The KITTI Vision Benchmark Suite

Bird's Eye View Evaluation 2017

The bird's eye view 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 bird's eye view 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 a bounding box overlap of 70% in bird's eye view, while for pedestrians and cyclists we require an 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	SE-SSD			91.84 %	95.68 %	86.72 %	0.03 s	1 core @ 2.5 Ghz (Python + C/C++)

2	ADLAB			91.66 %	95.56 %	86.92 %	0.05 s	1 core @ >3.5 Ghz (C/C++)

3	SPANet			91.59 %	95.59 %	86.53 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

4	PVGNet			91.26 %	94.36 %	86.63 %	0.05 s	1 core @ >3.5 Ghz (C/C++)

5	SA-SSD		code	91.03 %	95.03 %	85.96 %	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.
6	MMLab PV-RCNN		code	90.65 %	94.98 %	86.14 %	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.
7	CN			90.50 %	94.51 %	85.86 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)

8	HyBrid Feature Det			90.35 %	92.87 %	85.87 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)

9	Fast VP-RCNN		code	90.32 %	95.09 %	85.84 %	0.05 s	1 core @ 3.5 Ghz (C/C++)

10	LZY_RCNN			90.29 %	92.88 %	85.84 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

11	E^2-PV-RCNN			90.27 %	92.51 %	86.01 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)

12	DomainAdp+PVRCNN			90.23 %	92.73 %	86.09 %	0.09 s	GPU @ 2.5 Ghz (Python)

13	anonymous		code	90.22 %	94.86 %	85.73 %	0.05s	1 core @ >3.5 Ghz (python)

14	MSG-PGNN			90.20 %	92.89 %	85.80 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

15	DSA-PV-RCNN			90.13 %	92.42 %	85.93 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)

16	XView			90.12 %	92.27 %	85.94 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

17	FPC-RCNN			90.03 %	92.74 %	85.67 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

18	Associate-3Ddet_v2			90.00 %	95.55 %	84.72 %	0.04 s	1 core @ 2.5 Ghz (Python)

19	SAA-PV-RCNN			89.88 %	91.54 %	86.93 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

20	FSA-PVRCNN			89.87 %	92.30 %	85.71 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

21	EBM3DOD		code	89.86 %	95.64 %	84.56 %	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.
22	CIA-SSD		code	89.84 %	93.74 %	82.39 %	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.
23	HIKVISION-ADLab-HZ			89.83 %	93.21 %	84.88 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

24	AIMC-RUC			89.80 %	93.64 %	84.64 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

25	CLOCs_PVCas		code	89.80 %	93.05 %	86.57 %	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.
26	CIA-SSD v2			89.80 %	93.49 %	84.39 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

27	deprecated			89.77 %	93.68 %	82.31 %	deprecated	deprecated

28	CM3DV			89.77 %	95.54 %	84.49 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

29	EA-M-RCNN(BorderAtt)			89.76 %	94.67 %	86.73 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

30	AM-SSD			89.74 %	95.56 %	84.65 %	0.04 s	1 core @ 2.5 Ghz (Python)

31	CBi-GNN			89.74 %	95.92 %	84.54 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

32	OAP			89.72 %	93.13 %	82.25 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

33	D3D			89.72 %	93.37 %	84.72 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

34	EBM3DOD baseline		code	89.63 %	95.44 %	84.34 %	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.
35	3D-CVF at SPA			89.56 %	93.52 %	82.45 %	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.
36	CDE-Net(0.3)			89.54 %	95.26 %	82.31 %	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.
37	Cas-SSD			89.47 %	93.31 %	84.35 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

38	FCY			89.46 %	95.27 %	84.34 %	0.02 s	GPU @ 2.5 Ghz (Python)

39	PointRes			89.42 %	93.17 %	84.25 %	0.013 s	1 core @ 2.5 Ghz (Python + C/C++)

40	Seg-RCNN		code	89.39 %	93.36 %	81.93 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)

41	HUAWEI Octopus			89.39 %	92.58 %	86.55 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

42	CDE-Net(0.4)			89.38 %	94.91 %	84.29 %	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.
43	PLNL-3DSSD			89.36 %	93.00 %	84.18 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)

44	PSS			89.28 %	93.17 %	84.38 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

45	GNN-RCNN			89.28 %	92.13 %	85.49 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

46	CJJ			89.20 %	92.90 %	84.30 %	0.04 s	1 core @ 2.5 Ghz (C/C++)

47	STD		code	89.19 %	94.74 %	86.42 %	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.
48	Point-GNN		code	89.17 %	93.11 %	83.90 %	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.
49	PP-3D			89.17 %	93.11 %	83.90 %	0.1 s	1 core @ 2.5 Ghz (Python)

50	RoIFusion		code	89.03 %	92.88 %	83.94 %	0.22 s	1 core @ 3.0 Ghz (Python)

51	3DSSD		code	89.02 %	92.66 %	85.86 %	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.
52	CityBrainLab-TSD			88.91 %	92.78 %	84.12 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)

53	NLK-ALL		code	88.89 %	92.25 %	84.13 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)

54	H^23D R-CNN			88.87 %	92.85 %	86.07 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

55	TBD			88.83 %	92.96 %	86.27 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

56	Voxel R-CNN		code	88.83 %	94.85 %	86.13 %	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.
57	HVNet			88.82 %	92.83 %	83.38 %	0.03 s	GPU @ 2.0 Ghz (Python)
M. Ye, S. Xu and T. Cao: HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection. CVPR 2020.
58	RangeRCNN-LV			88.81 %	92.41 %	85.96 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

59	F-3DNet			88.76 %	92.68 %	83.63 %	0.5 s	GPU @ 2.5 Ghz (Python)

60	PV-RCNN-v2			88.74 %	92.66 %	85.97 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)

61	3DIoU+++			88.61 %	92.23 %	86.11 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

62	RangeIoUDet			88.59 %	92.28 %	85.83 %	0.02 s	1 core @ 2.5 Ghz (Python)

63	SIEV-Net			88.58 %	92.27 %	83.36 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

64	ISF-v2			88.57 %	92.15 %	83.91 %	0.04 s	1 core @ 2.5 Ghz (Python)

65	KNN-GCNN			88.57 %	91.73 %	83.32 %	0.4 s	1 core @ 2.5 Ghz (Python + C/C++)

66	PVF-NET			88.57 %	92.20 %	83.45 %	0.1 s	1 core @ 2.5 Ghz (Python)

67	CVRS VIC-Net			88.57 %	91.94 %	85.43 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

68	BLPNet_V2			88.55 %	92.24 %	83.44 %	0.04 s	1 core @ 2.5 Ghz (Python)

69	nonet			88.49 %	91.97 %	85.33 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

70	EPNet		code	88.47 %	94.22 %	83.69 %	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.
71	CenterNet3D			88.46 %	91.80 %	83.62 %	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.
72	deprecated			88.44 %	92.14 %	85.11 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

73	SIENet			88.44 %	92.00 %	85.90 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

74	PC-RGNN			88.43 %	92.08 %	85.81 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)

75	RangeRCNN			88.40 %	92.15 %	85.74 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, M. Zhang, Z. Zhang, X. Zhao and S. Pu: RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representation. arXiv preprint arXiv:2009.00206 2020.
76	Patches			88.39 %	92.72 %	83.19 %	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.
77	3D IoU-Net			88.38 %	94.76 %	81.93 %	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.
78	PF-GAP			88.35 %	92.16 %	85.64 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

79	ReFineNet			88.32 %	91.93 %	85.68 %	0.08 s	1 core @ 2.5 Ghz (Python)

80	CLOCs_SecCas			88.23 %	91.16 %	82.63 %	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.
81	MSL3D			88.23 %	91.64 %	85.53 %	0.03 s	GPU @ 2.5 Ghz (Python)

82	Multi-Sensor3D			88.23 %	91.64 %	85.53 %	0.03 s	GPU @ 2.5 Ghz (Python)

83	3DIoU_v2			88.22 %	92.52 %	85.90 %	0.2 s	1 core @ 2.5 Ghz (C/C++)

84	NLK-3D			88.22 %	91.54 %	83.33 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)

85	CVRS_PF			88.22 %	91.81 %	84.91 %	0.09 s	1 core @ 2.5 Ghz (C/C++)

86	SVGA-Net			88.21 %	91.98 %	85.46 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)

87	UberATG-MMF			88.21 %	93.67 %	81.99 %	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.
88	CVRS VIC-RCNN			88.20 %	92.35 %	85.64 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

89	Patches - EMP			88.17 %	94.49 %	84.75 %	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.
90	SRDL			88.17 %	92.01 %	85.43 %	0.15 s	GPU @ 2.5 Ghz (Python + C/C++)

91	3DIoU++			88.16 %	91.79 %	85.71 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

92	FPC3D			88.15 %	91.92 %	85.32 %	33 s	1 core @ 2.5 Ghz (C/C++)

93	Baseline of CA RCNN			88.13 %	91.91 %	85.40 %	0.1 s	GPU @ 2.5 Ghz (Python)

94	CVIS-DF3D			88.13 %	91.91 %	85.40 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

95	FPCR-CNN			88.12 %	92.62 %	85.18 %	0.05 s	1 core @ 2.5 Ghz (Python)

96	GAP-soft-filter			88.11 %	91.88 %	85.42 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

97	PointPainting			88.11 %	92.45 %	83.36 %	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.
98	SERCNN			88.10 %	94.11 %	83.43 %	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.
99	Associate-3Ddet		code	88.09 %	91.40 %	82.96 %	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.
100	HotSpotNet			88.09 %	94.06 %	83.24 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
Q. Chen, L. Sun, Z. Wang, K. Jia and A. Yuille: object as hotspots. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
101	Faraway-Frustum		code	88.08 %	91.90 %	85.35 %	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.
102	CVIS-DF3D_v2			88.06 %	91.85 %	85.37 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

103	Dccnet			88.01 %	92.09 %	82.45 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

104	UberATG-HDNET			87.98 %	93.13 %	81.23 %	0.05 s	GPU @ 2.5 Ghz (Python)
B. Yang, M. Liang and R. Urtasun: HDNET: Exploiting HD Maps for 3D Object Detection. 2nd Conference on Robot Learning (CoRL) 2018.
105	CCFNET			87.97 %	94.25 %	83.27 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

106	HV			87.94 %	91.76 %	83.03 %	0.02 s	GPU @ 2.5 Ghz (Python)

107	AIMC-RUC			87.91 %	93.92 %	82.70 %	0.11 s	1 core @ 2.5 Ghz (Python)

108	TBD			87.89 %	91.39 %	85.24 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

109	tbd		code	87.88 %	91.36 %	84.75 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

110	XView-PartA^2			87.84 %	91.94 %	85.22 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

111	Fast Point R-CNN			87.84 %	90.87 %	80.52 %	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.
112	TBD			87.83 %	91.80 %	85.19 %	0.07 s	1 core @ 2.5 Ghz (Python + C/C++)

113	MMLab-PartA^2		code	87.79 %	91.70 %	84.61 %	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.
114	MKFFNet			87.78 %	91.85 %	84.82 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

115	FPGNN			87.78 %	92.21 %	80.86 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

116	HRI-MSP-L			87.78 %	91.74 %	85.14 %	0.07 s	1 core @ 2.5 Ghz (C/C++)

117	SIF			87.76 %	91.44 %	85.15 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

118	MVAF-Net		code	87.73 %	91.95 %	85.00 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
G. Wang, B. Tian, Y. Zhang, L. Chen, D. Cao and J. Wu: Multi-View Adaptive Fusion Network for 3D Object Detection. arXiv preprint arXiv:2011.00652 2020.
119	MGACNet			87.68 %	90.93 %	84.60 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

120	deprecated			87.63 %	93.66 %	80.35 %	0.06 s	GPU @ >3.5 Ghz (Python)

121	VAL			87.63 %	93.57 %	79.89 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

122	MODet			87.56 %	90.80 %	82.69 %	0.05 s	GTX1080Ti
Y. Zhang, Z. Xiang, C. Qiao and S. Chen: Accurate and Real-Time Object Detection Based on Bird's Eye View on 3D Point Clouds. 2019 International Conference on 3D Vision (3DV) 2019.
123	VOXEL_3D			87.55 %	90.83 %	82.24 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

124	AB3DMOT		code	87.53 %	91.99 %	81.03 %	0.0047s	1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
125	V3D			87.53 %	90.83 %	82.30 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

126	PointRGCN			87.49 %	91.63 %	80.73 %	0.26 s	GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
127	AF_V1			87.47 %	92.70 %	82.19 %	0.1 s	1 core @ 2.5 Ghz (Python)

128	MKFFNet			87.41 %	91.62 %	84.67 %	0.01s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
129	MKFFNet			87.41 %	91.93 %	84.65 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

130	PC-CNN-V2			87.40 %	91.19 %	79.35 %	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.
131	MMLab-PointRCNN		code	87.39 %	92.13 %	82.72 %	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.
132	MAFF-Net(DAF-Pillar)			87.34 %	90.79 %	77.66 %	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.
133	VAR			87.31 %	90.68 %	82.67 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

134	DPointNet			87.29 %	88.96 %	82.61 %	0.07s	1 core @ 2.5 Ghz (C/C++)

135	HRI-VoxelFPN			87.21 %	92.75 %	79.82 %	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.
136	VGCN			87.16 %	90.67 %	82.98 %	0.09 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
137	MDA			87.13 %	90.67 %	82.80 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

138	epBRM		code	87.13 %	90.70 %	81.92 %	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.
139	Pointpillar_TV			87.08 %	90.50 %	81.98 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

140	SARPNET			86.92 %	92.21 %	81.68 %	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.
141	ARPNET			86.81 %	90.06 %	79.41 %	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.
142	C-GCN			86.78 %	91.11 %	80.09 %	0.147 s	GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
143	FLID			86.77 %	91.58 %	81.14 %	0.04 s	GPU @ 2.5 Ghz (Python)

144	CU-PointRCNN			86.69 %	92.65 %	82.66 %	0.1 s	GPU @ 1.5 Ghz (Python + C/C++)

145	tt		code	86.68 %	90.57 %	81.98 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

146	PointPillars		code	86.56 %	90.07 %	82.81 %	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.
147	TANet		code	86.54 %	91.58 %	81.19 %	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.
148	MVX-Net++			86.53 %	91.86 %	81.41 %	0.15 s	1 core @ 2.5 Ghz (C/C++)

149	SCNet			86.48 %	90.07 %	81.30 %	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.
150	Simple3D Net			86.46 %	89.82 %	82.60 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

151	SegVoxelNet			86.37 %	91.62 %	83.04 %	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.
152	IGRP+			86.29 %	92.20 %	81.48 %	0.18 s	1 core @ 2.5 Ghz (Python + C/C++)

153	3D IoU Loss			86.22 %	91.36 %	81.20 %	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.
154	IGRP			86.21 %	92.04 %	81.30 %	0.18 s	1 core @ 2.5 Ghz (Python + C/C++)

155	R-GCN			86.05 %	91.91 %	81.05 %	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.
156	UberATG-PIXOR++			86.01 %	93.28 %	80.11 %	0.035 s	GPU @ 2.5 Ghz (Python)
B. Yang, M. Liang and R. Urtasun: HDNET: Exploiting HD Maps for 3D Object Detection. 2nd Conference on Robot Learning (CoRL) 2018.
157	TBD			86.00 %	89.79 %	83.37 %	0.05 s	GPU @ 2.5 Ghz (Python)

158	IOU-SSD		code	85.98 %	90.18 %	80.74 %	0.045s	1 core @ 2.5 Ghz (C/C++)

159	TBD			85.91 %	90.88 %	80.95 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)

160	LSNet			85.89 %	92.12 %	80.80 %	0.09 s	GPU @ 2.5 Ghz (Python + C/C++)

161	DASS			85.85 %	91.74 %	80.97 %	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.
162	F-ConvNet		code	85.84 %	91.51 %	76.11 %	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.
163	PI-RCNN			85.81 %	91.44 %	81.00 %	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.
164	APL-Second			85.70 %	90.78 %	78.69 %	0.05 s	1 core @ 2.5 Ghz (Python)

165	UberATG-ContFuse			85.35 %	94.07 %	75.88 %	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.
166	3DBN_2			85.30 %	91.37 %	82.57 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
167	PFF3D			85.08 %	89.61 %	80.42 %	0.05 s	GPU @ 3.0 Ghz (Python + C/C++)

168	AVOD		code	84.95 %	89.75 %	78.32 %	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.
169	WS3D			84.93 %	90.96 %	77.96 %	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.
170	baseline			84.88 %	89.25 %	80.18 %	0.12 s	1 core @ 2.5 Ghz (C/C++)

171	FPC3D_all			84.85 %	91.05 %	80.23 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

172	AVOD-FPN		code	84.82 %	90.99 %	79.62 %	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.
173	F-PointNet		code	84.67 %	91.17 %	74.77 %	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.
174	3DBN			83.94 %	89.66 %	76.50 %	0.13s	1080Ti (Python+C/C++)
X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.
175	KMC		code	83.90 %	88.87 %	76.87 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

176	MLOD		code	82.68 %	90.25 %	77.97 %	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.
177	DAMNET		code	82.14 %	87.90 %	75.52 %	1 s	1 core @ 2.5 Ghz (C/C++)

178	voxelrcnn			81.41 %	88.21 %	75.26 %	15 s	1 core @ 2.5 Ghz (C/C++)

179	UberATG-PIXOR			80.01 %	83.97 %	74.31 %	0.035 s	TITAN Xp (Python)
B. Yang, W. Luo and R. Urtasun: PIXOR: Real-time 3D Object Detection from Point Clouds. CVPR 2018.
180	NLK			79.15 %	82.59 %	72.65 %	0.02 s	1 core @ 2.5 Ghz (Python)

181	MV3D (LIDAR)			78.98 %	86.49 %	72.23 %	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.
182	MV3D			78.93 %	86.62 %	69.80 %	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.
183	RCD			75.83 %	82.26 %	69.61 %	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.
184	LaserNet			74.52 %	79.19 %	68.45 %	12 ms	GPU @ 2.5 Ghz (C/C++)
G. Meyer, A. Laddha, E. Kee, C. Vallespi-Gonzalez and C. Wellington: LaserNet: An Efficient Probabilistic 3D Object Detector for Autonomous Driving. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
185	PL++ (SDN+GDC)		code	73.80 %	84.61 %	65.59 %	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.
186	A3DODWTDA		code	73.26 %	79.58 %	62.77 %	0.08 s	GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
187	VN3D			70.69 %	80.56 %	65.31 %	0.02 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
188	AEC3D			70.66 %	81.54 %	65.27 %	0.01 s	GPU @ 2.5 Ghz (Python)

189	stereo-tkc			69.70 %	87.15 %	62.51 %	0.4 s	GPU @ 2.0 Ghz (Python + C/C++)

190	Complexer-YOLO			68.96 %	77.24 %	64.95 %	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.
191	tiny-stereo-v2			68.87 %	86.89 %	59.95 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)

192	TopNet-Retina			68.16 %	80.16 %	63.43 %	52ms	GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
193	tiny-stereo			67.43 %	87.93 %	58.05 %	0.4 s	1 core @ 2.5 Ghz (Python + C/C++)

194	CG-Stereo			66.44 %	85.29 %	58.95 %	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.
195	PLUME			66.27 %	82.97 %	56.70 %	0.15 s	GPU @ 2.5 Ghz (Python)

196	CDN		code	66.24 %	83.32 %	57.65 %	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.
197	DSGN		code	65.05 %	82.90 %	56.60 %	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.
198	TopNet-DecayRate			64.60 %	79.74 %	58.04 %	92 ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
199	BirdNet+		code	63.33 %	84.80 %	61.23 %	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.
200	3D FCN			61.67 %	70.62 %	55.61 %	>5 s	1 core @ 2.5 Ghz (C/C++)
B. Li: 3D Fully Convolutional Network for Vehicle Detection in Point Cloud. IROS 2017.
201	CDN-PL++			61.04 %	81.27 %	52.84 %	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.
202	BirdNet			59.83 %	84.17 %	57.35 %	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.
203	TopNet-UncEst			59.67 %	72.05 %	51.67 %	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.
204	RT3D-GMP			59.00 %	69.14 %	45.49 %	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.
205	OSE+			58.65 %	79.80 %	50.52 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

206	Disp R-CNN (velo)		code	58.62 %	79.76 %	47.73 %	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.
207	SOD			58.50 %	81.25 %	49.08 %	0.1 s	1 core @ 2.5 Ghz (Python)

208	OSE			58.04 %	79.75 %	49.78 %	0.1 s	GPU @ 2.5 Ghz (C/C++)

209	Pseudo-LiDAR++		code	58.01 %	78.31 %	51.25 %	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.
210	Disp R-CNN		code	57.98 %	79.61 %	47.09 %	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.
211	NCL		code	57.66 %	50.87 %	57.99 %	NA s	1 core @ 2.5 Ghz (Python)

212	ZoomNet		code	54.91 %	72.94 %	44.14 %	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.
213	Neighbor-VoteNet			54.68 %	65.38 %	48.59 %	0.1 s	1 core @ 2.5 Ghz (Python)

214	VoxelJones		code	53.96 %	66.21 %	47.66 %	.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.
215	TopNet-HighRes			53.05 %	67.84 %	46.99 %	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.
216	RTS3D			51.79 %	72.17 %	43.19 %	0.03 s	GPU @ 2.5 Ghz (Python)

217	OC Stereo		code	51.47 %	68.89 %	42.97 %	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.
218	NVNet(BEV-3D)			50.41 %	61.32 %	44.74 %	0.1 s	1 core @ 2.5 Ghz (Python)

219	Stereo3D			50.28 %	76.10 %	36.86 %	0.1 s	GPU 1080Ti

220	RT3DStereo			46.82 %	58.81 %	38.38 %	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.
221	Pseudo-Lidar		code	45.00 %	67.30 %	38.40 %	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.
222	RT3D			44.00 %	56.44 %	42.34 %	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.
223	SC(DLA34+DCO)			42.12 %	62.97 %	35.37 %	0.07 s	GPU @ 2.5 Ghz (Python)

224	Stereo R-CNN		code	41.31 %	61.92 %	33.42 %	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.
225	StereoFENet			32.96 %	49.29 %	25.90 %	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.
226	LNET			29.68 %	34.30 %	25.11 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

227	Det3D			20.80 %	35.46 %	16.00 %	0.5 s	1 core @ 2.5 Ghz (C/C++)

228	LGDet3d			20.17 %	30.72 %	16.76 %	0.11 s	1 core @ 2.5 Ghz (C/C++)

229	ITS-MDPL			19.83 %	33.74 %	16.90 %	0.16 s	GPU @ 2.5 Ghz (Python)

230	MonoFlex			19.75 %	28.23 %	16.89 %	0.03 s	GPU @ 2.5 Ghz (Python)

231	MonoEF		code	19.70 %	29.03 %	17.26 %	0.03 s	1 core @ 2.5 Ghz (Python)

232	CaDDN			18.91 %	27.94 %	17.19 %	0.63 s	GPU @ 2.5 Ghz (Python)

233	DLE			18.89 %	24.79 %	16.00 %	0.04 s	GPU @ 2.5 Ghz (Python)

234	Object Transformer			18.78 %	26.43 %	15.94 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

235	PLDet3d			18.55 %	29.14 %	15.73 %	0.11 s	1 core @ 2.5 Ghz (C/C++)

236	MTMono3d			18.54 %	27.00 %	15.71 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

237	MonoGeo			18.42 %	24.40 %	16.03 %	0.05 s	1 core @ 2.5 Ghz (Python)

238	DDMP-3D			17.89 %	28.08 %	13.44 %	0.18 s	1 core @ 2.5 Ghz (Python)

239	IAFA			17.88 %	25.88 %	15.35 %	0.04 s	1 core @ 2.5 Ghz (C/C++)

240	RelationNet3D			17.66 %	25.56 %	15.52 %	0.04 s	GPU @ 2.5 Ghz (Python)

241	RefinedMPL			17.60 %	28.08 %	13.95 %	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.
242	Kinematic3D		code	17.52 %	26.69 %	13.10 %	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 .
243	MonoRUn			17.34 %	27.94 %	15.24 %	0.07 s	GPU @ 2.5 Ghz (Python + C/C++)

244	AM3D			17.32 %	25.03 %	14.91 %	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.
245	Deprecated			17.22 %	23.59 %	13.34 %	Deprecated	Deprecated

246	DAMono3D			17.17 %	23.73 %	13.46 %	0.09s	1 core @ 2.5 Ghz (C/C++)

247	YoloMono3D		code	17.15 %	26.79 %	12.56 %	0.05 s	GPU @ 2.5 Ghz (Python)

248	OCM3D			17.13 %	27.87 %	13.53 %	0.5 s	1 core @ 2.5 Ghz (C/C++)

249	IMA			17.08 %	23.93 %	14.75 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

250	MCA			17.07 %	25.93 %	14.80 %	0.04 s	1 core @ 2.5 Ghz (C/C++)

Table as LaTeX | Only published Methods

Pedestrian

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	ADLAB			52.58 %	58.39 %	49.13 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

2	SIEV-Net			52.15 %	60.78 %	48.54 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

3	TANet		code	51.38 %	60.85 %	47.54 %	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.
4	HIKVISION-AFree			50.67 %	56.54 %	48.60 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

5	MMLab PV-RCNN		code	50.57 %	59.86 %	46.74 %	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.
6	HotSpotNet			50.53 %	57.39 %	46.65 %	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.
7	VMVS			50.34 %	60.34 %	46.45 %	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.
8	AVOD-FPN		code	50.32 %	58.49 %	46.98 %	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.
9	3DSSD		code	49.94 %	60.54 %	45.73 %	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.
10	PointPainting			49.93 %	58.70 %	46.29 %	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.
11	SemanticVoxels			49.93 %	58.91 %	47.31 %	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.
12	MMLab-PartA^2		code	49.81 %	59.04 %	45.92 %	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.
13	HIKVISION-ADLab-HZ			49.62 %	55.94 %	47.39 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

14	SAA-PV-RCNN			49.58 %	57.07 %	46.49 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

15	F-PointNet		code	49.57 %	57.13 %	45.48 %	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.
16	F-ConvNet		code	48.96 %	57.04 %	44.33 %	0.47 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Wang and K. Jia: Frustum ConvNet: Sliding Frustums to Aggregate Local Point-Wise Features for Amodal 3D Object Detection. IROS 2019.
17	HVNet			48.86 %	54.84 %	46.33 %	0.03 s	GPU @ 2.0 Ghz (Python)
M. Ye, S. Xu and T. Cao: HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection. CVPR 2020.
18	STD		code	48.72 %	60.02 %	44.55 %	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.
19	PointPillars		code	48.64 %	57.60 %	45.78 %	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.
20	AF			48.46 %	56.09 %	44.90 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

21	TBD_IOU2			48.15 %	56.54 %	45.63 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

22	MVX-Net++			48.04 %	56.63 %	45.44 %	0.15 s	1 core @ 2.5 Ghz (C/C++)

23	Simple3D Net			47.27 %	56.05 %	44.70 %	0.02 s	GPU @ 2.5 Ghz (Python)

24	Point-GNN		code	47.07 %	55.36 %	44.61 %	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.
25	PP-3D			47.07 %	55.36 %	44.61 %	0.1 s	1 core @ 2.5 Ghz (Python)

26	KNN-GCNN			46.77 %	55.11 %	44.43 %	0.4 s	1 core @ 2.5 Ghz (Python + C/C++)

27	SCNet			46.73 %	56.87 %	42.74 %	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.
28	TBD_IOU1			46.59 %	53.92 %	44.29 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

29	XView-PartA^2			46.57 %	52.45 %	43.85 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

30	MMLab-PointRCNN		code	46.13 %	54.77 %	42.84 %	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.
31	GNN-RCNN			46.10 %	52.19 %	43.97 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

32	TBD_IOU			46.08 %	53.25 %	43.81 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

33	ARPNET			45.92 %	55.48 %	42.54 %	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.
34	E^2-PV-RCNN			45.85 %	52.35 %	44.00 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)

35	DSA-PV-RCNN			45.82 %	52.03 %	43.81 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)

36	epBRM		code	45.49 %	52.48 %	42.75 %	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.
37	MLOD		code	45.40 %	55.09 %	41.42 %	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.
38	FPCR-CNN			45.18 %	52.79 %	42.70 %	0.05 s	1 core @ 2.5 Ghz (Python)

39	PF-GAP			45.02 %	53.73 %	41.88 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

40	GAP-soft-filter			44.98 %	52.44 %	42.41 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

41	FPC-RCNN			44.96 %	51.54 %	42.88 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

42	Baseline of CA RCNN			44.85 %	52.42 %	42.56 %	0.1 s	GPU @ 2.5 Ghz (Python)

43	CVIS-DF3D			44.85 %	52.42 %	42.56 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

44	Fast VP-RCNN		code	44.84 %	51.19 %	42.63 %	0.05 s	1 core @ 3.5 Ghz (C/C++)

45	SRDL			44.84 %	52.42 %	42.56 %	0.15 s	GPU @ 2.5 Ghz (Python + C/C++)

46	TBD			44.65 %	50.72 %	42.61 %	0.07 s	1 core @ 2.5 Ghz (Python + C/C++)

47	SVGA-Net			44.57 %	51.45 %	42.45 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)

48	anonymous		code	44.50 %	50.60 %	42.26 %	0.05s	1 core @ >3.5 Ghz (python)

49	FSA-PVRCNN			44.49 %	49.33 %	42.58 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

50	SIF			44.28 %	52.05 %	42.03 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

51	CVRS VIC-RCNN			44.13 %	48.95 %	42.42 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

52	MGACNet			44.12 %	50.98 %	41.62 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

53	CVIS-DF3D_v2			43.97 %	51.14 %	41.94 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

54	Faraway-Frustum		code	43.85 %	52.15 %	41.68 %	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.
55	TBD			43.69 %	52.35 %	41.24 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

56	FPC3D_all			43.41 %	50.05 %	41.06 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

57	tbd			43.41 %	49.45 %	40.57 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

58	MKFFNet			43.29 %	50.71 %	40.97 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

59	CVRS VIC-Net			43.11 %	49.25 %	41.35 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

60	3DBN_2			42.97 %	50.99 %	40.49 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
61	MSL3D			42.82 %	48.81 %	40.13 %	0.03 s	GPU @ 2.5 Ghz (Python)

62	MKFFNet			42.58 %	49.79 %	40.62 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

63	XView			42.42 %	47.24 %	39.96 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

64	VGCN			42.33 %	50.02 %	40.05 %	0.09 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
65	IGRP+			41.86 %	50.15 %	38.98 %	0.18 s	1 core @ 2.5 Ghz (Python + C/C++)

66	deprecated			41.85 %	47.88 %	40.09 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

67	AF_MCLS			41.61 %	50.55 %	37.85 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

68	MKFFNet			41.33 %	47.80 %	39.39 %	0.01s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
69	TBD			41.12 %	48.24 %	39.06 %	0.05 s	GPU @ 2.5 Ghz (Python)

70	PFF3D			40.94 %	48.74 %	38.54 %	0.05 s	GPU @ 3.0 Ghz (Python + C/C++)

71	DAMNET		code	39.30 %	49.66 %	35.52 %	1 s	1 core @ 2.5 Ghz (C/C++)

72	NLK-3D			39.22 %	49.79 %	36.75 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)

73	AB3DMOT		code	38.79 %	47.51 %	35.85 %	0.0047s	1 core @ 2.5 Ghz (python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
74	BirdNet+		code	38.28 %	45.53 %	35.37 %	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.
75	RoIFusion		code	38.08 %	46.21 %	35.97 %	0.22 s	1 core @ 3.0 Ghz (Python)

76	CSW3D			37.96 %	49.27 %	33.83 %	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.
77	NLK-ALL		code	37.61 %	47.88 %	33.86 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)

78	IOU-SSD		code	36.90 %	43.81 %	35.11 %	0.045s	1 core @ 2.5 Ghz (C/C++)

79	CBi-GNN-persons			36.56 %	45.80 %	32.68 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

80	Pointpillar_TV			35.28 %	42.65 %	33.10 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

81	SparsePool		code	34.15 %	43.33 %	31.78 %	0.13 s	8 cores @ 2.5 Ghz (Python)
Z. Wang, W. Zhan and M. Tomizuka: Fusing bird view lidar point cloud and front view camera image for deep object detection. arXiv preprint arXiv:1711.06703 2017.
82	AVOD		code	33.57 %	42.58 %	30.14 %	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	33.22 %	41.55 %	29.66 %	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	FCY			32.64 %	41.16 %	29.35 %	0.02 s	GPU @ 2.5 Ghz (Python)

85	CG-Stereo			29.56 %	39.24 %	25.87 %	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.
86	tiny-stereo-v2			29.50 %	40.58 %	26.01 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)

87	Disp R-CNN		code	29.12 %	42.72 %	25.09 %	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.
88	stereo-tkc			28.44 %	37.74 %	25.86 %	0.4 s	GPU @ 2.0 Ghz (Python + C/C++)

89	Disp R-CNN (velo)		code	28.34 %	40.21 %	24.46 %	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.
90	tiny-stereo			28.22 %	38.17 %	24.73 %	0.4 s	1 core @ 2.5 Ghz (Python + C/C++)

91	NCL		code	27.06 %	31.78 %	25.63 %	NA s	1 core @ 2.5 Ghz (Python)

92	OSE+			26.02 %	36.60 %	22.89 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

93	VN3D			25.44 %	33.01 %	23.64 %	0.02 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
94	OSE			23.62 %	33.00 %	20.35 %	0.1 s	GPU @ 2.5 Ghz (C/C++)

95	BirdNet			23.06 %	28.20 %	21.65 %	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.
96	OC Stereo		code	20.80 %	29.79 %	18.62 %	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.
97	Stereo3D			20.76 %	31.01 %	18.41 %	0.1 s	GPU 1080Ti

98	DSGN		code	20.75 %	26.61 %	18.86 %	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.
99	Complexer-YOLO			18.26 %	21.42 %	17.06 %	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.
100	AEC3D			18.05 %	23.60 %	15.35 %	0.01 s	GPU @ 2.5 Ghz (Python)

101	SOD			15.49 %	23.56 %	13.38 %	0.1 s	1 core @ 2.5 Ghz (Python)

102	TopNet-Retina			14.57 %	18.04 %	12.48 %	52ms	GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
103	RT3D-GMP			14.22 %	19.92 %	12.83 %	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.
104	TopNet-HighRes			13.50 %	19.43 %	11.93 %	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	CaDDN			9.41 %	14.72 %	8.17 %	0.63 s	GPU @ 2.5 Ghz (Python)

106	RefinedMPL			7.92 %	13.09 %	7.25 %	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.
107	MonoRUn			7.59 %	11.70 %	6.34 %	0.07 s	GPU @ 2.5 Ghz (Python + C/C++)

108	MonoFlex			7.36 %	10.36 %	6.29 %	0.03 s	GPU @ 2.5 Ghz (Python)

109	MonoPair			7.04 %	10.99 %	6.29 %	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.
110	DLE			6.96 %	10.73 %	6.20 %	0.04 s	GPU @ 2.5 Ghz (Python)

111	TopNet-DecayRate			6.59 %	8.78 %	6.25 %	92 ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
112	RelationNet3D_res18			6.29 %	9.28 %	5.29 %	0.04 s	GPU @ 2.5 Ghz (Python)

113	DAMono3D			6.22 %	8.96 %	5.17 %	0.09s	1 core @ 2.5 Ghz (C/C++)

114	Deprecated			6.12 %	8.70 %	5.16 %	Deprecated	Deprecated

115	GA-Aug			5.86 %	8.83 %	4.77 %	0.04 s	GPU @ 2.5 Ghz (Python)

116	MonoGeo			5.73 %	8.14 %	4.74 %	0.05 s	1 core @ 2.5 Ghz (Python)

117	Shift R-CNN (mono)		code	5.66 %	8.58 %	4.49 %	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.
118	PG-MonoNet			5.43 %	7.06 %	4.55 %	0.19 s	GPU @ 2.5 Ghz (Python)

119	PLDet3d			4.91 %	7.18 %	3.93 %	0.11 s	1 core @ 2.5 Ghz (C/C++)

120	NL_M3D			4.66 %	6.20 %	3.99 %	0.2 s	1 core @ 2.5 Ghz (Python)

121	TopNet-UncEst			4.60 %	6.88 %	3.79 %	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.
122	MonoPSR		code	4.56 %	7.24 %	4.11 %	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.
123	CDI3D			4.55 %	6.63 %	3.88 %	0.03 s	GPU @ 2.5 Ghz (Python)

124	M3D-RPN(S-R)			4.46 %	6.53 %	4.10 %	0.16 s	GPU @ 1.5 Ghz (Python)

125	FADNet		code	4.45 %	6.46 %	3.70 %	0.04 s	GPU @ >3.5 Ghz (Python)

126	modat3D			4.23 %	6.62 %	3.39 %	0.03 s	GPU @ 2.5 Ghz (Python)

127	MP-Mono			4.22 %	5.87 %	3.42 %	0.16 s	GPU @ 2.5 Ghz (Python)

128	M3D-RPN		code	4.05 %	5.65 %	3.29 %	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 .
129	DDMP-3D			4.02 %	5.53 %	3.36 %	0.18 s	1 core @ 2.5 Ghz (Python)

130	DP3D			3.86 %	5.25 %	3.10 %	0.07 s	GPU @ 1.5 Ghz (Python + C/C++)

131	D4LCN		code	3.86 %	5.06 %	3.59 %	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.
132	Center3D			3.71 %	5.67 %	3.52 %	0.05 s	GPU @ 3.5 Ghz (Python)

133	RT3DStereo			3.65 %	4.72 %	3.00 %	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.
134	LAPNet			3.59 %	4.86 %	2.98 %	0.03 s	1 core @ 2.5 Ghz (Python)

135	MonoEF		code	3.05 %	4.61 %	2.85 %	0.03 s	1 core @ 2.5 Ghz (Python)

136	MTMono3d			2.38 %	3.11 %	1.89 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

137	SS3D			2.09 %	2.48 %	1.61 %	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.
138	SparVox3D			2.05 %	2.90 %	1.69 %	0.05 s	GPU @ 2.0 Ghz (Python)

139	UDI-mono3D			1.85 %	2.94 %	1.60 %	0.05 s	1 core @ 2.5 Ghz (Python)

140	UM3D_TUM			1.79 %	3.60 %	1.79 %	0.05 s	1 core @ 2.5 Ghz (Python)

141	UDI-mono3D			1.42 %	2.09 %	1.07 %	0.05 s	1 core @ 2.5 Ghz (Python)

142	PVNet			0.01 %	0.00 %	0.01 %	0,1 s	1 core @ 2.5 Ghz (Python)

143	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.
144	TBD			0.00 %	0.00 %	0.00 %	0.3 s	1 core @ 2.5 Ghz (C/C++)

Table as LaTeX | Only published Methods

Cyclist

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	HRI-MSP-L			75.24 %	89.91 %	67.01 %	0.07 s	1 core @ 2.5 Ghz (C/C++)

2	HIKVISION-AFree			74.08 %	87.14 %	66.16 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

3	DSA-PV-RCNN			72.61 %	83.93 %	65.82 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)

4	anonymous		code	72.55 %	85.63 %	65.33 %	0.05s	1 core @ >3.5 Ghz (python)

5	SAA-PV-RCNN			72.24 %	84.12 %	64.70 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

6	Fast VP-RCNN		code	72.07 %	84.39 %	65.02 %	0.05 s	1 core @ 3.5 Ghz (C/C++)

7	GNN-RCNN			71.90 %	85.09 %	65.27 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

8	E^2-PV-RCNN			71.89 %	84.41 %	65.15 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)

9	PV-RCNN-v2			71.86 %	84.60 %	63.84 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)

10	HIKVISION-ADLab-HZ			71.75 %	85.66 %	65.02 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

11	PointPainting			71.54 %	83.91 %	62.97 %	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.
12	RangeIoUDet			71.49 %	85.99 %	63.62 %	0.02 s	1 core @ 2.5 Ghz (Python)

13	HVNet			71.17 %	83.97 %	63.65 %	0.03 s	GPU @ 2.0 Ghz (Python)
M. Ye, S. Xu and T. Cao: HVNet: Hybrid Voxel Network for LiDAR Based 3D Object Detection. CVPR 2020.
14	FPC-RCNN			70.93 %	83.75 %	63.47 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

15	CVRS VIC-RCNN			70.05 %	85.46 %	63.44 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

16	FSA-PVRCNN			69.67 %	81.86 %	63.32 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

17	XView-PartA^2			69.43 %	83.48 %	63.18 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

18	TBD			69.41 %	82.71 %	61.77 %	0.07 s	1 core @ 2.5 Ghz (Python + C/C++)

19	CBi-GNN-persons			69.23 %	82.37 %	61.73 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

20	TBD			69.08 %	83.68 %	62.28 %	0.05 s	GPU @ 2.5 Ghz (Python)

21	TBD			69.08 %	83.68 %	62.28 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

22	MMLab PV-RCNN		code	68.89 %	82.49 %	62.41 %	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.
23	F-ConvNet		code	68.88 %	84.16 %	60.05 %	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.
24	MMLab-PartA^2		code	68.73 %	83.43 %	61.85 %	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.
25	MSL3D			68.57 %	81.23 %	62.01 %	0.03 s	GPU @ 2.5 Ghz (Python)

26	HotSpotNet			68.51 %	83.29 %	61.84 %	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.
27	NLK-ALL		code	68.30 %	83.07 %	60.31 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)

28	CVIS-DF3D_v2			68.21 %	80.74 %	60.44 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

29	CVRS VIC-Net			67.98 %	81.50 %	60.82 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

30	H^23D R-CNN			67.90 %	82.76 %	60.49 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

31	RoIFusion		code	67.71 %	83.13 %	61.70 %	0.22 s	1 core @ 3.0 Ghz (Python)

32	3DSSD		code	67.62 %	85.04 %	61.14 %	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.
33	MGACNet			67.40 %	82.29 %	60.71 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

34	TBD_IOU2			67.36 %	82.68 %	59.15 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

35	Point-GNN		code	67.28 %	81.17 %	59.67 %	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.
36	PP-3D			67.28 %	81.17 %	59.67 %	0.1 s	1 core @ 2.5 Ghz (Python)

37	MMLab-PointRCNN		code	67.24 %	82.56 %	60.28 %	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.
38	STD		code	67.23 %	81.36 %	59.35 %	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.
39	KNN-GCNN			67.22 %	83.35 %	59.51 %	0.4 s	1 core @ 2.5 Ghz (Python + C/C++)

40	tbd			67.20 %	81.16 %	59.56 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

41	FPCR-CNN			67.17 %	82.51 %	60.33 %	0.05 s	1 core @ 2.5 Ghz (Python)

42	MKFFNet			67.10 %	80.98 %	60.84 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

43	TBD_IOU			67.09 %	82.97 %	59.90 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

44	VGCN			67.04 %	81.50 %	59.45 %	0.09 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
45	TBD_IOU1			66.95 %	81.77 %	58.78 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

46	SVGA-Net			66.66 %	78.93 %	59.55 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)

47	deprecated			66.47 %	78.62 %	60.14 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

48	ARPNET			66.39 %	82.32 %	58.80 %	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.
49	MKFFNet			66.16 %	81.36 %	58.94 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

50	MKFFNet			66.09 %	78.95 %	59.58 %	0.01s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
51	AF			66.00 %	83.03 %	57.57 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

52	AB3DMOT		code	65.85 %	80.00 %	58.69 %	0.0047s	1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
53	MVX-Net++			64.84 %	78.89 %	58.15 %	0.15 s	1 core @ 2.5 Ghz (C/C++)

54	FPC3D_all			64.66 %	78.81 %	58.08 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

55	CCFNET			64.65 %	81.29 %	57.85 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
56	Faraway-Frustum		code	64.54 %	79.65 %	57.84 %	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.
57	Baseline of CA RCNN			64.53 %	79.62 %	57.91 %	0.1 s	GPU @ 2.5 Ghz (Python)

58	CVIS-DF3D			64.53 %	79.62 %	57.91 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

59	SRDL			64.52 %	79.64 %	57.90 %	0.15 s	GPU @ 2.5 Ghz (Python + C/C++)

60	AF_MCLS			64.34 %	82.45 %	57.39 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

61	3DBN_2			64.28 %	81.06 %	57.55 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
62	SIF			64.13 %	79.32 %	57.38 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

63	GAP-soft-filter			64.02 %	79.39 %	57.54 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

64	TANet		code	63.77 %	79.16 %	56.21 %	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	SIEV-Net			63.21 %	82.22 %	56.41 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

66	XView			63.06 %	81.32 %	56.65 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

67	NLK-3D			62.97 %	80.61 %	56.52 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)

68	PointPillars		code	62.73 %	79.90 %	55.58 %	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	PF-GAP			62.49 %	78.64 %	55.87 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

70	FCY			62.25 %	78.65 %	54.74 %	0.02 s	GPU @ 2.5 Ghz (Python)

71	IOU-SSD		code	61.78 %	74.11 %	55.70 %	0.045s	1 core @ 2.5 Ghz (C/C++)

72	F-PointNet		code	61.37 %	77.26 %	53.78 %	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	epBRM		code	59.79 %	75.13 %	53.36 %	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.
74	Pointpillar_TV			59.26 %	74.78 %	52.33 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

75	Simple3D Net			59.03 %	75.72 %	52.42 %	0.02 s	GPU @ 2.5 Ghz (Python)

76	IGRP+			57.94 %	76.25 %	51.86 %	0.18 s	1 core @ 2.5 Ghz (Python + C/C++)

77	AVOD-FPN		code	57.12 %	69.39 %	51.09 %	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	SCNet			56.39 %	73.73 %	49.99 %	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.
79	PFF3D			55.71 %	72.67 %	49.58 %	0.05 s	GPU @ 3.0 Ghz (Python + C/C++)

80	MLOD		code	55.06 %	73.03 %	48.21 %	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.
81	BirdNet+		code	52.15 %	72.45 %	46.57 %	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.
82	DAMNET		code	49.71 %	67.52 %	45.21 %	1 s	1 core @ 2.5 Ghz (C/C++)

83	AVOD		code	48.15 %	64.11 %	42.37 %	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.
84	BirdNet			41.56 %	58.64 %	36.94 %	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.
85	SparsePool		code	40.74 %	56.52 %	36.68 %	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	stereo-tkc			38.55 %	56.90 %	33.70 %	0.4 s	GPU @ 2.0 Ghz (Python + C/C++)

87	TopNet-Retina			36.83 %	47.48 %	33.58 %	52ms	GeForce 1080Ti (tensorflow-gpu, v1.12)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
88	tiny-stereo-v2			36.64 %	53.72 %	32.22 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)

89	CG-Stereo			36.25 %	55.33 %	32.17 %	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.
90	SparsePool		code	35.24 %	43.55 %	30.15 %	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.
91	tiny-stereo			33.48 %	52.03 %	29.11 %	0.4 s	1 core @ 2.5 Ghz (Python + C/C++)

92	SOD			28.81 %	44.90 %	24.37 %	0.1 s	1 core @ 2.5 Ghz (Python)

93	Disp R-CNN (velo)		code	27.04 %	44.19 %	23.58 %	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.
94	Disp R-CNN		code	27.04 %	44.19 %	23.58 %	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.
95	Complexer-YOLO			25.43 %	32.00 %	22.88 %	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.
96	OSE+			23.55 %	38.05 %	20.46 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

97	DSGN		code	21.04 %	31.23 %	18.93 %	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	VN3D			20.21 %	28.35 %	19.31 %	0.02 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
99	OSE			19.41 %	32.06 %	17.42 %	0.1 s	GPU @ 2.5 Ghz (C/C++)

100	OC Stereo		code	19.23 %	32.47 %	17.11 %	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.
101	TopNet-DecayRate			16.00 %	23.02 %	13.24 %	92 ms	NVIDIA GeForce 1080 Ti (tensorflow-gpu)
S. Wirges, T. Fischer, C. Stiller and J. Frias: Object Detection and Classification in Occupancy Grid Maps Using Deep Convolutional Networks. 2018 21st International Conference on Intelligent Transportation Systems (ITSC) 2018.
102	RT3D-GMP			13.92 %	20.59 %	12.74 %	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.
103	AEC3D			13.24 %	20.80 %	12.09 %	0.01 s	GPU @ 2.5 Ghz (Python)

104	TopNet-UncEst			9.18 %	12.31 %	8.14 %	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.
105	TopNet-HighRes			6.48 %	9.99 %	6.76 %	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.
106	MonoPSR		code	5.78 %	9.87 %	4.57 %	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.
107	CaDDN			5.38 %	9.67 %	4.75 %	0.63 s	GPU @ 2.5 Ghz (Python)

108	DAMono3D			4.18 %	7.05 %	4.31 %	0.09s	1 core @ 2.5 Ghz (C/C++)

109	RT3DStereo			4.10 %	7.03 %	3.88 %	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.
110	CDI3D			3.78 %	6.01 %	3.24 %	0.03 s	GPU @ 2.5 Ghz (Python)

111	MonoGeo			3.33 %	5.42 %	2.87 %	0.05 s	1 core @ 2.5 Ghz (Python)

112	RelationNet3D_res18			3.32 %	6.59 %	3.13 %	0.04 s	GPU @ 2.5 Ghz (Python)

113	DLE			3.28 %	5.34 %	2.83 %	0.04 s	GPU @ 2.5 Ghz (Python)

114	DDMP-3D			3.14 %	4.92 %	2.44 %	0.18 s	1 core @ 2.5 Ghz (Python)

115	modat3D			3.02 %	5.71 %	2.73 %	0.03 s	GPU @ 2.5 Ghz (Python)

116	MonoPair			2.87 %	4.76 %	2.42 %	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.
117	Deprecated			2.80 %	3.96 %	2.32 %	Deprecated	Deprecated

118	Center3D			2.76 %	5.28 %	2.72 %	0.05 s	GPU @ 3.5 Ghz (Python)

119	MonoFlex			2.67 %	4.41 %	2.50 %	0.03 s	GPU @ 2.5 Ghz (Python)

120	RefinedMPL			2.42 %	4.23 %	2.14 %	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.
121	UDI-mono3D			2.16 %	3.81 %	1.65 %	0.05 s	1 core @ 2.5 Ghz (Python)

122	UDI-mono3D			2.01 %	3.59 %	1.79 %	0.05 s	1 core @ 2.5 Ghz (Python)

123	NL_M3D			2.01 %	2.70 %	1.75 %	0.2 s	1 core @ 2.5 Ghz (Python)

124	PG-MonoNet			1.89 %	3.00 %	1.66 %	0.19 s	GPU @ 2.5 Ghz (Python)

125	SS3D			1.89 %	3.45 %	1.44 %	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.
126	DP3D			1.87 %	3.09 %	1.96 %	0.07 s	GPU @ 1.5 Ghz (Python + C/C++)

127	D4LCN		code	1.82 %	2.72 %	1.79 %	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.
128	GA-Aug			1.73 %	2.71 %	1.57 %	0.04 s	GPU @ 2.5 Ghz (Python)

129	MP-Mono			1.58 %	2.43 %	1.70 %	0.16 s	GPU @ 2.5 Ghz (Python)

130	PLDet3d			1.45 %	2.21 %	1.58 %	0.11 s	1 core @ 2.5 Ghz (C/C++)

131	MTMono3d			1.30 %	2.06 %	1.06 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

132	MonoEF		code	1.18 %	2.36 %	1.15 %	0.03 s	1 core @ 2.5 Ghz (Python)

133	LAPNet			1.03 %	1.71 %	1.04 %	0.03 s	1 core @ 2.5 Ghz (Python)

134	FADNet		code	0.94 %	1.54 %	0.79 %	0.04 s	GPU @ >3.5 Ghz (Python)

135	M3D-RPN		code	0.81 %	1.25 %	0.78 %	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 .
136	MonoRUn			0.73 %	1.14 %	0.66 %	0.07 s	GPU @ 2.5 Ghz (Python + C/C++)

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

138	Shift R-CNN (mono)		code	0.38 %	0.76 %	0.41 %	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.
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

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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

Bird's Eye View Evaluation 2017

Car

Pedestrian

Cyclist

Related Datasets

Citation

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