The KITTI Vision Benchmark Suite

The object detection and object orientation estimation benchmark consists of 7481 training images and 7518 test images, comprising a total of 80.256 labeled objects. All images are color and saved as png. For evaluation, we compute precision-recall curves for object detection and orientation-similarity-recall curves for joint object detection and orientation estimation. In the latter case not only the object 2D bounding box has to be located correctly, but also the orientation estimate in bird's eye view is evaluated. To rank the methods we compute average precision and average orientation similiarity. 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 evaluate object detection performance using the PASCAL criteria and object detection and orientation estimation performance using the measure discussed in our CVPR 2012 publication. For cars we require an overlap of 70%, while for pedestrians and cyclists we require an overlap of 50% for a detection. Detections in don't care areas or detections which are smaller than the minimum size do not count as false positive. 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 that for the hard evaluation ~2 % of the provided bounding boxes have not been recognized by humans, thereby upper bounding recall at 98 %. Hence, the hard evaluation is only given for reference.
Note 1: On 25.04.2017, we have fixed a bug in the object detection evaluation script. As of now, the submitted detections are filtered based on the min. bounding box height for the respective category which we have been done before only for the ground truth detections, thus leading to false positives for the category "Easy" when bounding boxes of height 25-39 Px were submitted (and to false positives for all categories if bounding boxes smaller than 25 Px were submitted). We like to thank Amy Wu, Matt Wilder, Pekka Jänis and Philippe Vandermersch for their feedback. The last leaderboards right before the changes can be found here!

Car

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	NFAF3D			96.20 %	96.78 %	93.26 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
2	VPFNet			96.15 %	96.64 %	91.14 %	0.06 s	2 cores @ 2.5 Ghz (Python)
3	ZEEWAIN-AI			96.14 %	95.22 %	88.94 %	0.3 s	GPU @ 2.5 Ghz (Python)
4	Anonymous			96.12 %	98.74 %	91.12 %	0.1 s	GPU @ 2.5 Ghz (Python)
5	SFD			96.12 %	99.07 %	91.12 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
6	CLOCs_PVCas		code	95.96 %	96.76 %	91.08 %	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.
7	CityBrainLab			95.94 %	98.58 %	90.92 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
8	Anonymous			95.91 %	96.55 %	92.86 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
9	EA-M-RCNN(BorderAtt)			95.88 %	96.68 %	90.89 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
10	sfd			95.86 %	98.93 %	90.98 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
11	NFAF3D-light			95.79 %	96.69 %	92.75 %	0.03 s	1 core @ 2.5 Ghz (Python + C/C++)
12	SECOND			95.79 %	96.44 %	90.55 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
13	Fast			95.75 %	96.69 %	90.95 %	0.1 s	GPU @ 2.5 Ghz (Python)
14	BANet		code	95.61 %	98.75 %	90.64 %	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.
15	SE-SSD		code	95.60 %	96.69 %	90.53 %	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.
16	JPVNet			95.52 %	96.41 %	90.72 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
17	Anonymous			95.50 %	98.36 %	90.63 %	0.1s	1 core @ 2.5 Ghz (C/C++)
18	SPANet			95.46 %	96.54 %	90.47 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
19	ISE-RCNN-PV			95.46 %	96.20 %	92.88 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
20	ISE-RCNN			95.43 %	96.38 %	92.82 %	0.09 s	1 core @ 2.5 Ghz (Python + C/C++)
21	PLNL-3DSSD			95.38 %	96.37 %	90.31 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
22	SPG_mini			95.32 %	96.23 %	92.68 %	0.09 s	GPU @ 2.5 Ghz (Python)
23	TBD			95.29 %	96.17 %	90.56 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
24	PC-CNN-V2			95.20 %	96.06 %	89.37 %	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.
25	VPFNet		code	95.17 %	96.06 %	92.66 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
26	F-PointNet		code	95.17 %	95.85 %	85.42 %	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.
27	EPNet++			95.17 %	96.73 %	92.10 %	0.1 s	GPU @ 2.5 Ghz (Python)
28	SA-SSD		code	95.16 %	97.92 %	90.15 %	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.
29	Pyramid R-CNN			95.13 %	95.88 %	92.62 %	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.
30	Voxel R-CNN		code	95.11 %	96.49 %	92.45 %	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.
31	TBD			95.10 %	96.48 %	92.62 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
32	3DSSD		code	95.10 %	97.69 %	92.18 %	0.04 s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Yang, Y. Sun, S. Liu and J. Jia: 3DSSD: Point-based 3D Single Stage Object Detector. CVPR 2020.
33	3DIoU++			95.06 %	96.37 %	90.52 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
34	PV-RCNN-v2			95.05 %	96.08 %	92.42 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
35	MVRA + I-FRCNN+			94.98 %	95.87 %	82.52 %	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.
36	SIENet		code	94.97 %	96.02 %	92.40 %	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.
37	VueronNet		code	94.97 %	97.85 %	89.68 %	0.06 s	1 core @ 2.0 Ghz (Python)
38	VoTr-TSD			94.94 %	95.97 %	92.44 %	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.
39	CSVoxel-RCNN			94.91 %	96.33 %	92.11 %	0.03 s	GPU @ 1.0 Ghz (Python)
40	FrustumRCNN			94.90 %	95.98 %	92.39 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
41	StructuralIF			94.81 %	96.14 %	92.12 %	0.02 s	8 cores @ 2.5 Ghz (Python)
42	E^2-PV-RCNN			94.80 %	95.95 %	92.26 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
43	SRIF-RCNN			94.79 %	95.63 %	92.35 %	0.0947 s	1 core @ 2.5 Ghz (C/C++)
44	ST-RCNN			94.79 %	98.06 %	92.12 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
45	ST-RCNN (SNLW-RCNN)		code	94.79 %	98.06 %	92.12 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
46	TPCG			94.78 %	95.96 %	92.25 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
47	XView			94.77 %	95.89 %	92.23 %	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.
48	SCIR-Net			94.76 %	96.15 %	91.99 %	0.03 s	GPU @ 2.5 Ghz (Python + C/C++)
49	MSG-PGNN			94.75 %	95.86 %	92.16 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
50	P2V-RCNN			94.73 %	96.03 %	92.34 %	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.
51	SARFE			94.73 %	95.94 %	92.12 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
52	FusionDetv2-v4			94.73 %	95.94 %	92.00 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
53	Generalized-SIENet			94.72 %	95.76 %	92.19 %	0.08 s	1 core @ 2.5 Ghz (Python)
54	SqueezeRCNN			94.72 %	96.02 %	92.12 %	0.08 s	1 core @ 2.5 Ghz (Python)
55	SPG		code	94.71 %	97.80 %	92.19 %	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.
56	MMLab PV-RCNN		code	94.70 %	98.17 %	92.04 %	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.
57	3DIoU_v2			94.70 %	96.15 %	92.37 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
58	HyBrid Feature Det			94.69 %	95.89 %	92.11 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
59	PC-RGNN			94.68 %	95.80 %	92.20 %	0.1 s	GPU @ 2.5 Ghz (Python + C/C++)
60	LZY_RCNN			94.65 %	95.81 %	92.08 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
61	FusionDetv2-v3			94.64 %	96.16 %	91.94 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
62	TransCyclistNet			94.64 %	96.08 %	92.10 %	0.08 s	1 core @ 2.5 Ghz (Python)
63	DSA-PV-RCNN		code	94.64 %	95.86 %	92.10 %	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.
64	FSA-PVRCNN			94.63 %	95.81 %	92.06 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
65	Fast VP-RCNN		code	94.62 %	98.00 %	91.91 %	0.05 s	1 core @ 3.5 Ghz (C/C++)
66	DD3D		code	94.62 %	95.32 %	89.89 %	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) .
67	TransDet3D			94.61 %	95.83 %	92.07 %	0.08 s	1 core @ 2.5 Ghz (Python)
68	RangeIoUDet			94.61 %	95.74 %	91.98 %	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.
69	Point Image Fusion			94.61 %	95.70 %	92.11 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
70	MSL3D			94.60 %	95.76 %	92.16 %	0.03 s	GPU @ 2.5 Ghz (Python)
71	Multi-Sensor3D			94.60 %	95.76 %	92.16 %	0.03 s	GPU @ 2.5 Ghz (Python)
72	ReFineNet			94.59 %	95.75 %	92.12 %	0.08 s	1 core @ 2.5 Ghz (Python)
73	SA-voxel-centernet		code	94.59 %	95.80 %	92.05 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
74	DVFENet			94.57 %	95.35 %	91.77 %	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.
75	WHUT-iou_ssd		code	94.54 %	95.77 %	91.91 %	0.045s	1 core @ 2.5 Ghz (C/C++)
76	sa-voxel-centernet		code	94.53 %	95.88 %	92.00 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
77	anonymous		code	94.53 %	97.51 %	91.80 %	0.05s	1 core @ >3.5 Ghz (python)
78	FPC3D			94.52 %	96.06 %	91.72 %	33 s	1 core @ 2.5 Ghz (C/C++)
79	FPC-RCNN			94.51 %	96.15 %	91.80 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
80	TuSimple		code	94.47 %	95.12 %	86.45 %	1.6 s	GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016. K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016.
81	PF-GAP			94.47 %	96.13 %	90.15 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
82	EPNet		code	94.44 %	96.15 %	89.99 %	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.
83	GNN-RCNN			94.44 %	95.85 %	91.96 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
84	SERCNN			94.42 %	96.33 %	89.96 %	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.
85	SAA-SECOND			94.39 %	95.67 %	91.63 %	38m s	1 core @ 2.5 Ghz (C/C++)
86	SVGA-Net			94.28 %	95.69 %	91.73 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
87	UberATG-MMF			94.25 %	97.41 %	89.87 %	0.08 s	GPU @ 2.5 Ghz (Python)
M. Liang*, B. Yang*, Y. Chen, R. Hu and R. Urtasun: Multi-Task Multi-Sensor Fusion for 3D Object Detection. CVPR 2019.
88	SRDL			94.24 %	95.86 %	91.80 %	0.15 s	GPU @ 2.5 Ghz (Python + C/C++)
89	FusionDetv1			94.23 %	95.84 %	91.80 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
90	FusionDetv2-v2			94.21 %	95.75 %	89.89 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
91	TBD			94.21 %	95.51 %	91.69 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
92	GAP-soft-filter			94.20 %	95.81 %	91.53 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
93	FPCR-CNN			94.13 %	95.95 %	91.20 %	0.05 s	1 core @ 2.5 Ghz (Python)
94	SAA-PV-RCNN			94.11 %	95.01 %	92.50 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
95	NV2P-RCNN			94.07 %	97.82 %	91.20 %	0.1 s	GPU @ 2.5 Ghz (Python)
96	RangeRCNN			94.03 %	95.48 %	91.74 %	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.
97	VueronNet			94.03 %	96.70 %	87.58 %	0.08 s	GPU @ 2.5 Ghz (Python)
98	Faraway-Frustum		code	93.99 %	95.81 %	91.72 %	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.
99	SIF			93.95 %	95.51 %	91.57 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
100	MGAF-3DSSD		code	93.87 %	94.45 %	86.37 %	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.
101	LPCG-Monoflex			93.86 %	96.90 %	83.94 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
102	MMLAB LIGA-Stereo		code	93.82 %	96.43 %	86.19 %	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.
103	LIGA-Stereo-old			93.77 %	96.66 %	83.97 %	0.375 s	Titan Xp
104	Associate-3Ddet_v2			93.77 %	96.83 %	88.57 %	0.04 s	1 core @ 2.5 Ghz (Python)
105	Patches - EMP			93.75 %	97.91 %	90.56 %	0.5 s	GPU @ 2.5 Ghz (Python)
J. Lehner, A. Mitterecker, T. Adler, M. Hofmarcher, B. Nessler and S. Hochreiter: Patch Refinement: Localized 3D Object Detection. arXiv preprint arXiv:1910.04093 2019.
106	CIA-SSD		code	93.72 %	96.87 %	86.20 %	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.
107	XView-PartA^2			93.71 %	95.42 %	91.26 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
108	HIKVISION-ADLab-HZ			93.69 %	96.70 %	88.66 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
109	QD-3DT		code	93.66 %	94.26 %	83.63 %	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.
110	MVAF-Net		code	93.66 %	95.37 %	90.90 %	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.
111	TBD			93.64 %	95.31 %	91.21 %	0.07 s	1 core @ 2.5 Ghz (Python + C/C++)
112	FusionDetv2-v5			93.61 %	95.33 %	89.22 %	0.05 s	1 core @ 2.5 Ghz (Java + C/C++)
113	CBi-GNN			93.60 %	98.89 %	88.47 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
114	KpNet			93.60 %	96.74 %	85.97 %	42 s	1 core @ 2.5 Ghz (C/C++)
115	AM-SSD			93.58 %	96.78 %	90.61 %	0.04 s	1 core @ 2.5 Ghz (Python)
116	IA-SSD (multi)			93.56 %	96.10 %	90.68 %	0.014 s	1 core @ 2.5 Ghz (C/C++)
117	MonoPair			93.55 %	96.61 %	83.55 %	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.
118	IA-SSD (single)			93.54 %	96.26 %	88.49 %	0.013 s	1 core @ 2.5 Ghz (C/C++)
119	EBM3DOD		code	93.54 %	96.81 %	88.33 %	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.
120	CM3DV			93.53 %	96.79 %	88.35 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
121	Deep MANTA			93.50 %	98.89 %	83.21 %	0.7 s	GPU @ 2.5 Ghz (Python + C/C++)
F. Chabot, M. Chaouch, J. Rabarisoa, C. Teulière and T. Chateau: Deep MANTA: A Coarse-to-fine Many-Task Network for joint 2D and 3D vehicle analysis from monocular image. CVPR 2017.
122	Point-GNN		code	93.50 %	96.58 %	88.35 %	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.
123	Seg-RCNN		code	93.49 %	96.74 %	88.10 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
124	BtcDet			93.47 %	96.23 %	88.55 %	0.09 s	GPU @ 2.5 Ghz (Python + C/C++)
125	Struc info fusion II			93.45 %	96.72 %	88.31 %	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.
126	EBM3DOD baseline		code	93.45 %	96.72 %	88.25 %	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.
127	PPAF			93.43 %	96.52 %	90.71 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
128	RRC		code	93.40 %	95.68 %	87.37 %	3.6 s	GPU @ 2.5 Ghz (C/C++)
J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y. Tai and L. Xu: Accurate Single Stage Detector Using Recurrent Rolling Convolution. CVPR 2017.
129	DGIST MT-CNN			93.39 %	95.16 %	85.50 %	0.09 s	GPU @ 1.0 Ghz (Python)
130	Sem-Aug v1		code	93.39 %	96.39 %	90.70 %	0.04 s	GPU @ 3.5 Ghz (Python)
131	TBD			93.38 %	94.17 %	88.39 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
132	3D-CVF at SPA			93.36 %	96.78 %	86.11 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
J. Yoo, Y. Kim, J. Kim and J. Choi: 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection. ECCV 2020.
133	PSS			93.36 %	96.64 %	90.52 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
134	ISF-v2			93.34 %	96.73 %	90.54 %	0.04 s	1 core @ 2.5 Ghz (Python)
135	TBD			93.33 %	96.02 %	90.50 %	TBD	GPU @ 2.5 Ghz (Python + C/C++)
136	Struc info fusion I			93.31 %	96.59 %	88.23 %	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.
137	RoIFusion		code	93.30 %	96.30 %	88.22 %	0.22 s	1 core @ 3.0 Ghz (Python)
138	CityBrainLab-CT3D		code	93.30 %	96.28 %	90.58 %	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.
139	STD		code	93.22 %	96.14 %	90.53 %	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.
140	SARPNET			93.21 %	96.07 %	88.09 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
Y. Ye, H. Chen, C. Zhang, X. Hao and Z. Zhang: SARPNET: Shape Attention Regional Proposal Network for LiDAR-based 3D Object Detection. Neurocomputing 2019.
141	demo			93.21 %	96.16 %	90.23 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
142	VFN			93.21 %	96.04 %	90.30 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
143	H^23D R-CNN		code	93.20 %	96.20 %	90.55 %	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.
144	Fast Point R-CNN			93.18 %	96.13 %	87.68 %	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.
145	ASCNet			93.17 %	96.09 %	90.43 %	0.09 s	1 core @ 2.5 Ghz (C/C++)
146	VCT			93.17 %	96.31 %	90.52 %	0.2 s	1 core @ 2.5 Ghz (Python)
147	sensekitti		code	93.17 %	94.79 %	84.38 %	4.5 s	GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.
148	Sem-Aug-PointRCNN		code	93.17 %	95.78 %	88.35 %	0.1 s	GPU @ 3.5 Ghz (C/C++)
149	MVOD			93.16 %	96.17 %	92.56 %	0.16 s	1 core @ 2.5 Ghz (C/C++)
150	MBDF-Net			93.15 %	96.26 %	90.25 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
151	SJTU-HW			93.11 %	96.30 %	82.21 %	0.85s	GPU @ 1.5 Ghz (Python + C/C++)
S. Zhang, X. Zhao, L. Fang, F. Haiping and S. Haitao: LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR. IEEE International Conference on Image Processing 2018. L. Fang, X. Zhao and S. Zhang: Small-objectness sensitive detection based on shifted single shot detector. Multimedia Tools and Applications 2018.
152	TBD			93.08 %	96.27 %	85.66 %	TBD	TBD
153	SGNet			93.08 %	96.43 %	90.53 %	0.09 s	GPU @ 2.5 Ghz (Python)
154	FromVoxelToPoint		code	93.06 %	96.08 %	90.53 %	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.
155	HVPR			93.04 %	95.91 %	87.88 %	0.02 s	GPU @ 2.5 Ghz (Python)
156	CLOCs_SecCas			92.95 %	95.43 %	89.21 %	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.
157	MBDF-Net-1			92.85 %	95.98 %	89.91 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
158	YF			92.85 %	96.04 %	89.96 %	0.04 s	GPU @ 2.5 Ghz (C/C++)
159	FPGNN			92.83 %	96.26 %	87.69 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
160	TBD			92.82 %	96.06 %	88.00 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
161	HotSpotNet			92.81 %	96.21 %	89.80 %	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.
162	DPointNet			92.77 %	95.55 %	89.63 %	0.07s	1 core @ 2.5 Ghz (C/C++)
163	SegVoxelNet			92.73 %	96.00 %	87.60 %	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.
164	Patches			92.72 %	96.34 %	87.63 %	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.
165	CenterNet3D			92.69 %	95.76 %	89.81 %	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.
166	R-GCN			92.67 %	96.19 %	87.66 %	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.
167	TBD			92.66 %	95.60 %	90.55 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
168	PI-RCNN			92.66 %	96.17 %	87.68 %	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.
169	PointPainting			92.58 %	98.39 %	89.71 %	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.
170	SIEV-Net			92.56 %	95.56 %	87.40 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
171	DASS			92.53 %	96.23 %	87.75 %	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.
172	3D IoU-Net			92.47 %	96.31 %	87.67 %	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.
173	Associate-3Ddet		code	92.45 %	95.61 %	87.32 %	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.
174	S-AT GCN			92.44 %	95.06 %	90.78 %	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.
175	3D-VDNet			92.35 %	95.42 %	89.37 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
176	PointRGCN			92.33 %	97.51 %	87.07 %	0.26 s	GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
177	CVFNet			92.27 %	95.61 %	88.75 %	28.1ms	1 core @ 2.5 Ghz (Python)
178	CCFNET			92.25 %	95.85 %	89.36 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
179	LSNet			92.23 %	96.06 %	87.35 %	0.09 s	GPU @ 2.5 Ghz (Python + C/C++)
180	F-ConvNet		code	92.19 %	95.85 %	80.09 %	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.
181	PFF3D		code	92.15 %	95.37 %	87.54 %	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.
182	SDP+RPN			92.03 %	95.16 %	79.16 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016. S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 2015.
183	RangeDet		code	92.03 %	95.20 %	87.14 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
184	AB3DMOT		code	92.00 %	95.88 %	86.98 %	0.0047s	1 core @ 2.5 Ghz (Python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
185	VGCN			91.97 %	94.91 %	89.34 %	0.09 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
186	MMLab-PointRCNN		code	91.90 %	95.92 %	87.11 %	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.
187	MKFFNet			91.88 %	95.29 %	89.21 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
188	AutoAlign			91.87 %	95.15 %	89.21 %	0.1 s	1 core @ 2.5 Ghz (Python)
189	MMLab-PartA^2		code	91.86 %	95.03 %	89.06 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
S. Shi, Z. Wang, J. Shi, X. Wang and H. Li: From Points to Parts: 3D Object Detection from Point Cloud with Part-aware and Part-aggregation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence 2020.
190	epBRM		code	91.77 %	94.59 %	88.45 %	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.
191	C-GCN			91.73 %	95.64 %	86.37 %	0.147 s	GPU @ V100 (Python)
J. Zarzar, S. Giancola and B. Ghanem: PointRGCN: Graph Convolution Networks for 3D Vehicles Detection Refinement. ArXiv 2019.
192	ITVD		code	91.73 %	95.85 %	79.31 %	0.3 s	GPU @ 2.5 Ghz (C/C++)
Y. Wei Liu: Improving Tiny Vehicle Detection in Complex Scenes. IEEE International Conference on Multimedia and Expo (ICME) 2018.
193	sscl-20p			91.71 %	97.14 %	88.72 %	0.02 s	1 core @ 2.5 Ghz (Python)
194	SINet+		code	91.67 %	94.17 %	78.60 %	0.3 s	TITAN X GPU
X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
195	VOXEL_3D			91.61 %	94.50 %	86.37 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
196	Cascade MS-CNN		code	91.60 %	94.26 %	78.84 %	0.25 s	GPU @ 2.5 Ghz (C/C++)
Z. Cai and N. Vasconcelos: Cascade R-CNN: High Quality Object Detection and Instance Segmentation. arXiv preprint arXiv:1906.09756 2019. Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A unified multi-scale deep convolutional neural network for fast object detection. European conference on computer vision 2016.
197	MKFFNet			91.54 %	95.32 %	89.02 %	0.01s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
198	V3D			91.52 %	94.46 %	86.34 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
199	MKFFNet			91.51 %	95.19 %	89.01 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
200	CA3D			91.48 %	95.19 %	82.01 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
201	PointRGBNet			91.48 %	95.40 %	86.50 %	0.08 s	4 cores @ 2.5 Ghz (Python + C/C++)
202	MAFF-Net(DAF-Pillar)			91.46 %	94.38 %	83.89 %	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.
203	AIMC-RUC			91.45 %	96.94 %	86.28 %	0.11 s	1 core @ 2.5 Ghz (Python)
204	HRI-VoxelFPN			91.44 %	96.65 %	86.18 %	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.
205	FPC3D_all			91.42 %	95.52 %	86.76 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
206	EgoNet		code	91.39 %	96.18 %	81.33 %	0.1 s	GPU @ 1.5 Ghz (Python)
S. Li, Z. Yan, H. Li and K. Cheng: Exploring intermediate representation for monocular vehicle pose estimation. The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
207	HNet-3DSSD		code	91.35 %	94.91 %	87.47 %	0.05 s	GPU @ 2.5 Ghz (Python)
208	SC(DLA34)			91.27 %	96.61 %	83.50 %	0.04 s	GPU @ 2.5 Ghz (Python)
209	GAA			91.20 %	94.50 %	82.97 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
210	PointPillars		code	91.19 %	94.00 %	88.17 %	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.
211	LTN			91.18 %	94.68 %	81.51 %	0.4 s	GPU @ >3.5 Ghz (Python)
T. Wang, X. He, Y. Cai and G. Xiao: Learning a Layout Transfer Network for Context Aware Object Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
212	WS3D			91.15 %	95.13 %	86.52 %	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.
213	KM3D		code	91.07 %	96.44 %	81.19 %	0.03 s	1 core @ 2.5 Ghz (Python)
P. Li: Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training. 2020.
214	Geo3D			91.04 %	94.28 %	78.91 %	0.04 s	GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
215	FII-CenterNet			91.03 %	94.48 %	83.00 %	0.09 s	GPU @ 2.5 Ghz (Python)
S. Fan, F. Zhu, S. Chen, H. Zhang, B. Tian, Y. Lv and F. Wang: FII-CenterNet: An Anchor-Free Detector With Foreground Attention for Traffic Object Detection. IEEE Transactions on Vehicular Technology 2021.
216	Aston-EAS			91.02 %	93.91 %	77.93 %	0.24 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Wei, J. He, Y. Zhou, K. Chen, Z. Tang and Z. Xiong: Enhanced Object Detection With Deep Convolutional Neural Networks for Advanced Driving Assistance. IEEE Transactions on Intelligent Transportation Systems 2019.
217	MonoFlex			91.02 %	96.01 %	83.38 %	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.
218	SCSTSV-MonoFlex			90.99 %	96.44 %	81.16 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
219	ARPNET			90.99 %	94.00 %	83.49 %	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.
220	GA-Aug			90.92 %	93.93 %	83.03 %	0.04 s	GPU @ 2.5 Ghz (Python)
221	MonoEF		code	90.88 %	96.32 %	83.27 %	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.
222	PatchNet		code	90.87 %	93.82 %	79.62 %	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.
223	MV3D			90.83 %	96.47 %	78.63 %	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.
224	monodle		code	90.81 %	93.83 %	80.93 %	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 .
225	3D IoU Loss			90.79 %	95.92 %	85.65 %	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.
226	SINet_VGG		code	90.79 %	93.59 %	77.53 %	0.2 s	TITAN X GPU
X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
227	APL-Second			90.70 %	95.82 %	85.51 %	0.05 s	1 core @ 2.5 Ghz (Python)
228	vadin-TBD			90.69 %	95.92 %	80.91 %	0.04 s	1 core @ 2.5 Ghz (Python)
229	TANet		code	90.67 %	93.67 %	85.31 %	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.
230	NF2			90.62 %	94.14 %	81.30 %	0.1 s	GPU @ 2.5 Ghz (Python)
231	MonoFlex			90.52 %	95.59 %	83.00 %	0.03 s	1 core @ 2.5 Ghz (Python)
232	FADNet		code	90.49 %	96.15 %	80.71 %	0.04 s	GPU @ >3.5 Ghz (Python)
233	IGRP+			90.42 %	96.03 %	87.63 %	0.18 s	1 core @ 2.5 Ghz (Python + C/C++)
234	CG-Stereo			90.38 %	96.31 %	82.80 %	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.
235	SCNet			90.30 %	95.59 %	85.09 %	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.
236	Deep3DBox			90.19 %	94.71 %	76.82 %	1.5 s	GPU @ 2.5 Ghz (C/C++)
A. Mousavian, D. Anguelov, J. Flynn and J. Kosecka: 3D Bounding Box Estimation Using Deep Learning and Geometry. CVPR 2017.
237	FQNet			90.17 %	94.72 %	76.78 %	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.
238	MonoGeo			90.14 %	95.11 %	80.19 %	0.05 s	1 core @ 2.5 Ghz (Python)
239	DeepStereoOP			90.06 %	95.15 %	79.91 %	3.4 s	GPU @ 3.5 Ghz (Matlab + C/C++)
C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.
240	SubCNN			89.98 %	94.26 %	79.78 %	2 s	GPU @ 3.5 Ghz (Python + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.
241	MLOD		code	89.97 %	94.88 %	84.98 %	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.
242	GPP		code	89.96 %	94.02 %	81.13 %	0.23 s	GPU @ 1.5 Ghz (Python + C/C++)
A. Rangesh and M. Trivedi: Ground plane polling for 6dof pose estimation of objects on the road. IEEE Transactions on Intelligent Vehicles 2020.
243	LCA			89.94 %	93.40 %	82.76 %	0.05 s	1 core @ 2.5 Ghz (Python)
244	AVOD		code	89.88 %	95.17 %	82.83 %	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.
245	SINet_PVA		code	89.86 %	92.72 %	76.47 %	0.11 s	TITAN X GPU
X. Hu, X. Xu, Y. Xiao, H. Chen, S. He, J. Qin and P. Heng: SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection. IEEE Transactions on Intelligent Transportation Systems 2019.
246	Digging_M3D			89.77 %	93.73 %	79.68 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
247	UDI-mono3D			89.67 %	94.39 %	80.29 %	0.05 s	1 core @ 2.5 Ghz (Python)
248	3DOP		code	89.55 %	92.96 %	79.38 %	3s	GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
249	IAFA			89.46 %	93.08 %	79.83 %	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.
250	Mono3D		code	89.37 %	94.52 %	79.15 %	4.2 s	GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.
251	4d-MSCNN		code	89.37 %	92.40 %	77.00 %	0.3 min	GPU @ 3.0 Ghz (Matlab + C/C++)
P. Ferraz, B. Oliveira, F. Ferreira, C. Silva Martins and others: Three-stage RGBD architecture for vehicle and pedestrian detection using convolutional neural networks and stereo vision. IET Intelligent Transport Systems 2020.
252	PPTrans			89.17 %	95.17 %	81.77 %	0.2 s	GPU @ 2.5 Ghz (Python)
253	FusionDetv2-v1			89.10 %	94.82 %	84.28 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
254	M3DSSD++			89.06 %	94.94 %	77.17 %	0.16s	1 core @ 2.5 Ghz (C/C++)
255	GAC3D++			88.93 %	94.23 %	79.09 %	0.25 s	1 core @ 2.5 Ghz (C/C++)
256	AVOD-FPN		code	88.92 %	94.70 %	84.13 %	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.
257	PCT			88.78 %	96.45 %	78.85 %	0.045 s	1 core @ 2.5 Ghz (C/C++)
258	FusionDetv2-baseline			88.76 %	94.28 %	85.65 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
259	AM3D			88.71 %	92.55 %	77.78 %	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.
260	EACV			88.70 %	94.51 %	81.15 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
261	MS-CNN		code	88.68 %	93.87 %	76.11 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection. ECCV 2016.
262	PMN			88.65 %	93.64 %	77.94 %	0.2 s	1 core @ 2.5 Ghz (Python)
263	MonoPSR		code	88.50 %	93.63 %	73.36 %	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.
264	BFF			88.49 %	90.84 %	78.84 %	8.4 s	4 cores @ 1.5 Ghz (Python)
265	Shift R-CNN (mono)		code	88.48 %	94.07 %	78.34 %	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.
266	RCD			88.46 %	92.52 %	83.73 %	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.
267	MM-MRFC			88.46 %	95.54 %	78.14 %	0.05 s	GPU @ 2.5 Ghz (C/C++)
A. Costea, R. Varga and S. Nedevschi: Fast Boosting based Detection using Scale Invariant Multimodal Multiresolution Filtered Features. CVPR 2017.
268	deleted			88.38 %	96.52 %	81.01 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)
269	MonoLCD			88.33 %	93.74 %	78.59 %	0.04 s	1 core @ 2.5 Ghz (Python)
270	3DBN			88.29 %	93.74 %	80.74 %	0.13s	1080Ti (Python+C/C++)
X. Li, J. Guivant, N. Kwok and Y. Xu: 3D Backbone Network for 3D Object Detection. CoRR 2019.
271	MonoCon			88.22 %	93.59 %	76.18 %	0.02 s	GPU @ 2.5 Ghz (Python)
272	CD-KD			87.93 %	94.62 %	82.27 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
273	MonoRUn		code	87.91 %	95.48 %	78.10 %	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.
274	Object Transformer			87.67 %	93.33 %	79.98 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
275	SMOKE		code	87.51 %	93.21 %	77.66 %	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.
276	weakm3d			87.35 %	91.21 %	82.29 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
277	CDN		code	87.19 %	95.85 %	79.43 %	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.
278	EG_DETR			87.10 %	93.04 %	79.05 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
279	RTM3D		code	86.93 %	91.82 %	77.41 %	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.
280	MonoRCNN		code	86.78 %	91.98 %	66.97 %	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.
281	BirdNet+		code	86.73 %	92.61 %	81.80 %	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.
282	HS3D		code	86.71 %	94.84 %	79.39 %	0.12 s	1 core @ 2.5 Ghz (Python + C/C++)
283	MP-Mono			86.54 %	90.66 %	65.72 %	0.16 s	GPU @ 2.5 Ghz (Python)
284	DSGN		code	86.43 %	95.53 %	78.75 %	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.
285	Stereo R-CNN		code	85.98 %	93.98 %	71.25 %	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.
286	StereoFENet			85.70 %	91.48 %	77.62 %	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.
287	PLDet3d			85.51 %	88.65 %	77.30 %	0.11 s	1 core @ 2.5 Ghz (C/C++)
288	ResNet-RRC_Car			85.33 %	91.45 %	74.27 %	0.06 s	GPU @ 1.5 Ghz (Python + C/C++)
H. Jeon and others: High-Speed Car Detection Using ResNet- Based Recurrent Rolling Convolution. Proceedings of the IEEE conference on systems, man, and cybernetics 2018.
289	AEC3D			85.22 %	90.74 %	80.82 %	18 ms	GPU @ 2.5 Ghz (Python)
290	PL++ (SDN+GDC)		code	85.15 %	94.95 %	77.78 %	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.
291	M3D-RPN		code	85.08 %	89.04 %	69.26 %	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 .
292	CDN-PL++			85.01 %	94.66 %	77.60 %	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.
293	SDP+CRC (ft)			85.00 %	92.06 %	71.71 %	0.6 s	GPU @ 2.5 Ghz (C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
294	LPCG-M3D			84.95 %	87.35 %	77.05 %	0.11 s	1 core @ 2.5 Ghz (C/C++)
295	SS3D			84.92 %	92.72 %	70.35 %	48 ms	Tesla V100 (Python)
E. Jörgensen, C. Zach and F. Kahl: Monocular 3D Object Detection and Box Fitting Trained End-to-End Using Intersection-over-Union Loss. CoRR 2019.
296	VN3D			84.80 %	90.73 %	78.41 %	0.02 s	1 core @ 2.5 Ghz (Python)
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297	MonoFENet			84.63 %	91.68 %	76.71 %	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.
298	MV3D (LIDAR)			84.39 %	93.08 %	79.27 %	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.
299	Complexer-YOLO			84.16 %	91.92 %	79.62 %	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.
300	ZoomNet		code	83.92 %	94.22 %	69.00 %	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.
301	OSE+			83.92 %	95.20 %	76.69 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
302	LAPNet			83.85 %	90.81 %	65.37 %	0.03 s	1 core @ 2.5 Ghz (Python)
303	D4LCN		code	83.67 %	90.34 %	65.33 %	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.
304	Deprecated			83.39 %	89.00 %	64.29 %	Deprecated	Deprecated
305	DA-Mono3D			83.36 %	88.94 %	64.23 %	0.09s	1 core @ 2.5 Ghz (C/C++)
306	Faster R-CNN		code	83.16 %	88.97 %	72.62 %	2 s	GPU @ 3.5 Ghz (Python + C/C++)
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards Real- Time Object Detection with Region Proposal Networks. NIPS 2015.
307	Mag			83.15 %	94.24 %	70.63 %	0.07 s	1 core @ 2.5 Ghz (C/C++)
308	MM			82.99 %	93.44 %	73.29 %	1 s	1 core @ 2.5 Ghz (C/C++)
309	Pseudo-LiDAR++		code	82.90 %	94.46 %	75.45 %	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.
310	Disp R-CNN		code	82.86 %	93.64 %	68.33 %	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.
311	BS3D			82.72 %	95.35 %	70.01 %	22 ms	Titan Xp
N. Gählert, J. Wan, M. Weber, J. Zöllner, U. Franke and J. Denzler: Beyond Bounding Boxes: Using Bounding Shapes for Real-Time 3D Vehicle Detection from Monocular RGB Images. 2019 IEEE Intelligent Vehicles Symposium (IV) 2019.
312	Disp R-CNN (velo)		code	82.64 %	93.45 %	70.45 %	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.
313	YOLOStereo3D		code	82.15 %	94.81 %	62.17 %	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.
314	Ground-Aware		code	82.05 %	92.33 %	62.08 %	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.
315	FRCNN+Or		code	82.00 %	92.91 %	68.79 %	0.09 s	Titan Xp GPU
C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine 2018. C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features. IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.
316	SwinMono3D			81.71 %	91.99 %	61.78 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
317	DDMP-3D			81.70 %	91.15 %	63.12 %	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.
318	ITS-MDPL			81.56 %	92.61 %	74.23 %	0.16 s	GPU @ 2.5 Ghz (Python)
319	A3DODWTDA (image)		code	81.25 %	78.96 %	70.56 %	0.8 s	GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
320	SOD			81.18 %	94.24 %	66.44 %	0.1 s	1 core @ 2.5 Ghz (Python)
321	none			81.07 %	91.14 %	68.85 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
322	RefineNet			81.01 %	91.91 %	65.67 %	0.20 s	GPU @ 2.5 Ghz (Matlab + C++)
R. Rajaram, E. Bar and M. Trivedi: RefineNet: Refining Object Detectors for Autonomous Driving. IEEE Transactions on Intelligent Vehicles 2016. R. Rajaram, E. Bar and M. Trivedi: RefineNet: Iterative Refinement for Accurate Object Localization. Intelligent Transportation Systems Conference 2016.
323	K3D			80.86 %	93.58 %	71.18 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
324	CaDDN		code	80.73 %	93.61 %	71.09 %	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.
325	GrooMeD-NMS		code	80.28 %	90.14 %	63.78 %	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.
326	3D-GCK			80.19 %	89.55 %	68.08 %	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.
327	COF3D			80.16 %	87.85 %	61.97 %	200 s	1 core @ 2.5 Ghz (Python + C/C++)
328	KMC		code	79.99 %	89.71 %	73.33 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
329	RelationNet3D_dla34		code	79.78 %	83.69 %	69.35 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
330	Lite-FPN			79.65 %	87.04 %	65.56 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
331	YoloMono3D		code	79.63 %	92.37 %	59.69 %	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.
332	E2E-DA			79.40 %	92.12 %	69.52 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
333	A3DODWTDA		code	79.15 %	82.98 %	68.30 %	0.08 s	GPU @ 3.0 Ghz (Python)
F. Gustafsson and E. Linder-Norén: Automotive 3D Object Detection Without Target Domain Annotations. 2018.
334	ImVoxelNet		code	79.09 %	89.80 %	69.45 %	0.2 s	GPU @ 2.5 Ghz (Python)
335	DFR-Net			78.81 %	90.13 %	60.40 %	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.
336	spLBP			78.66 %	81.66 %	61.69 %	1.5 s	8 cores @ 2.5 Ghz (Matlab + C/C++)
Q. Hu, S. Paisitkriangkrai, C. Shen, A. Hengel and F. Porikli: Fast Detection of Multiple Objects in Traffic Scenes With a Common Detection Framework. IEEE Trans. Intelligent Transportation Systems 2016.
337	MonoHMOO			78.21 %	92.33 %	61.58 %	0.2 s	1 core @ 2.5 Ghz (Python + C/C++)
338	E2E-DA-Lite (Res18)			78.21 %	90.79 %	66.16 %	0.01 s	GPU @ 2.5 Ghz (Python)
339	3D-SSMFCNN		code	78.19 %	77.92 %	69.19 %	0.1 s	GPU @ 1.5 Ghz (C/C++)
L. Novak: Vehicle Detection and Pose Estimation for Autonomous Driving. 2017.
340	MonoGRNet		code	77.94 %	88.65 %	63.31 %	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.
341	Aug3D-RPN			77.88 %	85.57 %	61.16 %	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.
342	AutoShape		code	77.66 %	86.51 %	64.40 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
343	Reinspect		code	77.48 %	90.27 %	66.73 %	2s	1 core @ 2.5 Ghz (C/C++)
R. Stewart, M. Andriluka and A. Ng: End-to-End People Detection in Crowded Scenes. CVPR 2016.
344	multi-task CNN			77.18 %	86.12 %	68.09 %	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.
345	Regionlets			76.99 %	88.75 %	60.49 %	1 s	>8 cores @ 2.5 Ghz (C/C++)
X. Wang, M. Yang, S. Zhu and Y. Lin: Regionlets for Generic Object Detection. T-PAMI 2015. W. Zou, X. Wang, M. Sun and Y. Lin: Generic Object Detection with Dense Neural Patterns and Regionlets. British Machine Vision Conference 2014. C. Long, X. Wang, G. Hua, M. Yang and Y. Lin: Accurate Object Detection with Location Relaxation and Regionlets Relocalization. Asian Conference on Computer Vision 2014.
346	3DVP		code	76.98 %	84.95 %	65.78 %	40 s	8 cores @ 3.5 Ghz (Matlab + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Data-Driven 3D Voxel Patterns for Object Category Recognition. IEEE Conference on Computer Vision and Pattern Recognition 2015.
347	RelationNet3D_res18		code	76.96 %	87.14 %	67.49 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
348	AutoShape			76.82 %	83.75 %	63.71 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
349	Mobile Stereo R-CNN			76.73 %	90.08 %	62.23 %	1.8 s	NVIDIA Jetson TX2
350	RelationNet3D			76.62 %	81.36 %	68.48 %	0.04 s	GPU @ 2.5 Ghz (Python)
351	ICCV			76.45 %	85.48 %	65.52 %	0.04 s	GPU @ 2.5 Ghz (Python)
352	SubCat		code	76.36 %	84.10 %	60.56 %	0.7 s	6 cores @ 3.5 Ghz (Matlab + C/C++)
E. Ohn-Bar and M. Trivedi: Learning to Detect Vehicles by Clustering Appearance Patterns. T-ITS 2015.
353	GS3D			76.35 %	86.23 %	62.67 %	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.
354	AOG		code	76.24 %	86.08 %	61.51 %	3 s	4 cores @ 2.5 Ghz (Matlab)
T. Wu, B. Li and S. Zhu: Learning And-Or Models to Represent Context and Occlusion for Car Detection and Viewpoint Estimation. TPAMI 2016. B. Li, T. Wu and S. Zhu: Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model. ECCV 2014.
355	Pose-RCNN			75.83 %	89.59 %	64.06 %	2 s	>8 cores @ 2.5 Ghz (Python)
M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on 2016.
356	3D FCN			74.65 %	86.74 %	67.85 %	>5 s	1 core @ 2.5 Ghz (C/C++)
B. Li: 3D Fully Convolutional Network for Vehicle Detection in Point Cloud. IROS 2017.
357	OC Stereo		code	74.60 %	87.39 %	62.56 %	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.
358	MAOLoss		code	73.79 %	89.31 %	63.59 %	0.05 s	1 core @ 2.5 Ghz (Python)
359	NCL		code	71.91 %	64.71 %	71.78 %	NA s	1 core @ 2.5 Ghz (Python)
360	Kinematic3D		code	71.73 %	89.67 %	54.97 %	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 .
361	AOG-View			71.26 %	85.01 %	55.73 %	3 s	1 core @ 2.5 Ghz (Matlab, C/C++)
B. Li, T. Wu and S. Zhu: Integrating Context and Occlusion for Car Detection by Hierarchical And-Or Model. ECCV 2014.
362	BEVC			70.93 %	79.97 %	64.46 %	35ms	GPU @ 1.5 Ghz (Python)
363	GAC3D			70.73 %	83.30 %	52.23 %	0.25 s	1 core @ 2.5 Ghz (Python)
364	MV-RGBD-RF			70.70 %	77.89 %	57.41 %	4 s	4 cores @ 2.5 Ghz (C/C++)
A. Gonzalez, D. Vazquez, A. Lopez and J. Amores: On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts.. IEEE Trans. on Cybernetics 2016. A. Gonzalez, G. Villalonga, J. Xu, D. Vazquez, J. Amores and A. Lopez: Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection. IEEE Intelligent Vehicles Symposium (IV) 2015.
365	Vote3Deep			70.30 %	78.95 %	63.12 %	1.5 s	4 cores @ 2.5 Ghz (C/C++)
M. Engelcke, D. Rao, D. Zeng Wang, C. Hay Tong and I. Posner: Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks. ArXiv e-prints 2016.
366	ROI-10D			70.16 %	76.56 %	61.15 %	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.
367	RetinaMono			69.83 %	74.54 %	60.95 %	0.02 s	1 core @ 2.5 Ghz (Python)
368	RetinaMono		code	69.01 %	75.18 %	58.98 %	0.02 s	1 core @ 2.5 Ghz (Python)
369	TBD			68.30 %	88.62 %	59.00 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
370	BirdNet+ (legacy)		code	68.05 %	92.10 %	65.61 %	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.
371	Decoupled-3D			67.92 %	87.78 %	54.53 %	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.
372	SparVox3D			67.88 %	83.76 %	52.56 %	0.05 s	GPU @ 2.0 Ghz (Python)
373	MDSNet			67.79 %	90.97 %	53.39 %	0.07 s	1 core @ 2.5 Ghz (Python)
374	Pseudo-Lidar		code	67.79 %	85.40 %	58.50 %	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.
375	OC-DPM			67.06 %	79.07 %	52.61 %	10 s	8 cores @ 2.5 Ghz (Matlab)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Occlusion Patterns for Object Class Detection. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2013.
376	DPM-VOC+VP			66.72 %	82.15 %	49.01 %	8 s	1 core @ 2.5 Ghz (C/C++)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015.
377	BdCost48LDCF		code	66.63 %	81.38 %	52.20 %	0.5 s	8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
378	RefinedMPL			65.24 %	88.29 %	53.20 %	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.
379	MDPM-un-BB			64.06 %	79.74 %	49.07 %	60 s	4 core @ 2.5 Ghz (MATLAB)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
380	Y4		code	63.60 %	81.79 %	56.20 %	0.03 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
381	TLNet (Stereo)		code	63.53 %	76.92 %	54.58 %	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.
382	PDV-Subcat			63.24 %	78.27 %	47.67 %	7 s	1 core @ 2.5 Ghz (C/C++)
J. Shen, X. Zuo, J. Li, W. Yang and H. Ling: A novel pixel neighborhood differential statistic feature for pedestrian and face detection . Pattern Recognition 2017.
383	MODet			62.54 %	66.06 %	60.04 %	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.
384	Graph-NMS			61.93 %	78.55 %	53.72 %	36 ms	GPU @ 2.5 Ghz (Python)
385	SubCat48LDCF		code	61.16 %	78.86 %	44.69 %	0.5 s	8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
386	DPM-C8B1			60.21 %	75.24 %	44.73 %	15 s	4 cores @ 2.5 Ghz (Matlab + C/C++)
J. Yebes, L. Bergasa and M. García-Garrido: Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes. Sensors 2015. J. Yebes, L. Bergasa, R. Arroyo and A. Lázaro: Supervised learning and evaluation of KITTI's cars detector with DPM. IV 2014.
387	FPIOD		code	60.04 %	78.81 %	50.13 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
388	SAMME48LDCF		code	58.38 %	77.47 %	44.43 %	0.5 s	8 cores @ 3.5 Ghz (Matlab + C/C++)
A. Fernández-Baldera, J. Buenaposada and L. Baumela: BAdaCost: Multi-class Boosting with Costs . Pattern Recognition 2018.
389	LSVM-MDPM-sv			58.36 %	71.11 %	43.22 %	10 s	4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010. A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout. NIPS 2011.
390	BirdNet			57.12 %	79.30 %	55.16 %	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.
391	ACF-SC			56.60 %	69.90 %	43.61 %	<0.3 s	1 core @ >3.5 Ghz (Matlab + C/C++)
C. Cadena, A. Dick and I. Reid: A Fast, Modular Scene Understanding System using Context-Aware Object Detection. Robotics and Automation (ICRA), 2015 IEEE International Conference on 2015.
392	LSVM-MDPM-us		code	55.95 %	68.94 %	41.45 %	10 s	4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
393	ACF			54.09 %	63.05 %	41.81 %	0.2 s	1 core @ >3.5 Ghz (Matlab + C/C++)
P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014. P. Doll\'ar: Piotr's Image and Video Matlab Toolbox (PMT). .
394	Mono3D_PLiDAR		code	53.36 %	80.85 %	44.80 %	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.
395	Graph-NMS-baseline			52.92 %	76.21 %	43.38 %	47 ms	GPU @ 2.5 Ghz (Python)
396	mono3d			52.11 %	41.51 %	50.08 %	0.03 s	GPU @ 2.5 Ghz (Python)
397	RT3D-GMP			51.95 %	62.41 %	39.14 %	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.
398	VeloFCN			51.82 %	70.53 %	45.70 %	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 .
399	Vote3D			45.94 %	54.38 %	40.48 %	0.5 s	4 cores @ 2.8 Ghz (C/C++)
D. Wang and I. Posner: Voting for Voting in Online Point Cloud Object Detection. Proceedings of Robotics: Science and Systems 2015.
400	TopNet-HighRes			45.85 %	58.04 %	41.11 %	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.
401	RT3DStereo			45.81 %	56.53 %	37.63 %	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.
402	Multimodal Detection		code	45.46 %	63.91 %	37.25 %	0.06 s	GPU @ 3.5 Ghz (Matlab + C/C++)
A. Asvadi, L. Garrote, C. Premebida, P. Peixoto and U. Nunes: Multimodal vehicle detection: fusing 3D- LIDAR and color camera data. Pattern Recognition Letters 2017.
403	RT3D			39.69 %	50.33 %	40.04 %	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.
404	R-AGNO-Net			36.55 %	49.87 %	35.20 %	0.15 s	1 core @ 2.5 Ghz (C/C++)
405	VoxelJones		code	36.31 %	43.89 %	34.16 %	.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.
406	CSoR		code	21.66 %	31.52 %	17.99 %	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.
407	mBoW			21.59 %	35.22 %	16.89 %	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.
408	DepthCN		code	21.18 %	37.45 %	16.08 %	2.3 s	GPU @ 3.5 Ghz (Matlab)
A. Asvadi, L. Garrote, C. Premebida, P. Peixoto and U. Nunes: DepthCN: vehicle detection using 3D- LIDAR and convnet. IEEE ITSC 2017.
409	YOLOv2		code	14.31 %	26.74 %	10.94 %	0.02 s	GPU @ 3.5 Ghz (C/C++)
J. Redmon, S. Divvala, R. Girshick and A. Farhadi: You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016. J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
410	TopNet-UncEst			6.24 %	7.24 %	5.42 %	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.
411	TopNet-Retina			5.00 %	6.82 %	4.52 %	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.
412	TopNet-DecayRate			0.01 %	0.00 %	0.01 %	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.
413	LaserNet			0.00 %	0.00 %	0.00 %	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.
414	TBD			0.00 %	0.00 %	0.00 %	0.1 s	1 core @ 2.5 Ghz (Python)
415	TBD			0.00 %	0.00 %	0.00 %	0.1 s	1 core @ 2.5 Ghz (Python)
416	Neighbor-Vote			0.00 %	0.00 %	0.00 %	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.

Pedestrian

	Method	Setting	Code	Moderate	Easy	Hard	Runtime	Environment
1	VueronNet			80.75 %	89.91 %	76.56 %	0.08 s	GPU @ 2.5 Ghz (Python)
2	F-PointNet		code	80.13 %	89.83 %	75.05 %	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.
3	NF2			79.59 %	88.28 %	75.47 %	0.1 s	GPU @ 2.5 Ghz (Python)
4	DGIST MT-CNN			79.38 %	88.58 %	74.83 %	0.09 s	GPU @ 1.0 Ghz (Python)
5	TuSimple		code	78.40 %	88.87 %	73.66 %	1.6 s	GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit all the layers: Fast and accurate cnn object detector with scale dependent pooling and cascaded rejection classifiers. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016. K. He, X. Zhang, S. Ren and J. Sun: Deep residual learning for image recognition. Proceedings of the IEEE conference on computer vision and pattern recognition 2016.
6	ZEEWAIN-AI			78.20 %	88.46 %	73.35 %	0.3 s	GPU @ 2.5 Ghz (Python)
7	RRC		code	76.61 %	85.98 %	71.47 %	3.6 s	GPU @ 2.5 Ghz (C/C++)
J. Ren, X. Chen, J. Liu, W. Sun, J. Pang, Q. Yan, Y. Tai and L. Xu: Accurate Single Stage Detector Using Recurrent Rolling Convolution. CVPR 2017.
8	WSSN			76.42 %	84.91 %	71.86 %	0.37 s	GPU @ >3.5 Ghz (Python + C/C++)
Z. Guo, W. Liao, Y. Xiao, P. Veelaert and W. Philips: Weak Segmentation Supervised Deep Neural Networks for Pedestrian Detection. Pattern Recognition 2021.
9	ECP Faster R-CNN			76.25 %	85.96 %	70.55 %	0.25 s	GPU @ 2.5 Ghz (Python)
M. Braun, S. Krebs, F. Flohr and D. Gavrila: The EuroCity Persons Dataset: A Novel Benchmark for Object Detection. CoRR 2018.
10	Aston-EAS			76.07 %	86.71 %	70.02 %	0.24 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Wei, J. He, Y. Zhou, K. Chen, Z. Tang and Z. Xiong: Enhanced Object Detection With Deep Convolutional Neural Networks for Advanced Driving Assistance. IEEE Transactions on Intelligent Transportation Systems 2019.
11	MHN			75.99 %	87.21 %	69.50 %	0.39 s	GPU @ 2.5 Ghz (Python)
J. Cao, Y. Pang, S. Zhao and X. Li: High-Level Semantic Networks for Multi- Scale Object Detection. IEEE Transactions on Circuits and Systems for Video Technology 2019.
12	FFNet		code	75.81 %	87.17 %	69.86 %	1.07 s	GPU @ 1.5 Ghz (Python)
C. Zhao, Y. Qian and M. Yang: Monocular Pedestrian Orientation Estimation Based on Deep 2D-3D Feedforward. Pattern Recognition 2019.
13	SJTU-HW			75.81 %	87.17 %	69.86 %	0.85s	GPU @ 1.5 Ghz (Python + C/C++)
S. Zhang, X. Zhao, L. Fang, F. Haiping and S. Haitao: LED: LOCALIZATION-QUALITY ESTIMATION EMBEDDED DETECTOR. IEEE International Conference on Image Processing 2018. L. Fang, X. Zhao and S. Zhang: Small-objectness sensitive detection based on shifted single shot detector. Multimedia Tools and Applications 2018.
14	MS-CNN		code	74.89 %	85.71 %	68.99 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
Z. Cai, Q. Fan, R. Feris and N. Vasconcelos: A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection. ECCV 2016.
15	F-ConvNet		code	72.91 %	83.63 %	67.18 %	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	GN			72.29 %	82.93 %	65.56 %	1 s	GPU @ 2.5 Ghz (Matlab + C/C++)
S. Jung and K. Hong: Deep network aided by guiding network for pedestrian detection. Pattern Recognition Letters 2017.
17	SubCNN			72.27 %	84.88 %	66.82 %	2 s	GPU @ 3.5 Ghz (Python + C/C++)
Y. Xiang, W. Choi, Y. Lin and S. Savarese: Subcategory-aware Convolutional Neural Networks for Object Proposals and Detection. IEEE Winter Conference on Applications of Computer Vision (WACV) 2017.
18	VMVS			71.82 %	82.80 %	66.85 %	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.
19	IVA		code	71.37 %	84.61 %	64.90 %	0.4 s	GPU @ 2.5 Ghz (C/C++)
Y. Zhu, J. Wang, C. Zhao, H. Guo and H. Lu: Scale-adaptive Deconvolutional Regression Network for Pedestrian Detection. ACCV 2016. S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in neural information processing systems 2015.
20	DD3D		code	70.79 %	84.61 %	65.69 %	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) .
21	MM-MRFC			70.76 %	83.79 %	64.81 %	0.05 s	GPU @ 2.5 Ghz (C/C++)
A. Costea, R. Varga and S. Nedevschi: Fast Boosting based Detection using Scale Invariant Multimodal Multiresolution Filtered Features. CVPR 2017.
22	SDP+RPN			70.42 %	82.07 %	65.09 %	0.4 s	GPU @ 2.5 Ghz (Python + C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016. S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems 2015.
23	3DOP		code	69.57 %	83.17 %	63.48 %	3s	GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Y. Zhu, A. Berneshawi, H. Ma, S. Fidler and R. Urtasun: 3D Object Proposals for Accurate Object Class Detection. NIPS 2015.
24	MonoPSR		code	68.56 %	85.60 %	63.34 %	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.
25	DeepStereoOP			68.46 %	83.00 %	63.35 %	3.4 s	GPU @ 3.5 Ghz (Matlab + C/C++)
C. Pham and J. Jeon: Robust Object Proposals Re-ranking for Object Detection in Autonomous Driving Using Convolutional Neural Networks. Signal Processing: Image Communiation 2017.
26	sensekitti		code	68.41 %	82.72 %	62.72 %	4.5 s	GPU @ 2.5 Ghz (Python + C/C++)
B. Yang, J. Yan, Z. Lei and S. Li: Craft Objects from Images. CVPR 2016.
27	FII-CenterNet			67.31 %	81.32 %	61.29 %	0.09 s	GPU @ 2.5 Ghz (Python)
S. Fan, F. Zhu, S. Chen, H. Zhang, B. Tian, Y. Lv and F. Wang: FII-CenterNet: An Anchor-Free Detector With Foreground Attention for Traffic Object Detection. IEEE Transactions on Vehicular Technology 2021.
28	Mono3D		code	67.29 %	80.30 %	62.23 %	4.2 s	GPU @ 2.5 Ghz (Matlab + C/C++)
X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler and R. Urtasun: Monocular 3D Object Detection for Autonomous Driving. CVPR 2016.
29	Faster R-CNN		code	66.24 %	79.97 %	61.09 %	2 s	GPU @ 3.5 Ghz (Python + C/C++)
S. Ren, K. He, R. Girshick and J. Sun: Faster R-CNN: Towards Real- Time Object Detection with Region Proposal Networks. NIPS 2015.
30	PMN			66.17 %	82.16 %	60.84 %	0.2 s	1 core @ 2.5 Ghz (Python)
31	VPFNet		code	65.68 %	75.03 %	61.95 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
32	SDP+CRC (ft)			64.36 %	79.22 %	59.16 %	0.6 s	GPU @ 2.5 Ghz (C/C++)
F. Yang, W. Choi and Y. Lin: Exploit All the Layers: Fast and Accurate CNN Object Detector with Scale Dependent Pooling and Cascaded Rejection Classifiers. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition 2016.
33	Pose-RCNN			63.54 %	80.07 %	57.02 %	2 s	>8 cores @ 2.5 Ghz (Python)
M. Braun, Q. Rao, Y. Wang and F. Flohr: Pose-RCNN: Joint object detection and pose estimation using 3D object proposals. Intelligent Transportation Systems (ITSC), 2016 IEEE 19th International Conference on 2016.
34	ADLAB			63.25 %	70.86 %	60.52 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
35	UDI-mono3D			63.24 %	77.94 %	57.35 %	0.05 s	1 core @ 2.5 Ghz (Python)
36	CFM			62.84 %	74.76 %	56.06 %	<2 s	GPU @ 2.5 Ghz (Matlab + C/C++)
Q. Hu, P. Wang, C. Shen, A. Hengel and F. Porikli: Pushing the Limits of Deep CNNs for Pedestrian Detection. IEEE Transactions on Circuits and Systems for Video Technology 2017.
37	HIKVISION-AFree			62.78 %	73.95 %	60.46 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
38	Fast			62.57 %	76.22 %	60.13 %	0.1 s	GPU @ 2.5 Ghz (Python)
39	TBD			62.35 %	79.37 %	57.42 %	TBD	TBD
40	HotSpotNet			62.31 %	71.43 %	59.24 %	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.
41	EACV			62.29 %	79.38 %	57.16 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
42	VCT			62.00 %	71.19 %	58.52 %	0.2 s	1 core @ 2.5 Ghz (Python)
43	GAA			61.92 %	77.67 %	56.56 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
44	P2V-RCNN			61.83 %	71.76 %	59.29 %	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.
45	TBD			61.74 %	70.58 %	59.45 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
46	MonoPair			61.57 %	78.81 %	56.51 %	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.
47	H^23D R-CNN			61.50 %	72.21 %	57.50 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
48	SAA-PV-RCNN			61.41 %	70.35 %	58.18 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
49	HIKVISION-ADLab-HZ			61.40 %	71.43 %	57.54 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
50	monodle		code	61.29 %	78.66 %	56.18 %	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 .
51	RPN+BF		code	61.22 %	77.06 %	55.22 %	0.6 s	GPU @ 2.5 Ghz (Matlab + C/C++)
L. Zhang, L. Lin, X. Liang and K. He: Is Faster R-CNN Doing Well for Pedestrian Detection?. ECCV 2016.
52	EA-M-RCNN(BorderAtt)			61.06 %	73.07 %	56.86 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
53	ISE-RCNN-PV			61.06 %	70.59 %	57.26 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
54	Regionlets			60.83 %	73.79 %	54.72 %	1 s	>8 cores @ 2.5 Ghz (C/C++)
X. Wang, M. Yang, S. Zhu and Y. Lin: Regionlets for Generic Object Detection. T-PAMI 2015. W. Zou, X. Wang, M. Sun and Y. Lin: Generic Object Detection with Dense Neural Patterns and Regionlets. British Machine Vision Conference 2014. C. Long, X. Wang, G. Hua, M. Yang and Y. Lin: Accurate Object Detection with Location Relaxation and Regionlets Relocalization. Asian Conference on Computer Vision 2014.
55	GA-Aug			60.78 %	76.78 %	55.00 %	0.04 s	GPU @ 2.5 Ghz (Python)
56	ISE-RCNN			60.70 %	69.41 %	58.49 %	0.09 s	1 core @ 2.5 Ghz (Python + C/C++)
57	3DSSD		code	60.51 %	72.33 %	56.28 %	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.
58	TBD			60.30 %	70.50 %	57.06 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
59	SIEV-Net			60.07 %	70.41 %	56.29 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
60	Generalized-SIENet			59.54 %	69.16 %	57.33 %	0.08 s	1 core @ 2.5 Ghz (Python)
61	AutoAlign			59.48 %	70.17 %	55.37 %	0.1 s	1 core @ 2.5 Ghz (Python)
62	Fast VP-RCNN		code	59.32 %	69.51 %	56.66 %	0.05 s	1 core @ 3.5 Ghz (C/C++)
63	QD-3DT		code	59.26 %	78.41 %	54.37 %	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.
64	TANet		code	59.07 %	69.90 %	56.44 %	0.035s	GPU @ 2.5 Ghz (Python + C/C++)
Z. Liu, X. Zhao, T. Huang, R. Hu, Y. Zhou and X. Bai: TANet: Robust 3D Object Detection from Point Clouds with Triple Attention. AAAI 2020.
65	anonymous		code	59.04 %	69.62 %	56.45 %	0.05s	1 core @ >3.5 Ghz (python)
66	GAP-soft-filter			58.89 %	68.54 %	56.08 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
67	DSA-PV-RCNN		code	58.81 %	66.93 %	56.57 %	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.
68	VFN			58.76 %	69.93 %	54.75 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
69	BFF			58.72 %	76.95 %	53.70 %	8.4 s	4 cores @ 1.5 Ghz (Python)
70	SRDL			58.70 %	68.45 %	56.23 %	0.15 s	GPU @ 2.5 Ghz (Python + C/C++)
71	FusionDetv1			58.68 %	68.44 %	56.22 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
72	PF-GAP			58.65 %	70.40 %	55.02 %	0.02 s	1 core @ 2.5 Ghz (C/C++)
73	P2V_PCV1			58.59 %	68.62 %	56.34 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
74	EG_DETR			58.58 %	74.53 %	53.76 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
75	MSL3D			58.57 %	69.07 %	55.86 %	0.03 s	GPU @ 2.5 Ghz (Python)
76	SA-voxel-centernet		code	58.50 %	66.89 %	56.25 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
77	MMLab PV-RCNN		code	58.37 %	68.88 %	55.38 %	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.
78	SCIR-Net			58.37 %	68.18 %	55.78 %	0.03 s	GPU @ 2.5 Ghz (Python + C/C++)
79	Point-GNN		code	58.20 %	71.59 %	54.06 %	0.6 s	GPU @ 2.5 Ghz (Python)
W. Shi and R. Rajkumar: Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud. CVPR 2020.
80	TPCG			58.17 %	67.39 %	55.55 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
81	XView-PartA^2			58.17 %	67.12 %	55.81 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
82	DeepParts			58.15 %	71.47 %	51.92 %	~1 s	GPU @ 2.5 Ghz (Matlab)
Y. Tian, P. Luo, X. Wang and X. Tang: Deep Learning Strong Parts for Pedestrian Detection. ICCV 2015.
83	CompACT-Deep			58.14 %	70.93 %	52.29 %	1 s	1 core @ 2.5 Ghz (Matlab + C/C++)
Z. Cai, M. Saberian and N. Vasconcelos: Learning Complexity-Aware Cascades for Deep Pedestrian Detection. ICCV 2015.
84	EPNet++			58.10 %	68.58 %	55.58 %	0.1 s	GPU @ 2.5 Ghz (Python)
85	WHUT-iou_ssd		code	58.03 %	66.60 %	55.82 %	0.045s	1 core @ 2.5 Ghz (C/C++)
86	E^2-PV-RCNN			58.01 %	67.39 %	55.77 %	0.08 s	1 core @ 2.5 Ghz (Python + C/C++)
87	MMLab-PartA^2		code	57.96 %	68.78 %	54.01 %	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.
88	Point Image Fusion			57.91 %	66.47 %	55.48 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
89	AVOD-FPN		code	57.87 %	67.95 %	55.23 %	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.
90	PPAF			57.81 %	67.54 %	54.30 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
91	sa-voxel-centernet		code	57.79 %	66.03 %	55.61 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
92	FSA-PVRCNN			57.67 %	65.80 %	55.34 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
93	SVGA-Net			57.57 %	67.48 %	55.11 %	0.08 s	GPU @ 2.5 Ghz (Python + C/C++)
94	TBD			57.56 %	66.43 %	55.21 %	0.07 s	1 core @ 2.5 Ghz (Python + C/C++)
95	FPC-RCNN			57.46 %	66.88 %	55.09 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
96	SGNet			57.36 %	65.93 %	53.82 %	0.09 s	GPU @ 2.5 Ghz (Python)
97	Faraway-Frustum		code	57.35 %	67.88 %	54.42 %	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.
98	MVOD			57.33 %	66.72 %	54.31 %	0.16 s	1 core @ 2.5 Ghz (C/C++)
99	SIF			57.32 %	67.78 %	54.86 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
P. An: SIF. Submitted to CVIU 2021.
100	GNN-RCNN			57.32 %	66.78 %	55.77 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
101	FromVoxelToPoint		code	57.26 %	68.26 %	54.74 %	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.
102	SemanticVoxels			57.22 %	67.62 %	54.90 %	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.
103	FPCR-CNN			57.14 %	66.59 %	54.43 %	0.05 s	1 core @ 2.5 Ghz (Python)
104	FusionDetv2-v3			56.93 %	65.96 %	54.73 %	0.05 s	1 core @ 2.5 Ghz (Python + C/C++)
105	IA-SSD (single)			56.87 %	66.69 %	54.68 %	0.013 s	1 core @ 2.5 Ghz (C/C++)
106	FRCNN+Or		code	56.68 %	71.64 %	51.53 %	0.09 s	Titan Xp GPU
C. Guindel, D. Martin and J. Armingol: Fast Joint Object Detection and Viewpoint Estimation for Traffic Scene Understanding. IEEE Intelligent Transportation Systems Magazine 2018. C. Guindel, D. Martin and J. Armingol: Joint Object Detection and Viewpoint Estimation using CNN features. IEEE International Conference on Vehicular Electronics and Safety (ICVES) 2017.
107	FusionDetv2-v4			56.60 %	65.80 %	54.55 %	0.06 s	1 core @ 2.5 Ghz (Python + C/C++)
108	SARFE			56.56 %	66.10 %	54.15 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
109	FilteredICF			56.53 %	69.79 %	50.32 %	~ 2 s	>8 cores @ 2.5 Ghz (Matlab + C/C++)
S. Zhang, R. Benenson and B. Schiele: Filtered Channel Features for Pedestrian Detection. CVPR 2015.
110	ARPNET			56.42 %	69.08 %	52.69 %	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.
111	FusionDetv2-v5			56.40 %	65.17 %	53.89 %	0.05 s	1 core @ 2.5 Ghz (Java + C/C++)
112	MonoRUn		code	56.40 %	73.05 %	51.40 %	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.
113	MV-RGBD-RF			56.18 %	72.99 %	49.72 %	4 s	4 cores @ 2.5 Ghz (C/C++)
A. Gonzalez, D. Vazquez, A. Lopez and J. Amores: On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts.. IEEE Trans. on Cybernetics 2016. A. Gonzalez, G. Villalonga, J. Xu, D. Vazquez, J. Amores and A. Lopez: Multiview Random Forest of Local Experts Combining RGB and LIDAR data for Pedestrian Detection. IEEE Intelligent Vehicles Symposium (IV) 2015.
114	TBD_IOU1			56.02 %	66.44 %	53.62 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
115	SAA-SECOND			55.95 %	65.50 %	52.88 %	38m s	1 core @ 2.5 Ghz (C/C++)
116	TBD_IOU			55.90 %	64.94 %	53.60 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
117	MGAF-3DSSD		code	55.80 %	66.31 %	52.02 %	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.
118	Mag			55.74 %	71.91 %	50.92 %	0.07 s	1 core @ 2.5 Ghz (C/C++)
119	MLOD		code	55.62 %	68.42 %	51.45 %	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.
120	ST-RCNN			55.46 %	64.50 %	52.89 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
121	TBD			55.43 %	65.62 %	51.97 %	0.06 s	1 core @ 2.5 Ghz (C/C++)
122	FusionDetv2-v2			55.32 %	62.74 %	52.43 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
123	PointPillars		code	55.10 %	65.29 %	52.39 %	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.
124	FPC3D_all			55.08 %	64.74 %	52.59 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
125	STD		code	55.04 %	68.33 %	50.85 %	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.
126	GAC3D++			54.96 %	74.08 %	50.06 %	0.25 s	1 core @ 2.5 Ghz (C/C++)
127	Vote3Deep			54.80 %	67.99 %	51.17 %	1.5 s	4 cores @ 2.5 Ghz (C/C++)
M. Engelcke, D. Rao, D. Zeng Wang, C. Hay Tong and I. Posner: Vote3Deep: Fast Object Detection in 3D Point Clouds Using Efficient Convolutional Neural Networks. ArXiv e-prints 2016.
128	epBRM		code	54.13 %	62.90 %	51.95 %	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.
129	DVFENet			54.13 %	63.54 %	51.79 %	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.
130	OSE+			54.12 %	68.48 %	49.93 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
131	XView			53.83 %	62.27 %	51.61 %	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.
132	PointPainting			53.76 %	61.86 %	50.61 %	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.
133	MKFFNet			53.64 %	63.25 %	51.07 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
134	MKFFNet			53.55 %	62.18 %	50.80 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
135	PDV2			53.54 %	65.59 %	47.65 %	3.7 s	1 core @ 3.0 Ghz Matlab (C/C++)
J. Shen, X. Zuo, J. Li, W. Yang and H. Ling: A novel pixel neighborhood differential statistic feature for pedestrian and face detection . Pattern Recognition 2017.
136	TBD			53.34 %	64.17 %	51.00 %	TBD	GPU @ 2.5 Ghz (Python + C/C++)
137	ASCNet			53.28 %	62.40 %	50.88 %	0.09 s	1 core @ 2.5 Ghz (C/C++)
138	TAFT			53.15 %	67.62 %	47.08 %	0.2 s	1 core @ 2.5 Ghz (C/C++)
J. Shen, X. Zuo, W. Yang, D. Prokhorov, X. Mei and H. Ling: Differential Features for Pedestrian Detection: A Taylor Series Perspective. IEEE Transactions on Intelligent Transportation Systems 2018.
139	Disp R-CNN		code	52.98 %	71.79 %	48.20 %	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.
140	FusionDetv2-baseline			52.96 %	58.96 %	50.94 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
141	Disp R-CNN (velo)		code	52.90 %	71.63 %	48.15 %	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.
142	demo			52.90 %	63.79 %	49.54 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
143	pAUCEnsT			52.88 %	65.84 %	46.97 %	60 s	1 core @ 2.5 Ghz (Matlab + C/C++)
S. Paisitkriangkrai, C. Shen and A. Hengel: Pedestrian Detection with Spatially Pooled Features and Structured Ensemble Learning. arXiv 2014.
144	SparVox3D			52.84 %	69.33 %	48.49 %	0.05 s	GPU @ 2.0 Ghz (Python)
145	VGCN			52.80 %	61.86 %	50.66 %	0.09 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
146	tbd			52.78 %	63.45 %	50.79 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
147	PFF3D		code	52.53 %	62.12 %	50.27 %	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.
148	NV2P-RCNN			52.52 %	61.80 %	50.24 %	0.1 s	GPU @ 2.5 Ghz (Python)
149	IA-SSD (multi)			52.45 %	65.07 %	50.20 %	0.014 s	1 core @ 2.5 Ghz (C/C++)
150	S-AT GCN			52.30 %	62.01 %	50.10 %	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.
151	MMLAB LIGA-Stereo		code	52.18 %	65.59 %	49.29 %	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.
152	SCSTSV-MonoFlex			52.18 %	67.51 %	45.90 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
153	MKFFNet			51.96 %	60.31 %	49.70 %	0.01s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
154	YF			51.82 %	61.37 %	48.59 %	0.04 s	GPU @ 2.5 Ghz (C/C++)
155	Shift R-CNN (mono)		code	51.30 %	70.86 %	46.37 %	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.
156	AF_MCLS			49.95 %	62.85 %	45.78 %	0.1 s	1 core @ 2.5 Ghz (Python + C/C++)
157	SCNet			49.61 %	60.95 %	46.91 %	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.
158	MMLab-PointRCNN		code	49.41 %	58.93 %	46.44 %	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.
159	Y4		code	49.24 %	68.07 %	44.42 %	0.03 s	1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
160	vadin-TBD			48.97 %	63.77 %	44.60 %	0.04 s	1 core @ 2.5 Ghz (Python)
161	MonoFlex			48.64 %	61.96 %	44.17 %	0.03 s	1 core @ 2.5 Ghz (Python)
162	ACFD		code	48.63 %	61.62 %	44.15 %	0.2 s	4 cores @ >3.5 Ghz (C/C++)
M. Dimitrievski, P. Veelaert and W. Philips: Semantically aware multilateral filter for depth upsampling in automotive LiDAR point clouds. IEEE Intelligent Vehicles Symposium, IV 2017, Los Angeles, CA, USA, June 11-14, 2017 2017.
163	R-CNN			48.57 %	62.88 %	43.05 %	4 s	GPU @ 3.3 Ghz (C/C++)
J. Hosang, M. Omran, R. Benenson and B. Schiele: Taking a Deeper Look at Pedestrians. arXiv 2015.
164	IGRP+			48.46 %	59.37 %	44.82 %	0.18 s	1 core @ 2.5 Ghz (Python + C/C++)
165	RelationNet3D_dla34		code	47.93 %	63.34 %	43.42 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
166	LIGA-Stereo-old			47.75 %	58.61 %	44.39 %	0.375 s	Titan Xp
167	MonoFlex			47.58 %	62.64 %	43.15 %	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.
168	BirdNet+		code	47.50 %	54.78 %	45.53 %	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.
169	RoIFusion		code	46.81 %	56.26 %	44.58 %	0.22 s	1 core @ 3.0 Ghz (Python)
170	deleted			46.14 %	58.97 %	42.48 %	0.3 s	GPU @ 2.5 Ghz (Python + C/C++)
171	SS3D			45.79 %	61.58 %	41.14 %	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.
172	ACF			45.67 %	59.81 %	40.88 %	1 s	1 core @ 3.5 Ghz (Matlab + C/C++)
P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014.
173	Fusion-DPM		code	44.99 %	58.93 %	40.19 %	~ 30 s	1 core @ 3.5 Ghz (Matlab + C/C++)
C. Premebida, J. Carreira, J. Batista and U. Nunes: Pedestrian Detection Combining RGB and Dense LIDAR Data. IROS 2014.
174	M3DSSD++			44.89 %	58.54 %	40.66 %	0.16s	1 core @ 2.5 Ghz (C/C++)
175	CBi-GNN-persons			44.88 %	58.17 %	40.50 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
176	ACF-MR			44.79 %	58.29 %	39.94 %	0.6 s	1 core @ 3.5 Ghz (C/C++)
R. Rajaram, E. Ohn-Bar and M. Trivedi: Looking at Pedestrians at Different Scales: A Multi-resolution Approach and Evaluations. T-ITS 2016.
177	Deprecated			44.65 %	60.33 %	38.51 %	Deprecated	Deprecated
178	MonoGeo			44.63 %	58.49 %	40.41 %	0.05 s	1 core @ 2.5 Ghz (Python)
179	mono3d			44.43 %	57.25 %	40.11 %	0.03 s	GPU @ 2.5 Ghz (Python)
180	Geo3D			44.42 %	58.74 %	40.13 %	0.04 s	GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
181	LPCG-Monoflex			44.13 %	62.44 %	39.46 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
182	HA-SSVM			43.87 %	58.76 %	38.81 %	21 s	1 core @ >3.5 Ghz (Matlab + C/C++)
J. Xu, S. Ramos, D. Vázquez and A. López: Hierarchical Adaptive Structural SVM for Domain Adaptation. IJCV 2016.
183	AB3DMOT		code	43.86 %	54.55 %	40.99 %	0.0047s	1 core @ 2.5 Ghz (python)
X. Weng and K. Kitani: A Baseline for 3D Multi-Object Tracking. arXiv:1907.03961 2019.
184	MonoEF		code	43.73 %	58.79 %	39.45 %	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.
185	MonoLCD			43.71 %	57.69 %	39.60 %	0.04 s	1 core @ 2.5 Ghz (Python)
186	DA-Mono3D			43.57 %	59.80 %	39.23 %	0.09s	1 core @ 2.5 Ghz (C/C++)
187	D4LCN		code	43.50 %	59.55 %	37.12 %	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.
188	DPM-VOC+VP			43.26 %	59.21 %	38.12 %	8 s	1 core @ 2.5 Ghz (C/C++)
B. Pepik, M. Stark, P. Gehler and B. Schiele: Multi-view and 3D Deformable Part Models. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 2015.
189	ACF-SC			42.97 %	53.30 %	38.12 %	<0.3 s	1 core @ >3.5 Ghz (Matlab + C/C++)
C. Cadena, A. Dick and I. Reid: A Fast, Modular Scene Understanding System using Context-Aware Object Detection. Robotics and Automation (ICRA), 2015 IEEE International Conference on 2015.
190	SquaresICF		code	42.61 %	57.08 %	37.85 %	1 s	GPU @ >3.5 Ghz (C/C++)
R. Benenson, M. Mathias, T. Tuytelaars and L. Gool: Seeking the strongest rigid detector. CVPR 2013.
191	CG-Stereo			42.54 %	54.64 %	38.45 %	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.
192	MonoCon			42.44 %	56.57 %	36.34 %	0.02 s	GPU @ 2.5 Ghz (Python)
193	RelationNet3D_res18		code	42.25 %	57.61 %	37.93 %	0.04 s	1 core @ 2.5 Ghz (C/C++)
194	BirdNet+ (legacy)		code	41.97 %	51.38 %	40.15 %	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.
195	ICCV			41.88 %	55.14 %	37.55 %	0.04 s	GPU @ 2.5 Ghz (Python)
196	PLDet3d			41.86 %	55.94 %	37.64 %	0.11 s	1 core @ 2.5 Ghz (C/C++)
197	DDMP-3D			41.54 %	56.73 %	35.52 %	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.
198	CSW3D			41.50 %	53.76 %	37.25 %	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.
199	M3D-RPN(S-R)			41.46 %	56.64 %	37.31 %	0.16 s	GPU @ 1.5 Ghz (Python)
200	M3D-RPN		code	41.46 %	56.64 %	37.31 %	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 .
201	YOLOStereo3D		code	41.46 %	56.20 %	37.07 %	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.
202	HS3D		code	41.36 %	49.82 %	39.47 %	0.12 s	1 core @ 2.5 Ghz (Python + C/C++)
203	MP-Mono			40.87 %	55.79 %	36.81 %	0.16 s	GPU @ 2.5 Ghz (Python)
204	SwinMono3D			40.54 %	56.73 %	36.18 %	0.08 s	1 core @ 2.5 Ghz (C/C++)
205	SubCat			40.50 %	53.75 %	35.66 %	1.2 s	6 cores @ 2.5 Ghz (Matlab + C/C++)
E. Ohn-Bar and M. Trivedi: Fast and Robust Object Detection Using Visual Subcategories. Computer Vision and Pattern Recognition Workshops Mobile Vision 2014.
206	DSGN		code	39.93 %	49.28 %	38.13 %	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.
207	RT3D-GMP			39.83 %	55.56 %	35.18 %	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.
208	FusionDetv2-v1			39.60 %	46.77 %	38.75 %	0.04 s	1 core @ 2.5 Ghz (Python + C/C++)
209	SparsePool		code	39.59 %	50.81 %	35.91 %	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.
210	Graph-NMS-baseline			39.46 %	52.32 %	35.60 %	47 ms	GPU @ 2.5 Ghz (Python)
211	SparsePool		code	39.43 %	50.94 %	35.78 %	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.
212	AVOD		code	39.43 %	50.90 %	35.75 %	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.
213	CD-KD			39.33 %	51.68 %	35.52 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
214	ACF			39.12 %	48.42 %	35.03 %	0.2 s	1 core @ >3.5 Ghz (Matlab + C/C++)
P. Doll\'ar, R. Appel, S. Belongie and P. Perona: Fast Feature Pyramids for Object Detection. PAMI 2014. P. Doll\'ar: Piotr's Image and Video Matlab Toolbox (PMT). .
215	Graph-NMS			38.40 %	51.24 %	35.24 %	36 ms	GPU @ 2.5 Ghz (Python)
216	LSVM-MDPM-sv			37.26 %	50.74 %	33.13 %	10 s	4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010. A. Geiger, C. Wojek and R. Urtasun: Joint 3D Estimation of Objects and Scene Layout. NIPS 2011.
217	multi-task CNN			37.00 %	49.38 %	33.46 %	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.
218	Lite-FPN			36.93 %	50.63 %	32.88 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
219	NCL		code	36.89 %	42.81 %	34.76 %	NA s	1 core @ 2.5 Ghz (Python)
220	Complexer-YOLO			36.45 %	42.16 %	32.91 %	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.
221	LAPNet			36.22 %	48.96 %	32.27 %	0.03 s	1 core @ 2.5 Ghz (Python)
222	LSVM-MDPM-us		code	35.92 %	48.73 %	31.70 %	10 s	4 cores @ 3.0 Ghz (C/C++)
P. Felzenszwalb, R. Girshick, D. McAllester and D. Ramanan: Object Detection with Discriminatively Trained Part-Based Models. PAMI 2010.
223	COF3D			35.34 %	50.45 %	31.28 %	200 s	1 core @ 2.5 Ghz (Python + C/C++)
224	Aug3D-RPN			34.95 %	47.22 %	30.64 %	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.
225	MonoHMOO			34.74 %	49.26 %	30.37 %	0.2 s	1 core @ 2.5 Ghz (Python + C/C++)
226	PPTrans			34.45 %	45.02 %	30.94 %	0.2 s	GPU @ 2.5 Ghz (Python)
227	PointRGBNet			33.92 %	44.35 %	30.43 %	0.08 s	4 cores @ 2.5 Ghz (Python + C/C++)
228	Vote3D			33.04 %	42.66 %	30.59 %	0.5 s	4 cores @ 2.8 Ghz (C/C++)
D. Wang and I. Posner: Voting for Voting in Online Point Cloud Object Detection. Proceedings of Robotics: Science and Systems 2015.
229	FADNet		code	32.64 %	42.43 %	29.13 %	0.04 s	GPU @ >3.5 Ghz (Python)
230	MM			32.48 %	45.03 %	28.58 %	1 s	1 core @ 2.5 Ghz (C/C++)
231	CaDDN		code	32.42 %	46.35 %	29.98 %	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.
232	DFR-Net			31.84 %	45.20 %	27.94 %	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.
233	K3D			31.71 %	44.40 %	27.90 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
234	FPIOD		code	30.96 %	45.07 %	28.48 %	0.05 s	1 core @ 2.5 Ghz (C/C++)
235	OC Stereo		code	30.79 %	43.50 %	28.40 %	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.
236	mBoW			30.26 %	41.52 %	26.34 %	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.
237	BirdNet			30.07 %	36.82 %	28.40 %	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.
238	SOD			29.47 %	46.61 %	26.97 %	0.1 s	1 core @ 2.5 Ghz (Python)
239	RT3DStereo			29.30 %	41.12 %	25.25 %	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.
240	AEC3D			28.32 %	37.26 %	24.98 %	18 ms	GPU @ 2.5 Ghz (Python)
241	BEVC			26.52 %	35.31 %	24.72 %	35ms	GPU @ 1.5 Ghz (Python)
242	DPM-C8B1			25.34 %	36.40 %	22.00 %	15 s	4 cores @ 2.5 Ghz (Matlab + C/C++)
J. Yebes, L. Bergasa and M. García-Garrido: Visual Object Recognition with 3D-Aware Features in KITTI Urban Scenes. Sensors 2015. J. Yebes, L. Bergasa, R. Arroyo and A. Lázaro: Supervised learning and evaluation of KITTI's cars detector with DPM. IV 2014.
243	VN3D			23.61 %	30.51 %	21.97 %	0.02 s	1 core @ 2.5 Ghz (Python)
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244	MAOLoss		code	23.60 %	32.07 %	21.55 %	0.05 s	1 core @ 2.5 Ghz (Python)
245	RefinedMPL			20.81 %	30.41 %	18.72 %	0.15 s	GPU @ 2.5 Ghz (Python + C/C++)
J. Vianney, S. Aich and B. Liu: RefinedMPL: Refined Monocular PseudoLiDAR for 3D Object Detection in Autonomous Driving. arXiv preprint arXiv:1911.09712 2019.
246	E2E-DA			19.96 %	29.93 %	17.46 %	0.03 s	1 core @ 2.5 Ghz (C/C++)
247	E2E-DA-Lite (Res18)			19.87 %	32.25 %	17.37 %	0.01 s	GPU @ 2.5 Ghz (Python)
248	TopNet-Retina			16.45 %	22.37 %	15.43 %	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.
249	TopNet-HighRes			15.28 %	21.22 %	13.89 %	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.
250	YOLOv2		code	11.46 %	15.37 %	9.67 %	0.02 s	GPU @ 3.5 Ghz (C/C++)
J. Redmon, S. Divvala, R. Girshick and A. Farhadi: You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016. J. Redmon and A. Farhadi: YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017.
251	TopNet-UncEst			8.58 %	13.00 %	7.38 %	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.
252	BIP-HETERO			7.05 %	8.51 %	6.30 %	~2 s	1 core @ 2.5 Ghz (C/C++)
A. Mekonnen, F. Lerasle, A. Herbulot and C. Briand: People Detection with Heterogeneous Features and Explicit Optimization on Computation Time. Pattern Recognition (ICPR), 2014 22nd International Conference on 2014.
253	TopNet-DecayRate			0.01 %	0.01 %	0.01 %	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.