The KITTI Vision Benchmark Suite

The stereo / flow benchmark consists of 194 training image pairs and 195 test image pairs, saved in loss less png format. Our evaluation server computes the average number of bad pixels for all non-occluded or occluded (=all groundtruth) pixels. 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 disparity maps and flow fields.

• Download stereo/optical flow data set (2 GB)
• Download stereo/optical flow calibration files (1 MB)
• Download multi-view extension (20 frames per scene, all cameras) (17 GB)
• Download stereo/optical flow development kit (3 MB)
• Semantic and instance labels for 60 images and car labels for all training images (1 MB)

Our evaluation table ranks all methods according to the number of non-occluded erroneous pixels at the specified disparity / end-point error threshold. All methods providing less than 100 % density have been interpolated using simple background interpolation as explained in the corresponding header file in the development kit. For each method we show:

• Out-Noc: Percentage of erroneous pixels in non-occluded areas
• Out-All: Percentage of erroneous pixels in total
• Avg-Noc: Average disparity / end-point error in non-occluded areas
• Avg-All: Average disparity / end-point error in total
• Density: Percentage of pixels for which ground truth has been provided by the method

Note: On 04.11.2013 we have improved the ground truth disparity maps and flow fields leading to slightly improvements for all methods. Please download the stereo/flow dataset with the improved ground truth for training again, if you have downloaded the dataset prior to 04.11.2013. Please consider reporting these new number for all future submissions. Links to last leaderboards before the updates: stereo and flow!

	Method	Setting	Code	Out-Noc	Out-All	Avg-Noc	Avg-All	Density	Runtime	Environment
1	ICG-ACV		code	1.03 %	1.34 %	0.4 px	0.5 px	100.00 %	0.25 s	1 core @ 2.5 Ghz (Python)
2	PCWNet			1.04 %	1.37 %	0.4 px	0.5 px	100.00 %	0.44 s	1 core @ 2.5 Ghz (C/C++)
3	LaC+GANet		code	1.05 %	1.42 %	0.4 px	0.5 px	100.00 %	1.8 s	1 core @ 2.5 Ghz (C/C++)
B. Liu, H. Yu and Y. Long: Local Similarity Pattern and Cost Self- Reassembling for Deep Stereo Matching Networks. Proceedings of the AAAI Conference on Artificial Intelligence 2022.
4	GwcNet+ADL			1.05 %	1.42 %	0.4 px	0.5 px	100.00 %	0.34 s	1 core @ 2.5 Ghz (Python)
5	MDANet			1.08 %	1.45 %	0.4 px	0.5 px	100.00 %	0.43 s	1 core @ 2.5 Ghz (C/C++)
6	gwcnet+L_norm			1.09 %	1.56 %	0.4 px	0.5 px	100.00 %	0.4 s	1 core @ 2.5 Ghz (Python)
7	HCRNet			1.09 %	1.42 %	0.4 px	0.4 px	100.00 %	0.19 s	GPU @ 2.5 Ghz (Python)
8	GwcNet+			1.10 %	1.49 %	0.4 px	0.5 px	100.00 %	0.32 s	GPU @ 2.5 Ghz (Python)
9	RT+AANet			1.10 %	1.49 %	0.4 px	0.5 px	100.00 %	0.05 s	GPU @ 2.5 Ghz (Python)
10	PRNet			1.11 %	1.43 %	0.4 px	0.5 px	100.00 %	0.2 s	1 core @ 2.5 Ghz (Python)
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11	NLCA-Net v2		code	1.11 %	1.46 %	0.4 px	0.5 px	100.00 %	0.67 s	GPU @ >3.5 Ghz (Python)
Z. Rao, D. Yuchao, S. Zhelun and H. Renjie: Rethinking Training Strategy in Stereo Matching. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS .
12	Astar			1.11 %	1.46 %	0.4 px	0.5 px	100.00 %	0.02 s	1 core @ 2.5 Ghz (Python)
13	UCFNet			1.12 %	1.49 %	0.4 px	0.5 px	100.00 %	0.21 s	1 core @ 2.5 Ghz (C/C++)
14	CVP-AAR			1.12 %	1.49 %	0.4 px	0.5 px	100.00 %	0.15s	GPU @ 2.5 Ghz (Python)
15	UPFNet			1.12 %	1.46 %	0.4 px	0.5 px	100.00 %	0.25 s	1 core @ 2.5 Ghz (Python)
16	LaC+GwcNet		code	1.13 %	1.49 %	0.5 px	0.5 px	100.00 %	0.65 s	GPU @ 2.5 Ghz (Python)
B. Liu, H. Yu and Y. Long: Local Similarity Pattern and Cost Self- Reassembling for Deep Stereo Matching Networks. Proceedings of the AAAI Conference on Artificial Intelligence 2022.
17	ACVNet		code	1.13 %	1.47 %	0.4 px	0.5 px	100.00 %	0.2 s	1 core @ 2.5 Ghz (Python)
G. Xu, J. Cheng, P. Guo and X. Yang: Attention Concatenation Volume for Accurate and Efficient Stereo Matching. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2022.
18	ASM			1.13 %	1.50 %	0.4 px	0.4 px	100.00 %	0.40 s	1 core @ 2.5 Ghz (Python)
19	LEAStereo		code	1.13 %	1.45 %	0.5 px	0.5 px	100.00 %	0.3 s	GPU @ 2.5 Ghz (Python)
X. Cheng, Y. Zhong, M. Harandi, Y. Dai, X. Chang, H. Li, T. Drummond and Z. Ge: Hierarchical Neural Architecture Search for Deep Stereo Matching. Advances in Neural Information Processing Systems 2020.
20	PSMNet+ADL			1.14 %	1.50 %	0.4 px	0.5 px	100.00 %	0.43 s	GPU @ 1.5 Ghz (Python)
21	CREStereo		code	1.14 %	1.46 %	0.4 px	0.5 px	100.00 %	0.40 s	GPU @ >3.5 Ghz (C/C++)
J. Li, P. Wang, P. Xiong, T. Cai, Z. Yan, L. Yang, J. Liu, H. Fan and S. Liu: Practical Stereo Matching via Cascaded Recurrent Network with Adaptive Correlation. 2022.
22	DMCA			1.14 %	1.49 %	0.4 px	0.5 px	100.00 %	0.28 s	1 core @ 2.5 Ghz (C/C++)
23	PSMNet+			1.14 %	1.54 %	0.4 px	0.5 px	100.00 %	0.41 s	GPU @ 2.5 Ghz (Python)
24	CTNet			1.15 %	1.47 %	0.4 px	0.5 px	100.00 %	0.4 s	8 cores @ 2.5 Ghz (Python)
25	FENet			1.15 %	1.53 %	0.4 px	0.5 px	100.00 %	0.4 s	1 core @ 2.5 Ghz (C/C++)
26	GwcNet-DCA			1.16 %	1.50 %	0.5 px	0.5 px	100.00 %	0.24 s	GPU @ 2.5 Ghz (Python)
27	CFNet-RSSM			1.16 %	1.50 %	0.4 px	0.5 px	100.00 %	0.15 s	1 core @ 2.5 Ghz (Python)
28	ICG-Gwc			1.17 %	1.52 %	0.4 px	0.5 px	100.00 %	0.26 s	1 core @ 2.5 Ghz (C/C++)
29	AcfNet		code	1.17 %	1.54 %	0.5 px	0.5 px	100.00 %	0.48 s	1 core @ 2.5 Ghz (Python)
Y. Zhang, Y. Chen, X. Bai, S. Yu, K. Yu, Z. Li and K. Yang: Adaptive Unimodal Cost Volume Filtering for Deep Stereo Matching. AAAI 2020.
30	DMLMBCVNet			1.17 %	1.57 %	0.4 px	0.5 px	100.00 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
31	Abc-Net			1.18 %	1.59 %	0.4 px	0.5 px	100.00 %	0.72 s	4 cores @ 2.5 Ghz (Python)
X. Li, Y. Fan, G. Lv and H. Ma: Area-based correlation and non-local attention network for stereo matching. The Visual Computer 2021.
32	DSNet			1.18 %	1.57 %	0.5 px	0.5 px	100.00 %	0.36 s	1 core @ 2.5 Ghz (C/C++)
33	XXnet			1.18 %	1.62 %	0.4 px	0.5 px	100.00 %	0.36 s	8 cores @ 2.5 Ghz (Python)
34	DSFCA		code	1.19 %	1.53 %	0.4 px	0.5 px	100.00 %	0.31 s	1 core @ 2.5 Ghz (Python)
35	CAL-Net			1.19 %	1.53 %	0.4 px	0.5 px	100.00 %	0.44 s	4 cores @ 2.5 Ghz (Python)
S. Chen, B. Li, W. Wang, H. Zhang, H. Li and Z. Wang: Cost Affinity Learning Network for Stereo Matching. IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2021, Toronto, ON, Canada, June 6-11, 2021 2021.
36	GANet-deep		code	1.19 %	1.60 %	0.4 px	0.5 px	100.00 %	1.8 s	GPU @ 2.5 Ghz (Python)
F. Zhang, V. Prisacariu, R. Yang and P. Torr: GA-Net: Guided Aggregation Net for End-to-end Stereo Matching. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
37	DADCA		code	1.20 %	1.54 %	0.5 px	0.5 px	100.00 %	0.28 s	1 core @ 2.5 Ghz (C/C++)
38	OptStereo			1.20 %	1.61 %	0.4 px	0.5 px	100.00 %	0.10 s	GPU @ 2.5 Ghz (Python)
H. Wang, R. Fan, P. Cai and M. Liu: PVStereo: Pyramid voting module for end-to-end self-supervised stereo matching. IEEE Robotics and Automation Letters 2021.
39	CL-G			1.21 %	1.57 %	0.4 px	0.5 px	100.00 %	0.32 s	GPU @ 2.5 Ghz (Python)
40	RDNet			1.21 %	1.57 %	0.4 px	0.5 px	100.00 %	0.60 s	1 core @ 2.5 Ghz (C/C++)
41	ICG-PSM			1.21 %	1.57 %	0.5 px	0.5 px	100.00 %	0.3 s	1 core @ 2.5 Ghz (Python)
42	NLCA-Net-3		code	1.21 %	1.60 %	0.4 px	0.5 px	100.00 %	0.44 s	GPU @ 2.5 Ghz (Python)
Z. Rao, M. He, Y. Dai, Z. Zhu, B. Li and R. He: NLCA-Net: a non-local context attention network for stereo matching. APSIPA Transactions on Signal and Information Processing 2020.
43	CVCNet			1.21 %	1.57 %	0.5 px	0.5 px	100.00 %	0.07 s	1 core @ 2.5 Ghz (C/C++)
44	DCANet			1.22 %	1.60 %	0.5 px	0.5 px	100.00 %	0.10s	GTX30900TI
45	CFNet		code	1.23 %	1.58 %	0.4 px	0.5 px	100.00 %	0.18 s	1 core @ 2.5 Ghz (Python)
Z. Shen, Y. Dai and Z. Rao: CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
46	ATSA_net		code	1.23 %	1.58 %	0.5 px	0.5 px	100.00 %	0.07 s	1 core @ 2.5 Ghz (Python)
47	PFSMNet		code	1.23 %	1.58 %	0.5 px	0.5 px	100.00 %	0.31 s	1 core @ 2.5 Ghz (C/C++)
K. Zeng, Y. Wang, Q. Zhu, J. Mao and H. Zhang: Deep Progressive Fusion Stereo Network. IEEE Transactions on Intelligent Transportation Systems 2021.
48	ATSA_net			1.23 %	1.58 %	0.5 px	0.5 px	100.00 %	0.07 s	1 core @ 2.5 Ghz (Python)
49	DMLMBCV			1.24 %	1.61 %	0.4 px	0.5 px	100.00 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
50	AGNet			1.24 %	1.62 %	0.4 px	0.5 px	100.00 %	0.42 s	1 core @ 2.5 Ghz (C/C++)
51	PCR-GwcNet			1.24 %	1.67 %	0.5 px	0.5 px	100.00 %	0.32 s	1 core @ 2.5 Ghz (C/C++)
52	UCNet			1.25 %	1.70 %	0.5 px	0.5 px	100.00 %	0.6 s	1 core @ 2.5 Ghz (C/C++)
53	NLCA-Net		code	1.25 %	1.62 %	0.4 px	0.5 px	100.00 %	0.6 s	GPU @ 2.5 Ghz (C/C++)
Z. Rao, M. He, Y. Dai, Z. Zhu, B. Li and R. He: NLCA-Net: a non-local context attention network for stereo matching. APSIPA Transactions on Signal and Information Processing 2020.
54	HFRANet			1.26 %	1.63 %	0.4 px	0.5 px	100.00 %	0.56s	1 core @ 2.5 Ghz (C/C++)
55	SCV-Stereo		code	1.27 %	1.68 %	0.5 px	0.5 px	100.00 %	0.08 s	GPU @ 2.5 Ghz (Python)
H. Wang, R. Fan and M. Liu: SCV-Stereo: Learning stereo matching from a sparse cost volume. 2021 IEEE International Conference on Image Processing (ICIP) 2021.
56	AGNet			1.30 %	1.68 %	0.4 px	0.5 px	100.00 %	0.40 s	1 core @ 2.5 Ghz (Python)
57	DPCTF-S			1.31 %	1.72 %	0.5 px	0.5 px	100.00 %	0.11 s	GPU @ 2.5 Ghz (Python)
Y. Deng, J. Xiao, S. Zhou and J. Feng: Detail Preserving Coarse-to-Fine Matching for Stereo Matching and Optical Flow. IEEE Transactions on Image Processing 2021.
58	AMNet			1.32 %	1.73 %	0.5 px	0.5 px	100.00 %	0.9 s	GPU @ 2.5 Ghz (Python)
X. Du, M. El-Khamy and J. Lee: AMNet: Deep Atrous Multiscale Stereo Disparity Estimation Networks. 2019.
59	GwcNet-gc		code	1.32 %	1.70 %	0.5 px	0.5 px	100.00 %	0.32 s	GPU @ 2.0 Ghz (Java + C/C++)
X. Guo, K. Yang, W. Yang, X. Wang and H. Li: Group-wise correlation stereo network. CVPR 2019.
60	PGNet			1.32 %	1.79 %	0.5 px	0.5 px	100.00 %	0.7 s	1 core @ 2.5 Ghz (python)
S. Chen, Z. Xiang, C. Qiao, Y. Chen and T. Bai: PGNet: Panoptic parsing guided deep stereo matching. Neurocomputing 2021.
61	DFL-CCA-Net			1.35 %	1.73 %	0.5 px	0.5 px	100.00 %	0.46 s	Nvidia Titan Xp
62	PF			1.35 %	1.72 %	0.5 px	0.5 px	100.00 %	1.35 s	1 core @ 2.5 Ghz (C/C++)
63	multi attention			1.35 %	1.81 %	0.5 px	0.5 px	100.00 %	1.60 s	1 core @ 2.5 Ghz (C/C++)
64	CGF-F-B			1.35 %	1.73 %	0.5 px	0.5 px	100.00 %	0.26 s	GPU @ 2.5 Ghz (Python)
65	GANet-15		code	1.36 %	1.80 %	0.5 px	0.5 px	100.00 %	0.36 s	1 core @ 2.5 Ghz (C/C++)
F. Zhang, V. Prisacariu, R. Yang and P. Torr: GA-Net: Guided Aggregation Net for End-to-end Stereo Matching. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
66	cal++			1.36 %	1.78 %	0.5 px	0.5 px	100.00 %	0.44 s	8 cores @ 2.5 Ghz (C/C++)
67	SGNet			1.38 %	1.85 %	0.5 px	0.5 px	100.00 %	0.6 s	1 core @ 2.5 Ghz (Python + C/C++)
S. Chen, Z. Xiang, C. Qiao, Y. Chen and T. Bai: SGNet: Semantics Guided Deep Stereo Matching. Proceedings of the Asian Conference on Computer Vision (ACCV) 2020.
68	HD^3-Stereo		code	1.40 %	1.80 %	0.5 px	0.5 px	100.00 %	0.14 s	NVIDIA Pascal Titan XP
Z. Yin, T. Darrell and F. Yu: Hierarchical Discrete Distribution Decomposition for Match Density Estimation. CVPR 2019.
69	HITNet		code	1.41 %	1.89 %	0.4 px	0.5 px	100.00 %	0.02 s	GPU @ 2.5 Ghz (Python + C/C++)
V. Tankovich, C. Häne, Y. Zhang, A. Kowdle, S. Fanello and S. Bouaziz: HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching. CVPR 2021.
70	ICG-Stereo			1.41 %	1.76 %	0.5 px	0.5 px	100.00 %	0.02 s	NVIDIA RTX 3090 (PyTorch)
71	CFP-Net		code	1.41 %	1.83 %	0.5 px	0.5 px	100.00 %	0.95 s	8 cores @ 2.5 Ghz (Python)
Z. Zhu, M. He, Y. Dai, Z. Rao and B. Li: Multi-scale Cross-form Pyramid Network for Stereo Matching. arXiv preprint 2019.
72	WSMCnet		code	1.42 %	1.90 %	0.6 px	0.6 px	100.00 %	0.39 s	GPU @ Nvidia GTX 1070 (Pytorch)
Y. Wang, H. Wang, G. Yu, M. Yang, Y. Yuan and J. Quan: Stereo Matching Algorithm Based on Three-Dimensional Convolutional Neural Network. Acta Optica Sinica 2019.
73	sCroCo_RVC		code	1.43 %	1.72 %	0.5 px	0.5 px	100.00 %	1.1 s	1 core @ 2.5 Ghz (Python)
74	Fast-ACV+		code	1.45 %	1.85 %	0.5 px	0.5 px	100.00 %	0.04 s	1 core @ 2.5 Ghz (Python)
75	AAP+			1.45 %	1.84 %	0.5 px	0.5 px	100.00 %	0.05 s	1 core @ 2.5 Ghz (Python)
76	EdgeStereo-V2			1.46 %	1.83 %	0.4 px	0.5 px	100.00 %	0.32 s	Nvidia GTX Titan Xp
X. Song, X. Zhao, L. Fang, H. Hu and Y. Yu: Edgestereo: An effective multi-task learning network for stereo matching and edge detection. International Journal of Computer Vision (IJCV) 2019.
77	MABNet_origin		code	1.47 %	1.89 %	0.5 px	0.5 px	100.00 %	0.38 s	Nvidia rtx2080ti (Python)
J. Xing, Z. Qi, J. Dong, J. Cai and H. Liu: MABNet: A Lightweight Stereo Network Based on Multibranch Adjustable Bottleneck Module. .
78	SSPCVNet			1.47 %	1.90 %	0.5 px	0.6 px	100.00 %	0.9 s	1 core @ 2.5 Ghz (Python)
Z. Wu, X. Wu, X. Zhang, S. Wang and L. Ju: Semantic Stereo Matching With Pyramid Cost Volumes. The IEEE International Conference on Computer Vision (ICCV) 2019.
79	Syn2PSMNet		code	1.47 %	1.86 %	0.5 px	0.6 px	100.00 %	0.21 s	GPU @ 2.5 Ghz (Python)
80	AAPNet			1.47 %	1.86 %	0.5 px	0.5 px	100.00 %	0.08 s	1 core @ 2.5 Ghz (Python)
81	TemporalStereo(M)			1.47 %	1.61 %	0.6 px	0.6 px	100.00 %	0.048 s	GPU @ 2.5 Ghz (Python)
82	PSMNet		code	1.49 %	1.89 %	0.5 px	0.6 px	100.00 %	0.41 s	Nvidia Titan Xp
J. Chang and Y. Chen: Pyramid Stereo Matching Network. arXiv preprint arXiv:1803.08669 2018.
83	HSM		code	1.53 %	1.99 %	0.5 px	0.6 px	100.00 %	0.15 s	Titan X Pascal
G. Yang, J. Manela, M. Happold and D. Ramanan: Hierarchical Deep Stereo Matching on High- Resolution Images. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
84	EBNet			1.53 %	1.97 %	0.5 px	0.6 px	100.00 %	0.02 s	GPU @ 2.5 Ghz (Python)
85	ISMF-EMA			1.54 %	1.95 %	0.5 px	0.6 px	100.00 %	0.2 s	1 core @ 2.5 Ghz (Python)
86	CGFNet		code	1.55 %	1.93 %	0.5 px	0.5 px	100.00 %	0.03 s	1 core @ 2.5 Ghz (Python)
87	AANet+		code	1.55 %	2.04 %	0.4 px	0.5 px	100.00 %	0.06 s	NVIDIA V100 GPU
H. Xu and J. Zhang: AANet: Adaptive Aggregation Network for Efficient Stereo Matching. CVPR 2020.
88	CoEx		code	1.55 %	1.93 %	0.5 px	0.5 px	100.00 %	0.027 s	RTX 2080Ti (Python)
A. Bangunharcana, J. Cho, S. Lee, I. Kweon, K. Kim and S. Kim: Correlate-and-Excite: Real-Time Stereo Matching via Guided Cost Volume Excitation. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2021.
89	CGNet		code	1.59 %	1.98 %	0.5 px	0.5 px	100.00 %	0.03 s	GPU @ >3.5 Ghz (Python)
90	BGNet+			1.62 %	2.03 %	0.5 px	0.6 px	100.00 %	0.02 s	GPU @ 2.5 Ghz (Python)
B. Xu, Y. Xu, X. Yang, W. Jia and Y. Guo: Bilateral Grid Learning for Stereo Matching Network. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
91	TemporalStereo(S)			1.62 %	1.94 %	0.6 px	0.6 px	100.00 %	0.044 s	GPU @ 2.5 Ghz (Python)
92	MSDC-Net			1.63 %	2.09 %	0.5 px	0.6 px	100.00 %	0.6 s	1 core @ 2.5 Ghz (C/C++)
Z. Rao, M. He, Y. Dai, Z. Zhu, B. Li and R. He: MSDC-Net: Multi-Scale Dense and Contextual Networks for Stereo Matching. 2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) 2019.
93	WaveletStereo			1.66 %	2.18 %	0.5 px	0.6 px	100.00 %	0.27 s	1 core @ 2.5 Ghz (C/C++)
Anonymous: WaveletStereo: Learning wavelet coefficients for stereo matching. arXiv: Computer Vision and Pattern Recognition 2019.
94	AcdNet			1.67 %	2.20 %	0.5 px	0.5 px	100.00 %	0.36 s	1 core @ 2.5 Ghz (C/C++)
95	SegStereo		code	1.68 %	2.03 %	0.5 px	0.6 px	100.00 %	0.6 s	Nvidia GTX Titan Xp
G. Yang, H. Zhao, J. Shi, Z. Deng and J. Jia: SegStereo: Exploiting Semantic Information for Disparity Estimation. ECCV 2018.
96	Fast-ACV		code	1.69 %	2.12 %	0.5 px	0.6 px	100.00 %	0.04 s	1 core @ 2.5 Ghz (Python)
97	LWNet			1.69 %	2.13 %	0.5 px	0.6 px	100.00 %	0.07 s	1 core @ 2.5 Ghz (C/C++)
98	AutoDispNet-CSS		code	1.70 %	2.05 %	0.5 px	0.5 px	100.00 %	0.9 s	1 core @ 2.5 Ghz (C/C++)
T. Saikia, Y. Marrakchi, A. Zela, F. Hutter and T. Brox: AutoDispNet: Improving Disparity Estimation with AutoML. The IEEE International Conference on Computer Vision (ICCV) 2019.
99	iResNet-i2		code	1.71 %	2.16 %	0.5 px	0.6 px	100.00 %	0.12 s	1 core @ 2.5 Ghz (C/C++)
Z. Liang, Y. Feng, Y. Guo, H. Liu, W. Chen, L. Qiao, L. Zhou and J. Zhang: Learning for disparity estimation through feature constancy. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2018.
100	GC-NET			1.77 %	2.30 %	0.6 px	0.7 px	100.00 %	0.9 s	Nvidia GTX Titan X
A. Kendall, H. Martirosyan, S. Dasgupta, P. Henry, R. Kennedy, A. Bachrach and A. Bry: End-to-End Learning of Geometry and Context for Deep Stereo Regression. Proceedings of the International Conference on Computer Vision (ICCV) 2017.
101	ERSCNet			1.80 %	2.30 %	0.5 px	0.6 px	100.00 %	0.28 s	GPU @ 2.5 Ghz (Python)
Anonymous: ERSCNet. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
102	APNet			1.84 %	2.27 %	0.5 px	0.6 px	100.00 %	0.03 s	1 core @ 2.5 Ghz (Python)
103	EASNet			1.89 %	2.41 %	0.6 px	0.6 px	100.00 %	0.1 s	GPU @ 1.5 Ghz (Python)
104	AANet		code	1.91 %	2.42 %	0.5 px	0.6 px	100.00 %	0.06 s	GPU @ 2.5 Ghz (Python)
H. Xu and J. Zhang: AANet: Adaptive Aggregation Network for Efficient Stereo Matching. CVPR 2020.
105	PDSNet			1.92 %	2.53 %	0.9 px	1.0 px	100.00 %	0.5 s	1 core @ 2.5 Ghz (Python + C/C++)
S. Tulyakov, A. Ivanov and F. Fleuret: Practical Deep Stereo (PDS): Toward applications-friendly deep stereo matching. Proceedings of the international conference on Neural Information Processing Systems (NIPS) 2018.
106	EASNet-M			1.96 %	2.47 %	0.6 px	0.6 px	100.00 %	0.8 s	GPU @ 1.5 Ghz (Python)
107	PVStereo			1.98 %	2.47 %	0.7 px	0.8 px	100.00 %	0.10 s	1 core @ 2.5 Ghz (Python)
H. Wang, R. Fan, P. Cai and M. Liu: PVStereo: Pyramid voting module for end-to-end self-supervised stereo matching. IEEE Robotics and Automation Letters 2021.
108	FADNet		code	2.04 %	2.46 %	0.5 px	0.6 px	100.00 %	0.05 s	Tesla V100 (Python)
Q. Wang, S. Shi, S. Zheng, K. Zhao and X. Chu: FADNet: A Fast and Accurate Network for Disparity Estimation. arXiv preprint arXiv:2003.10758 2020.
109	MMStereo			2.04 %	2.52 %	0.6 px	0.7 px	100.00 %	0.04 s	Nvidia Titan RTX (Python)
K. Shankar, M. Tjersland, J. Ma, K. Stone and M. Bajracharya: A Learned Stereo Depth System for Robotic Manipulation in Homes. .
110	RecResNet		code	2.21 %	2.94 %	0.6 px	0.7 px	100.00 %	0.3 s	GPU @ NVIDIA TITAN X (Tensorflow)
K. Batsos and P. Mordohai: RecResNet: A Recurrent Residual CNN Architecture for Disparity Map Enhancement. In International Conference on 3D Vision (3DV) 2018.
111	L-ResMatch		code	2.27 %	3.40 %	0.7 px	1.0 px	100.00 %	48 s	Titan X (Torch7, CUDA)
A. Shaked and L. Wolf: Improved Stereo Matching with Constant Highway Networks and Reflective Loss. arXiv preprint arxiv:1701.00165 2016.
112	CNNF+SGM			2.28 %	3.48 %	0.7 px	0.9 px	100.00 %	71 s	TESLA K40C
F. Zhang and B. Wah: Fundamental Principles on Learning New Features for Effective Dense Matching. IEEE Transactions on Image Processing 2018.
113	stroco		code	2.28 %	2.67 %	0.6 px	0.7 px	100.00 %	1.1 s	1 core @ 2.5 Ghz (Python)
114	SGM-Net			2.29 %	3.50 %	0.7 px	0.9 px	100.00 %	67 s	Titan X
A. Seki and M. Pollefeys: SGM-Nets: Semi-Global Matching With Neural Networks. CVPR 2017.
115	SsSMnet			2.30 %	3.00 %	0.7 px	0.8 px	100.00 %	0.8 s	Titan Xp
Y. Zhong, Y. Dai and H. Li: Self-Supervised Learning for Stereo Matching with Self-Improving Ability. arXiv:1709.00930 2017.
116	PBCP			2.36 %	3.45 %	0.7 px	0.9 px	100.00 %	68 s	Nvidia GTX Titan X
A. Seki and M. Pollefeys: Patch Based Confidence Prediction for Dense Disparity Map. British Machine Vision Conference (BMVC) 2016.
117	Displets v2		code	2.37 %	3.09 %	0.7 px	0.8 px	100.00 %	265 s	>8 cores @ 3.0 Ghz (Matlab + C/C++)
F. Guney and A. Geiger: Displets: Resolving Stereo Ambiguities using Object Knowledge. Conference on Computer Vision and Pattern Recognition (CVPR) 2015.
118	RTSnet		code	2.43 %	2.90 %	0.7 px	0.7 px	100.00 %	0.02 s	1 core @ 2.5 Ghz (Python)
H. Lee and Y. Shin: Real-Time Stereo Matching Network with High Accuracy. 2019 IEEE International Conference on Image Processing (ICIP) 2019.
119	MC-CNN-acrt		code	2.43 %	3.63 %	0.7 px	0.9 px	100.00 %	67 s	Nvidia GTX Titan X (CUDA, Lua/Torch7)
J. Zbontar and Y. LeCun: Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches. Submitted to JMLR .
120	cfusion		code	2.46 %	2.69 %	0.8 px	0.8 px	99.93 %	70 s	GPU (Matlab + CUDA)
V. Ntouskos and F. Pirri: Confidence driven TGV fusion. arXiv preprint arXiv:1603.09302 2016.
121	Displets		code	2.47 %	3.27 %	0.7 px	0.9 px	100.00 %	265 s	>8 cores @ 3.0 Ghz (Matlab + C/C++)
F. Guney and A. Geiger: Displets: Resolving Stereo Ambiguities using Object Knowledge. Conference on Computer Vision and Pattern Recognition (CVPR) 2015.
122	EASNet-S			2.57 %	3.19 %	0.7 px	0.7 px	100.00 %	0.6 s	GPU @ 1.5 Ghz (Python)
123	MC-CNN			2.61 %	3.84 %	0.8 px	1.0 px	100.00 %	100 s	Nvidia GTX Titan (CUDA, Lua/Torch7)
J. Zbontar and Y. LeCun: Computing the Stereo Matching Cost with a Convolutional Neural Network. Conference on Computer Vision and Pattern Recognition (CVPR) 2015.
124	Fast DS-CS		code	2.61 %	3.20 %	0.7 px	0.8 px	100.00 %	0.02 s	GPU @ 2.0 Ghz (Python + C/C++)
K. Yee and A. Chakrabarti: Fast Deep Stereo with 2D Convolutional Processing of Cost Signatures. WACV 2020 (to appear).
125	MABNet_tiny		code	2.71 %	3.31 %	0.7 px	0.8 px	100.00 %	0.11 s	1 core @ 2.5 Ghz (Python)
J. Xing, Z. Qi, J. Dong, J. Cai and H. Liu: MABNet: A Lightweight Stereo Network Based on Multibranch Adjustable Bottleneck Module. .
126	PRSM		code	2.78 %	3.00 %	0.7 px	0.7 px	100.00 %	300 s	1 core @ 2.5 Ghz (C/C++)
C. Vogel, K. Schindler and S. Roth: 3D Scene Flow Estimation with a Piecewise Rigid Scene Model. ijcv 2015.
127	SPS-StFl			2.83 %	3.64 %	0.8 px	0.9 px	100.00 %	35 s	1 core @ 3.5 Ghz (C/C++)
K. Yamaguchi, D. McAllester and R. Urtasun: Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation. ECCV 2014.
128	MC-CNN-WS		code	3.02 %	4.45 %	0.8 px	1.0 px	100.00 %	1.35 s	1 core 2.5 Ghz + K40 NVIDIA, Lua-Torch
S. Tulyakov, A. Ivanov and F. Fleuret: Weakly supervised learning of deep metrics for stereo reconstruction. ICCV 2017.
129	VC-SF			3.05 %	3.31 %	0.8 px	0.8 px	100.00 %	300 s	1 core @ 2.5 Ghz (C/C++)
C. Vogel, S. Roth and K. Schindler: View-Consistent 3D Scene Flow Estimation over Multiple Frames. Proceedings of European Conference on Computer Vision. Lecture Notes in, Computer Science 2014.
130	Content-CNN			3.07 %	4.29 %	0.8 px	1.0 px	100.00 %	0.7 s	Nvidia GTX Titan X (Torch)
W. Luo, A. Schwing and R. Urtasun: Efficient Deep Learning for Stereo Matching. CVPR 2016.
131	Deep Embed			3.10 %	4.24 %	0.9 px	1.1 px	100.00 %	3 s	1 core @ 2.5 Ghz (C/C++)
Z. Chen, X. Sun, Y. Yu, L. Wang and C. Huang: A Deep Visual Correspondence Embedding Model for Stereo Matching Costs. ICCV 2015.
132	JSOSM			3.15 %	3.94 %	0.8 px	0.9 px	100.00 %	105 s	8 cores @ 2.5 Ghz (C/C++)
X. Li and J. Liu: EFFICIENT STEREO MATCHING USING SEGMENT OPTIMIZATION. ICIP 2016.
133	SA-NET			3.15 %	3.77 %	0.8 px	0.9 px	100.00 %	0.07 s	1 core @ 2.5 Ghz (Python)
134	FD-Fusion		code	3.16 %	3.85 %	0.7 px	0.8 px	100.00 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
M. Ferrera, A. Boulch and J. Moras: Fast Stereo Disparity Maps Refinement By Fusion of Data-Based And Model-Based Estimations. International Conference on 3D Vision (3DV) 2019.
135	OSF		code	3.28 %	4.07 %	0.8 px	0.9 px	99.98 %	50 min	1 core @ 3.0 Ghz (Matlab + C/C++)
M. Menze and A. Geiger: Object Scene Flow for Autonomous Vehicles. Conference on Computer Vision and Pattern Recognition (CVPR) 2015.
136	CoR		code	3.30 %	4.10 %	0.8 px	0.9 px	100.00 %	6 s	6 cores @ 3.3 Ghz (Matlab + C/C++)
A. Chakrabarti, Y. Xiong, S. Gortler and T. Zickler: Low-level Vision by Consensus in a Spatial Hierarchy of Regions. CVPR 2015.
137	TCD-CRF			3.32 %	5.24 %	0.9 px	1.9 px	100.00 %	60 s	4 cores @ 3.5 Ghz (C/C++)
S. Arjomand Bigdeli, G. Budweiser and M. Zwicker: Temporally Coherent Disparity Maps Using CRFs with Fast 4D Filtering. Proc. ACPR 2015.
138	SPS-St		code	3.39 %	4.41 %	0.9 px	1.0 px	100.00 %	2 s	1 core @ 3.5 Ghz (C/C++)
K. Yamaguchi, D. McAllester and R. Urtasun: Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation. ECCV 2014.
139	PCBP-SS			3.40 %	4.72 %	0.8 px	1.0 px	100.00 %	5 min	4 cores @ 2.5 Ghz (Matlab + C/C++)
K. Yamaguchi, D. McAllester and R. Urtasun: Robust Monocular Epipolar Flow Estimation. CVPR 2013.
140	CBMV		code	3.56 %	4.73 %	0.9 px	1.1 px	100.00 %	250 s	6 cores@3.0Ghz(Python,C/C++,CUDA TitanX)
K. Batsos, C. Cai and P. Mordohai: CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation. 2018.
141	FAT-Stereo			3.68 %	4.41 %	0.8 px	0.9 px	100.00 %	0.8 s	1 core @ 2.5 Ghz (C/C++)
142	DDS-SS			3.83 %	4.59 %	0.9 px	1.0 px	100.00 %	1 min	1 core @ 2.5 Ghz (Matlab + C/C++)
D. Wei, C. Liu and W. Freeman: A Data-driven Regularization Model for Stereo and Flow. 3DTV-Conference, 2014 International Conference on 2014.
143	StereoSLIC			3.92 %	5.11 %	0.9 px	1.0 px	99.89 %	2.3 s	1 core @ 3.0 Ghz (C/C++)
K. Yamaguchi, D. McAllester and R. Urtasun: Robust Monocular Epipolar Flow Estimation. CVPR 2013.
144	SMCM			3.94 %	5.24 %	0.9 px	1.1 px	100.00 %	1800 s	Nvidia GTX 1080 (Caffe)
M. Yang, Y. Liu, Y. Cai and Z. You: Stereo matching based on classification of materials. Neurocomputing 2016.
145	PR-Sf+E			4.02 %	4.87 %	0.9 px	1.0 px	100.00 %	200 s	4 cores @ 3.0 Ghz (Matlab + C/C++)
C. Vogel, K. Schindler and S. Roth: Piecewise Rigid Scene Flow. International Conference on Computer Vision (ICCV) 2013.
146	PCBP			4.04 %	5.37 %	0.9 px	1.1 px	100.00 %	5 min	4 cores @ 2.5 Ghz (Matlab + C/C++)
K. Yamaguchi, T. Hazan, D. McAllester and R. Urtasun: Continuous Markov Random Fields for Robust Stereo Estimation. ECCV 2012.
147	DispNetC		code	4.11 %	4.65 %	0.9 px	1.0 px	100.00 %	0.06 s	Nvidia GTX Titan X (Caffe)
N. Mayer, E. Ilg, P. Häusser, P. Fischer, D. Cremers, A. Dosovitskiy and T. Brox: A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation. CVPR 2016.
148	CSPMS			4.13 %	5.92 %	1.2 px	1.6 px	100.00 %	6 s	4 cores @ 2.5 Ghz (C/C++)
J. Cho and M. Humenberger: Fast PatchMatch Stereo Matching Using Multi-Scale Cost Fusion for Automotive Applications. IV 2015.
149	SGM-post			4.27 %	5.33 %	1.0 px	1.1 px	100.00 %	5 s	4 cores @ 2.5 Ghz (C/C++)
Z. Zhong: Efficient Learning based Semi-Global Stereo Matching. 2015 submitted.
150	MBM			4.35 %	5.43 %	1.0 px	1.1 px	100.00 %	0.2 s	1 core @ 3.0 Ghz (C/C++)
N. Einecke and J. Eggert: A Multi-Block-Matching Approach for Stereo. IV 2015.
151	PR-Sceneflow			4.36 %	5.22 %	0.9 px	1.1 px	100.00 %	150 sec	4 core @ 3.0 Ghz (Matlab - C/C++)
C. Vogel, K. Schindler and S. Roth: Piecewise Rigid Scene Flow. International Conference on Computer Vision (ICCV) 2013.
152	CRD-Fusion		code	4.38 %	5.40 %	0.9 px	1.1 px	100.00 %	0.02 s	GPU @ 2.5 Ghz (Python)
X. Fan, S. Jeon and B. Fidan: Occlusion-Aware Self-Supervised Stereo Matching with Confidence Guided Raw Disparity Fusion. Conference on Robots and Vision 2022.
153	CoR-Conf		code	4.49 %	5.26 %	1.0 px	1.2 px	96.37 %	6 s	6 cores @ 3.3 Ghz (Matlab + C/C++)
A. Chakrabarti, Y. Xiong, S. Gortler and T. Zickler: Low-level Vision by Consensus in a Spatial Hierarchy of Regions. CVPR 2015.
154	Flow2Stereo			4.58 %	5.11 %	1.0 px	1.1 px	100.00 %	0.05 s	GPU @ 2.5 Ghz (Python)
P. Liu, I. King, M. Lyu and J. Xu: Flow2Stereo: Effective Self-Supervised Learning of Optical Flow and Stereo Matching. CVPR 2020.
155	DispSegNet			4.68 %	5.66 %	0.9 px	1.0 px	100.00 %	0.9 s	GPU @ 2.5 Ghz (Python)
J. Zhang, K. Skinner, R. Vasudevan and M. Johnson-Roberson: DispSegNet: Leveraging Semantics for End- to-End Learning of Disparity Estimation From Stereo Imagery. IEEE Robotics and Automation Letters 2019.
156	pSGM			4.68 %	6.13 %	1.0 px	1.4 px	100.00 %	7.92 s	4 cores @ 3.5 Ghz (C/C++)
Y. Lee, M. Park, Y. Hwang, Y. Shin and C. Kyung: Memory-Efficient Parametric Semiglobal Matching. IEEE Signal Processing Letters 2018.
157	AARBM			4.86 %	5.94 %	1.0 px	1.2 px	100.00 %	0.25 s	1 core @ 3.0 Ghz (C/C++)
N. Einecke and J. Eggert: Block-Matching Stereo with Relaxed Fronto-Parallel Assumption. IV 2014.
158	wSGM			4.97 %	6.18 %	1.3 px	1.6 px	97.03 %	6s	1 core @ 3.5 Ghz (C/C++)
R. Spangenberg, T. Langner and R. Rojas: Weighted Semi-Global Matching and Center-Symmetric Census Transform for Robust Driver Assistance. CAIP 2013.
159	AABM			4.97 %	6.04 %	1.0 px	1.2 px	100.00 %	0.12 s	1 core @ 3.1 Ghz (C/C++)
N. Einecke and J. Eggert: Stereo Image Warping for Improved Depth Estimation of Road Surfaces. IV 2013.
160	ATGV			5.02 %	6.88 %	1.0 px	1.6 px	100.00 %	6 min	>8 cores @ 3.0 Ghz (Matlab + C/C++)
R. Ranftl, T. Pock and H. Bischof: Minimizing TGV-based Variational Models with Non-Convex Data terms. ICSSVM 2013.
161	rSGM		code	5.03 %	6.60 %	1.1 px	1.5 px	97.22 %	0.2 s	4 cores @ 2.6 Ghz (C/C++)
R. Spangenberg, T. Langner, S. Adfeldt and R. Rojas: Large Scale Semi-Global Matching on the CPU. IV 2014.
162	iSGM			5.11 %	7.15 %	1.2 px	2.1 px	94.70 %	8 s	2 cores @ 2.5 Ghz (C/C++)
S. Hermann and R. Klette: Iterative Semi-Global Matching for Robust Driver Assistance Systems. ACCV 2012.
163	RBM			5.18 %	6.21 %	1.1 px	1.3 px	100.00 %	0.2 s	1 core @ 3.0 Ghz (C/C++)
N. Einecke and J. Eggert: Block-Matching Stereo with Relaxed Fronto-Parallel Assumption. IV 2014.
164	ARW		code	5.20 %	6.87 %	1.2 px	1.5 px	99.33 %	4.6s	1 core @ 3.5 Ghz (MATLAB+C/C++)
S. Lee, J. Lee, J. Lim and I. Suh: Robust Stereo Matching using Adaptive Random Walk with Restart Algorithm. Image and vision computing (accepted) 2015.
165	DLP			5.28 %	7.21 %	1.2 px	2.0 px	100.00 %	60 s	8 cores @ >3.5 Ghz (C/C++)
V. Nguyen, H. Nguyen and J. Jeon: Robust Stereo Data Cost With a Learning Strategy. IEEE Transactions on Intelligent Transportation Systems 2017.
166	Ensemble			5.34 %	6.91 %	1.5 px	2.0 px	100.00 %	135 s	2 cores @ >3.5 Ghz (Matlab)
A. Spyropoulos and P. Mordohai: Ensemble Classifier for Combining Stereo Matching Algorithms. International Conference on 3D Vision (3DV) 2015.
167	ALTGV			5.36 %	6.49 %	1.1 px	1.2 px	100.00 %	20 s	GPU @ 2.5 Ghz (C/C++)
G. Kuschk and D. Cremers: Fast and Accurate Large-scale Stereo Reconstruction using Variational Methods. ICCV Workshop on Big Data in 3D Computer Vision 2013.
168	SNCC			5.40 %	6.44 %	1.2 px	1.3 px	100.00 %	0.11 s	1 core @ 3.1 Ghz (C/C++)
N. Einecke and J. Eggert: A Two-Stage Correlation Method for Stereoscopic Depth Estimation. DICTA 2010.
169	CAT			5.45 %	6.54 %	1.1 px	1.2 px	100.00 %	10 s	1 core @ 3.5 Ghz (C/C++)
J. Ha, J. Jeon, G. Bae, S. Jo and H. Jeong: Cost Aggregation Table: Cost Aggregation Method Using Summed Area Table Scheme for Dense Stereo Correspondence. Advances in Visual Computing 2014.
170	SGM			5.76 %	7.00 %	1.2 px	1.3 px	85.80 %	3.7 s	1 core @ 3.0 Ghz (C/C++)
H. Hirschmueller: Stereo Processing by Semi-Global Matching and Mutual Information. PAMI 2008.
171	mSGM-LDE			6.01 %	8.22 %	1.4 px	2.4 px	100.00 %	55 s	2 cores @ 2.5 Ghz (C/C++)
V. Nguyen, D. Nguyen, S. Lee and J. Jeon: Local Density Encoding for Robust Stereo Matching. TCSVT 2014.
172	AAFS			6.10 %	6.94 %	1.2 px	1.3 px	100.00 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
J. Chang, P. Chang and Y. Chen: Attention-Aware Feature Aggregation for Real-time Stereo Matching on Edge Devices. Proceedings of the Asian Conference on Computer Vision 2020.
173	Toast2			6.16 %	7.42 %	1.2 px	1.4 px	95.39 %	0.03 s	4 cores @ 3.5 Ghz (C/C++)
B. Ranft and T. Strau\ss: Modeling Arbitrarily Oriented Slanted Planes for Efficient Stereo Vision based on Block Matching. Intelligent Transportation Systems (ITSC), 2014 IEEE 17th International Conference on 2014.
174	ITGV			6.20 %	7.30 %	1.3 px	1.5 px	100.00 %	7 s	1 core @ 3.0 Ghz (Matlab + C/C++)
R. Ranftl, S. Gehrig, T. Pock and H. Bischof: Pushing the Limits of Stereo Using Variational Stereo Estimation. IV 2012.
175	OASM-Net			6.39 %	8.60 %	1.3 px	2.0 px	100.00 %	0.73 s	GPU @ 2.5 Ghz (Python)
A. Li and Z. Yuan: Occlusion Aware Stereo Matching via Cooperative Unsupervised Learning. Proceedings of the Asian Conference on Computer Vision, ACCV 2018.
176	Permutation Stereo			7.39 %	8.48 %	1.6 px	1.8 px	99.93 %	30 s	GPU @ 2.5 Ghz (Matlab)
P. Brousseau and S. Roy: A Permutation Model for the Self- Supervised Stereo Matching Problem. 2022 19th Conference on Robots and Vision (CRV) 2022.
177	OCV-SGBM		code	7.64 %	9.13 %	1.8 px	2.0 px	86.50 %	1.1 s	1 core @ 2.5 Ghz (C/C++)
H. Hirschmueller: Stereo processing by semiglobal matching and mutual information. PAMI 2008.
178	SSMW			7.83 %	8.95 %	1.6 px	1.8 px	99.99 %	2.5 min	8 cores @ 2.5 Ghz (C/C++)
X. Li, J. Liu, G. Chen and H. Fu: Efficient Methods Using Slanted Support Windows for Slanted Surfaces. IET Computer Vision, http://ietdl.org/t/5QsTxb 2016.
179	MSMW		code	8.01 %	9.24 %	1.6 px	1.7 px	72.39 %	3 min	4 cores @ 2.5 Ghz (C/C++)
A. Buades and G. Facciolo: On the performance of local methods for stereovision. 2013 submitted.
180	HSMA			8.15 %	10.33 %	1.9 px	2.9 px	100.00 %	44s	1 core @ 3.0 Ghz (C/C++)
O. Zeglazi, M. Rziza, A. Amine and C. Demonceaux: A hierarchical stereo matching algorithm based on adaptive support region aggregation method. Pattern Recognition Letters 2018.
181	ELAS		code	8.24 %	9.96 %	1.4 px	1.6 px	94.55 %	0.3 s	1 core @ 2.5 Ghz (C/C++)
A. Geiger, M. Roser and R. Urtasun: Efficient Large-Scale Stereo Matching. ACCV 2010.
182	linBP			8.56 %	10.70 %	1.7 px	2.7 px	99.89 %	1.6 min	1 core @ 3.0 Ghz (C/C++)
W. Khan, V. Suaste, D. Caudillo and R. Klette: Belief Propagation Stereo Matching Compared to iSGM on Binocular or Trinocular Video Data. IV 2013.
183	ADSM			8.71 %	10.05 %	2.1 px	2.7 px	100.00 %	125 s	1 core @ 2.0 Ghz (C/C++)
O. Zeglazi, M. Rziza, A. Amine and C. Demonceaux: Accurate dense stereo matching for road scenes. 2017 IEEE International Conference on Image Processing, ICIP 2017, Beijing, China, September 17-20, 2017 .
184	Deep-Raw			8.93 %	11.07 %	3.9 px	4.9 px	100.00 %	1 s	1 core @ 2.5 Ghz (C/C++)
Z. Chen, X. Sun, Y. Yu, L. Wang and C. Huang: A Deep Visual Correspondence Embedding Model for Stereo Matching Costs. ICCV 2015.
185	S+GF (Cen)		code	9.03 %	11.21 %	2.1 px	3.4 px	100.00 %	140 s	1 core @ 3.0 Ghz (C/C++)
K. Zhang, Y. Fang, D. Min, L. Sun, S. Yang, S. Yan and Q. Tian: Cross-Scale Cost Aggregation for Stereo Matching. CVPR 2014.
186	CrossCensus			9.46 %	10.86 %	2.3 px	2.7 px	100.00 %	30 s	1 core @ 2.5 Ghz (C/C++)
K. Zhang, J. Lu and G. Lafruit: Cross-Based Local Stereo Matching Using Orthogonal Integral Images. Circuits and Systems for Video Technology, IEEE Transactions on 2009.
187	SymST-GP			9.79 %	11.66 %	2.5 px	3.3 px	100.00 %	0.254 s	Dual - Nvidia GTX Titan (CUDA)
R. Ralha, G. Falcao, J. Amaro, V. Mota, M. Antunes, J. Barreto and U. Nunes: Parallel refinement of slanted 3D reconstruction using dense stereo induced from symmetry. Journal of Real-Time Image Processing 2016.
188	SM_GPTM			9.79 %	11.38 %	2.1 px	2.6 px	100.00 %	6.5 s	2 cores @ 2.5 Ghz (C/C++)
C. Cigla and A. Alatan: An Improved Stereo Matching Algorithm with Ground Plane and Temporal Smoothness Constraints. ECCV Workshops 2012.
189	LAMC-DSΜ			9.82 %	11.49 %	2.1 px	2.7 px	99.96 %	10.8 min	2 cores @ 2.5 Ghz (Matlab)
C. Stentoumis, L. Grammatikopoulos, I. Kalisperakis, E. Petsa and G. Karras: A local adaptive approach for dense stereo matching in architectural scene reconstruction. ISPRS 2013.
190	IIW			10.78 %	12.62 %	3.3 px	4.3 px	70.85 %	5.5 s	1 core @ 2.5 Ghz (C/C++)
A. Murarka and N. Einecke: A meta-technique for increasing density of local stereo methods through iterative interpolation and warping. Canadian Conference on Computer and Robot Vision 2014.
191	SDM		code	10.95 %	12.14 %	2.0 px	2.3 px	63.58 %	1 min	1 core @ 2.5 Ghz (C/C++)
J. Kostkova: Stratified dense matching for stereopsis in complex scenes. BMVC 2003.
192	HLSC_mesh			11.22 %	12.82 %	2.3 px	2.9 px	100.00 %	800 s	1 core @ 2.5 Ghz (Matlab + C/C++)
S. Hadfield, K. Lebeda and R. Bowden: Stereo reconstruction using top-down cues. Computer Vision and Image Understanding 2016.
193	GF (Census)		code	11.65 %	13.76 %	4.5 px	5.6 px	100.00 %	120 s	1 core @ 3.0 Ghz (C/C++)
A. Hosni, C. Rhemann, M. Bleyer, C. Rother and M. Gelautz: Fast Cost-Volume Filtering for Visual Correspondence and Beyond. TPAMI 2013. K. Zhang, Y. Fang, D. Min, L. Sun, S. Yang, S. Yan and Q. Tian: Cross-Scale Cost Aggregation for Stereo Matching. CVPR 2014.
194	BSM		code	11.74 %	13.44 %	2.2 px	2.8 px	97.02 %	2.5 min	1 core @ 3.0 Ghz (C/C++)
K. Zhang, J. Li, Y. Li, W. Hu, L. Sun and S. Yang: Binary stereo matching. Pattern Recognition (ICPR), 2012 21st International Conference on 2012.
195	GCSF		code	12.05 %	13.24 %	1.9 px	2.1 px	60.77 %	2.4 s	1 core @ 2.5 Ghz (C/C++)
J. Cech, J. Sanchez-Riera and R. Horaud: Scene Flow Estimation by Growing Correspondence Seeds. CVPR 2011.
196	OCV-BM-post		code	12.28 %	13.76 %	2.1 px	2.3 px	47.11 %	0.1 s	1 core @ 2.5 Ghz (C/C++)
G. Bradski: The OpenCV Library. Dr. Dobb's Journal of Software Tools 2000.
197	GCS		code	13.38 %	14.54 %	2.1 px	2.3 px	51.06 %	2.2 s	1 core @ 2.5 Ghz (C/C++)
J. Cech and R. Sara: Efficient Sampling of Disparity Space for Fast And Accurate Matching. BenCOS 2007.
198	GLDS		code	17.22 %	18.63 %	2.8 px	3.2 px	100.00 %	26 s	GPU @ 1.5 Ghz (C/C++)
K. Oguri and Y. Shibata: A new stereo formulation not using pixel and disparity models. 2018.
199	CostFilter		code	19.99 %	21.08 %	5.0 px	5.4 px	100.00 %	4 min	1 core @ 2.5 Ghz (Matlab)
C. Rhemann, A. Hosni, M. Bleyer, C. Rother and M. Gelautz: Fast Cost-Volume Filtering for Visual Correspondence and Beyond. CVPR 2011.
200	GC+occ		code	33.49 %	34.73 %	8.6 px	9.2 px	87.57 %	6 min	1 core @ 2.5 Ghz (C/C++)
V. Kolmogorov and R. Zabih: Computing Visual Correspondence with Occlusions using Graph Cuts. ICCV 2001.
201	VariableCros			34.84 %	36.11 %	12.4 px	12.9 px	95.66 %	30 s	1 core @ 2.5 Ghz (Matlab)
K. Zhang, J. Lu and G. Lafruit: Cross-Based Local Stereo Matching Using Orthogonal Integral Images. Circuits and Systems for Video Technology, IEEE Transactions on 2009.
202	ALE-Stereo		code	50.48 %	51.19 %	13.0 px	13.5 px	100.00 %	50 min	1 core @ 3.0 Ghz (C/C++)
L. Ladicky, P. Sturgess, C. Russell, S. Sengupta, Y. Bastanlar, W. Clocksin and P. Torr: Joint Optimisation for Object Class Segmentation and Dense Stereo Reconstruction. BMVC 2010.
203	MEDIAN			52.61 %	53.67 %	7.7 px	8.2 px	99.95 %	0.01 s	1 core @ 2.5 Ghz (C/C++)
204	AVERAGE			61.62 %	62.49 %	8.0 px	8.6 px	99.95 %	0.01 s	1 core @ 2.5 Ghz (C/C++)

• HCI/Bosch Robust Vision Challenge: Optical flow and stereo vision challenge on high resolution imagery recorded at a high frame rate under diverse weather conditions (e.g., sunny, cloudy, rainy). The Robert Bosch AG provides a prize for the best performing method.
• Image Sequence Analysis Test Site (EISATS): Synthetic image sequences with ground truth information provided by UoA and Daimler AG. Some of the images come with 3D range sensor information.
• Middlebury Stereo Evaluation: The classic stereo evaluation benchmark, featuring four test images in version 2 of the benchmark, with very accurate ground truth from a structured light system. 38 image pairs are provided in total.
• Daimler Stereo Dataset: Stereo bad weather highway scenes with partial ground truth for freespace
• Make3D Range Image Data: Images with small-resolution ground truth used to learn and evaluate depth from single monocular images.
• Lubor Ladicky's Stereo Dataset: Stereo Images with manually labeled ground truth based on polygonal areas.

When using this dataset in your research, we will be happy if you cite us:
@INPROCEEDINGS{Geiger2012CVPR,
  author = {Andreas Geiger and Philip Lenz and Raquel Urtasun},
  title = {Are we ready for Autonomous Driving? The KITTI Vision Benchmark Suite},
  booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
  year = {2012}
}