP+			53.22 %	69.87 %	47.55 %	0.18 s	1 core @ 2.5 Ghz (Python + C/C++)

51	SCNet			50.79 %	67.98 %	45.15 %	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.
52	AVOD-FPN		code	50.55 %	63.76 %	44.93 %	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.
53	MLOD		code	49.43 %	68.81 %	42.84 %	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.
54	BirdNet+		code	47.72 %	67.38 %	42.89 %	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. arXiv:2003.04188 [cs.CV] 2020.
55	PFF3D			46.78 %	63.27 %	41.37 %	0.05 s	GPU @ 3.0 Ghz (Python + C/C++)

56	DAMNET		code	42.82 %	58.71 %	38.74 %	1 s	1 core @ 2.5 Ghz (C/C++)

57	AVOD		code	42.08 %	57.19 %	38.29 %	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.
58	SparsePool		code	37.33 %	52.61 %	33.39 %	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.
59	SparsePool		code	32.61 %	40.87 %	29.05 %	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.
60	CG-Stereo			30.89 %	47.40 %	27.23 %	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.
61	BirdNet			30.25 %	43.98 %	27.21 %	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.
62	Disp R-CNN (velo)		code	23.75 %	39.72 %	20.47 %	0.42 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.
63	Disp R-CNN		code	23.75 %	39.72 %	20.47 %	0.42 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.
64	Complexer-YOLO			18.53 %	24.27 %	17.31 %	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.
65	DSGN		code	18.17 %	27.76 %	16.21 %	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.
66	PB3D			17.28 %	28.50 %	15.56 %	0.42 s	1 core @ 2.5 Ghz (C/C++)

67	OC Stereo		code	16.63 %	29.40 %	14.72 %	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.
68	RT3D-GMP			4.90 %	7.75 %	4.18 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)

69	MonoPSR		code	4.74 %	8.37 %	3.68 %	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.
70	TopNet-UncEst			4.54 %	7.13 %	3.81 %	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.
71	I2BEV			3.41 %	7.00 %	3.30 %	0.63 s	GPU @ 2.5 Ghz (Python)

72	RT3DStereo			3.37 %	5.29 %	2.57 %	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.
73	vxnet			2.75 %	4.58 %	2.38 %	1 s	1 core @ 2.5 Ghz (C/C++)

74	CDI3D			2.69 %	4.15 %	2.45 %	0.03 s	GPU @ 2.5 Ghz (Python)

75	Center3D			2.35 %	4.32 %	2.06 %	0.05 s	GPU @ 3.5 Ghz (Python)

76	Mono3CN			2.17 %	3.68 %	2.02 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

77	SS3D_HW			2.17 %	4.29 %	2.00 %	0.4 s	GPU @ 2.5 Ghz (Python)

78	MonoPair			2.12 %	3.79 %	1.83 %	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.
79	RefinedMPL			1.82 %	3.23 %	1.77 %	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.
80	UDI-mono3D			1.74 %	3.29 %	1.37 %	0.05 s	1 core @ 2.5 Ghz (Python)

81	TopNet-HighRes			1.67 %	2.49 %	1.88 %	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.
82	D4LCN		code	1.67 %	2.45 %	1.36 %	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.
83	DP3D			1.66 %	2.77 %	1.31 %	0.07 s	GPU @ 1.5 Ghz (Python + C/C++)

84	NL_M3D			1.51 %	2.10 %	1.58 %	0.2 s	1 core @ 2.5 Ghz (Python)

85	UDI-mono3D			1.47 %	3.01 %	1.47 %	0.05 s	1 core @ 2.5 Ghz (Python)

86	SS3D			1.45 %	2.80 %	1.35 %	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.
87	PG-MonoNet			1.43 %	2.41 %	1.23 %	0.19 s	GPU @ 2.5 Ghz (Python)

88	MP-Mono			1.42 %	1.89 %	1.29 %	0.16 s	GPU @ 2.5 Ghz (Python)

89	DP3D			1.39 %	2.04 %	1.12 %	0.05 s	GPU @ 1.5 Ghz (Python + C/C++)

90	MTMono3d			0.90 %	1.59 %	0.96 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

91	LAPNet			0.89 %	1.37 %	0.62 %	0.03 s	1 core @ 2.5 Ghz (Python)

92	M3D-RPN		code	0.65 %	0.94 %	0.47 %	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 .
93	UM3D_TUM			0.62 %	0.45 %	0.62 %	0.05 s	1 core @ 2.5 Ghz (Python)

94	Shift R-CNN (mono)		code	0.29 %	0.48 %	0.31 %	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.
95	PVNet			0.00 %	0.00 %	0.00 %	0,1 s	1 core @ 2.5 Ghz (Python)

96	mBoW			0.00 %	0.00 %	0.00 %	10 s	1 core @ 2.5 Ghz (C/C++)
J. Behley, V. Steinhage and A. Cremers: Laser-based Segment Classification Using a Mixture of Bag-of-Words. Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2013.

Table as LaTeX | Only published Methods

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Citation

When using this dataset in your research, we will be happy if you cite us:
@INPROCEEDINGS{Geiger2012CVPR,
author = {Andreas Geiger and Philip Lenz and Raquel Urtasun},
title = {Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite},
booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2012}
}

The KITTI Vision Benchmark Suite

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

3D Object Detection Evaluation 2017

Car

Pedestrian

Cyclist

Related Datasets

Citation

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