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	HIKVISION-ADLab-HZ			82.83 %	89.00 %	76.00 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

2	SE-SSD			82.54 %	91.49 %	77.15 %	0.03 s	1 core @ 2.5 Ghz (Python + C/C++)

3	EA-M-RCNN(BorderAtt)			82.33 %	87.77 %	77.37 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

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

5	ADLAB			82.08 %	90.92 %	77.36 %	0.05 s	1 core @ >3.5 Ghz (C/C++)

6	PV-RCNN-v2			81.88 %	90.14 %	77.15 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)

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

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

9	E^2-PV-RCNN			81.70 %	88.33 %	77.20 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)

10	PLNL-3DSSD			81.69 %	88.98 %	74.90 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)

11	DomainAdp+PVRCNN			81.66 %	88.64 %	77.08 %	0.09 s	GPU @ 2.5 Ghz (Python)

12	Fast VP-RCNN		code	81.62 %	90.97 %	76.90 %	0.05 s	1 core @ 3.5 Ghz (C/C++)

13	Voxel R-CNN		code	81.62 %	90.90 %	77.06 %	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 . arXiv preprint arXiv:2012.15712 2020.
14	HyBrid Feature Det			81.59 %	88.77 %	76.92 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)

15	3DIoU+++			81.58 %	88.53 %	77.37 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

16	CityBrainLab-TSD			81.57 %	88.13 %	77.00 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)

17	CVRS VIC-RCNN			81.57 %	88.60 %	77.09 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

18	H^23D R-CNN			81.55 %	90.43 %	77.22 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

19	anonymous		code	81.55 %	90.94 %	76.74 %	0.05s	1 core @ >3.5 Ghz (python)

20	LZY_RCNN			81.52 %	88.77 %	78.59 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

21	TBD			81.51 %	88.96 %	77.27 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

22	SIENet			81.50 %	87.83 %	77.08 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

23	MSG-PGNN			81.50 %	88.70 %	76.88 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

24	DSA-PV-RCNN			81.46 %	88.25 %	76.96 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)

25	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.
26	PC-RGNN			81.38 %	87.94 %	76.88 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)

27	XView			81.35 %	89.21 %	76.87 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

28	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.
29	FSA-PVRCNN			81.31 %	88.01 %	76.75 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

30	ReFineNet			81.24 %	87.70 %	76.77 %	0.08 s	1 core @ 2.5 Ghz (Python)

31	MSL3D			81.15 %	87.27 %	76.56 %	0.03 s	GPU @ 2.5 Ghz (Python)

32	Multi-Sensor3D			81.15 %	87.27 %	76.56 %	0.03 s	GPU @ 2.5 Ghz (Python)

33	SAA-PV-RCNN			81.09 %	87.24 %	78.05 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

34	FPC-RCNN			81.08 %	88.68 %	76.46 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

35	AIMC-RUC			80.83 %	90.14 %	73.59 %	0.11 s	1 core @ 2.5 Ghz (Python)

36	SVGA-Net			80.82 %	87.40 %	76.23 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)

37	GNN-RCNN			80.81 %	87.94 %	76.53 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

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

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

40	CLOCs_PVCas		code	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.
41	AIMC-RUC			80.63 %	89.90 %	75.32 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

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

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

44	CVRS VIC-Net			80.61 %	88.25 %	75.83 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

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

46	XView-PartA^2			80.41 %	87.72 %	76.22 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

47	SRDL			80.38 %	87.73 %	76.27 %	0.15 s	GPU @ 2.5 Ghz (Python + C/C++)

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

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

50	AM-SSD			80.30 %	89.58 %	75.02 %	0.04 s	1 core @ 2.5 Ghz (Python)

51	CIA-SSD		code	80.28 %	89.59 %	72.87 %	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.
52	Baseline of CA RCNN			80.28 %	87.45 %	76.21 %	0.1 s	GPU @ 2.5 Ghz (Python)

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

54	TBD			80.24 %	87.67 %	76.27 %	0.07 s	1 core @ 2.5 Ghz (Python + C/C++)

55	GAP-soft-filter			80.18 %	87.43 %	76.21 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

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

57	TBD			80.17 %	86.83 %	75.96 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

58	deprecated			80.16 %	89.48 %	72.75 %	deprecated	deprecated

59	EBM3DOD		code	80.12 %	91.05 %	72.78 %	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.
60	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.
61	CN			79.89 %	90.55 %	76.31 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)

62	SIF			79.88 %	86.84 %	75.89 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

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

64	CM3DV			79.87 %	89.00 %	72.59 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

65	RangeIoUDet			79.80 %	88.60 %	76.76 %	0.02 s	1 core @ 2.5 Ghz (Python)

66	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.
67	Seg-RCNN		code	79.73 %	89.16 %	72.28 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)

68	CJJ			79.72 %	88.98 %	74.71 %	0.04 s	1 core @ 2.5 Ghz (C/C++)

69	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.
70	PSS			79.71 %	89.13 %	74.78 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

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

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

73	CDE-Net(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: Multi sensor fusion 3d object detection method. Submitted to OSA 2021.
74	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.
75	PointRes			79.55 %	88.73 %	74.17 %	0.013 s	1 core @ 2.5 Ghz (Python + C/C++)

76	EBM3DOD baseline		code	79.52 %	88.80 %	72.30 %	0.05 s	1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.
77	Cas-SSD			79.50 %	88.73 %	72.46 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

78	CDE-Net(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: Multi sensor fusion 3d object detection method. Submitted to OSA 2021.
79	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.
80	PP-3D			79.47 %	88.33 %	72.29 %	0.1 s	1 core @ 2.5 Ghz (Python)

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

82	RoIFusion		code	79.36 %	88.09 %	72.51 %	0.22 s	1 core @ 3.0 Ghz (Python)

83	3DIoU_v2			79.30 %	88.22 %	76.96 %	0.2 s	1 core @ 2.5 Ghz (C/C++)

84	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.
85	PF-GAP			79.27 %	87.65 %	76.43 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

86	FPCR-CNN			79.25 %	88.45 %	75.69 %	0.05 s	1 core @ 2.5 Ghz (Python)

87	3DIoU++			79.22 %	87.49 %	76.68 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

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

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

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

91	Faraway-Frustum		code	79.05 %	87.45 %	76.14 %	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.
92	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.
93	CCFNET			78.97 %	88.20 %	74.14 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

94	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.
95	deprecated			78.83 %	87.89 %	73.52 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

96	FPC3D			78.81 %	87.61 %	75.49 %	33 s	1 core @ 2.5 Ghz (C/C++)

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

98	MVAF-Net		code	78.71 %	87.87 %	75.48 %	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.
99	ISF-v2			78.67 %	87.54 %	74.03 %	0.04 s	1 core @ 2.5 Ghz (Python)

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

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

102	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.
103	F-3DNet			78.48 %	85.48 %	71.62 %	0.5 s	GPU @ 2.5 Ghz (Python)

104	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.
105	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.
106	MKFFNet			78.40 %	85.25 %	73.75 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

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

108	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.
109	MKFFNet			78.30 %	87.25 %	73.66 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

110	MKFFNet			78.30 %	86.86 %	73.80 %	0.01s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
111	KNN-GCNN			78.26 %	86.37 %	71.14 %	0.4 s	1 core @ 2.5 Ghz (Python + C/C++)

112	HV			77.92 %	86.38 %	73.04 %	0.02 s	GPU @ 2.5 Ghz (Python)

113	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.
114	V3D			77.87 %	86.58 %	72.52 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

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

116	VOXEL_3D			77.69 %	86.45 %	72.20 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

117	VGCN			77.65 %	84.47 %	73.36 %	0.09 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
118	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.
119	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.
120	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.
121	Dccnet			77.22 %	86.67 %	69.97 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

122	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.
123	VAR			77.08 %	84.92 %	72.21 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

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

125	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.
126	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.
127	TBD			76.57 %	85.33 %	72.05 %	0.05 s	GPU @ 2.5 Ghz (Python)

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

129	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.
130	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.
131	DPointNet			76.34 %	81.67 %	70.34 %	0.07s	1 core @ 2.5 Ghz (C/C++)

132	SIEV-Net			76.18 %	85.21 %	70.60 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

133	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.
134	NLK-3D			76.08 %	84.47 %	70.93 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)

135	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.
136	IGRP			75.90 %	86.27 %	69.31 %	0.18 s	1 core @ 2.5 Ghz (Python + C/C++)

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

138	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.
139	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.
140	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.
141	MDA			75.39 %	83.72 %	71.98 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

142	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.
143	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.
144	3DBN_2			75.06 %	84.90 %	72.10 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
145	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.
146	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.
147	FPGNN			74.77 %	83.82 %	67.93 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

148	FPC3D_all			74.55 %	85.50 %	69.91 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

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

150	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.
151	IOU-SSD		code	74.09 %	85.00 %	68.42 %	0.045s	1 core @ 2.5 Ghz (C/C++)

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

153	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.
154	tt		code	73.92 %	84.14 %	69.15 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

155	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.
156	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.
157	baseline			73.55 %	82.92 %	67.42 %	0.12 s	1 core @ 2.5 Ghz (C/C++)

158	LSNet			73.55 %	86.13 %	68.58 %	0.09 s	GPU @ 2.5 Ghz (Python + C/C++)

159	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.
160	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.
161	TBD			73.02 %	82.74 %	67.97 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)

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

163	APL-Second			72.87 %	82.75 %	67.91 %	0.05 s	1 core @ 2.5 Ghz (Python)

164	DASS			72.31 %	81.85 %	65.99 %	0.09 s	1 core @ 2.0 Ghz (Python)
O. Unal, L. Gool and D. Dai: Improving Point Cloud Semantic Segmentation by Learning 3D Object Detection. 2020.
165	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.
166	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.
167	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.
168	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.
169	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.
170	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.
171	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.
172	DAMNET		code	65.52 %	76.25 %	59.54 %	1 s	1 core @ 2.5 Ghz (C/C++)

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

174	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.
175	KMC		code	62.74 %	74.45 %	56.76 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

176	RCD			60.56 %	70.54 %	55.58 %	0.1 s	GPU @ 2.5 Ghz (Python)
A. Bewley, P. Sun, T. Mensink, D. Anguelov and C. Sminchisescu: Range Conditioned Dilated Convolutions for Scale Invariant 3D Object Detection. Conference on Robot Learning (CoRL) 2020.
177	stereo-tkc			59.21 %	78.26 %	52.47 %	0.4 s	GPU @ 2.0 Ghz (Python + C/C++)

178	tiny-stereo-v2			57.11 %	76.87 %	50.05 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)

179	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.
180	tiny-stereo			56.44 %	79.17 %	48.07 %	0.4 s	1 core @ 2.5 Ghz (Python + C/C++)

181	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.
182	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.
183	CDN		code	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.
184	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.
185	AEC3D			52.50 %	66.83 %	48.48 %	0.01 s	GPU @ 2.5 Ghz (Python)

186	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.
187	VN3D			52.16 %	64.68 %	48.17 %	0.02 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
188	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.
189	NCL		code	50.07 %	46.58 %	50.33 %	NA s	1 core @ 2.5 Ghz (Python)

190	SOD			48.69 %	70.90 %	40.12 %	0.1 s	1 core @ 2.5 Ghz (Python)

191	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.
192	Disp R-CNN (velo)		code	45.78 %	68.21 %	37.73 %	0.387 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
193	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. Advances in Neural Information Processing Systems 2020.
194	Disp R-CNN		code	43.27 %	67.02 %	36.43 %	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.
195	OSE			43.27 %	64.78 %	37.13 %	0.1 s	GPU @ 2.5 Ghz (C/C++)

196	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.
197	OSE+			41.60 %	62.67 %	35.82 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

198	Stereo3D			41.25 %	65.68 %	30.42 %	0.1 s	GPU 1080Ti

199	RT3D-GMP			38.76 %	45.79 %	30.00 %	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.
200	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.
201	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.
202	RTS3D			37.38 %	58.51 %	31.12 %	0.03 s	GPU @ 2.5 Ghz (Python)

203	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.
204	SC(DLA34+DCO)			31.30 %	49.94 %	25.62 %	0.07 s	GPU @ 2.5 Ghz (Python)

205	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.
206	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.
207	NVNet(BEV-3D)			24.87 %	33.30 %	21.96 %	0.1 s	1 core @ 2.5 Ghz (Python)

208	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.
209	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.
210	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.
211	LGDet3d			14.82 %	22.73 %	12.88 %	0.11 s	1 core @ 2.5 Ghz (C/C++)

212	ITS-MDPL			14.23 %	24.26 %	11.95 %	0.16 s	GPU @ 2.5 Ghz (Python)

213	MonoFlex			13.89 %	19.94 %	12.07 %	0.03 s	GPU @ 2.5 Ghz (Python)

214	MonoEF		code	13.87 %	21.29 %	11.71 %	0.03 s	1 core @ 2.5 Ghz (Python)

215	CaDDN			13.41 %	19.17 %	11.46 %	0.63 s	GPU @ 2.5 Ghz (Python)

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

217	PLDet3d			12.85 %	20.72 %	11.11 %	0.11 s	1 core @ 2.5 Ghz (C/C++)

218	DDMP-3D			12.78 %	19.71 %	9.80 %	0.18 s	1 core @ 2.5 Ghz (Python)

219	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 .
220	DAMono3D			12.66 %	16.99 %	9.97 %	0.09s	1 core @ 2.5 Ghz (C/C++)

221	RelationNet3D			12.60 %	17.57 %	10.95 %	0.04 s	GPU @ 2.5 Ghz (Python)

222	Object Transformer			12.58 %	17.87 %	10.87 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

223	MonoGeo			12.57 %	16.87 %	11.16 %	0.05 s	1 core @ 2.5 Ghz (Python)

224	MTMono3d			12.44 %	18.54 %	10.09 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

225	MonoRUn			12.30 %	19.65 %	10.58 %	0.07 s	GPU @ 2.5 Ghz (Python + C/C++)

226	Deprecated			12.30 %	16.48 %	9.14 %	Deprecated	Deprecated

227	DLE			12.26 %	17.23 %	10.29 %	0.04 s	GPU @ 2.5 Ghz (Python)

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

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

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

231	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.
232	GA-Aug			11.67 %	17.46 %	9.69 %	0.04 s	GPU @ 2.5 Ghz (Python)

233	MP-Mono			11.65 %	16.78 %	9.01 %	0.16 s	GPU @ 2.5 Ghz (Python)

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

235	RetinaMono		code	11.61 %	16.68 %	9.57 %	0.02 s	1 core @ 2.5 Ghz (Python)

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

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

238	TBD			11.47 %	19.53 %	9.17 %	0.3 s	1 core @ 2.5 Ghz (C/C++)

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

240	KM3D		code	11.45 %	16.73 %	9.92 %	0.03 s	1 core @ 2.5 Ghz (Python)
P. Li: Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training. 2020.
241	IMA			11.34 %	16.24 %	9.44 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

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

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

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

245	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.
246	UM3D_TUM			11.13 %	15.30 %	9.31 %	0.05 s	1 core @ 2.5 Ghz (Python)

247	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.
248	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.
249	OCM3D			10.44 %	17.48 %	7.87 %	0.5 s	1 core @ 2.5 Ghz (C/C++)

250	RTM3D		code	10.34 %	14.41 %	8.77 %	0.05 s	GPU @ 1.0 Ghz (Python)

Table as LaTeX | Only published Methods

Pedestrian

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	HIKVISION-AFree			46.88 %	52.75 %	43.50 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

2	ADLAB			46.18 %	53.59 %	43.28 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

3	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.
4	SAA-PV-RCNN			45.00 %	52.55 %	41.82 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

5	SIEV-Net			44.80 %	54.00 %	41.11 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

6	HIKVISION-ADLab-HZ			44.78 %	52.09 %	42.57 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

7	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.
8	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.
9	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.
10	PP-3D			43.77 %	51.92 %	40.14 %	0.1 s	1 core @ 2.5 Ghz (Python)

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

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

13	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.
14	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.
15	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.
16	TBD_IOU2			43.28 %	51.80 %	40.71 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

17	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.
18	AF			43.09 %	50.65 %	39.65 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

19	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.
20	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.
21	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.
22	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.
23	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.
24	TBD_IOU1			41.65 %	49.00 %	39.31 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

25	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.
26	TBD_IOU			41.45 %	48.25 %	39.17 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

27	GNN-RCNN			41.32 %	47.48 %	38.97 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

28	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.
29	DSA-PV-RCNN			40.89 %	46.97 %	38.80 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)

30	XView-PartA^2			40.71 %	47.73 %	38.47 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

31	E^2-PV-RCNN			40.47 %	46.61 %	38.60 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)

32	FPCR-CNN			40.32 %	48.33 %	37.66 %	0.05 s	1 core @ 2.5 Ghz (Python)

33	Simple3D Net			40.20 %	48.41 %	37.50 %	0.02 s	GPU @ 2.5 Ghz (Python)

34	FPC-RCNN			40.13 %	46.41 %	37.84 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

35	SVGA-Net			39.88 %	47.59 %	37.57 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)

36	anonymous		code	39.74 %	46.09 %	37.41 %	0.05s	1 core @ >3.5 Ghz (python)

37	Fast VP-RCNN		code	39.65 %	45.95 %	37.29 %	0.05 s	1 core @ 3.5 Ghz (C/C++)

38	PF-GAP			39.53 %	47.63 %	36.44 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

39	TBD			39.48 %	45.46 %	37.35 %	0.07 s	1 core @ 2.5 Ghz (Python + C/C++)

40	GAP-soft-filter			39.47 %	46.93 %	36.99 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

41	CVRS VIC-RCNN			39.46 %	45.19 %	37.16 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

42	SRDL			39.43 %	47.30 %	36.99 %	0.15 s	GPU @ 2.5 Ghz (Python + C/C++)

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

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

45	FSA-PVRCNN			39.39 %	44.14 %	37.13 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

46	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.
47	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.
48	TBD			39.31 %	46.85 %	36.11 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

49	tbd			38.89 %	45.98 %	35.94 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

50	SIF			38.74 %	46.23 %	36.06 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

51	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.
52	Faraway-Frustum		code	38.58 %	46.33 %	35.71 %	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.
53	MSL3D			38.58 %	45.00 %	35.72 %	0.03 s	GPU @ 2.5 Ghz (Python)

54	CVIS-DF3D_v2			38.31 %	45.10 %	36.15 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

55	AF_MCLS			38.29 %	47.07 %	34.67 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

56	3DBN_2			38.23 %	46.79 %	35.57 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
57	MKFFNet			38.05 %	46.01 %	35.72 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

58	IGRP+			38.05 %	46.26 %	34.53 %	0.18 s	1 core @ 2.5 Ghz (Python + C/C++)

59	FPC3D_all			37.95 %	45.49 %	35.60 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

60	VGCN			37.60 %	45.28 %	34.96 %	0.09 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
61	MGACNet			37.50 %	43.55 %	35.33 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

62	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.
63	TBD			37.37 %	43.60 %	34.36 %	0.05 s	GPU @ 2.5 Ghz (Python)

64	CVRS VIC-Net			37.18 %	43.82 %	35.35 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

65	XView			36.79 %	42.44 %	34.96 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

66	MKFFNet			36.66 %	43.94 %	34.56 %	0.01s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
67	MKFFNet			36.65 %	44.00 %	34.59 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

68	PFF3D			36.07 %	43.93 %	32.86 %	0.05 s	GPU @ 3.0 Ghz (Python + C/C++)

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

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

71	RoIFusion		code	35.14 %	42.22 %	32.92 %	0.22 s	1 core @ 3.0 Ghz (Python)

72	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.
73	NLK-ALL		code	34.46 %	44.30 %	30.83 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)

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

75	CBi-GNN-persons			32.92 %	41.65 %	29.19 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

76	IOU-SSD		code	32.57 %	39.15 %	30.24 %	0.045s	1 core @ 2.5 Ghz (C/C++)

77	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.
78	Pointpillar_TV			30.79 %	38.56 %	28.57 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

79	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.
80	FCY			29.38 %	37.28 %	26.19 %	0.02 s	GPU @ 2.5 Ghz (Python)

81	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.
82	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.
83	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.
84	Disp R-CNN (velo)		code	25.80 %	37.12 %	22.04 %	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.
85	Disp R-CNN		code	25.40 %	35.75 %	21.79 %	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.
86	tiny-stereo-v2			25.13 %	35.02 %	22.36 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)

87	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.
88	stereo-tkc			23.83 %	31.66 %	21.37 %	0.4 s	GPU @ 2.0 Ghz (Python + C/C++)

89	tiny-stereo			23.66 %	32.51 %	21.08 %	0.4 s	1 core @ 2.5 Ghz (Python + C/C++)

90	NCL		code	23.33 %	27.75 %	21.66 %	NA s	1 core @ 2.5 Ghz (Python)

91	VN3D			21.03 %	26.46 %	18.40 %	0.02 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
92	OSE			20.65 %	28.68 %	17.65 %	0.1 s	GPU @ 2.5 Ghz (C/C++)

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

94	OSE+			19.67 %	28.30 %	17.17 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

95	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.
96	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.
97	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.
98	AEC3D			14.98 %	19.85 %	13.66 %	0.01 s	GPU @ 2.5 Ghz (Python)

99	SOD			14.68 %	21.13 %	12.67 %	0.1 s	1 core @ 2.5 Ghz (Python)

100	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.
101	RT3D-GMP			11.41 %	16.23 %	10.12 %	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.
102	CaDDN			8.14 %	12.87 %	6.76 %	0.63 s	GPU @ 2.5 Ghz (Python)

103	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.
104	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.
105	MonoRUn			6.78 %	10.88 %	5.83 %	0.07 s	GPU @ 2.5 Ghz (Python + C/C++)

106	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.
107	DLE			6.55 %	9.64 %	5.44 %	0.04 s	GPU @ 2.5 Ghz (Python)

108	MonoFlex			6.31 %	9.43 %	5.26 %	0.03 s	GPU @ 2.5 Ghz (Python)

109	DAMono3D			5.68 %	7.86 %	4.81 %	0.09s	1 core @ 2.5 Ghz (C/C++)

110	Deprecated			5.62 %	7.52 %	4.71 %	Deprecated	Deprecated

111	RelationNet3D_res18			5.25 %	8.34 %	4.72 %	0.04 s	GPU @ 2.5 Ghz (Python)

112	GA-Aug			4.89 %	8.04 %	4.32 %	0.04 s	GPU @ 2.5 Ghz (Python)

113	MonoGeo			4.78 %	7.59 %	4.41 %	0.05 s	1 core @ 2.5 Ghz (Python)

114	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.
115	PG-MonoNet			4.50 %	5.76 %	3.93 %	0.19 s	GPU @ 2.5 Ghz (Python)

116	PLDet3d			4.25 %	6.31 %	3.49 %	0.11 s	1 core @ 2.5 Ghz (C/C++)

117	M3D-RPN(S-R)			4.11 %	5.70 %	3.37 %	0.16 s	GPU @ 1.5 Ghz (Python)

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

119	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.
120	NL_M3D			3.87 %	5.16 %	3.08 %	0.2 s	1 core @ 2.5 Ghz (Python)

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

122	DDMP-3D			3.55 %	4.93 %	3.01 %	0.18 s	1 core @ 2.5 Ghz (Python)

123	FADNet		code	3.53 %	5.40 %	3.31 %	0.04 s	GPU @ >3.5 Ghz (Python)

124	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 .
125	Center3D			3.43 %	4.86 %	2.78 %	0.05 s	GPU @ 3.5 Ghz (Python)

126	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.
127	modat3D			3.37 %	5.53 %	3.02 %	0.03 s	GPU @ 2.5 Ghz (Python)

128	DP3D			3.37 %	4.77 %	2.77 %	0.07 s	GPU @ 1.5 Ghz (Python + C/C++)

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

130	MonoEF		code	2.79 %	4.27 %	2.21 %	0.03 s	1 core @ 2.5 Ghz (Python)

131	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.
132	MTMono3d			2.05 %	2.40 %	1.68 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

133	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.
134	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.
135	UM3D_TUM			1.74 %	3.49 %	1.74 %	0.05 s	1 core @ 2.5 Ghz (Python)

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

137	SparVox3D			1.35 %	1.93 %	1.04 %	0.05 s	GPU @ 2.0 Ghz (Python)

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

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

140	TBD			0.00 %	0.00 %	0.00 %	0.3 s	1 core @ 2.5 Ghz (C/C++)

141	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	HIKVISION-AFree			69.92 %	84.65 %	63.33 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

3	anonymous		code	69.13 %	83.09 %	61.35 %	0.05s	1 core @ >3.5 Ghz (python)

4	Fast VP-RCNN		code	69.02 %	83.81 %	61.51 %	0.05 s	1 core @ 3.5 Ghz (C/C++)

5	SAA-PV-RCNN			68.96 %	82.06 %	61.54 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

6	HIKVISION-ADLab-HZ			68.83 %	84.82 %	60.75 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

7	DSA-PV-RCNN			68.54 %	82.19 %	61.33 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)

8	E^2-PV-RCNN			68.03 %	81.55 %	60.51 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)

9	RangeIoUDet			67.77 %	83.12 %	60.26 %	0.02 s	1 core @ 2.5 Ghz (Python)

10	FPC-RCNN			67.57 %	82.79 %	60.69 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

11	GNN-RCNN			67.49 %	81.25 %	61.15 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

12	PV-RCNN-v2			67.33 %	82.22 %	60.04 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)

13	CBi-GNN-persons			66.49 %	79.95 %	59.12 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

14	XView-PartA^2			66.33 %	80.65 %	59.72 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

15	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.
16	TBD			65.64 %	82.29 %	57.98 %	0.05 s	GPU @ 2.5 Ghz (Python)

17	TBD			65.64 %	82.29 %	57.98 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

18	FSA-PVRCNN			65.20 %	80.68 %	59.14 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

19	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.
20	CVRS VIC-RCNN			64.99 %	81.47 %	58.62 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

21	TBD			64.60 %	80.49 %	57.18 %	0.07 s	1 core @ 2.5 Ghz (Python + C/C++)

22	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.
23	RoIFusion		code	64.05 %	80.84 %	58.37 %	0.22 s	1 core @ 3.0 Ghz (Python)

24	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.
25	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.
26	TBD_IOU			63.68 %	79.74 %	56.63 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

27	CVRS VIC-Net			63.65 %	78.29 %	57.27 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

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

29	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.
30	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.
31	PP-3D			63.48 %	78.60 %	57.08 %	0.1 s	1 core @ 2.5 Ghz (Python)

32	AF			63.43 %	80.64 %	55.15 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

33	TBD_IOU2			63.41 %	81.49 %	56.40 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

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

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

36	tbd			62.75 %	78.45 %	56.30 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

37	H^23D R-CNN			62.74 %	78.67 %	55.78 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

38	TBD_IOU1			62.67 %	80.32 %	55.99 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

39	FPCR-CNN			62.56 %	79.61 %	55.82 %	0.05 s	1 core @ 2.5 Ghz (Python)

40	VGCN			62.36 %	78.47 %	55.88 %	0.09 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
41	MSL3D			62.27 %	76.74 %	56.20 %	0.03 s	GPU @ 2.5 Ghz (Python)

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

43	GAP-soft-filter			62.04 %	77.06 %	55.09 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

44	SRDL			62.02 %	77.35 %	55.52 %	0.15 s	GPU @ 2.5 Ghz (Python + C/C++)

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

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

47	Faraway-Frustum		code	62.00 %	77.36 %	55.40 %	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.
48	MGACNet			62.00 %	78.73 %	55.18 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

49	SVGA-Net			61.86 %	75.45 %	54.68 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)

50	MKFFNet			61.80 %	78.08 %	54.89 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

51	SIF			61.61 %	77.13 %	55.11 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

52	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.
53	MVX-Net++			61.03 %	76.07 %	53.41 %	0.15 s	1 core @ 2.5 Ghz (C/C++)

54	AF_MCLS			60.89 %	78.82 %	54.13 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

55	3DBN_2			60.88 %	78.10 %	54.10 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
56	MKFFNet			60.48 %	76.68 %	54.20 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

57	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.
58	CCFNET			60.18 %	78.05 %	53.42 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
59	SIEV-Net			59.99 %	78.75 %	52.37 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

60	PF-GAP			59.92 %	77.88 %	53.48 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

61	XView			59.55 %	77.24 %	53.47 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

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

63	FPC3D_all			59.45 %	74.75 %	52.71 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

64	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.
65	NLK-3D			59.30 %	76.45 %	51.82 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)

66	MKFFNet			59.14 %	75.64 %	52.97 %	0.01s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
67	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.
68	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.
69	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.
70	IOU-SSD		code	57.65 %	71.77 %	52.31 %	0.045s	1 core @ 2.5 Ghz (C/C++)

71	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.
72	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.
73	Pointpillar_TV			54.69 %	71.61 %	48.22 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

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

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

76	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.
77	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.
78	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.
79	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.
80	PFF3D			46.78 %	63.27 %	41.37 %	0.05 s	GPU @ 3.0 Ghz (Python + C/C++)

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

82	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.
83	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.
84	stereo-tkc			34.53 %	51.87 %	30.01 %	0.4 s	GPU @ 2.0 Ghz (Python + C/C++)

85	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.
86	tiny-stereo-v2			31.31 %	46.37 %	27.66 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)

87	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.
88	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.
89	tiny-stereo			27.76 %	42.80 %	24.10 %	0.4 s	1 core @ 2.5 Ghz (Python + C/C++)

90	SOD			25.29 %	40.51 %	21.32 %	0.1 s	1 core @ 2.5 Ghz (Python)

91	Disp R-CNN (velo)		code	24.40 %	40.05 %	21.12 %	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.
92	Disp R-CNN		code	24.40 %	40.04 %	21.12 %	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.
93	OSE+			20.75 %	32.62 %	17.23 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

94	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.
95	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.
96	VN3D			17.46 %	24.83 %	16.73 %	0.02 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
97	OSE			17.28 %	28.50 %	15.56 %	0.1 s	GPU @ 2.5 Ghz (C/C++)

98	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.
99	RT3D-GMP			12.99 %	18.31 %	10.63 %	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.
100	AEC3D			10.76 %	18.08 %	9.99 %	0.01 s	GPU @ 2.5 Ghz (Python)

101	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.
102	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.
103	DAMono3D			3.80 %	5.65 %	3.23 %	0.09s	1 core @ 2.5 Ghz (C/C++)

104	CaDDN			3.41 %	7.00 %	3.30 %	0.63 s	GPU @ 2.5 Ghz (Python)

105	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.
106	MonoGeo			3.05 %	4.74 %	2.69 %	0.05 s	1 core @ 2.5 Ghz (Python)

107	Deprecated			2.71 %	3.89 %	2.27 %	Deprecated	Deprecated

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

109	DLE			2.66 %	4.59 %	2.45 %	0.04 s	GPU @ 2.5 Ghz (Python)

110	DDMP-3D			2.50 %	4.18 %	2.32 %	0.18 s	1 core @ 2.5 Ghz (Python)

111	modat3D			2.39 %	4.16 %	1.85 %	0.03 s	GPU @ 2.5 Ghz (Python)

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

113	MonoFlex			2.35 %	4.17 %	2.04 %	0.03 s	GPU @ 2.5 Ghz (Python)

114	RelationNet3D_res18			2.33 %	4.51 %	2.22 %	0.04 s	GPU @ 2.5 Ghz (Python)

115	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.
116	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.
117	UDI-mono3D			1.74 %	3.29 %	1.37 %	0.05 s	1 core @ 2.5 Ghz (Python)

118	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.
119	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.
120	DP3D			1.66 %	2.77 %	1.31 %	0.07 s	GPU @ 1.5 Ghz (Python + C/C++)

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

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

123	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.
124	PG-MonoNet			1.43 %	2.41 %	1.23 %	0.19 s	GPU @ 2.5 Ghz (Python)

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

126	GA-Aug			1.20 %	1.99 %	1.09 %	0.04 s	GPU @ 2.5 Ghz (Python)

127	MonoEF		code	0.92 %	1.80 %	0.71 %	0.03 s	1 core @ 2.5 Ghz (Python)

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

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

130	PLDet3d			0.80 %	1.24 %	0.89 %	0.11 s	1 core @ 2.5 Ghz (C/C++)

131	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 .
132	FADNet		code	0.64 %	1.44 %	0.67 %	0.04 s	GPU @ >3.5 Ghz (Python)

133	UM3D_TUM			0.62 %	0.45 %	0.62 %	0.05 s	1 core @ 2.5 Ghz (Python)

134	MonoRUn			0.61 %	1.01 %	0.48 %	0.07 s	GPU @ 2.5 Ghz (Python + C/C++)

135	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.
136	PVNet			0.00 %	0.00 %	0.00 %	0,1 s	1 core @ 2.5 Ghz (Python)

137	TBD			0.00 %	0.00 %	0.00 %	0.3 s	1 core @ 2.5 Ghz (C/C++)

138	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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TME Motorway Dataset: 28 video sequences with vehicle annotations captured from VisLab's BRAiVE vehicle.
LabelMe: Online annotation tool to build image databases for computer vision research.
MIT Street Scenes: Street-side images with labels for 9 object categories (including cars, pedestrians, buildings, trees).
Daimler Pedestrian Datasets: Datasets focusing on pedestrian detection for autonomous driving.
Caltech Pedestrian Detection Benchmark: 10 hours of video with 350.000 annotated pedestrian bounding boxes.
Robust Multi-Person Tracking from Mobile Platforms: Videos with annotated pedestrians captured from a stroller.

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