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

2	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.
3	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.
4	CN			90.50 %	94.51 %	85.86 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)

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

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

7	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.
8	CIA-SSD			89.77 %	93.68 %	82.31 %	0.03 s	1 core @ 2.5 Ghz (Python + C/C++)

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

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

11	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.
12	scssd-normal(0.3)			89.54 %	95.26 %	82.31 %	0.05 s	GPU @ 2.5 Ghz (Python)

13	Cas-SSD			89.47 %	93.31 %	84.35 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

14	PC-RGNN			89.47 %	95.36 %	84.29 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)

15	scssd-normal(0.4)			89.38 %	94.91 %	84.29 %	0.05 s	1 core @ 2.5 Ghz (Python)

16	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.
17	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.
18	Noah CV Lab - SSL			89.16 %	90.18 %	81.73 %	0.1 s	GPU @ 2.5 Ghz (Python)

19	HUAWEI Octopus-LIS			89.13 %	92.25 %	86.37 %	0.1 s	1 core @ 2.5 Ghz (Python)

20	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.
21	CLOCs_PointCas			88.99 %	92.60 %	81.74 %	0.1 s	GPU @ 2.5 Ghz (Python)

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

23	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.
24	PointCSE			88.81 %	92.58 %	83.64 %	0.02 s	1 core @ 2.5 Ghz (C/C++)

25	ELE			88.80 %	94.52 %	85.69 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)

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

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

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

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

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

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

32	DENFIDet			88.56 %	92.42 %	83.76 %	0.02 s	GPU @ 2.5 Ghz (C/C++)

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

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

35	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.
36	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.
37	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.
38	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.
39	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.
40	NLK-3D			88.22 %	91.54 %	83.33 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)

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

42	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.
43	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.
44	FDet			88.13 %	91.91 %	85.40 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)

45	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.
46				88.11 %	93.73 %	84.98 %

47	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.
48	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.
49	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.
50	DEFT			88.06 %	92.06 %	83.22 %	1 s	GPU @ 2.5 Ghz (Python)

51	OneCoLab SicNet			88.06 %	92.17 %	83.60 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

52	OneCoLab SicNet V2			88.06 %	91.49 %	85.44 %	0.08 s	1 core @ 2.5 Ghz (C/C++)

53	deprecated			88.05 %	91.96 %	83.21 %	0.05 s	GPU @ >3.5 Ghz (Python)

54	deprecated			88.04 %	91.97 %	83.22 %	-	-

55	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.
56				87.95 %	93.59 %	83.21 %

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

58	SPA			87.90 %	91.70 %	83.18 %	0.1 s	1 core @ 2.5 Ghz (Python)

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

60	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.
61	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.
62	HRI-MSP-L			87.78 %	91.74 %	85.14 %	0.07 s	1 core @ 2.5 Ghz (C/C++)

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

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

65	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.
66	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.
67	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.
68	deprecated			87.46 %	92.54 %	77.39 %	0.05 s	1 core @ 2.5 Ghz (Python)

69	SAIC-SA-3D			87.45 %	92.34 %	83.72 %	0.05 s	GPU @ 2.5 Ghz (Python)

70	IE-PointRCNN			87.43 %	92.11 %	81.10 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

71	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.
72	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.
73	VAR			87.31 %	90.68 %	82.67 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

74	deprecated			87.28 %	90.44 %	75.09 %	0.05 s	GPU @ 2.0 Ghz (Python)

75	PiP			87.25 %	90.87 %	83.38 %	0.033 s	1 core @ 2.5 Ghz (Python)

76	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.
77	CentrNet-v1			87.19 %	90.72 %	83.34 %	0.03 s	GPU @ 2.5 Ghz (Python)

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

79	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.
80	Roadstar.ai			87.12 %	92.42 %	81.88 %	0.08 s	GPU @ 2.0 Ghz (Python)

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

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

83	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.
84	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.
85	PointPiallars_SECA			86.79 %	90.15 %	82.87 %	0.06 s	1 core @ 2.5 Ghz (C/C++)

86	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.
87	FLID			86.77 %	91.58 %	81.14 %	0.04 s	GPU @ 2.5 Ghz (Python)

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

89	Tencent_ADlab_Lidar			86.72 %	90.27 %	81.35 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)

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

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

92	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.
93	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.
94	MVX-Net++			86.53 %	91.86 %	81.41 %	0.15 s	1 core @ 2.5 Ghz (C/C++)

95	PointRCNN-deprecated			86.52 %	92.51 %	81.39 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)

96	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.
97	RUC			86.46 %	90.06 %	82.20 %	0.12 s	1 core @ 2.5 Ghz (C/C++)

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

99	DDB			86.45 %	89.91 %	82.21 %	0.05 s	GPU @ 2.5 Ghz (Python)

100	PPFNet		code	86.44 %	92.35 %	81.48 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

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

102	HR-SECOND		code	86.40 %	91.68 %	81.40 %	0.11 s	1 core @ 2.5 Ghz (Python + C/C++)

103	SECOND-V1.5		code	86.37 %	91.81 %	81.04 %	0.04 s	GPU @ 2.0 Ghz (Python + C/C++)

104	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.
105	VOXEL_FPN_HR			86.36 %	90.28 %	81.20 %	0.12 s	8 cores @ 2.5 Ghz (Python)
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106	CP			86.30 %	92.14 %	82.97 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

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

108	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.
109	IGRP			86.21 %	92.04 %	81.30 %	0.18 s	1 core @ 2.5 Ghz (Python + C/C++)

110	MP			86.16 %	90.24 %	78.86 %	0.2 s	1 core @ 2.5 Ghz (C/C++)

111	FCY			86.11 %	89.74 %	80.99 %	0.02 s	GPU @ 2.5 Ghz (Python)

112	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.
113	RethinkDet3D			86.05 %	91.32 %	81.13 %	0.15 s	1 core @ 2.5 Ghz (Python)

114	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.
115	TBD			86.00 %	89.79 %	83.37 %	0.05 s	GPU @ 2.5 Ghz (Python)

116	PPBA			85.85 %	91.30 %	80.92 %	NA s	GPU @ 2.5 Ghz (Python)

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

118	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.
119	RUC		code	85.84 %	88.54 %	81.15 %	0.12 s	1 core @ 2.5 Ghz (C/C++)

120	BVVF			85.83 %	91.20 %	80.76 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

121	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.
122	SAANet			85.69 %	91.72 %	78.77 %	0.10 s	1 core @ 2.5 Ghz (Python)

123	SFB-SECOND			85.63 %	91.38 %	78.60 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

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

125	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.
126	3DBN_2			85.30 %	91.37 %	82.57 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
127	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.
128	WS3D			84.93 %	90.96 %	77.96 %	0.1 s	GPU @ 2.5 Ghz (Python)

129	baseline			84.88 %	89.25 %	80.18 %	0.12 s	1 core @ 2.5 Ghz (C/C++)

130	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.
131	Prune			84.81 %	90.48 %	77.40 %	0.11 s	1 core @ 2.5 Ghz (C/C++)

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

133	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.
134	RUC		code	84.40 %	89.11 %	79.33 %	0.12 s	1 core @ 2.5 Ghz (C/C++)

135	MVSLN			84.26 %	90.30 %	78.94 %	0.1s s	1 core @ 2.5 Ghz (C/C++)

136	PP-3D			84.11 %	89.16 %	76.81 %	0.1 s	1 core @ 2.5 Ghz (Python)

137	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.
138	RADNet-Fusion			83.84 %	91.81 %	78.80 %	0.1 s	1 core @ 2.5 Ghz (Python)

139	RADNet-LIDAR			83.74 %	92.43 %	77.00 %	0.1 s	1 core @ 2.5 Ghz (Python)

140	3DNN			83.68 %	88.06 %	77.00 %	0.09 s	GPU @ 2.5 Ghz (Python)

141	yl_net			82.70 %	87.27 %	80.23 %	0.03 s	GPU @ 2.5 Ghz (Python)

142	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.
143	SECA			82.58 %	90.37 %	75.75 %	1 s	GPU @ 2.5 Ghz (Python)

144	SDP-Net-s			81.93 %	86.57 %	75.83 %	12ms	GPU @ 2.5 Ghz (Python)

145	PointRes			81.60 %	90.60 %	76.03 %	0.013 s	1 core @ 2.5 Ghz (Python + C/C++)

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

147	RuiRUC			80.20 %	86.90 %	67.77 %	0.12 s	1 core @ 2.5 Ghz (Python)

148	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.
149	NLK			79.15 %	82.59 %	72.65 %	0.02 s	1 core @ 2.5 Ghz (Python)

150	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.
151	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.
152	VoxelNet(Unofficial)			78.39 %	87.95 %	71.29 %	0.5 s	GPU @ 2.0 Ghz (Python)

153	seivl			77.43 %	85.43 %	75.51 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

154	RCD			75.83 %	82.26 %	69.61 %	0.1 s	GPU @ 2.5 Ghz (Python)

155	ANM			75.40 %	84.78 %	61.28 %	0.12 s	1 core @ 2.5 Ghz (Python)

156	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.
157	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.
158	anm			73.63 %	82.59 %	62.87 %	3 s	1 core @ 2.5 Ghz (C/C++)

159	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.
160	avodC			72.78 %	84.61 %	66.02 %	0.1 s	GPU @ 2.5 Ghz (Python)

161	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.
162	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.
163	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.
164	CDN			66.24 %	83.32 %	57.65 %	0.6 s	GPU @ 2.5 Ghz (Python)

165	SF			65.74 %	74.20 %	58.35 %	0.5 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
166	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.
167	tiny-stereo-volume			64.69 %	81.55 %	55.97 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)

168	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.
169	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.
170	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.
171	CDN-PL++			61.04 %	81.27 %	52.84 %	0.4 s	GPU @ 2.5 Ghz (C/C++)

172	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.
173	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.
174	Pseudo-LiDAR E2E			58.84 %	79.58 %	52.06 %	0.4 s	GPU @ 2.5 Ghz (Python)

175	PB3D			58.04 %	79.75 %	49.78 %	0.42 s	1 core @ 2.5 Ghz (C/C++)

176	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.
177	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.
178	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.
179	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.
180	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.
181	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.
182	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.
183	Stereo3D			50.28 %	76.10 %	36.86 %	0.1 s	GPU 1080Ti

184	stereo_sa			49.61 %	71.47 %	42.71 %	0.3 s	GPU @ 2.5 Ghz (Python)

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

186	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.
187	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.
188	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.
189	m-prcnn			42.81 %	67.82 %	33.63 %	0.43 s	1 core @ 2.5 Ghz (Python)

190	IDA-3D			42.47 %	61.87 %	34.59 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)

191	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.
192	Licar			38.47 %	46.67 %	35.78 %	0.09 s	GPU @ 2.0 Ghz (Python)

193	ASOD			33.63 %	54.61 %	26.76 %	0.28 s	GPU @ 2.5 Ghz (Python)

194	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.
195	deprecated			30.56 %	34.56 %	25.69 %		1 core @ 2.5 Ghz (C/C++)

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

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

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

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

200	ITS-MDPL			17.97 %	32.01 %	15.68 %	0.16 s	GPU @ 2.5 Ghz (Python)

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

202	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.
203	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 .
204	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.
205	P3D			17.30 %	29.27 %	13.38 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

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

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

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

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

210	PatchNet			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.
211	UM3D_TUM			16.69 %	23.63 %	14.17 %	0.05 s	1 core @ 2.5 Ghz (Python)

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

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

214	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.
215	NL_M3D			15.93 %	24.15 %	12.11 %	0.2 s	1 core @ 2.5 Ghz (Python)

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

217	HG-Mono			15.86 %	22.96 %	12.02 %	0.46 s	GPU @ 2.5 Ghz (C/C++)

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

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

220	MonoPair			14.83 %	19.28 %	12.89 %	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.
221	Decoupled-3D			14.82 %	23.16 %	11.25 %	0.08 s	GPU @ 2.5 Ghz (C/C++)
Y. Cai, B. Li, Z. Jiao, H. Li, X. Zeng and X. Wang: Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation. AAAI 2020.
222	Decoupled-3D v2			14.66 %	24.62 %	11.46 %	0.08 s	GPU @ 2.5 Ghz (C/C++)

223	MonoSS			14.52 %	20.91 %	12.63 %	0.03 s	GPU @ 2.5 Ghz (Python + C/C++)

224	SMOKE		code	14.49 %	20.83 %	12.75 %	0.03 s	GPU @ 2.5 Ghz (Python)
Z. Liu, Z. Wu and R. Tóth: SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation. 2020.
225	RTM3D		code	14.20 %	19.17 %	11.99 %	0.05 s	GPU @ 1.0 Ghz (Python)
P. Li, H. Zhao, P. Liu and F. Cao: RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving. 2020.
226	Mono3CN			14.17 %	19.82 %	12.30 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

227	LCD3D			13.99 %	21.97 %	11.43 %	0.03 s	GPU @ 2.5 Ghz (Python)

228	Center3D			13.98 %	18.89 %	12.44 %	0.05 s	GPU @ 3.5 Ghz (Python)

229	Mono3D_PLiDAR		code	13.92 %	21.27 %	11.25 %	0.1 s	NVIDIA GeForce 1080 (pytorch)
X. Weng and K. Kitani: Monocular 3D Object Detection with Pseudo-LiDAR Point Cloud. arXiv:1903.09847 2019.
230	SS3D_HW			13.70 %	20.28 %	9.86 %	0.4 s	GPU @ 2.5 Ghz (Python)

231	M3D-RPN		code	13.67 %	21.02 %	10.23 %	0.16 s	GPU @ 1.5 Ghz (Python)
G. Brazil and X. Liu: M3D-RPN: Monocular 3D Region Proposal Network for Object Detection . ICCV 2019 .
232	SSL-RTM3D Res18			13.37 %	19.71 %	11.10 %	0.02 s	GPU @ 2.5 Ghz (Python)

233	CSoR			13.07 %	18.67 %	10.34 %	3.5 s	4 cores @ >3.5 Ghz (Python + C/C++)
L. Plotkin: PyDriver: Entwicklung eines Frameworks für räumliche Detektion und Klassifikation von Objekten in Fahrzeugumgebung. 2015.
234	RAR-Net			13.01 %	20.63 %	10.19 %	0.5 s	1 core @ 2.5 Ghz (C/C++)

235	MonoPSR		code	12.58 %	18.33 %	9.91 %	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.
236	SS3D			11.52 %	16.33 %	9.93 %	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.
237	MonoGRNet		code	11.17 %	18.19 %	8.73 %	0.04s	NVIDIA P40
Z. Qin, J. Wang and Y. Lu: MonoGRNet: A Geometric Reasoning Network for 3D Object Localization. The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19) 2019.
238	MonoFENet			11.03 %	17.03 %	9.05 %	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.
239	anonymous			10.96 %	20.42 %	9.23 %	1 s	1 core @ 2.5 Ghz (C/C++)

240	RADNet-Mono			10.57 %	15.22 %	8.74 %	0.1 s	1 core @ 2.5 Ghz (Python)

241	OACV			10.13 %	16.24 %	8.28 %	0.23 s	GPU @ 2.5 Ghz (Python)

242	anonymous			10.06 %	18.80 %	8.56 %	1 s	1 core @ 2.5 Ghz (C/C++)

243	A3DODWTDA (image)		code	8.66 %	10.37 %	7.06 %	0.8 s	GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
244	TLNet (Stereo)		code	7.69 %	13.71 %	6.73 %	0.1 s	1 core @ 2.5 Ghz (Python)
Z. Qin, J. Wang and Y. Lu: Triangulation Learning Network: from Monocular to Stereo 3D Object Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
245	Shift R-CNN (mono)		code	6.82 %	11.84 %	5.27 %	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.
246	AACL			6.75 %	8.55 %	5.68 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

247	GS3D			6.08 %	8.41 %	4.94 %	2 s	1 core @ 2.5 Ghz (C/C++)
B. Li, W. Ouyang, L. Sheng, X. Zeng and X. Wang: GS3D: An Efficient 3D Object Detection Framework for Autonomous Driving. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
248	MVRA + I-FRCNN+			5.84 %	9.05 %	4.50 %	0.18 s	GPU @ 2.5 Ghz (Python)
H. Choi, H. Kang and Y. Hyun: Multi-View Reprojection Architecture for Orientation Estimation. The IEEE International Conference on Computer Vision (ICCV) Workshops 2019.
249	ROI-10D			4.91 %	9.78 %	3.74 %	0.2 s	GPU @ 3.5 Ghz (Python)
F. Manhardt, W. Kehl and A. Gaidon: ROI-10D: Monocular Lifting of 2D Detection to 6D Pose and Metric Shape. Computer Vision and Pattern Recognition (CVPR) 2019.
250	3D-GCK			4.57 %	5.79 %	3.64 %	24 ms	Tesla V100

Table as LaTeX | Only published Methods

Pedestrian

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	DENFIDet			51.96 %	61.15 %	49.03 %	0.02 s	GPU @ 2.5 Ghz (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	Noah CV Lab - SSL			50.66 %	57.27 %	46.55 %	0.1 s	GPU @ 2.5 Ghz (Python)

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	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.
12	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.
13				49.48 %	55.90 %	45.79 %

14	PPBA			49.34 %	57.23 %	46.86 %	NA s	GPU @ 2.5 Ghz (Python)

15	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.
16	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.
17	RethinkDet3D			48.84 %	58.96 %	46.20 %	0.15 s	1 core @ 2.5 Ghz (Python)

18	CentrNet-v1			48.78 %	57.58 %	45.94 %	0.03 s	GPU @ 2.5 Ghz (Python)

19	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.
20	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.
21	DDB			48.35 %	57.68 %	45.44 %	0.05 s	GPU @ 2.5 Ghz (Python)

22	PiP			48.14 %	56.16 %	45.27 %	0.033 s	1 core @ 2.5 Ghz (Python)

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

24	PPFNet		code	47.92 %	55.04 %	44.95 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)

25	LDAM			47.35 %	52.08 %	45.23 %	24 ms	GTX 1080 ti GPU

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

27	Tencent_ADlab_Lidar			47.24 %	56.06 %	44.61 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)

28	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.
29	KNN-GCNN			46.77 %	55.11 %	44.43 %	0.4 s	1 core @ 2.5 Ghz (Python + C/C++)

30	TBU			46.76 %	55.15 %	44.60 %	NA s	GPU @ 2.5 Ghz (Python)

31	PP-3D			46.74 %	56.74 %	44.01 %	0.1 s	1 core @ 2.5 Ghz (Python)

32	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.
33	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.
34	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.
35	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.
36	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.
37	FDet			44.85 %	52.42 %	42.56 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)

38				44.59 %	50.87 %	42.14 %

39	Roadstar.ai			44.35 %	49.63 %	41.39 %	0.08 s	GPU @ 2.0 Ghz (Python)

40	FCY			43.88 %	51.21 %	41.41 %	0.02 s	GPU @ 2.5 Ghz (Python)

41	3DBN_2			42.97 %	50.99 %	40.49 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
42	VOXEL_FPN_HR			41.62 %	50.18 %	38.30 %	0.12 s	8 cores @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
43	deprecated			41.32 %	53.09 %	37.16 %	0.05 s	GPU @ 2.0 Ghz (Python)

44	TBD			41.12 %	48.24 %	39.06 %	0.05 s	GPU @ 2.5 Ghz (Python)

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

46	HR-SECOND		code	40.06 %	50.05 %	36.47 %	0.11 s	1 core @ 2.5 Ghz (Python + C/C++)

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

48	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.
49	MP			38.77 %	47.59 %	35.50 %	0.2 s	1 core @ 2.5 Ghz (C/C++)

50	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.
51	anm			38.01 %	49.07 %	34.00 %	3 s	1 core @ 2.5 Ghz (C/C++)

52	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.
53	Pointpillar_TV			35.28 %	42.65 %	33.10 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

54	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.
55	SAANet			33.94 %	42.34 %	31.75 %	0.10 s	1 core @ 2.5 Ghz (Python)

56	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.
57	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.
58				32.32 %	40.87 %	29.52 %

59	SF			29.77 %	37.16 %	26.61 %	0.5 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
60	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.
61	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.
62	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.
63	PB3D			23.62 %	33.00 %	20.35 %	0.42 s	1 core @ 2.5 Ghz (C/C++)

64	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.
65	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.
66	Stereo3D			20.76 %	31.01 %	18.41 %	0.1 s	GPU 1080Ti

67	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.
68	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.
69	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.
70	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.
71	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.
72	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.
73	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.
74	RT3D-GMP			5.73 %	7.93 %	5.62 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)

75	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.
76	SS3D_HW			5.47 %	8.81 %	4.79 %	0.4 s	GPU @ 2.5 Ghz (Python)

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

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

79	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.
80	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.
81	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 .
82	Mono3CN			4.02 %	6.03 %	3.40 %	0.1 s	1 core @ 2.5 Ghz (C/C++)

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

84	HG-Mono			3.90 %	5.66 %	3.67 %	0.46 s	GPU @ 2.5 Ghz (C/C++)

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

86	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.
87	Center3D			3.71 %	5.67 %	3.52 %	0.05 s	GPU @ 3.5 Ghz (Python)

88	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.
89	MTMono3d			2.38 %	3.11 %	1.89 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

90	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.
91	UM3D_TUM			1.79 %	3.60 %	1.79 %	0.05 s	1 core @ 2.5 Ghz (Python)

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

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

94	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	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.
4	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.
5	TBD			69.08 %	83.68 %	62.28 %	0.05 s	GPU @ 2.5 Ghz (Python)

6	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.
7	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.
8	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.
9	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.
10	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.
11	PPBA			67.28 %	82.69 %	60.53 %	NA s	GPU @ 2.5 Ghz (Python)

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

13	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.
14	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.
15	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.
16	KNN-GCNN			67.22 %	83.35 %	59.51 %	0.4 s	1 core @ 2.5 Ghz (Python + C/C++)

17				67.20 %	79.66 %	61.04 %

18				66.86 %	82.13 %	60.86 %

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

20	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.
21	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.
22	DENFIDet			65.49 %	82.13 %	57.76 %	0.02 s	GPU @ 2.5 Ghz (C/C++)

23	PiP			65.12 %	79.51 %	58.25 %	0.033 s	1 core @ 2.5 Ghz (Python)

24	VOXEL_FPN_HR			65.02 %	81.07 %	58.44 %	0.12 s	8 cores @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
25	MVX-Net++			64.84 %	78.89 %	58.15 %	0.15 s	1 core @ 2.5 Ghz (C/C++)

26	FDet			64.53 %	79.62 %	57.91 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)

27	3DBN_2			64.28 %	81.06 %	57.55 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
28	HR-SECOND		code	64.21 %	78.79 %	57.82 %	0.11 s	1 core @ 2.5 Ghz (Python + C/C++)

29	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.
30	PBASN		code	63.34 %	79.45 %	57.01 %	NA s	GPU @ 2.5 Ghz (Python)

31	LDAM			63.17 %	77.22 %	57.34 %	24 ms	GTX 1080 ti GPU

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

33	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.
34	Tencent_ADlab_Lidar			62.34 %	78.91 %	55.37 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)

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

36	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.
37	MP			60.16 %	77.57 %	54.01 %	0.2 s	1 core @ 2.5 Ghz (C/C++)

38	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.
39	Roadstar.ai			59.50 %	68.73 %	53.60 %	0.08 s	GPU @ 2.0 Ghz (Python)

40	FCY			59.35 %	76.73 %	52.63 %	0.02 s	GPU @ 2.5 Ghz (Python)

41	Pointpillar_TV			59.26 %	74.78 %	52.33 %	0.05 s	1 core @ 2.5 Ghz (C/C++)

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

43	CentrNet-v1			58.05 %	75.80 %	51.17 %	0.03 s	GPU @ 2.5 Ghz (Python)

44	SAANet			57.98 %	74.71 %	51.95 %	0.10 s	1 core @ 2.5 Ghz (Python)

45	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.
46	DDB			57.01 %	73.70 %	50.71 %	0.05 s	GPU @ 2.5 Ghz (Python)

47	deprecated			56.42 %	81.02 %	49.28 %	0.05 s	GPU @ 2.0 Ghz (Python)

48	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.
49	PP-3D			55.06 %	71.94 %	48.10 %	0.1 s	1 core @ 2.5 Ghz (Python)

50	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.
51	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.
52	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.
53	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.
54	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.
55	anm			38.56 %	56.94 %	34.06 %	3 s	1 core @ 2.5 Ghz (C/C++)

56	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.
57	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.
58	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.
59	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.
60	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.
61	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.
62	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.
63	PB3D			19.41 %	32.06 %	17.42 %	0.42 s	1 core @ 2.5 Ghz (C/C++)

64	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.
65	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.
66	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.
67	RT3D-GMP			6.90 %	10.09 %	6.14 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)

68	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.
69	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.
70	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.
71	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.
72	SS3D_HW			2.78 %	5.03 %	2.36 %	0.4 s	GPU @ 2.5 Ghz (Python)

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

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

75	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.
76	UDI-mono3D			2.01 %	3.59 %	1.79 %	0.05 s	1 core @ 2.5 Ghz (Python)

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

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

79	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.
80	DP3D			1.87 %	3.09 %	1.96 %	0.07 s	GPU @ 1.5 Ghz (Python + C/C++)

81	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.
82	DP3D			1.57 %	2.32 %	1.29 %	0.05 s	GPU @ 1.5 Ghz (Python + C/C++)

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

84	HG-Mono			1.27 %	2.16 %	1.34 %	0.46 s	GPU @ 2.5 Ghz (C/C++)

85	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 .
86	UM3D_TUM			0.62 %	0.45 %	0.62 %	0.05 s	1 core @ 2.5 Ghz (Python)

87	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.
88	PVNet			0.00 %	0.00 %	0.00 %	0,1 s	1 core @ 2.5 Ghz (Python)

89	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