The KITTI Vision Benchmark Suite

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.

Car

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	NFAF3D			91.97 %	95.72 %	86.97 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
2	VPFNet			91.86 %	93.02 %	86.94 %	0.06 s	2 cores @ 2.5 Ghz (Python)
3	SE-SSD		code	91.84 %	95.68 %	86.72 %	0.03 s	1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, L. Jiang and C. Fu: SE-SSD: Self-Ensembling Single-Stage Object Detector From Point Cloud. CVPR 2021.
4	CityBrainLab			91.60 %	94.75 %	86.67 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
5	SPANet			91.59 %	95.59 %	86.53 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
6	Anonymous			91.53 %	95.04 %	86.69 %	0.1 s	GPU @ 2.5 Ghz (Python)
7	SFD			91.38 %	95.23 %	86.71 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
8	BANet		code	91.32 %	95.23 %	86.48 %	0.11 s	1 core @ 2.5 Ghz (C/C++)
R. Qian, X. Lai and X. Li: Boundary-Aware 3D Object Detection from Point Clouds. 2021.
9	Anonymous			91.04 %	94.31 %	86.31 %	0.1s	1 core @ 2.5 Ghz (C/C++)
10	SFD_NEW			91.04 %	94.76 %	86.31 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
11	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.
12	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.
13	VueronNet		code	90.56 %	94.67 %	85.31 %	0.06 s	1 core @ 2.0 Ghz (Python)
14	ST-RCNN			90.53 %	94.58 %	86.08 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
15	ST-RCNN (SNLW-RCNN)		code	90.53 %	94.58 %	86.08 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
16	VPFNet		code	90.52 %	93.94 %	86.25 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
17	SARFE			90.45 %	94.39 %	85.97 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
18	HyBrid Feature Det			90.35 %	92.87 %	85.87 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
19	VoTr-TSD			90.34 %	94.03 %	86.14 %	0.07 s	1 core @ 2.5 Ghz (C/C++)
J. Mao, Y. Xue, M. Niu, H. Bai, J. Feng, X. Liang, H. Xu and C. Xu: Voxel Transformer for 3D Object Detection. ICCV 2021.
20	TransCyclistNet			90.33 %	92.68 %	85.90 %	0.08 s	1 core @ 2.5 Ghz (Python)
21	Fast VP-RCNN		code	90.32 %	95.09 %	85.84 %	0.05 s	1 core @ 3.5 Ghz (C/C++)
22	WHUT-iou_ssd		code	90.31 %	94.22 %	85.83 %	0.045s	1 core @ 2.5 Ghz (C/C++)
23	LZY_RCNN			90.29 %	92.88 %	85.84 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
24	E^2-PV-RCNN			90.27 %	92.51 %	86.01 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
25	anonymous		code	90.22 %	94.86 %	85.73 %	0.05s	1 core @ >3.5 Ghz (python)
26	MSG-PGNN			90.20 %	92.89 %	85.80 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
27	DSA-PV-RCNN		code	90.13 %	92.42 %	85.93 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
28	XView			90.12 %	92.27 %	85.94 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
29	Generalized-SIENet			90.09 %	92.12 %	85.88 %	0.08 s	1 core @ 2.5 Ghz (Python)
30	SCIR-Net			90.04 %	92.11 %	85.63 %	0.03 s	GPU @ 2.5 Ghz (Python + C/C++)
31	FPC-RCNN			90.03 %	92.74 %	85.67 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
32	TPCG			90.02 %	92.22 %	85.88 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
33	Associate-3Ddet_v2			90.00 %	95.55 %	84.72 %	0.04 s	1 core @ 2.5 Ghz (Python)
34	TransDet3D			89.98 %	92.44 %	85.71 %	0.08 s	1 core @ 2.5 Ghz (Python)
35	SAA-PV-RCNN			89.88 %	91.54 %	86.93 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
36	FSA-PVRCNN			89.87 %	92.30 %	85.71 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
37	EBM3DOD		code	89.86 %	95.64 %	84.56 %	0.12 s	1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.
38	CIA-SSD		code	89.84 %	93.74 %	82.39 %	0.03 s	1 core @ 2.5 Ghz (Python + C/C++)
W. Zheng, W. Tang, S. Chen, L. Jiang and C. Fu: CIA-SSD: Confident IoU-Aware Single-Stage Object Detector From Point Cloud. AAAI 2021.
39	HIKVISION-ADLab-HZ			89.83 %	93.21 %	84.88 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
40	CLOCs_PVCas		code	89.80 %	93.05 %	86.57 %	0.1 s	1 core @ 2.5 Ghz (Python)
S. Pang, D. Morris and H. Radha: CLOCs: Camera-LiDAR Object Candidates Fusion for 3D Object Detection . 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2020.
41	CM3DV			89.77 %	95.54 %	84.49 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
42	NFAF3D-light			89.76 %	95.41 %	86.42 %	0.03 s	1 core @ 2.5 Ghz (Python + C/C++)
43	EA-M-RCNN(BorderAtt)			89.76 %	94.67 %	86.73 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
44	sa-voxel-centernet		code	89.74 %	92.02 %	85.69 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
45	AM-SSD			89.74 %	95.56 %	84.65 %	0.04 s	1 core @ 2.5 Ghz (Python)
46	CBi-GNN			89.74 %	95.92 %	84.54 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
47	EBM3DOD baseline		code	89.63 %	95.44 %	84.34 %	0.05 s	1 core @ 2.5 Ghz (Python)
F. Gustafsson, M. Danelljan and T. Schön: Accurate 3D Object Detection using Energy- Based Models. arXiv preprint arXiv:2012.04634 2020.
48	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.
49	Struc info fusion II			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: Struc info fusion. Submitted to CVIU 2021.
50	Fast			89.49 %	93.03 %	86.40 %	0.1 s	GPU @ 2.5 Ghz (Python)
51	KpNet			89.49 %	93.29 %	81.92 %	42 s	1 core @ 2.5 Ghz (C/C++)
52	IA-SSD (single)			89.48 %	93.14 %	84.42 %	0.013 s	1 core @ 2.5 Ghz (C/C++)
53	Seg-RCNN		code	89.39 %	93.36 %	81.93 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
54	Struc info fusion I			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: Struc info fusion. Submitted to CVIU 2021.
55	JPVNet			89.36 %	92.78 %	84.37 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
56	PLNL-3DSSD			89.36 %	93.00 %	84.18 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
57	ASCNet			89.36 %	92.85 %	86.45 %	0.09 s	1 core @ 2.5 Ghz (C/C++)
58	BtcDet			89.34 %	92.81 %	84.55 %	0.09 s	GPU @ 2.5 Ghz (Python + C/C++)
59	IA-SSD (multi)			89.33 %	92.79 %	84.35 %	0.014 s	1 core @ 2.5 Ghz (C/C++)
60	PPAF			89.29 %	92.94 %	84.46 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
61	PSS			89.28 %	93.17 %	84.38 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
62	GNN-RCNN			89.28 %	92.13 %	85.49 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
63	TBD			89.21 %	92.74 %	84.23 %	TBD	GPU @ 2.5 Ghz (Python + C/C++)
64	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.
65	MBDF-Net			89.18 %	95.36 %	84.24 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
66	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.
67	Anonymous			89.15 %	92.47 %	85.97 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
68	SGNet			89.14 %	93.04 %	86.54 %	0.09 s	GPU @ 2.5 Ghz (Python)
69	SPG_mini			89.12 %	92.80 %	86.27 %	0.09 s	GPU @ 2.5 Ghz (Python)
70	TBD			89.11 %	92.42 %	84.26 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
71	RoIFusion		code	89.03 %	92.88 %	83.94 %	0.22 s	1 core @ 3.0 Ghz (Python)
72	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.
73	EPNet++			89.00 %	95.41 %	85.73 %	0.1 s	GPU @ 2.5 Ghz (Python)
74	SECOND			88.98 %	92.01 %	83.67 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
75	ISE-RCNN			88.97 %	92.86 %	86.28 %	0.09 s	1 core @ 2.5 Ghz (Python + C/C++)
76	Sem-Aug v1		code	88.92 %	92.59 %	84.29 %	0.04 s	GPU @ 3.5 Ghz (Python)
77	MBDF-Net-1			88.90 %	94.68 %	83.98 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
78	VCT			88.90 %	93.01 %	84.23 %	0.2 s	1 core @ 2.5 Ghz (Python)
79	H^23D R-CNN		code	88.87 %	92.85 %	86.07 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
J. Deng, W. Zhou, Y. Zhang and H. Li: From Multi-View to Hollow-3D: Hallucinated Hollow-3D R-CNN for 3D Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2021.
80	MVOD			88.85 %	92.50 %	86.19 %	0.16 s	1 core @ 2.5 Ghz (C/C++)
81	Pyramid R-CNN			88.84 %	92.19 %	86.21 %	0.07 s	1 core @ 2.5 Ghz (C/C++)
J. Mao, M. Niu, H. Bai, X. Liang, H. Xu and C. Xu: Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection. ICCV 2021.
82	TBD			88.83 %	92.96 %	86.27 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
83	CityBrainLab-CT3D		code	88.83 %	92.36 %	84.07 %	0.07 s	1 core @ 2.5 Ghz (Python + C/C++)
H. Sheng, S. Cai, Y. Liu, B. Deng, J. Huang, X. Hua and M. Zhao: Improving 3D Object Detection with Channel- wise Transformer. ICCV 2021.
84	Voxel R-CNN		code	88.83 %	94.85 %	86.13 %	0.04 s	GPU @ 3.0 Ghz (C/C++)
J. Deng, S. Shi, P. Li, W. Zhou, Y. Zhang and H. Li: Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection . AAAI 2021.
85	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.
86	SRIF-RCNN			88.77 %	92.10 %	86.06 %	0.0947 s	1 core @ 2.5 Ghz (C/C++)
87	TBD			88.75 %	92.30 %	84.01 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
88	PV-RCNN-v2			88.74 %	92.66 %	85.97 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
89	SPG		code	88.70 %	94.33 %	85.98 %	0.09 s	1 core @ 2.5 Ghz (Python + C/C++)
Q. Xu, Y. Zhou, W. Wang, C. Qi and D. Anguelov: SPG: Unsupervised Domain Adaptation for 3D Object Detection via Semantic Point Generation. Proceedings of the IEEE conference on computer vision and pattern recognition (ICCV) 2021.
90	ISE-RCNN-PV			88.69 %	92.31 %	86.10 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
91	SIENet		code	88.65 %	92.38 %	86.03 %	0.08 s	1 core @ 2.5 Ghz (Python)
Z. Li, Y. Yao, Z. Quan, W. Yang and J. Xie: SIENet: Spatial Information Enhancement Network for 3D Object Detection from Point Cloud. 2021.
92	P2V-RCNN			88.63 %	92.72 %	86.14 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.
93	demo			88.62 %	92.35 %	83.56 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
94	FromVoxelToPoint		code	88.61 %	92.23 %	86.11 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to- Point Decoder. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
95	RangeIoUDet			88.59 %	92.28 %	85.83 %	0.02 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Liang, Z. Zhang, M. Zhang, X. Zhao and S. Pu: RangeIoUDet: Range Image Based Real-Time 3D Object Detector Optimized by Intersection Over Union. CVPR 2021.
96	SIEV-Net			88.58 %	92.27 %	83.36 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
97	ISF-v2			88.57 %	92.15 %	83.91 %	0.04 s	1 core @ 2.5 Ghz (Python)
98	SqueezeRCNN			88.52 %	92.65 %	85.82 %	0.08 s	1 core @ 2.5 Ghz (Python)
99	FusionDetv2-v3			88.47 %	92.55 %	85.63 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
100	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.
101	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.
102	FrustumRCNN			88.44 %	92.00 %	85.90 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
103	PC-RGNN			88.43 %	92.08 %	85.81 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
104	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.
105	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.
106	Point Image Fusion			88.39 %	92.14 %	85.78 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
107	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.
108	StructuralIF			88.38 %	91.78 %	85.67 %	0.02 s	8 cores @ 2.5 Ghz (Python)
109	CSVoxel-RCNN			88.38 %	92.09 %	85.59 %	0.03 s	GPU @ 1.0 Ghz (Python)
110	PF-GAP			88.35 %	92.16 %	85.64 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
111	ReFineNet			88.32 %	91.93 %	85.68 %	0.08 s	1 core @ 2.5 Ghz (Python)
112	SA-voxel-centernet		code	88.28 %	91.80 %	85.73 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
113	FusionDetv2-v4			88.27 %	92.05 %	85.38 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
114	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.
115	MSL3D			88.23 %	91.64 %	85.53 %	0.03 s	GPU @ 2.5 Ghz (Python)
116	Multi-Sensor3D			88.23 %	91.64 %	85.53 %	0.03 s	GPU @ 2.5 Ghz (Python)
117	3DIoU_v2			88.22 %	92.52 %	85.90 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
118	SVGA-Net			88.21 %	91.98 %	85.46 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
119	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.
120	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.
121	SRDL			88.17 %	92.01 %	85.43 %	0.15 s	GPU @ 2.5 Ghz (Python + C/C++)
122	3DIoU++			88.16 %	91.79 %	85.71 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
123	FPC3D			88.15 %	91.92 %	85.32 %	33 s	1 core @ 2.5 Ghz (C/C++)
124	3D-VDNet			88.15 %	91.72 %	84.65 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
125	SAA-SECOND			88.14 %	91.32 %	85.23 %	38m s	1 core @ 2.5 Ghz (C/C++)
126	FusionDetv1			88.13 %	91.91 %	85.40 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
127	FPCR-CNN			88.12 %	92.62 %	85.18 %	0.05 s	1 core @ 2.5 Ghz (Python)
128	GAP-soft-filter			88.11 %	91.88 %	85.42 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
129	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.
130	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.
131	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.
132	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.
133	Faraway-Frustum		code	88.08 %	91.90 %	85.35 %	0.1 s	GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion. arXiv preprint arXiv:2011.01404 2020.
134	NV2P-RCNN			88.08 %	93.44 %	85.32 %	0.1 s	GPU @ 2.5 Ghz (Python)
135	VFN			88.06 %	91.23 %	83.26 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
136	FusionDetv2-v2			88.04 %	91.77 %	85.29 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
137	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.
138	CCFNET			87.97 %	94.25 %	83.27 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
139	HVPR			87.94 %	91.76 %	83.03 %	0.02 s	GPU @ 2.5 Ghz (Python)
140	AIMC-RUC			87.91 %	93.92 %	82.70 %	0.11 s	1 core @ 2.5 Ghz (Python)
141	TBD			87.89 %	91.39 %	85.24 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
142	CVFNet			87.87 %	93.65 %	82.29 %	28.1ms	1 core @ 2.5 Ghz (Python)
143	FusionDetv2-v5			87.86 %	91.92 %	83.07 %	0.05 s	1 core @ 2.5 Ghz (Java + C/C++)
144	XView-PartA^2			87.84 %	91.94 %	85.22 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
145	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.
146	TBD			87.83 %	91.80 %	85.19 %	0.07 s	1 core @ 2.5 Ghz (Python + C/C++)
147	YF			87.81 %	92.11 %	83.07 %	0.04 s	GPU @ 2.5 Ghz (C/C++)
148	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.
149	MKFFNet			87.78 %	91.85 %	84.82 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
150	FPGNN			87.78 %	92.21 %	80.86 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
151	SIF			87.76 %	91.44 %	85.15 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
152	MVAF-Net		code	87.73 %	91.95 %	85.00 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
G. Wang, B. Tian, Y. Zhang, L. Chen, D. Cao and J. Wu: Multi-View Adaptive Fusion Network for 3D Object Detection. arXiv preprint arXiv:2011.00652 2020.
153	DVFENet			87.68 %	90.93 %	84.60 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection. Neurocomputing 2021.
154	S-AT GCN			87.68 %	90.85 %	84.20 %	0.02 s	GPU @ 2.0 Ghz (Python)
L. Wang, C. Wang, X. Zhang, T. Lan and J. Li: S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection. CoRR 2021.
155	AutoAlign			87.60 %	91.72 %	84.44 %	0.1 s	1 core @ 2.5 Ghz (Python)
156	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.
157	VOXEL_3D			87.55 %	90.83 %	82.24 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
158	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.
159	V3D			87.53 %	90.83 %	82.30 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
160	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.
161	MGAF-3DSSD		code	87.47 %	92.70 %	82.19 %	0.1 s	1 core @ 2.5 Ghz (Python)
J. Li, H. Dai, L. Shao and Y. Ding: Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
162	MKFFNet			87.41 %	91.62 %	84.67 %	0.01s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
163	MKFFNet			87.41 %	91.93 %	84.65 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
164	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.
165	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.
166	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.
167	DPointNet			87.29 %	88.96 %	82.61 %	0.07s	1 core @ 2.5 Ghz (C/C++)
168	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.
169	VGCN			87.16 %	90.67 %	82.98 %	0.09 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
170	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.
171	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.
172	sscl-20p			86.82 %	91.43 %	82.06 %	0.02 s	1 core @ 2.5 Ghz (Python)
173	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.
174	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.
175	HNet-3DSSD		code	86.69 %	91.65 %	81.05 %	0.05 s	GPU @ 2.5 Ghz (Python)
176	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.
177	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.
178	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.
179	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.
180	IGRP+			86.29 %	92.20 %	81.48 %	0.18 s	1 core @ 2.5 Ghz (Python + C/C++)
181	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.
182	APL-Second			86.16 %	91.45 %	81.08 %	0.05 s	1 core @ 2.5 Ghz (Python)
183	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.
184	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.
185	TBD			85.91 %	90.88 %	80.95 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
186	LSNet			85.89 %	92.12 %	80.80 %	0.09 s	GPU @ 2.5 Ghz (Python + C/C++)
187	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.
188	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.
189	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.
190	PointRGBNet			85.73 %	91.39 %	80.68 %	0.08 s	4 cores @ 2.5 Ghz (Python + C/C++)
191	Sem-Aug-PointRCNN		code	85.50 %	89.75 %	83.13 %	0.1 s	GPU @ 3.5 Ghz (C/C++)
192	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.
193	PFF3D		code	85.08 %	89.61 %	80.42 %	0.05 s	GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
194	RangeDet		code	85.06 %	89.88 %	80.23 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
195	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.
196	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.
197	FPC3D_all			84.85 %	91.05 %	80.23 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
198	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.
199	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.
200	FusionDetv2-v1			84.45 %	89.64 %	79.73 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
201	FusionDetv2-baseline			84.31 %	90.38 %	79.23 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
202	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.
203	KMC		code	83.90 %	88.87 %	76.87 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
204	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.
205	HS3D		code	82.00 %	87.40 %	74.95 %	0.12 s	1 core @ 2.5 Ghz (Python + C/C++)
206	BirdNet+		code	81.85 %	87.43 %	75.36 %	0.11 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. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
207	AEC3D			80.37 %	86.81 %	74.26 %	18 ms	GPU @ 2.5 Ghz (Python)
208	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.
209	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.
210	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.
211	VN3D			77.45 %	86.35 %	71.59 %	0.02 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
212	MMLAB LIGA-Stereo		code	76.78 %	88.15 %	67.40 %	0.4 s	1 core @ 2.5 Ghz (Python + C/C++)
X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
213	R-AGNO-Net			76.24 %	80.10 %	70.38 %	0.15 s	1 core @ 2.5 Ghz (C/C++)
214	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.
215	LIGA-Stereo-old			74.76 %	88.33 %	65.31 %	0.375 s	Titan Xp
216	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.
217	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.
218	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.
219	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.
220	deleted			68.87 %	86.89 %	59.95 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)
221	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.
222	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.
223	PLUME			66.27 %	82.97 %	56.70 %	0.15 s	GPU @ 2.5 Ghz (Python)
Y. Wang, B. Yang, R. Hu, M. Liang and R. Urtasun: PLUME: Efficient 3D Object Detection from Stereo Images. IROS 2021.
224	CDN		code	66.24 %	83.32 %	57.65 %	0.6 s	GPU @ 2.5 Ghz (Python)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems (NeurIPS) 2020.
225	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.
226	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.
227	BirdNet+ (legacy)		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. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
228	BEVC			61.89 %	69.00 %	56.32 %	35ms	GPU @ 1.5 Ghz (Python)
229	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.
230	CDN-PL++			61.04 %	81.27 %	52.84 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
D. Garg, Y. Wang, B. Hariharan, M. Campbell, K. Weinberger and W. Chao: Wasserstein Distances for Stereo Disparity Estimation. Advances in Neural Information Processing Systems 2020.
231	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.
232	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.
233	RT3D-GMP			59.00 %	69.14 %	45.49 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
234	OSE+			58.65 %	79.80 %	50.52 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
235	Disp R-CNN (velo)		code	58.62 %	79.76 %	47.73 %	0.387 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
236	SOD			58.50 %	81.25 %	49.08 %	0.1 s	1 core @ 2.5 Ghz (Python)
237	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.
238	Disp R-CNN		code	57.98 %	79.61 %	47.09 %	0.387 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
239	NCL		code	57.66 %	50.87 %	57.99 %	NA s	1 core @ 2.5 Ghz (Python)
240	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.
241	TBD			54.68 %	65.38 %	48.59 %	0.1 s	1 core @ 2.5 Ghz (Python)
242	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.
243	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.
244	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.
245	TBD			50.41 %	61.32 %	44.74 %	0.1 s	1 core @ 2.5 Ghz (Python)
246	YOLOStereo3D		code	50.28 %	76.10 %	36.86 %	0.1 s	GPU 1080Ti
Y. Liu, L. Wang and M. Liu: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
247	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.
248	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.
249	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.
250	SC(DLA34)			42.12 %	62.97 %	35.37 %	0.04 s	GPU @ 2.5 Ghz (Python)
251	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.
252	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.
253	Digging_M3D			28.84 %	39.74 %	26.08 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
254	Mobile Stereo R-CNN			28.78 %	44.51 %	22.30 %	1.8 s	NVIDIA Jetson TX2
255	LPCG-Monoflex			24.81 %	35.96 %	21.86 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
256	SCSTSV-MonoFlex			23.71 %	34.59 %	20.41 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
257	CD-KD			23.10 %	33.69 %	20.67 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
258	DD3D		code	22.56 %	30.98 %	20.03 %	n/a s	1 core @ 2.5 Ghz (C/C++)
D. Park, R. Ambrus, V. Guizilini, J. Li and A. Gaidon: Is Pseudo-Lidar needed for Monocular 3D Object detection?. IEEE/CVF International Conference on Computer Vision (ICCV) .
259	MonoCon			22.10 %	31.12 %	19.00 %	0.02 s	GPU @ 2.5 Ghz (Python)
260	CA3D			20.77 %	29.57 %	17.88 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
261	MM			20.68 %	29.92 %	17.77 %	1 s	1 core @ 2.5 Ghz (C/C++)
262	vadin-TBD			20.68 %	29.60 %	17.81 %	0.04 s	1 core @ 2.5 Ghz (Python)
263	MonoFlex			20.67 %	30.95 %	17.72 %	0.03 s	1 core @ 2.5 Ghz (Python)
264	LPCG-M3D			20.17 %	30.72 %	16.76 %	0.11 s	1 core @ 2.5 Ghz (C/C++)
265	AutoShape		code	20.08 %	30.66 %	15.95 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
266	M3DSSD++			20.03 %	32.18 %	16.47 %	0.16s	1 core @ 2.5 Ghz (C/C++)
267	MAOLoss		code	19.95 %	28.29 %	16.94 %	0.05 s	1 core @ 2.5 Ghz (Python)
268	TBD			19.75 %	27.98 %	17.32 %	TBD	TBD
269	MonoFlex			19.75 %	28.23 %	16.89 %	0.03 s	GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.
270	MonoEF		code	19.70 %	29.03 %	17.26 %	0.03 s	1 core @ 2.5 Ghz (Python)
Y. Zhou, Y. He, H. Zhu, C. Wang, H. Li and Q. Jiang: Monocular 3D Object Detection: An Extrinsic Parameter Free Approach. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
271	mono3d			19.67 %	28.15 %	16.73 %	0.03 s	GPU @ 2.5 Ghz (Python)
272	K3D			19.60 %	28.31 %	17.62 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
273	ITS-MDPL			19.54 %	33.02 %	17.56 %	0.16 s	GPU @ 2.5 Ghz (Python)
274	DFR-Net			19.17 %	28.17 %	14.84 %	0.18 s	1080 Ti (Pytorch)
Z. Zou, X. Ye, L. Du, X. Cheng, X. Tan, L. Zhang, J. Feng, X. Xue and E. Ding: The devil is in the task: Exploiting reciprocal appearance-localization features for monocular 3d object detection . ICCV 2021.
275	SwinMono3D			19.15 %	29.65 %	14.09 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
276	GAC3D++			19.05 %	26.94 %	16.48 %	0.25 s	1 core @ 2.5 Ghz (C/C++)
277	PCT			19.03 %	29.65 %	15.92 %	0.045 s	1 core @ 2.5 Ghz (C/C++)
278	E2E-DA			19.03 %	27.41 %	16.46 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
279	MonoGeo			18.99 %	25.86 %	16.19 %	0.05 s	1 core @ 2.5 Ghz (Python)
280	CaDDN		code	18.91 %	27.94 %	17.19 %	0.63 s	GPU @ 2.5 Ghz (Python)
C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.
281	monodle		code	18.89 %	24.79 %	16.00 %	0.04 s	GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for Monocular 3D Object Detection. CVPR 2021 .
282	Object Transformer			18.78 %	26.43 %	15.94 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
283	MonoLCD			18.68 %	25.89 %	16.30 %	0.04 s	1 core @ 2.5 Ghz (Python)
284	none			18.66 %	26.19 %	15.79 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
285	Neighbor-Vote			18.65 %	27.39 %	16.54 %	0.1 s	GPU @ 2.5 Ghz (Python)
X. Chu, J. Deng, Y. Li, Z. Yuan, Y. Zhang, J. Ji and Y. Zhang: Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance Voting. ACM MM 2021.
286	MDSNet			18.65 %	30.92 %	14.53 %	0.07 s	1 core @ 2.5 Ghz (Python)
287	PLDet3d			18.55 %	29.14 %	15.73 %	0.11 s	1 core @ 2.5 Ghz (C/C++)
288	GrooMeD-NMS		code	18.27 %	26.19 %	14.05 %	0.12 s	1 core @ 2.5 Ghz (Python)
A. Kumar, G. Brazil and X. Liu: GrooMeD-NMS: Grouped Mathematically Differentiable NMS for Monocular 3D Object Detection. CVPR 2021.
289	AutoShape			18.12 %	28.25 %	14.84 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
290	PPTrans			18.12 %	28.05 %	15.41 %	0.2 s	GPU @ 2.5 Ghz (Python)
291	MonoRCNN		code	18.11 %	25.48 %	14.10 %	0.07 s	GPU @ 2.5 Ghz (Python)
X. Shi, Q. Ye, X. Chen, C. Chen, Z. Chen and T. Kim: Geometry-based Distance Decomposition for Monocular 3D Object Detection. ICCV 2021.
292	Ground-Aware		code	17.98 %	29.81 %	13.08 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
Y. Liu, Y. Yuan and M. Liu: Ground-aware Monocular 3D Object Detection for Autonomous Driving. IEEE Robotics and Automation Letters 2021.
293	MP-Mono			17.96 %	25.36 %	13.84 %	0.16 s	GPU @ 2.5 Ghz (Python)
294	Aug3D-RPN			17.89 %	26.00 %	14.18 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth. 2021.
295	DDMP-3D			17.89 %	28.08 %	13.44 %	0.18 s	1 core @ 2.5 Ghz (Python)
L. Wang, L. Du, X. Ye, Y. Fu, G. Guo, X. Xue, J. Feng and L. Zhang: Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection. CVPR 2020.
296	IAFA			17.88 %	25.88 %	15.35 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
D. Zhou, X. Song, Y. Dai, J. Yin, F. Lu, M. Liao, J. Fang and L. Zhang: IAFA: Instance-Aware Feature Aggregation for 3D Object Detection from a Single Image. Proceedings of the Asian Conference on Computer Vision 2020.
297	RelationNet3D_dla34		code	17.74 %	24.27 %	15.38 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
298	TBD			17.70 %	29.97 %	15.04 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
299	RelationNet3D			17.66 %	25.56 %	15.52 %	0.04 s	GPU @ 2.5 Ghz (Python)
300	MonoHMOO			17.60 %	27.39 %	13.25 %	0.2 s	1 core @ 2.5 Ghz (Python + C/C++)
301	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.
302	Lite-FPN			17.58 %	26.67 %	14.61 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
303	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 .
304	MonoRUn		code	17.34 %	27.94 %	15.24 %	0.07 s	GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
305	RetinaMono			17.33 %	26.12 %	15.26 %	0.02 s	1 core @ 2.5 Ghz (Python)
306	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.
307	Deprecated			17.22 %	23.59 %	13.34 %	Deprecated	Deprecated
308	DA-Mono3D			17.17 %	23.73 %	13.46 %	0.09s	1 core @ 2.5 Ghz (C/C++)
309	YoloMono3D		code	17.15 %	26.79 %	12.56 %	0.05 s	GPU @ 2.5 Ghz (Python)
Y. Liu, L. Wang and L. Ming: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
310	GAC3D			16.93 %	25.80 %	12.50 %	0.25 s	1 core @ 2.5 Ghz (Python)
311	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.
312	RetinaMono		code	16.85 %	24.52 %	14.02 %	0.02 s	1 core @ 2.5 Ghz (Python)
313	ImVoxelNet		code	16.37 %	25.19 %	13.58 %	0.2 s	GPU @ 2.5 Ghz (Python)
314	TBD			16.22 %	24.21 %	14.11 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
315	KM3D		code	16.20 %	23.44 %	14.47 %	0.03 s	1 core @ 2.5 Ghz (Python)
P. Li: Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training. 2020.
316	E2E-DA-Lite (Res18)			16.06 %	23.49 %	13.55 %	0.01 s	GPU @ 2.5 Ghz (Python)
317	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.
318	LAPNet			15.76 %	25.10 %	12.30 %	0.03 s	1 core @ 2.5 Ghz (Python)
319	COF3D			15.39 %	25.36 %	11.34 %	200 s	1 core @ 2.5 Ghz (Python + C/C++)
320	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.
321	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.
322	QD-3DT		code	14.71 %	20.16 %	12.76 %	0.03 s	GPU @ 2.5 Ghz (Python)
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.
323	RelationNet3D_res18		code	14.59 %	20.54 %	12.51 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
324	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.
325	ICCV			14.30 %	19.93 %	12.37 %	0.04 s	GPU @ 2.5 Ghz (Python)
326	FADNet		code	14.22 %	23.00 %	12.56 %	0.04 s	GPU @ >3.5 Ghz (Python)
327	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.
328	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.
329	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 .
330	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.
331	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.
332	Geo3D			11.86 %	16.31 %	10.26 %	0.04 s	GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
333	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.
334	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.
335	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.
336	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.
337	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.
338	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.
339	SparVox3D			6.39 %	10.20 %	5.06 %	0.05 s	GPU @ 2.0 Ghz (Python)
340	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.
341	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.
342	weakm3d			5.31 %	8.19 %	3.83 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
343	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.
344	3D-GCK			4.57 %	5.79 %	3.64 %	24 ms	Tesla V100
N. Gählert, J. Wan, N. Jourdan, J. Finkbeiner, U. Franke and J. Denzler: Single-Shot 3D Detection of Vehicles from Monocular RGB Images via Geometrically Constrained Keypoints in Real-Time. 2020 IEEE Intelligent Vehicles Symposium (IV) 2020.
345	FQNet			3.23 %	5.40 %	2.46 %	0.5 s	1 core @ 2.5 Ghz (Python)
L. Liu, J. Lu, C. Xu, Q. Tian and J. Zhou: Deep Fitting Degree Scoring Network for Monocular 3D Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
346	UDI-mono3D			3.08 %	3.93 %	2.55 %	0.05 s	1 core @ 2.5 Ghz (Python)
347	3D-SSMFCNN		code	2.63 %	3.20 %	2.40 %	0.1 s	GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.
348	VeloFCN			0.14 %	0.02 %	0.21 %	1 s	GPU @ 2.5 Ghz (Python + C/C++)
B. Li, T. Zhang and T. Xia: Vehicle Detection from 3D Lidar Using Fully Convolutional Network. RSS 2016 .
349	GAA			0.00 %	0.00 %	0.00 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
350	multi-task CNN			0.00 %	0.00 %	0.00 %	25.1 ms	GPU @ 2.0 Ghz (Python)
M. Oeljeklaus, F. Hoffmann and T. Bertram: A Fast Multi-Task CNN for Spatial Understanding of Traffic Scenes. IEEE Intelligent Transportation Systems Conference 2018.
351	GA-Aug			0.00 %	0.00 %	0.00 %	0.04 s	GPU @ 2.5 Ghz (Python)
352	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.

Pedestrian

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	ADLAB			52.58 %	58.39 %	49.13 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
2	VPFNet		code	52.41 %	60.07 %	50.28 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
3	SIEV-Net			52.15 %	60.78 %	48.54 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
4	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.
5	ISE-RCNN			51.06 %	55.64 %	47.76 %	0.09 s	1 core @ 2.5 Ghz (Python + C/C++)
6	HIKVISION-AFree			50.67 %	56.54 %	48.60 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
7	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.
8	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.
9	H^23D R-CNN			50.43 %	58.14 %	46.72 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
10	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.
11	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.
12	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.
13	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.
14	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.
15	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.
16	HIKVISION-ADLab-HZ			49.62 %	55.94 %	47.39 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
17	SAA-PV-RCNN			49.58 %	57.07 %	46.49 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
18	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.
19	AutoAlign			49.27 %	59.28 %	46.14 %	0.1 s	1 core @ 2.5 Ghz (Python)
20	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.
21	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.
22	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.
23	EA-M-RCNN(BorderAtt)			48.68 %	57.06 %	45.01 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
24	VCT			48.67 %	54.64 %	46.62 %	0.2 s	1 core @ 2.5 Ghz (Python)
25	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.
26	EPNet++			48.47 %	56.24 %	45.73 %	0.1 s	GPU @ 2.5 Ghz (Python)
27	MGAF-3DSSD		code	48.46 %	56.09 %	44.90 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: Anchor-free 3D Single Stage Detector with Mask-Guided Attention for Point Cloud. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
28	Fast			48.27 %	57.19 %	44.55 %	0.1 s	GPU @ 2.5 Ghz (Python)
29	FromVoxelToPoint		code	48.15 %	56.54 %	45.63 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
J. Li, H. Dai, L. Shao and Y. Ding: From Voxel to Point: IoU-guided 3D Object Detection for Point Cloud with Voxel-to- Point Decoder. MM '21: The 29th ACM International Conference on Multimedia (ACM MM) 2021.
30	TBD			47.95 %	53.09 %	45.90 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
31	ISE-RCNN-PV			47.85 %	55.63 %	45.80 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
32	P2V-RCNN			47.36 %	54.15 %	45.10 %	0.1 s	2 cores @ 2.5 Ghz (Python)
J. Li, S. Luo, Z. Zhu, H. Dai, A. Krylov, Y. Ding and L. Shao: P2V-RCNN: Point to Voxel Feature Learning for 3D Object Detection from Point Clouds. IEEE Access 2021.
33	SGNet			47.29 %	53.84 %	44.10 %	0.09 s	GPU @ 2.5 Ghz (Python)
34	VFN			47.12 %	56.71 %	44.42 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
35	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.
36	SCIR-Net			46.76 %	53.47 %	43.72 %	0.03 s	GPU @ 2.5 Ghz (Python + C/C++)
37	PPAF			46.74 %	53.44 %	43.76 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
38	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.
39	TBD_IOU1			46.59 %	53.92 %	44.29 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
40	XView-PartA^2			46.57 %	52.45 %	43.85 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
41	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.
42	GNN-RCNN			46.10 %	52.19 %	43.97 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
43	TBD_IOU			46.08 %	53.25 %	43.81 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
44	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.
45	E^2-PV-RCNN			45.85 %	52.35 %	44.00 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
46	DSA-PV-RCNN		code	45.82 %	52.03 %	43.81 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
P. Bhattacharyya, C. Huang and K. Czarnecki: SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection. 2021.
47	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.
48	SAA-SECOND			45.47 %	53.95 %	42.77 %	38m s	1 core @ 2.5 Ghz (C/C++)
49	FusionDetv2-v3			45.41 %	51.16 %	42.72 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
50	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.
51	Generalized-SIENet			45.39 %	51.66 %	43.51 %	0.08 s	1 core @ 2.5 Ghz (Python)
52	SA-voxel-centernet		code	45.35 %	51.16 %	43.33 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
53	WHUT-iou_ssd		code	45.24 %	50.30 %	43.28 %	0.045s	1 core @ 2.5 Ghz (C/C++)
54	sa-voxel-centernet		code	45.20 %	51.01 %	43.25 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
55	FPCR-CNN			45.18 %	52.79 %	42.70 %	0.05 s	1 core @ 2.5 Ghz (Python)
56	TPCG			45.17 %	51.44 %	43.03 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
57	IA-SSD (single)			45.07 %	52.73 %	42.75 %	0.013 s	1 core @ 2.5 Ghz (C/C++)
58	Point Image Fusion			45.07 %	50.56 %	42.92 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
59	PF-GAP			45.02 %	53.73 %	41.88 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
60	GAP-soft-filter			44.98 %	52.44 %	42.41 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
61	FPC-RCNN			44.96 %	51.54 %	42.88 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
62	FusionDetv1			44.85 %	52.42 %	42.56 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
63	Fast VP-RCNN		code	44.84 %	51.19 %	42.63 %	0.05 s	1 core @ 3.5 Ghz (C/C++)
64	SRDL			44.84 %	52.42 %	42.56 %	0.15 s	GPU @ 2.5 Ghz (Python + C/C++)
65	FusionDetv2-v2			44.80 %	50.61 %	42.91 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
66	TBD			44.65 %	50.72 %	42.61 %	0.07 s	1 core @ 2.5 Ghz (Python + C/C++)
67	FusionDetv2-v5			44.64 %	51.44 %	42.32 %	0.05 s	1 core @ 2.5 Ghz (Java + C/C++)
68	SVGA-Net			44.57 %	51.45 %	42.45 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
69	anonymous		code	44.50 %	50.60 %	42.26 %	0.05s	1 core @ >3.5 Ghz (python)
70	FSA-PVRCNN			44.49 %	49.33 %	42.58 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
71	FusionDetv2-v4			44.47 %	50.88 %	42.18 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
72	P2V_PCV1			44.33 %	49.29 %	41.71 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
73	MVOD			44.32 %	50.38 %	42.37 %	0.16 s	1 core @ 2.5 Ghz (C/C++)
74	SIF			44.28 %	52.05 %	42.03 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
75	ST-RCNN			44.14 %	49.78 %	41.95 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
76	DVFENet			44.12 %	50.98 %	41.62 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
Y. He, G. Xia, Y. Luo, L. Su, Z. Zhang, W. Li and P. Wang: DVFENet: Dual-branch Voxel Feature Extraction Network for 3D Object Detection. Neurocomputing 2021.
77	SARFE			44.00 %	49.85 %	41.99 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
78	Faraway-Frustum		code	43.85 %	52.15 %	41.68 %	0.1 s	GPU @ 2.5 Ghz (Python)
H. Zhang, D. Yang, E. Yurtsever, K. Redmill and U. Ozguner: Faraway-Frustum: Dealing with Lidar Sparsity for 3D Object Detection using Fusion. arXiv preprint arXiv:2011.01404 2020.
79	TBD			43.69 %	52.35 %	41.24 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
80	S-AT GCN			43.43 %	50.63 %	41.58 %	0.02 s	GPU @ 2.0 Ghz (Python)
L. Wang, C. Wang, X. Zhang, T. Lan and J. Li: S-AT GCN: Spatial-Attention Graph Convolution Network based Feature Enhancement for 3D Object Detection. CoRR 2021.
81	FPC3D_all			43.41 %	50.05 %	41.06 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
82	tbd			43.41 %	49.45 %	40.57 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
83	MKFFNet			43.29 %	50.71 %	40.97 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
84	demo			43.29 %	51.78 %	40.79 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
85	BirdNet+		code	42.87 %	48.90 %	40.59 %	0.11 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. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
86	MSL3D			42.82 %	48.81 %	40.13 %	0.03 s	GPU @ 2.5 Ghz (Python)
87	IA-SSD (multi)			42.61 %	51.76 %	40.51 %	0.014 s	1 core @ 2.5 Ghz (C/C++)
88	MKFFNet			42.58 %	49.79 %	40.62 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
89	FusionDetv2-baseline			42.53 %	47.08 %	40.71 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
90	YF			42.43 %	50.18 %	39.99 %	0.04 s	GPU @ 2.5 Ghz (C/C++)
91	XView			42.42 %	47.24 %	39.96 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
L. Xie, G. Xu, D. Cai and X. He: X-view: Non-egocentric Multi-View 3D Object Detector. 2021.
92	VGCN			42.33 %	50.02 %	40.05 %	0.09 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
93	IGRP+			41.86 %	50.15 %	38.98 %	0.18 s	1 core @ 2.5 Ghz (Python + C/C++)
94	TBD			41.70 %	49.81 %	39.43 %	TBD	GPU @ 2.5 Ghz (Python + C/C++)
95	AF_MCLS			41.61 %	50.55 %	37.85 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
96	ASCNet			41.46 %	47.25 %	38.83 %	0.09 s	1 core @ 2.5 Ghz (C/C++)
97	MKFFNet			41.33 %	47.80 %	39.39 %	0.01s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
98	PFF3D		code	40.94 %	48.74 %	38.54 %	0.05 s	GPU @ 3.0 Ghz (Python + C/C++)
L. Wen and K. Jo: Fast and Accurate 3D Object Detection for Lidar-Camera-Based Autonomous Vehicles Using One Shared Voxel-Based Backbone. IEEE Access 2021.
99	NV2P-RCNN			40.71 %	46.83 %	38.86 %	0.1 s	GPU @ 2.5 Ghz (Python)
100	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.
101	BirdNet+ (legacy)		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. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
102	HS3D		code	38.11 %	44.05 %	34.73 %	0.12 s	1 core @ 2.5 Ghz (Python + C/C++)
103	RoIFusion		code	38.08 %	46.21 %	35.97 %	0.22 s	1 core @ 3.0 Ghz (Python)
104	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.
105	CBi-GNN-persons			36.56 %	45.80 %	32.68 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
106	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.
107	MMLAB LIGA-Stereo		code	34.13 %	44.71 %	30.42 %	0.4 s	1 core @ 2.5 Ghz (Python + C/C++)
X. Guo, S. Shi, X. Wang and H. Li: LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) 2021.
108	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.
109	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.
110	FusionDetv2-v1			32.24 %	37.46 %	31.61 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
111	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.
112	deleted			29.50 %	40.58 %	26.01 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)
113	PointRGBNet			29.32 %	38.07 %	26.94 %	0.08 s	4 cores @ 2.5 Ghz (Python + C/C++)
114	Disp R-CNN		code	29.12 %	42.72 %	25.09 %	0.387 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
115	LIGA-Stereo-old			28.84 %	36.99 %	25.78 %	0.375 s	Titan Xp
116	Disp R-CNN (velo)		code	28.34 %	40.21 %	24.46 %	0.387 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Sun, L. Chen, Y. Xie, S. Zhang, Q. Jiang, X. Zhou and H. Bao: Disp R-CNN: Stereo 3D Object Detection via Shape Prior Guided Instance Disparity Estimation. CVPR 2020.
117	NCL		code	27.06 %	31.78 %	25.63 %	NA s	1 core @ 2.5 Ghz (Python)
118	OSE+			26.02 %	36.60 %	22.89 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
119	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.
120	AEC3D			22.40 %	28.59 %	20.67 %	18 ms	GPU @ 2.5 Ghz (Python)
121	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.
122	YOLOStereo3D		code	20.76 %	31.01 %	18.41 %	0.1 s	GPU 1080Ti
Y. Liu, L. Wang and M. Liu: YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection. 2021 International Conference on Robotics and Automation (ICRA) 2021.
123	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.
124	BEVC			20.50 %	26.84 %	18.71 %	35ms	GPU @ 1.5 Ghz (Python)
125	VN3D			19.12 %	23.51 %	16.26 %	0.02 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
126	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.
127	SOD			15.49 %	23.56 %	13.38 %	0.1 s	1 core @ 2.5 Ghz (Python)
128	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.
129	RT3D-GMP			14.22 %	19.92 %	12.83 %	0.06 s	GPU @ 2.5 Ghz (Python + C/C++)
H. Königshof and C. Stiller: Learning-Based Shape Estimation with Grid Map Patches for Realtime 3D Object Detection for Automated Driving. 2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC) 2020.
130	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.
131	DD3D		code	10.85 %	15.90 %	9.41 %	n/a s	1 core @ 2.5 Ghz (C/C++)
D. Park, R. Ambrus, V. Guizilini, J. Li and A. Gaidon: Is Pseudo-Lidar needed for Monocular 3D Object detection?. IEEE/CVF International Conference on Computer Vision (ICCV) .
132	SwinMono3D			9.82 %	14.55 %	8.31 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
133	SCSTSV-MonoFlex			9.62 %	14.45 %	8.14 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
134	CaDDN		code	9.41 %	14.72 %	8.17 %	0.63 s	GPU @ 2.5 Ghz (Python)
C. Reading, A. Harakeh, J. Chae and S. Waslander: Categorical Depth Distribution Network for Monocular 3D Object Detection. CVPR 2021.
135	MM			9.39 %	15.39 %	8.61 %	1 s	1 core @ 2.5 Ghz (C/C++)
136	K3D			9.06 %	14.56 %	7.59 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
137	MonoFlex			8.91 %	13.26 %	7.38 %	0.03 s	1 core @ 2.5 Ghz (Python)
138	MonoLCD			8.89 %	12.73 %	7.72 %	0.04 s	1 core @ 2.5 Ghz (Python)
139	vadin-TBD			8.81 %	13.26 %	7.41 %	0.04 s	1 core @ 2.5 Ghz (Python)
140	MonoCon			8.73 %	13.55 %	7.83 %	0.02 s	GPU @ 2.5 Ghz (Python)
141	mono3d			8.30 %	12.67 %	6.94 %	0.03 s	GPU @ 2.5 Ghz (Python)
142	LPCG-Monoflex			7.92 %	12.11 %	6.61 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
143	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.
144	MonoRUn		code	7.59 %	11.70 %	6.34 %	0.07 s	GPU @ 2.5 Ghz (Python + C/C++)
H. Chen, Y. Huang, W. Tian, Z. Gao and L. Xiong: MonoRUn: Monocular 3D Object Detection by Reconstruction and Uncertainty Propagation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
145	MonoFlex			7.36 %	10.36 %	6.29 %	0.03 s	GPU @ 2.5 Ghz (Python)
Y. Zhang, J. Lu and J. Zhou: Objects are Different: Flexible Monocular 3D Object Detection. CVPR 2021.
146	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.
147	monodle		code	6.96 %	10.73 %	6.20 %	0.04 s	GPU @ 2.5 Ghz (Python)
X. Ma, Y. Zhang, D. Xu, D. Zhou, S. Yi, H. Li and W. Ouyang: Delving into Localization Errors for Monocular 3D Object Detection. CVPR 2021 .
148	GAC3D++			6.92 %	10.56 %	5.70 %	0.25 s	1 core @ 2.5 Ghz (C/C++)
149	MonoGeo			6.77 %	9.54 %	5.83 %	0.05 s	1 core @ 2.5 Ghz (Python)
150	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.
151	RelationNet3D_dla34		code	6.54 %	10.17 %	5.51 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
152	ICCV			6.29 %	9.28 %	5.29 %	0.04 s	GPU @ 2.5 Ghz (Python)
153	DA-Mono3D			6.22 %	8.96 %	5.17 %	0.09s	1 core @ 2.5 Ghz (C/C++)
154	M3DSSD++			6.19 %	9.24 %	5.54 %	0.16s	1 core @ 2.5 Ghz (C/C++)
155	E2E-DA			6.15 %	10.33 %	5.99 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
156	Deprecated			6.12 %	8.70 %	5.16 %	Deprecated	Deprecated
157	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.
158	MonoHMOO			5.62 %	8.69 %	5.25 %	0.2 s	1 core @ 2.5 Ghz (Python + C/C++)
159	RelationNet3D_res18		code	5.50 %	8.86 %	5.04 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
160	Aug3D-RPN			5.22 %	7.14 %	4.21 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
C. He, J. Huang, X. Hua and L. Zhang: Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth. 2021.
161	PLDet3d			4.91 %	7.18 %	3.93 %	0.11 s	1 core @ 2.5 Ghz (C/C++)
162	Lite-FPN			4.79 %	7.13 %	4.26 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
163	COF3D			4.78 %	7.20 %	4.55 %	200 s	1 core @ 2.5 Ghz (Python + C/C++)
164	MAOLoss		code	4.74 %	6.63 %	4.19 %	0.05 s	1 core @ 2.5 Ghz (Python)
165	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.
166	E2E-DA-Lite (Res18)			4.59 %	5.98 %	3.53 %	0.01 s	GPU @ 2.5 Ghz (Python)
167	MP-Mono			4.59 %	6.04 %	3.96 %	0.16 s	GPU @ 2.5 Ghz (Python)
168	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.
169	DFR-Net			4.52 %	6.66 %	3.71 %	0.18 s	1080 Ti (Pytorch)
Z. Zou, X. Ye, L. Du, X. Cheng, X. Tan, L. Zhang, J. Feng, X. Xue and E. Ding: The devil is in the task: Exploiting reciprocal appearance-localization features for monocular 3d object detection . ICCV 2021.
170	M3D-RPN(S-R)			4.46 %	6.53 %	4.10 %	0.16 s	GPU @ 1.5 Ghz (Python)
171	FADNet		code	4.45 %	6.46 %	3.70 %	0.04 s	GPU @ >3.5 Ghz (Python)
172	QD-3DT		code	4.23 %	6.62 %	3.39 %	0.03 s	GPU @ 2.5 Ghz (Python)
H. Hu, Y. Yang, T. Fischer, F. Yu, T. Darrell and M. Sun: Monocular Quasi-Dense 3D Object Tracking. ArXiv:2103.07351 2021.
173	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 .
174	DDMP-3D			4.02 %	5.53 %	3.36 %	0.18 s	1 core @ 2.5 Ghz (Python)
L. Wang, L. Du, X. Ye, Y. Fu, G. Guo, X. Xue, J. Feng and L. Zhang: Depth-conditioned Dynamic Message Propagation for Monocular 3D Object Detection. CVPR 2020.
175	Geo3D			3.95 %	6.18 %	3.66 %	0.04 s	GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
176	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.
177	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.
178	LAPNet			3.59 %	4.86 %	2.98 %	0.03 s	1 core @ 2.5 Ghz (Python)
179	TBD			3.26 %	4.19 %	2.72 %	TBD	TBD
180	CD-KD			3.26 %	4.75 %	2.72 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
181	MonoEF		code	3.05 %	4.61 %	2.85 %	0.03 s	1 core @ 2.5 Ghz (Python)
Y. Zhou, Y. He, H. Zhu, C. Wang, H. Li and Q. Jiang: Monocular 3D Object Detection: An Extrinsic Parameter Free Approach. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
182	TBD			2.58 %	3.89 %	2.25 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
183	PPTrans			2.58 %	4.01 %	2.37 %	0.2 s	GPU @ 2.5 Ghz (Python)
184	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.
185	SparVox3D			2.05 %	2.90 %	1.69 %	0.05 s	GPU @ 2.0 Ghz (Python)
186	UDI-mono3D			1.85 %	2.94 %	1.60 %	0.05 s	1 core @ 2.5 Ghz (Python)
187	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.