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	Fast VP-RCNN		code	90.46 %	92.83 %	85.94 %	0.05 s	1 core @ >3.5 Ghz (python)

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

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

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

12	EBM3DOD			89.86 %	95.64 %	84.56 %	0.08 s	1 core @ 2.5 Ghz (Python)

13	CIA-SSD			89.84 %	93.74 %	82.39 %	0.03 s	1 core @ 2.5 Ghz (Python + C/C++)

14	HRI-ADLab-HZ			89.83 %	93.21 %	84.88 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

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

16	CLOCs_PVCas			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.
17	CIA-SSD v2			89.80 %	93.49 %	84.39 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

18	deprecated			89.77 %	93.68 %	82.31 %	deprecated	deprecated

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

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

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

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

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

24	EBM3DOD baseline			89.63 %	95.44 %	84.34 %	0.08 s	1 core @ 2.5 Ghz (Python)

25	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.
26	scssd-normal(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: SCSSD for multi sensor fusion 3d object detection method. Submitted to Information Science 2020.
27	Cas-SSD			89.47 %	93.31 %	84.35 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

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

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

30	LZnet			89.39 %	93.36 %	81.93 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)

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

32	scssd-normal(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: SCSSD for multi sensor fusion 3d object detection method. Submitted to Information Science 2020.
33	ISF			89.28 %	93.17 %	84.38 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

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

35	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.
36	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.
37	PP-3D			89.17 %	93.11 %	83.90 %	0.1 s	1 core @ 2.5 Ghz (Python)

38	Noah CV Lab - SSL			89.16 %	90.18 %	81.73 %	0.1 s	GPU @ 2.5 Ghz (Python)

39	RoIFusion		code	89.06 %	92.90 %	83.96 %	0.22 s	1 core @ 3.0 Ghz (Python)

40	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.
41	Discrete-PointDet			88.95 %	94.56 %	83.56 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

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

43	Voxel R-CNN			88.83 %	94.85 %	86.13 %	0.04 s	GPU @ 3.0 Ghz (C/C++)

44	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.
45	PointCSE			88.81 %	92.58 %	83.64 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

46	RangeRCNN-LV			88.81 %	92.41 %	85.96 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

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

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

49	cvMax			88.64 %	92.12 %	83.72 %	0.04 s	GPU @ >3.5 Ghz (Python)

50	deprecated			88.59 %	92.18 %	83.60 %	0.04 s	GPU @ 2.5 Ghz (Python)

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

52	VICNet			88.58 %	92.27 %	83.36 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

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

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

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

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

57	MuRF			88.56 %	91.57 %	83.46 %	0.05 s	GPU @ 1.5 Ghz (Python + C/C++)

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

59	Chovy			88.54 %	92.34 %	83.68 %	0.04 s	GPU @ 2.5 Ghz (Python)

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

61	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.
62	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.
63	deprecated			88.44 %	92.14 %	85.11 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

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

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

66	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.
67	MSG-PGNN			88.40 %	91.97 %	85.87 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

68	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.
69	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.
70	PF-GAP			88.35 %	92.16 %	85.64 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

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

72	HyBrid Feature Det			88.27 %	92.09 %	85.69 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)

73	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.
74	MSL3D			88.23 %	91.64 %	85.53 %	0.03 s	GPU @ 2.5 Ghz (Python)

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

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

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

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

79	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.
80	CVRS VIC-RCNN			88.20 %	92.35 %	85.64 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

81	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.
82	SRDL			88.17 %	92.01 %	85.43 %	0.15 s	GPU @ 2.5 Ghz (Python + C/C++)

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

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

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

86	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.
87	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.
88	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.
89	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.
90	CVIS-DF3D_v2			88.06 %	91.85 %	85.37 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

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

92	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.
93	CCFNET			87.97 %	94.25 %	83.27 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

94	HVPR			87.94 %	91.76 %	83.03 %	0.02 s	GPU @ 2.5 Ghz (Python)

95	LZY_RCNN			87.94 %	91.74 %	83.64 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

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

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

98	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.
99	TBD			87.83 %	91.80 %	85.19 %	0.07 s	1 core @ 2.5 Ghz (Python + C/C++)

100	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.
101	HRI-MSP-L			87.78 %	91.74 %	85.14 %	0.07 s	1 core @ 2.5 Ghz (C/C++)

102	MGACNet			87.68 %	90.93 %	84.60 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

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

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

105	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.
106	VOXEL_3D			87.55 %	90.83 %	82.24 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

107	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.
108	V3D			87.53 %	90.83 %	82.30 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

109	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.
110	AF_V1			87.47 %	92.70 %	82.19 %	0.1 s	1 core @ 2.5 Ghz (Python)

111	PKRCNN			87.41 %	91.62 %	84.67 %	0.01s	1 core @ 2.5 Ghz (Python)
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112	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.
113	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.
114	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.
115	VAR			87.31 %	90.68 %	82.67 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

116	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.
117	MDA			87.13 %	90.67 %	82.80 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

118	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.
119	Pointpillar_TV			87.08 %	90.50 %	81.98 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

120	EPENet			87.00 %	90.98 %	82.99 %	0.04 s	1 core @ 2.5 Ghz (C/C++)

121	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.
122	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.
123	PointPiallars_SECA			86.79 %	90.15 %	82.87 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

124	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.
125	FLID			86.77 %	91.58 %	81.14 %	0.04 s	GPU @ 2.5 Ghz (Python)

126	CentrNet-FG			86.72 %	90.30 %	82.99 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

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

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

129	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.
130	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.
131	MVX-Net++			86.53 %	91.86 %	81.41 %	0.15 s	1 core @ 2.5 Ghz (C/C++)

132	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.
133	Simple3D Net			86.46 %	89.82 %	82.60 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

134	autonet			86.42 %	89.81 %	81.25 %	0.12 s	1 core @ 2.5 Ghz (C/C++)

135	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.
136	IGRP+			86.29 %	92.20 %	81.48 %	0.18 s	1 core @ 2.5 Ghz (Python + C/C++)

137	Bit			86.27 %	89.74 %	81.19 %	0.11 s	1 core @ 2.5 Ghz (C/C++)

138	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.
139	IGRP			86.21 %	92.04 %	81.30 %	0.18 s	1 core @ 2.5 Ghz (Python + C/C++)

140	DPointNet			86.12 %	88.55 %	79.82 %	0.09 s	1 core @ 2.5 Ghz (C/C++)

141	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.
142	RethinkDet3D			86.05 %	91.32 %	81.13 %	0.15 s	1 core @ 2.5 Ghz (Python)

143	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.
144	TBD			86.00 %	89.79 %	83.37 %	0.05 s	GPU @ 2.5 Ghz (Python)

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

146	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.
147	PPBA			85.85 %	91.30 %	80.92 %	NA s	GPU @ 2.5 Ghz (Python)

148	TBU			85.85 %	91.30 %	80.92 %	NA s	GPU @ 2.5 Ghz (Python)

149	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.
150	RUC		code	85.84 %	88.54 %	81.15 %	0.12 s	1 core @ 2.5 Ghz (C/C++)

151	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.
152	APL-Second			85.70 %	90.78 %	78.69 %	0.05 s	1 core @ 2.5 Ghz (Python)

153	PBASN		code	85.62 %	90.95 %	80.49 %	NA s	GPU @ 2.5 Ghz (Python)

154	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.
155	3DBN_2			85.30 %	91.37 %	82.57 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
156	PFF3D			85.08 %	89.61 %	80.42 %	0.05 s	GPU @ 3.0 Ghz (Python + C/C++)

157	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.
158	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.
159	baseline			84.88 %	89.25 %	80.18 %	0.12 s	1 core @ 2.5 Ghz (C/C++)

160	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.
161	Prune			84.81 %	90.48 %	77.40 %	0.11 s	1 core @ 2.5 Ghz (C/C++)

162	autoRUC			84.80 %	90.44 %	77.43 %	0.12 s	1 core @ 2.5 Ghz (C/C++)

163	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.
164	RUC		code	84.40 %	89.11 %	79.33 %	0.12 s	1 core @ 2.5 Ghz (C/C++)

165	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.
166	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.
167	DAMNET		code	82.14 %	87.90 %	75.52 %	1 s	1 core @ 2.5 Ghz (C/C++)

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

169	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.
170	NLK			79.15 %	82.59 %	72.65 %	0.02 s	1 core @ 2.5 Ghz (Python)

171	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.
172	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.
173	seivl			77.43 %	85.43 %	75.51 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

174	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.
175	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.
176	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.
177	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.
178	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.
179	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.
180	tiny-stereo-v1			66.68 %	85.24 %	57.62 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)

181	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.
182	tiny-stereo-v2			66.41 %	85.91 %	57.27 %	0.4 s	1 core @ 2.5 Ghz (Python + C/C++)

183	PLUME			66.27 %	82.97 %	56.70 %	0.15 s	GPU @ 2.5 Ghz (Python)

184	CDN			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.
185	SF			65.74 %	74.20 %	58.35 %	0.5 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
186	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.
187	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.
188	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.
189	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.
190	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. arXiv preprint arXiv:2007.03085 2020.
191	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.
192	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.
193	OSE			58.04 %	79.75 %	49.78 %	0.1 s	GPU @ 2.5 Ghz (C/C++)

194	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.
195	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.
196	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.
197	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.
198	Disp R-CNN		code	52.37 %	73.87 %	43.67 %	0.42 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
199	Disp R-CNN (velo)		code	52.37 %	74.12 %	43.79 %	0.42 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
200	RTS3D			51.79 %	72.17 %	43.19 %	0.03 s	GPU @ 2.5 Ghz (Python)

201	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.
202	Stereo3D			50.28 %	76.10 %	36.86 %	0.1 s	GPU 1080Ti

203	RT3D-GMP			49.57 %	61.28 %	38.70 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)

204	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.
205	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.
206	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.
207	IDA-3D			42.47 %	61.87 %	34.59 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)

208	SC(DLA34+DCO)			42.12 %	62.97 %	35.37 %	0.07 s	GPU @ 2.5 Ghz (Python)

209	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.
210	ASOD			33.63 %	54.61 %	26.76 %	0.28 s	GPU @ 2.5 Ghz (Python)

211	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.
212	deprecated			30.56 %	34.56 %	25.69 %		1 core @ 2.5 Ghz (C/C++)

213	S3D			30.44 %	35.25 %	25.68 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

214	LNET			29.68 %	34.30 %	25.11 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

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

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

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

218	ITS-MDPL			19.52 %	32.80 %	16.96 %	0.16 s	GPU @ 2.5 Ghz (Python)

219	PSMD			19.33 %	28.63 %	15.31 %	0.1 s	GPU @ 2.5 Ghz (Python)

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

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

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

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

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

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

226	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.
227	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 .
228	MonoRUn			17.34 %	27.94 %	15.24 %	0.07 s	GPU @ 2.5 Ghz (Python + C/C++)

229	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.
230	Deprecated			17.22 %	23.59 %	13.34 %	Deprecated	Deprecated

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

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

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

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

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

236	DP3D			16.96 %	26.51 %	12.82 %	0.07 s	GPU @ 1.5 Ghz (Python + C/C++)

237	PatchNet		code	16.86 %	22.97 %	14.97 %	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.
238	UM3D_TUM			16.69 %	23.63 %	14.17 %	0.05 s	1 core @ 2.5 Ghz (Python)

239	GA-Aug			16.45 %	24.64 %	14.15 %	0.04 s	GPU @ 2.5 Ghz (Python)

240	PG-MonoNet			16.31 %	23.31 %	13.03 %	0.19 s	GPU @ 2.5 Ghz (Python)

241	SSL-RTM3D			16.20 %	23.44 %	14.47 %	0.03 s	1 core @ 2.5 Ghz (Python)

242	CDI3D			16.06 %	22.06 %	13.43 %	0.03 s	GPU @ 2.5 Ghz (Python)

243	D4LCN		code	16.02 %	22.51 %	12.55 %	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.
244	MP-Mono			16.01 %	23.45 %	12.07 %	0.16 s	GPU @ 2.5 Ghz (Python)

245	NL_M3D			15.93 %	24.15 %	12.11 %	0.2 s	1 core @ 2.5 Ghz (Python)

246	DA-3Ddet			15.90 %	23.35 %	12.11 %	0.4 s	GPU @ 2.5 Ghz (Python)

247	LAPNet			15.76 %	25.10 %	12.30 %	0.03 s	1 core @ 2.5 Ghz (Python)

248	DP3D			15.44 %	23.98 %	12.24 %	0.05 s	GPU @ 1.5 Ghz (Python + C/C++)

249	MA			15.43 %	22.01 %	14.01 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

250	MonoPair			14.83 %	19.28 %	12.89 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)

Table as LaTeX | Only published Methods

Pedestrian

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	VICNet			52.15 %	60.78 %	48.54 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

2	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.
3	CentrNet-FG			50.87 %	60.56 %	48.16 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

4	HRI-ADLab-HZ-AFree			50.67 %	56.54 %	48.60 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

5	Noah CV Lab - SSL			50.66 %	57.27 %	46.55 %	0.1 s	GPU @ 2.5 Ghz (Python)

6	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.
7	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.
8	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.
9	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.
10	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.
11	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.
12	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.
13	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.
14	HRI-ADLab-HZ			49.62 %	55.94 %	47.39 %	0.1 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	PPBA			49.34 %	57.23 %	46.86 %	NA s	GPU @ 2.5 Ghz (Python)

17	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.
18	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.
19	RethinkDet3D			48.84 %	58.96 %	46.20 %	0.15 s	1 core @ 2.5 Ghz (Python)

20	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.
21	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.
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	TBU			46.76 %	55.15 %	44.60 %	NA s	GPU @ 2.5 Ghz (Python)

28	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.
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	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.
32	DSA-PV-RCNN			45.82 %	52.03 %	43.81 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)

33	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.
34	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.
35	Fast VP-RCNN		code	45.07 %	51.16 %	42.92 %	0.05 s	1 core @ >3.5 Ghz (python)

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

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

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

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

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

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

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

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

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

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

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

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

48	3DBN_2			42.97 %	50.99 %	40.49 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
49	MSL3D			42.82 %	48.81 %	40.13 %	0.03 s	GPU @ 2.5 Ghz (Python)

50	IGRP+			41.86 %	50.15 %	38.98 %	0.18 s	1 core @ 2.5 Ghz (Python + C/C++)

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

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

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

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

56	PBASN		code	40.63 %	46.80 %	38.41 %	NA s	GPU @ 2.5 Ghz (Python)

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

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

59	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.
60	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.
61	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.
62	NLK-ALL		code	37.61 %	47.88 %	33.86 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)

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

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

65	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.
66	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.
67	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.
68	FCY			32.64 %	41.16 %	29.35 %	0.02 s	GPU @ 2.5 Ghz (Python)

69	SF			29.77 %	37.16 %	26.61 %	0.5 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
70	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.
71	Disp R-CNN		code	25.36 %	36.06 %	21.62 %	0.42 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
72	Disp R-CNN (velo)		code	24.95 %	35.39 %	21.30 %	0.42 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
73	OSE			23.62 %	33.00 %	20.35 %	0.1 s	GPU @ 2.5 Ghz (C/C++)

74	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.
75	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.
76	Stereo3D			20.76 %	31.01 %	18.41 %	0.1 s	GPU 1080Ti

77	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.
78	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.
79	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.
80	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.
81	CaDDN			9.41 %	14.72 %	8.17 %	0.63 s	GPU @ 2.5 Ghz (Python)

82	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.
83	MonoRUn			7.59 %	11.70 %	6.34 %	0.07 s	GPU @ 2.5 Ghz (Python + C/C++)

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

85	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.
86	DLE			6.96 %	10.73 %	6.20 %	0.04 s	GPU @ 2.5 Ghz (Python)

87	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.
88	DAMono3D			6.22 %	8.96 %	5.17 %	0.09s	1 core @ 2.5 Ghz (C/C++)

89	Deprecated			6.12 %	8.70 %	5.16 %	Deprecated	Deprecated

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

91	RT3D-GMP			5.73 %	7.93 %	5.62 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)

92	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.
93	PG-MonoNet			5.43 %	7.06 %	4.55 %	0.19 s	GPU @ 2.5 Ghz (Python)

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

95	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.
96	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.
97	CDI3D			4.55 %	6.63 %	3.88 %	0.03 s	GPU @ 2.5 Ghz (Python)

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

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

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

101	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 .
102	Mono3CN			4.02 %	6.03 %	3.40 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

103	DDMP-3D			4.02 %	5.53 %	3.36 %	0.18 s	1 core @ 2.5 Ghz (Python)

104	DP3D			4.01 %	5.71 %	3.64 %	0.05 s	GPU @ 1.5 Ghz (Python + C/C++)

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

106	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.
107	Center3D			3.71 %	5.67 %	3.52 %	0.05 s	GPU @ 3.5 Ghz (Python)

108	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.
109	LAPNet			3.59 %	4.86 %	2.98 %	0.03 s	1 core @ 2.5 Ghz (Python)

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

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

112	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.
113	UDI-mono3D			1.85 %	2.94 %	1.60 %	0.05 s	1 core @ 2.5 Ghz (Python)

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

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

116	SparVox3D			0.44 %	0.55 %	0.30 %	0.05 s	GPU @ 2.0 Ghz (Python)

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

118	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			75.24 %	89.91 %	67.01 %	0.07 s	1 core @ 2.5 Ghz (C/C++)

2	Noah CV Lab - SSL			74.45 %	85.96 %	64.23 %	0.1 s	GPU @ 2.5 Ghz (Python)

3	HRI-ADLab-HZ-AFree			74.08 %	87.14 %	66.16 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

4	Fast VP-RCNN		code	73.69 %	87.08 %	66.03 %	0.05 s	1 core @ >3.5 Ghz (python)

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

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

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

8	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.
9	RangeIoUDet			71.49 %	85.99 %	63.62 %	0.02 s	1 core @ 2.5 Ghz (Python)

10	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.
11	CVRS VIC-RCNN			70.05 %	85.46 %	63.44 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

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

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

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

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

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

17	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.
18	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.
19	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.
20	MSL3D			68.57 %	81.23 %	62.01 %	0.03 s	GPU @ 2.5 Ghz (Python)

21	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.
22	NLK-ALL		code	68.30 %	83.07 %	60.31 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)

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

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

25	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.
26	MGACNet			67.40 %	82.29 %	60.71 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

27	PPBA			67.28 %	82.69 %	60.53 %	NA s	GPU @ 2.5 Ghz (Python)

28	TBU			67.28 %	82.69 %	60.53 %	NA s	GPU @ 2.5 Ghz (Python)

29	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.
30	PP-3D			67.28 %	81.17 %	59.67 %	0.1 s	1 core @ 2.5 Ghz (Python)

31	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.
32	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.
33	KNN-GCNN			67.22 %	83.35 %	59.51 %	0.4 s	1 core @ 2.5 Ghz (Python + C/C++)

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

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

36	RethinkDet3D			66.42 %	82.73 %	59.60 %	0.15 s	1 core @ 2.5 Ghz (Python)

37	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.
38	PKRCNN			66.09 %	78.95 %	59.58 %	0.01s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
39	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.
40	MVX-Net++			64.84 %	78.89 %	58.15 %	0.15 s	1 core @ 2.5 Ghz (C/C++)

41	CCFNET			64.65 %	81.29 %	57.85 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
42	Baseline of CA RCNN			64.53 %	79.62 %	57.91 %	0.1 s	GPU @ 2.5 Ghz (Python)

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

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

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

46	3DBN_2			64.28 %	81.06 %	57.55 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
47	GAP-soft-filter			64.02 %	79.39 %	57.54 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

48	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.
49	PBASN		code	63.34 %	79.45 %	57.01 %	NA s	GPU @ 2.5 Ghz (Python)

50	VICNet			63.21 %	82.22 %	56.41 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)

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

52	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.
53	PF-GAP			62.49 %	78.64 %	55.87 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

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

55	CentrNet-FG			62.10 %	76.94 %	54.94 %	0.03 s	1 core @ 2.5 Ghz (C/C++)

56	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.
57	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.
58	Pointpillar_TV			59.26 %	74.78 %	52.33 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

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

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

61	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.
62	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.
63	PFF3D			55.71 %	72.67 %	49.58 %	0.05 s	GPU @ 3.0 Ghz (Python + C/C++)

64	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.
65	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.
66	DAMNET		code	49.71 %	67.52 %	45.21 %	1 s	1 core @ 2.5 Ghz (C/C++)

67	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.
68	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.
69	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.
70	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.
71	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.
72	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.
73	Disp R-CNN (velo)		code	26.46 %	43.41 %	22.46 %	0.42 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
74	Disp R-CNN		code	26.46 %	43.41 %	22.46 %	0.42 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
75	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.
76	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.
77	OSE			19.41 %	32.06 %	17.42 %	0.1 s	GPU @ 2.5 Ghz (C/C++)

78	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.
79	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.
80	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.
81	RT3D-GMP			6.90 %	10.09 %	6.14 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)

82	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.
83	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.
84	CaDDN			5.38 %	9.67 %	4.75 %	0.63 s	GPU @ 2.5 Ghz (Python)

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

86	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.
87	CDI3D			3.78 %	6.01 %	3.24 %	0.03 s	GPU @ 2.5 Ghz (Python)

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

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

90	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.
91	Deprecated			2.80 %	3.96 %	2.32 %	Deprecated	Deprecated

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

93	Mono3CN			2.69 %	3.92 %	2.19 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

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

95	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.
96	UDI-mono3D			2.16 %	3.81 %	1.65 %	0.05 s	1 core @ 2.5 Ghz (Python)

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

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

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

100	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.
101	DP3D			1.87 %	3.09 %	1.96 %	0.07 s	GPU @ 1.5 Ghz (Python + C/C++)

102	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.
103	GA-Aug			1.73 %	2.71 %	1.57 %	0.04 s	GPU @ 2.5 Ghz (Python)

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

105	DP3D			1.57 %	2.32 %	1.29 %	0.05 s	GPU @ 1.5 Ghz (Python + C/C++)

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

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

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

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

110	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 .
111	MonoRUn			0.73 %	1.14 %	0.66 %	0.07 s	GPU @ 2.5 Ghz (Python + C/C++)

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

113	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.
114	PVNet			0.00 %	0.00 %	0.00 %	0,1 s	1 core @ 2.5 Ghz (Python)

115	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