Stereo Evaluation 2015


The stereo 2015 / flow 2015 / scene flow 2015 benchmark consists of 200 training scenes and 200 test scenes (4 color images per scene, saved in loss less png format). Compared to the stereo 2012 and flow 2012 benchmarks, it comprises dynamic scenes for which the ground truth has been established in a semi-automatic process. Our evaluation server computes the percentage of bad pixels averaged over all ground truth pixels of all 200 test images. For this benchmark, we consider a pixel to be correctly estimated if the disparity or flow end-point error is <3px or <5% (for scene flow this criterion needs to be fulfilled for both disparity maps and the flow map). 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. More details can be found in .

Our evaluation table ranks all methods according to the number of erroneous pixels. 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. Legend:

  • D1: Percentage of stereo disparity outliers in first frame
  • D2: Percentage of stereo disparity outliers in second frame
  • Fl: Percentage of optical flow outliers
  • SF: Percentage of scene flow outliers (=outliers in either D0, D1 or Fl)
  • bg: Percentage of outliers averaged only over background regions
  • fg: Percentage of outliers averaged only over foreground regions
  • all: Percentage of outliers averaged over all ground truth pixels


Note: On 13.03.2017 we have fixed several small errors in the flow (noc+occ) ground truth of the dynamic foreground objects and manually verified all images for correctness by warping them according to the ground truth. As a consequence, all error numbers have decreased slightly. Please download the devkit and the annotations with the improved ground truth for the training set again if you have downloaded the files prior to 13.03.2017 and consider reporting these new number in all future publications. The last leaderboards before these corrections can be found here (optical flow 2015) and here (scene flow 2015). The leaderboards for the KITTI 2015 stereo benchmarks did not change.

Important Policy Update: As more and more non-published work and re-implementations of existing work is submitted to KITTI, we have established a new policy: from now on, only submissions with significant novelty that are leading to a peer-reviewed paper in a conference or journal are allowed. Minor modifications of existing algorithms or student research projects are not allowed. Such work must be evaluated on a split of the training set. To ensure that our policy is adopted, new users must detail their status, describe their work and specify the targeted venue during registration. Furthermore, we will regularly delete all entries that are 6 months old but are still anonymous or do not have a paper associated with them. For conferences, 6 month is enough to determine if a paper has been accepted and to add the bibliography information. For longer review cycles, you need to resubmit your results.
Additional information used by the methods
  • Flow: Method uses optical flow (2 temporally adjacent images)
  • Multiview: Method uses more than 2 temporally adjacent images
  • Motion stereo: Method uses epipolar geometry for computing optical flow
  • Additional training data: Use of additional data sources for training (see details)

Evaluation ground truth        Evaluation area

Method Setting Code D1-bg D1-fg D1-all Density Runtime Environment
1 RAS-Net(SAIT China) 1.42 % 2.73 % 1.64 % 100.00 % 0.3 s 1 core @ 2.5 Ghz (Python)
2 ADLAB-RFDisp 1.41 % 2.77 % 1.64 % 100.00 % 0.4 s GPU @ 2.5 Ghz (Python)
3 LEAStereo code 1.40 % 2.91 % 1.65 % 100.00 % 0.30 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.
4 GA-fw 1.52 % 2.49 % 1.68 % 100.00 % 1.8 s 1 core @ 2.5 Ghz (Python)
5 OptStereo 1.44 % 2.95 % 1.69 % 100.00 % 0.05 s GPU @ 2.5 Ghz (Python)
6 MSMD-Net(only MS) 1.41 % 3.13 % 1.69 % 100.00 % 0.32 s 1 core @ 2.5 Ghz (C/C++)
7 Dahua_SF
This method uses optical flow information.
1.48 % 2.83 % 1.71 % 100.00 % 0.5 s 1 core @ 2.5 Ghz (Python)
8 Dahua_Stereo 1.48 % 2.83 % 1.71 % 100.00 % 1.52 s GPU @ 2.5 Ghz (Python)
9 SCV-Stereo 1.44 % 3.05 % 1.71 % 100.00 % 0.08 s GPU @ 2.5 Ghz (Python)
10 CANet 1.45 % 3.11 % 1.72 % 100.00 % 0.70 s 1 core @ 2.5 Ghz (C/C++)
11 CSPN 1.51 % 2.88 % 1.74 % 100.00 % 1.0 s GPU @ 2.5 Ghz (Python)
X. Cheng, P. Wang and R. Yang: Learning Depth with Convolutional Spatial Propagation Network. IEEE Transactions on Pattern Analysis and Machine Intelligence(T-PAMI) 2019.
12 GA_CSA 1.49 % 3.01 % 1.74 % 100.00 % 1.8 s 1 core @ 2.5 Ghz (Python)
13 DJIStereo 1.46 % 3.20 % 1.75 % 100.00 % 1.5 s 1 core @ 2.5 Ghz (Python)
14 DHSM 1.54 % 2.92 % 1.77 % 100.00 % 2 s 1 core @ 2.5 Ghz (Python)
15 NLCA-Net_V2 1.41 % 3.56 % 1.77 % 100.00 % 0.67 s 1 core @ 2.5 Ghz (C/C++)
16 HPA-Net 1.50 % 3.31 % 1.80 % 100.00 % 0.42 s GPU @ 2.5 Ghz (Python)
17 SUW-Stereo 1.47 % 3.45 % 1.80 % 100.00 % 1.8 s 1 core @ 2.5 Ghz (C/C++)
H. Ren, A. Raj, M. El-Khamy and J. Lee: SUW-Learn: Joint Supervised, Unsupervised, Weakly Supervised Deep Learning for Monocular Depth Estimation. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops 2020.
18 nsg 1.47 % 3.46 % 1.80 % 100.00 % 1.82 s GPU @ 1.5 Ghz (Python)
19 RME
This method uses optical flow information.
1.48 % 3.46 % 1.81 % 100.00 % 2 s GPU @ 2.5 Ghz (Python + C/C++)
ERROR: Wrong syntax in BIBTEX file.
20 GANet-deep code 1.48 % 3.46 % 1.81 % 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.
21 MaskRCNN+ISF
This method uses optical flow information.
1.48 % 3.46 % 1.81 % 100.00 % 3.3 s GPU @ 2.5 Ghz (Python)
22 Stereo expansion
This method uses optical flow information.
code 1.48 % 3.46 % 1.81 % 100.00 % 2 s GPU @ 2.5 Ghz (Python)
G. Yang and D. Ramanan: Upgrading Optical Flow to 3D Scene Flow through Optical Expansion. CVPR 2020.
23 PSMNet-NL 1.58 % 3.01 % 1.82 % 100.00 % 0.41 s GPU @ 2.5 Ghz (Python)
24 GA-Net+G 1.49 % 3.47 % 1.82 % 100.00 % 0.5 s GPU (Python)
25 HDU-FCC code 1.50 % 3.45 % 1.82 % 100.00 % 0.7 s 1 core @ 2.5 Ghz (C/C++)
26 PVStereo 1.50 % 3.43 % 1.82 % 100.00 % 0.05 s GPU @ 2.5 Ghz (Python)
27 gwcnet+DCA 1.49 % 3.51 % 1.83 % 100.00 % 0.27 s GPU @ 2.5 Ghz (Python)
28 CMF 1.44 % 3.76 % 1.83 % 100.00 % 0.5 s GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
29 NLCA-Net-3 code 1.45 % 3.78 % 1.83 % 100.00 % 0.44 s >8 cores @ 3.5 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.
30 HD3+_Flow
This method uses optical flow information.
1.63 % 2.89 % 1.84 % 100.00 % 0.04 s GPU @ 2.5 Ghz (Python)
31 AMNet 1.53 % 3.43 % 1.84 % 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.
32 Gwc-RSSM 1.54 % 3.42 % 1.85 % 100.00 % 0.20 s 1 core @ 2.5 Ghz (Python)
33 UnDAF-GANet 1.53 % 3.49 % 1.86 % 100.00 % 1.8 s GPU @ 2.5 Ghz (Python)
34 MFM-Net 1.51 % 3.67 % 1.87 % 100.00 % 0.47 s GPU @ 1.5 Ghz (Python)
35 RAS-Net 1.61 % 3.16 % 1.87 % 100.00 % 0.23 s 1 core @ 2.5 Ghz (C/C++)
36 pcr_psm 1.53 % 3.62 % 1.88 % 100.00 % 0.46 s GPU @ 2.5 Ghz (Python)
37 CFNet 1.54 % 3.56 % 1.88 % 100.00 % 0.18 s 1 core @ 2.5 Ghz (Python)
38 RigidMask+ISF
This method uses optical flow information.
1.53 % 3.65 % 1.89 % 100.00 % 3.3 s GPU @ 2.5 Ghz (Python)
39 AcfNet code 1.51 % 3.80 % 1.89 % 100.00 % 0.48 s GPU @ 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.
40 LISAStereo 1.62 % 3.24 % 1.89 % 100.00 % 0.09 s 4 cores @ 2.5 Ghz (Python)
41 CAIS+PSMNet 1.57 % 3.62 % 1.91 % 100.00 % 0.38 s GPU @ 2.5 Ghz (Python)
42 NLCA_NET_v2_RVC 1.51 % 3.97 % 1.92 % 100.00 % 0.67 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 CDN code 1.66 % 3.20 % 1.92 % 100.00 % 0.4 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 2020.
44 Abc-Net 1.47 % 4.20 % 1.92 % 100.00 % 0.83 s 4 core @ 2.5 Ghz (Python)
45 HCGANet 1.64 % 3.38 % 1.93 % 100.00 % 0.064 s GPU @ 2.5 Ghz (Python)
46 GANet-15 code 1.55 % 3.82 % 1.93 % 100.00 % 0.36 s GPU (Pytorch)
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.
47 CAL-Net 1.59 % 3.76 % 1.95 % 100.00 % 0.44 s 2 cores @ 2.5 Ghz (Python)
48 GWC_pcr 1.57 % 3.91 % 1.96 % 100.00 % 0.32 s 1 core @ 2.5 Ghz (Python)
49 NLCA-Net code 1.53 % 4.09 % 1.96 % 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: NLCA-Net: a non-local context attention network for stereo matching. APSIPA Transactions on Signal and Information Processing 2020.
50 CFNet_RVC 1.65 % 3.53 % 1.96 % 100.00 % 0.22 s GPU @ 2.5 Ghz (Python)
51 MSRFNet 1.68 % 3.38 % 1.96 % 100.00 % 0.056 s GPU @ 2.5 Ghz (Python)
52 FWSM 1.69 % 3.37 % 1.97 % 100.00 % 0.42 s 1 core @ 2.5 Ghz (Python)
53 MonoStereo 1.63 % 3.73 % 1.98 % 100.00 % 0.05 s GPU @ 2.5 Ghz (Python)
54 UGwc 1.64 % 3.70 % 1.98 % 100.00 % 0.8 s 1 core @ 2.5 Ghz (Python)
55 WTHNet 1.63 % 3.75 % 1.98 % 100.00 % 0.5 s 1 core @ 2.5 Ghz (C/C++)
56 HITNet 1.74 % 3.20 % 1.98 % 100.00 % 0.015 s Titan V,
57 TS_FAD 1.85 % 2.69 % 1.99 % 100.00 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
58 SGNet 1.63 % 3.76 % 1.99 % 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.
59 gwc_dcr_300 1.63 % 3.82 % 1.99 % 100.00 % 0.32 s GPU @ 2.5 Ghz (Python)
60 CSN code 1.59 % 4.03 % 2.00 % 100.00 % 0.6 s 1 core @ 2.5 Ghz (Python)
X. Gu, Z. Fan, S. Zhu, Z. Dai, F. Tan and P. Tan: Cascade cost volume for high-resolution multi-view stereo and stereo matching. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition 2020.
61 GwcNet-cmd 1.57 % 4.14 % 2.00 % 100.00 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
62 MANet-Selected 1.58 % 4.13 % 2.00 % 100.00 % 0.88 s GPU @ 2.5 Ghz (Python)
63 HD^3-Stereo code 1.70 % 3.63 % 2.02 % 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.
64 CoT-Stereo 1.67 % 3.78 % 2.02 % 100.00 % 0.3 s GPU @ 2.5 Ghz (Python)
65 CVL 1.71 % 3.59 % 2.02 % 100.00 % 0.36 s 1 core @ 2.5 Ghz (Python)
66 PSMNet-fw 1.74 % 3.50 % 2.03 % 100.00 % 0.41 s 1 core @ 2.5 Ghz (C/C++)
67 nsa 1.65 % 3.95 % 2.03 % 100.00 % 0.08 s GPU @ 1.5 Ghz (Python)
68 AANet+ code 1.65 % 3.96 % 2.03 % 100.00 % 0.06 s NVIDIA V100 GPU
H. Xu and J. Zhang: AANet: Adaptive Aggregation Network for Efficient Stereo Matching. CVPR 2020.
69 GwcNet_CSA 1.73 % 3.57 % 2.03 % 100.00 % 0.37 s 1 core @ 2.5 Ghz (C/C++)
70 MANet-Medium 1.63 % 4.15 % 2.05 % 100.00 % 0.88 s GPU @ 2.5 Ghz (Python)
71 SM^3Net code 1.65 % 4.03 % 2.05 % 100.00 % 0.54 s 1 core @ 2.5 Ghz (Python)
72 DDP_out_HD3 1.75 % 3.55 % 2.05 % 100.00 % 10 min 1 GPU (Python)
73 CAEF-Net 1.68 % 3.92 % 2.05 % 100.00 % 0.44 s 1 core @ 2.5 Ghz (Python)
74 MANet-Selected 1.61 % 4.29 % 2.06 % 100.00 % 0.88 s GPU @ 2.5 Ghz (Python)
75 DSA-Net 1.68 % 3.95 % 2.06 % 100.00 % 0.46 s GPU @ 2.5 Ghz (Python)
76 LR-PSMNet code 1.65 % 4.13 % 2.06 % 100.00 % 0.5 s GPU @ 2.5 Ghz (Python)
W. Chuah, R. Tennakoon, R. Hoseinnezhad, A. Bab-Hadiashar and D. Suter: Adjusting Bias in Long Range Stereo Matching: A semantics guided approach. 2020.
77 PSMNet++ 1.63 % 4.27 % 2.07 % 100.00 % 0.36 s GPU @ >3.5 Ghz (Python)
78 DeepStereo 1.71 % 3.87 % 2.07 % 100.00 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
79 PSMNet_pcr 1.71 % 3.89 % 2.07 % 100.00 % 0.32 s GPU @ 2.5 Ghz (Python)
80 UnDAF-SENSE
This method uses optical flow information.
1.75 % 3.70 % 2.07 % 100.00 % 0.32 s GPU @ 2.5 Ghz (Python)
81 MANet-Large 1.61 % 4.41 % 2.08 % 100.00 % 0.88 s GPU @ 2.5 Ghz (Python)
82 MGSNet 1.68 % 4.06 % 2.08 % 100.00 % 0.65 s GPU @ 2.5 Ghz (Python)
83 PSM + SMD-Nets 1.69 % 4.01 % 2.08 % 100.00 % 0.41 s 1 core @ 2.5 Ghz (Python + C/C++)
84 EdgeStereo-V2 1.84 % 3.30 % 2.08 % 100.00 % 0.32s 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.
85 DDP_in_HD3 1.78 % 3.64 % 2.09 % 100.00 % 1 s 1 core @ 2.5 Ghz (Python + C/C++)
86 GwcNet-gc ++ 1.67 % 4.19 % 2.09 % 100.00 % 0.32 s GPU @ 2.5 Ghz (Python)
87 PSM-BCD 1.67 % 4.20 % 2.09 % 100.00 % 0.32 s NVIDIA Titan Xp, 8 core 1.7 Ghz, Pytorch
88 MDA-Net(New) 1.76 % 3.77 % 2.10 % 100.00 % 0.4 s 1 core @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
89 GwcNet-g code 1.74 % 3.93 % 2.11 % 100.00 % 0.32 s GPU @ 2.0 Ghz (Python + C/C++)
X. Guo, K. Yang, W. Yang, X. Wang and H. Li: Group-wise correlation stereo network. CVPR 2019.
90 False 1.75 % 3.93 % 2.11 % 100.00 % 0.4 s 1 core @ 2.5 Ghz (Python)
91 SSPCVNet 1.75 % 3.89 % 2.11 % 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.
92 E-GWCNet 1.67 % 4.34 % 2.11 % 100.00 % 0.49 s 1 core @ 2.5 Ghz (Python)
93 WSMCnet code 1.72 % 4.19 % 2.13 % 100.00 % 0.39s 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.
94 DEINet+ft 1.72 % 4.26 % 2.14 % 100.00 % 0.23 s GPU @ 2.5 Ghz (Python + C/C++)
95 HSM-1.8x code 1.80 % 3.85 % 2.14 % 100.00 % 0.14 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.
96 DSMNet 1.78 % 3.97 % 2.14 % 100.00 % 0.67 s 1 core @ 2.5 Ghz (Python)
97 DeepPruner (best) code 1.87 % 3.56 % 2.15 % 100.00 % 0.18 s 1 core @ 2.5 Ghz (C/C++)
S. Duggal, S. Wang, W. Ma, R. Hu and R. Urtasun: DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch. ICCV 2019.
98 DC3DC 1.84 % 3.75 % 2.16 % 100.00 % 1.5 s 1 core @ 2.5 Ghz (C/C++)
99 GANet++ 1.88 % 3.53 % 2.16 % 100.00 % 0.04 s GPU @ 2.5 Ghz (Python)
100 PSM+CRF 1.86 % 3.66 % 2.16 % 100.00 % 0.32 s GPU @ 2.0 Ghz (C/C++)
101 Stereo-fusion-SJTU 1.87 % 3.61 % 2.16 % 100.00 % 0.7 s Nvidia GTX Titan Xp
X. Song, X. Zhao, H. Hu and L. Fang: EdgeStereo: A Context Integrated Residual Pyramid Network for Stereo Matching. Asian Conference on Computer Vision 2018.
102 AutoDispNet-CSS code 1.94 % 3.37 % 2.18 % 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.
103 BGNet+ 1.81 % 4.09 % 2.19 % 100.00 % 0.03 s GPU @ 2.5 Ghz (Python)
104 Bi3D code 1.95 % 3.48 % 2.21 % 100.00 % 0.48 s GPU @ 1.5 Ghz (Python)
A. Badki, A. Troccoli, K. Kim, J. Kautz, P. Sen and O. Gallo: Bi3D: Stereo Depth Estimation via Binary Classifications. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
105 MFRANet 1.78 % 4.35 % 2.21 % 100.00 % 0.32 s GPU @ 2.5 Ghz (Python)
106 ICANet 1.81 % 4.23 % 2.21 % 100.00 % 0.47 s GPU @ 2.5 Ghz (Python)
107 dh 1.86 % 4.01 % 2.22 % 100.00 % 1.9 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.
108 JS^3M(only SM) code 1.79 % 4.37 % 2.22 % 100.00 % 0.45 s GPU @ 2.5 Ghz (Python)
109 SENSE
This method uses optical flow information.
code 2.07 % 3.01 % 2.22 % 100.00 % 0.32s GPU, GTX 2080Ti
H. Jiang, D. Sun, V. Jampani, Z. Lv, E. Learned-Miller and J. Kautz: SENSE: A Shared Encoder Network for Scene-Flow Estimation. The IEEE International Conference on Computer Vision (ICCV) 2019.
110 PSMNet+D 1.85 % 4.15 % 2.23 % 100.00 % 0.4 s GPU @ 2.5 Ghz (Python)
111 GA-TeNet 1.89 % 3.94 % 2.23 % 100.00 % 0.49 s 1 core @ 2.5 Ghz (C/C++)
112 MA-P 1.75 % 4.65 % 2.23 % 100.00 % 0.33 s GPU @ 2.5 Ghz (Python)
113 CTFNet-v2 1.80 % 4.46 % 2.24 % 100.00 % 0.3 s 8 cores @ 2.5 Ghz (Python)
114 SegStereo code 1.88 % 4.07 % 2.25 % 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.
115 PSMNet+GLR 1.85 % 4.25 % 2.25 % 100.00 % 0.3 s GPU (Python)
116 PDR_Net 1.85 % 4.24 % 2.25 % 100.00 % 0.19 s 1 core @ 2.5 Ghz (Python)
117 DRNet 1.82 % 4.42 % 2.25 % 100.00 % 0.45 s 8 cores @ 2.5 Ghz (Python)
118 DTF_SENSE
This method uses optical flow information.
This method makes use of multiple (>2) views.
2.08 % 3.13 % 2.25 % 100.00 % 0.76 s 1 core @ 2.5 Ghz (C/C++)
R. Schuster, C. Unger and D. Stricker: A Deep Temporal Fusion Framework for Scene Flow Using a Learnable Motion Model and Occlusions. IEEE Winter Conference on Applications of Computer Vision (WACV) 2021.
119 SuperB 1.99 % 3.63 % 2.26 % 100.00 % 0.1 s NVIDIA Tesla V100 + PyTorch 1.2.0
120 E-PSMNet 1.89 % 4.17 % 2.27 % 100.00 % 0.68 s 1 core @ 2.5 Ghz (Python)
121 CTFNet 1.81 % 4.56 % 2.27 % 100.00 % 0.7 s 8 cores @ 2.5 Ghz (Python)
122 MCV-MFC 1.95 % 3.84 % 2.27 % 100.00 % 0.35 s 1 core @ 2.5 Ghz (C/C++)
Z. Liang, Y. Guo, Y. Feng, W. Chen, L. Qiao, L. Zhou, J. Zhang and H. Liu: Stereo Matching Using Multi-level Cost Volume and Multi-scale Feature Constancy. IEEE transactions on pattern analysis and machine intelligence 2019.
123 CTFNet-v1 1.85 % 4.35 % 2.27 % 100.00 % 0.6 s 8 cores @ 2.5 Ghz (Python)
124 HSM-1.5x code 1.95 % 3.93 % 2.28 % 100.00 % 0.085 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.
125 CVANet_RVC 1.74 % 4.98 % 2.28 % 100.00 % 0.8 s 1 core @ 2.5 Ghz (C/C++)
126 CCNet 1.74 % 4.98 % 2.28 % 100.00 % 0.8 s 1 core @ 2.5 Ghz (C/C++)
127 MSFGNet 1.79 % 4.73 % 2.28 % 100.00 % 0.14 s GPU @ >3.5 Ghz (Python)
128 PSMNet-Naifan 1.82 % 4.64 % 2.29 % 100.00 % 0.4 s GPU @ 2.5 Ghz (Python)
129 SGNet 1.82 % 4.69 % 2.30 % 100.00 % 0.3 s 1 core @ 2.5 Ghz (Python)
130 DWA code 1.99 % 3.92 % 2.31 % 100.00 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
131 CFP-Net code 1.90 % 4.39 % 2.31 % 100.00 % 0.9 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.
132 PSMNet code 1.86 % 4.62 % 2.32 % 100.00 % 0.41 s Nvidia GTX Titan Xp
J. Chang and Y. Chen: Pyramid Stereo Matching Network. arXiv preprint arXiv:1803.08669 2018.
133 GANetREF_RVC code 1.88 % 4.58 % 2.33 % 100.00 % 1.62 s GPU @ >3.5 Ghz (Python + 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 2019.
134 DEINet+ 1.82 % 4.88 % 2.33 % 100.00 % 0.21 s GPU @ 2.5 Ghz (Python + C/C++)
135 JDCNet 1.91 % 4.47 % 2.33 % 100.00 % 0.079s NVIDIA V100
136 JPSMNet 1.93 % 4.40 % 2.34 % 100.00 % 0.47 s GPU @ 2.5 Ghz (Python)
137 PSM+LGF55 1.89 % 4.76 % 2.37 % 100.00 % 0.05 s 1 core @ 2.5 Ghz (C/C++)
138 VH-Net 2.11 % 3.71 % 2.38 % 100.00 % 0.4 s GPU @ >3.5 Ghz (Python)
139 PSM+LGF551 1.88 % 4.91 % 2.39 % 100.00 % 0.4 s 1 core @ 2.5 Ghz (Python)
140 MTLnet 2.07 % 4.01 % 2.39 % 100.00 % 0.09 s RTX 2070(pytorch)
141 ASM 1.97 % 4.60 % 2.41 % 100.00 % 0.02 s 1 core @ 2.5 Ghz (Python)
142 MABNet_origin code 1.89 % 5.02 % 2.41 % 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. .
143 MFN_U_SF_DS_K code 2.15 % 3.74 % 2.42 % 100.00 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
144 HybridNet code 1.93 % 4.90 % 2.42 % 100.00 % 0.12 s GPU @ 2.5 Ghz (Python)
145 FBNet 1.96 % 4.86 % 2.45 % 100.00 % 0.6 s 8 cores @ 2.5 Ghz (Python)
146 ANM3 1.95 % 5.19 % 2.49 % 100.00 % 0.4 s 1 core @ 2.5 Ghz (Python)
147 ANM1 1.99 % 5.05 % 2.50 % 100.00 % 0.41 s 1 core @ 2.5 Ghz (Python)
148 ERSCNet 2.11 % 4.46 % 2.50 % 100.00 % 0.28 s GPU @ 2.5 Ghz (Python)
Anonymous: ERSCNet. Proceedings of the European Conference on Computer Vision (ECCV) 2020.
149 BGNet 2.07 % 4.74 % 2.51 % 100.00 % 0.02 s GPU @ >3.5 Ghz (Python)
150 LFENet 2.26 % 3.88 % 2.53 % 100.00 % 0.09 s GPU @ 2.5 Ghz (Python)
ERROR: Wrong syntax in BIBTEX file.
151 MDA-Net 2.12 % 4.63 % 2.54 % 100.00 % 0.7 s 1 core @ 2.5 Ghz (Python)
152 UberATG-DRISF
This method uses optical flow information.
2.16 % 4.49 % 2.55 % 100.00 % 0.75 s CPU+GPU @ 2.5 Ghz (Python)
W. Ma, S. Wang, R. Hu, Y. Xiong and R. Urtasun: Deep Rigid Instance Scene Flow. CVPR 2019.
153 AANet code 1.99 % 5.39 % 2.55 % 100.00 % 0.062 s NVIDIA V100 GPU
H. Xu and J. Zhang: AANet: Adaptive Aggregation Network for Efficient Stereo Matching. CVPR 2020.
154 PDSNet 2.29 % 4.05 % 2.58 % 100.00 % 0.5 s 1 core @ 2.5 Ghz (Python)
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.
155 SPP 2.10 % 5.02 % 2.59 % 100.00 % 0.41 s 4 cores @ 2.0 Ghz (Python)
156 DeepPruner (fast) code 2.32 % 3.91 % 2.59 % 100.00 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
S. Duggal, S. Wang, W. Ma, R. Hu and R. Urtasun: DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch. ICCV 2019.
157 FADNet code 2.50 % 3.10 % 2.60 % 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.
158 MSC_U_SF_DS_K code 2.29 % 4.17 % 2.60 % 100.00 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
ERROR: Wrong syntax in BIBTEX file.
159 SCV code 2.22 % 4.53 % 2.61 % 100.00 % 0.36 s Nvidia GTX 1080 Ti
C. Lu, H. Uchiyama, D. Thomas, A. Shimada and R. Taniguchi: Sparse Cost Volume for Efficient Stereo Matching. Remote Sensing 2018.
160 2DFuseNet 2.08 % 5.42 % 2.63 % 100.00 % 0.11 s 1 core @ 2.5 Ghz (C/C++)
161 WaveletStereo: 2.24 % 4.62 % 2.63 % 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.
162 MCDRNet 2.09 % 5.42 % 2.65 % 100.00 % 0.032 s 1 core @ 2.5 Ghz (C/C++)
163 AANet_RVC 2.23 % 4.89 % 2.67 % 100.00 % 0.1 s GPU @ 2.5 Ghz (Python)
H. Xu and J. Zhang: AANet: Adaptive Aggregation Network for Efficient Stereo Matching. CVPR 2020.
164 CRL code 2.48 % 3.59 % 2.67 % 100.00 % 0.47 s Nvidia GTX 1080
J. Pang, W. Sun, J. Ren, C. Yang and Q. Yan: Cascade residual learning: A two-stage convolutional neural network for stereo matching. ICCV Workshop on Geometry Meets Deep Learning 2017.
165 GC+CRF 2.11 % 5.71 % 2.71 % 100.00 % 0.27 s GPU @ 2.0 Ghz (C/C++)
166 MSCVNet 2.31 % 5.41 % 2.82 % 100.00 % 0.04 s 1 core @ 2.5 Ghz (C/C++)
167 PCStereo 2.39 % 4.98 % 2.82 % 100.00 % 0.2 s GPU @ 2.5 Ghz (Python)
168 NVstereo2D 2.57 % 4.20 % 2.84 % 100.00 % 0.01 s GPU @ 2.5 Ghz (Python)
169 GC-NET 2.21 % 6.16 % 2.87 % 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.
170 BSDCNet 2.49 % 4.98 % 2.90 % 100.00 % 0.025s 1 core @ 2.5 Ghz (C/C++)
171 LRCR 2.55 % 5.42 % 3.03 % 100.00 % 49.2 s Nvidia GTX Titan X
Z. Jie, P. Wang, Y. Ling, B. Zhao, Y. Wei, J. Feng and W. Liu: Left-Right Comparative Recurrent Model for Stereo Matching. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018.
172 DCNet 2.70 % 4.70 % 3.04 % 100.00 % 0.025s GPU @ Nvidia GTX 1080 (Tensorflow)
173 SLNet 2.61 % 5.31 % 3.06 % 100.00 % 0.6 s 1 core @ 2.5 Ghz (Python)
174 Fast DS-CS code 2.83 % 4.31 % 3.08 % 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).
175 FDNet 2.83 % 4.31 % 3.08 % 100.00 % 0.7 s 1 core @ 2.5 Ghz (C/C++)
176 AdaStereo 2.59 % 5.55 % 3.08 % 100.00 % 0.41 s GPU @ 2.5 Ghz (Python)
X. Song, G. Yang, X. Zhu, H. Zhou, Z. Wang and J. Shi: AdaStereo: A Simple and Efficient Approach for Adaptive Stereo Matching. arXiv preprint arXiv:2004.04627 2020.
177 RecResNet code 2.46 % 6.30 % 3.10 % 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.
178 NVStereoNet code 2.62 % 5.69 % 3.13 % 100.00 % 0.6 s NVIDIA Titan Xp
N. Smolyanskiy, A. Kamenev and S. Birchfield: On the Importance of Stereo for Accurate Depth Estimation: An Efficient Semi-Supervised Deep Neural Network Approach. arXiv preprint arXiv:1803.09719 2018.
179 NineNet2 2.83 % 4.64 % 3.13 % 100.00 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
180 PMY-net 2.63 % 5.72 % 3.15 % 100.00 % 1 s 1 core @ 2.5 Ghz (Python)
181 Net3_2015 2.69 % 5.44 % 3.15 % 100.00 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
182 DRR 2.58 % 6.04 % 3.16 % 100.00 % 0.4 s Nvidia GTX Titan X
S. Gidaris and N. Komodakis: Detect, Replace, Refine: Deep Structured Prediction For Pixel Wise Labeling. arXiv preprint arXiv:1612.04770 2016.
183 NineNet3 2.69 % 5.57 % 3.17 % 100.00 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
184 SimpleStereo 2.69 % 5.60 % 3.17 % 100.00 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
185 Net3_2015 2.71 % 5.55 % 3.18 % 100.00 % 0.3 s 1 core @ 2.5 Ghz (Python + C/C++)
186 NineNet 2.70 % 5.94 % 3.24 % 100.00 % 0.07 s 1 core @ 2.5 Ghz (C/C++)
187 DMSNet 2.81 % 5.39 % 3.24 % 100.00 % 0.015625 s 1 core @ 2.5 Ghz (Python)
188 FPN 2.85 % 5.37 % 3.27 % 100.00 % 1 s 1 core @ 2.5 Ghz (Python)
189 SFFNet 2.69 % 6.23 % 3.28 % 100.00 % 0.07 s GPU @ 2.5 Ghz (Python)
190 DMSNetv2 2.80 % 5.85 % 3.31 % 100.00 % 0.02 s 1 core @ 2.5 Ghz (Python)
191 DWARF
This method uses optical flow information.
3.20 % 3.94 % 3.33 % 100.00 % 0.14s - 1.43s TitanXP - JetsonTX2
F. Aleotti, M. Poggi, F. Tosi and S. Mattoccia: Learning end-to-end scene flow by distilling single tasks knowledge. Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20) 2020.
192 AbNet1 3.14 % 4.43 % 3.35 % 100.00 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
193 CaTeNet2 2.71 % 6.67 % 3.37 % 100.00 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
194 SsSMnet 2.70 % 6.92 % 3.40 % 100.00 % 0.8 s P100
Y. Zhong, Y. Dai and H. Li: Self-Supervised Learning for Stereo Matching with Self-Improving Ability. arXiv:1709.00930 2017.
195 L-ResMatch code 2.72 % 6.95 % 3.42 % 100.00 % 48 s 1 core @ 2.5 Ghz (C/C++)
A. Shaked and L. Wolf: Improved Stereo Matching with Constant Highway Networks and Reflective Loss. arXiv preprint arxiv:1701.00165 2016.
196 Displets v2 code 3.00 % 5.56 % 3.43 % 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.
197 LBPS code 2.85 % 6.35 % 3.44 % 100.00 % 0.39 s GPU @ 2.5 Ghz (C/C++)
P. Knöbelreiter, C. Sormann, A. Shekhovtsov, F. Fraundorfer and T. Pock: Belief Propagation Reloaded: Learning BP-Layers for Labeling Problems. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2020.
198 CaTeNet 2.65 % 8.21 % 3.58 % 100.00 % 0.06 s 1 core @ 2.5 Ghz (C/C++)
199 ACOSF
This method uses optical flow information.
2.79 % 7.56 % 3.58 % 100.00 % 5 min 1 core @ 3.0 Ghz (Matlab + C/C++)
C. Li, H. Ma and Q. Liao: Two-Stage Adaptive Object Scene Flow Using Hybrid CNN-CRF Model. International Conference on Pattern Recognition (ICPR) 2020.
200 CNNF+SGM 2.78 % 7.69 % 3.60 % 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.
201 PBCP 2.58 % 8.74 % 3.61 % 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.
202 ASMNet 3.18 % 5.98 % 3.64 % 100.00 % 0.04 s 4 cores @ 2.5 Ghz (Python)
203 SGM-Net 2.66 % 8.64 % 3.66 % 100.00 % 67 s Titan X
A. Seki and M. Pollefeys: SGM-Nets: Semi-Global Matching With Neural Networks. CVPR 2017.
204 STTR 3.23 % 6.06 % 3.70 % 99.98 % 0.6 s 1 core @ 2.5 Ghz (C/C++)
205 Three3 2.99 % 7.33 % 3.71 % 100.00 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
206 HSM-Net_RVC code 2.74 % 8.73 % 3.74 % 100.00 % 0.97 s GPU @ 2.5 Ghz (Python)
G. Yang, J. Manela, M. Happold and D. Ramanan: Hierarchical deep stereo matching on high-resolution images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2019.
207 CasNet 2.94 % 7.77 % 3.75 % 100.00 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
208 ECCV_RVC 3.28 % 6.40 % 3.80 % 100.00 % 0.6 s GPU @ 1.0 Ghz (Python)
209 MABNet_tiny code 3.04 % 8.07 % 3.88 % 100.00 % 0.11 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. .
210 MC-CNN-acrt code 2.89 % 8.88 % 3.89 % 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 .
211 FD-Fusion code 3.22 % 7.44 % 3.92 % 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.
212 Net3_3 3.52 % 6.22 % 3.97 % 100.00 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
213 Reversing-PSMNet code 3.13 % 8.70 % 4.06 % 100.00 % 0.41 s 1 core @ 1.5 Ghz (Python)
F. Aleotti, F. Tosi, L. Zhang, M. Poggi and S. Mattoccia: Reversing the cycle: self-supervised deep stereo through enhanced monocular distillation. European Conference on Computer Vision (ECCV) 2020.
214 Simpnet 3.26 % 8.09 % 4.06 % 100.00 % 0.6 s 1 core @ 2.5 Ghz (C/C++)
215 DAStereo 3.67 % 6.83 % 4.20 % 100.00 % 0.32 s 1 core @ 2.5 Ghz (Python)
216 F407NJJ-FLOW
This method uses optical flow information.
3.25 % 9.11 % 4.22 % 100.00 % -1 s 1 core @ 2.5 Ghz (C/C++)
217 PRSM
This method uses optical flow information.
This method makes use of multiple (>2) views.
code 3.02 % 10.52 % 4.27 % 99.99 % 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.
218 DispNetC code 4.32 % 4.41 % 4.34 % 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.
219 SGM-Forest 3.11 % 10.74 % 4.38 % 99.92 % 6 seconds 1 core @ 3.0 Ghz (Python/C/C++)
J. Schönberger, S. Sinha and M. Pollefeys: Learning to Fuse Proposals from Multiple Scanline Optimizations in Semi-Global Matching. European Conference on Computer Vision (ECCV) 2018.
220 SSF
This method uses optical flow information.
3.55 % 8.75 % 4.42 % 100.00 % 5 min 1 core @ 2.5 Ghz (Matlab + C/C++)
Z. Ren, D. Sun, J. Kautz and E. Sudderth: Cascaded Scene Flow Prediction using Semantic Segmentation. International Conference on 3D Vision (3DV) 2017.
221 SMV 3.45 % 9.32 % 4.43 % 100.00 % 0.5 s GPU @ 2.5 Ghz (C/C++)
222 ISF
This method uses optical flow information.
4.12 % 6.17 % 4.46 % 100.00 % 10 min 1 core @ 3 Ghz (C/C++)
A. Behl, O. Jafari, S. Mustikovela, H. Alhaija, C. Rother and A. Geiger: Bounding Boxes, Segmentations and Object Coordinates: How Important is Recognition for 3D Scene Flow Estimation in Autonomous Driving Scenarios?. International Conference on Computer Vision (ICCV) 2017.
223 Content-CNN 3.73 % 8.58 % 4.54 % 100.00 % 1 s Nvidia GTX Titan X (Torch)
W. Luo, A. Schwing and R. Urtasun: Efficient Deep Learning for Stereo Matching. CVPR 2016.
224 MADnet code 3.75 % 9.20 % 4.66 % 100.00 % 0.02 s GPU @ 2.5 Ghz (Python)
A. Tonioni, F. Tosi, M. Poggi, S. Mattoccia and L. Di Stefano: Real-Time self-adaptive deep stereo. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
225 DTF_PWOC
This method uses optical flow information.
This method makes use of multiple (>2) views.
3.91 % 8.57 % 4.68 % 100.00 % 0.38 s RTX 2080 Ti
R. Schuster, C. Unger and D. Stricker: A Deep Temporal Fusion Framework for Scene Flow Using a Learnable Motion Model and Occlusions. IEEE Winter Conference on Applications of Computer Vision (WACV) 2021.
226 VN 4.29 % 7.65 % 4.85 % 100.00 % 0.5 s GPU @ 3.5 Ghz (Python + C/C++)
P. Knöbelreiter and T. Pock: Learned Collaborative Stereo Refinement. German Conference on Pattern Recognition (GCPR) 2019.
227 LWANet 4.28 % 8.22 % 4.94 % 100.00 % 0.02 s GPU @ 2.5 Ghz (Python)
228 naive-stereo 4.43 % 7.65 % 4.96 % 100.00 % 0.05 s 1 core @ 2.5 Ghz (Python)
229 MC-CNN-WS code 3.78 % 10.93 % 4.97 % 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.
230 3DMST 3.36 % 13.03 % 4.97 % 100.00 % 93 s 1 core @ >3.5 Ghz (C/C++)
X. Lincheng Li and L. Zhang: 3D Cost Aggregation with Multiple Minimum Spanning Trees for Stereo Matching. submitted to Applied Optics .
231 CBMV_ROB code 3.55 % 12.09 % 4.97 % 100.00 % 250 s 6 core @ 3.0 Ghz (Python + C/C++)
K. Batsos, C. Cai and P. Mordohai: CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018.
232 OSF+TC
This method uses optical flow information.
This method makes use of multiple (>2) views.
4.11 % 9.64 % 5.03 % 100.00 % 50 min 1 core @ 2.5 Ghz (C/C++)
M. Neoral and J. Šochman: Object Scene Flow with Temporal Consistency. 22nd Computer Vision Winter Workshop (CVWW) 2017.
233 CBMV code 4.17 % 9.53 % 5.06 % 100.00 % 250 s 6 cores @ 3.0 Ghz (Python,C/C++,CUDA Nvidia TitanX)
K. Batsos, C. Cai and P. Mordohai: CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation. 2018.
234 PWOC-3D
This method uses optical flow information.
code 4.19 % 9.82 % 5.13 % 100.00 % 0.13 s GTX 1080 Ti
R. Saxena, R. Schuster, O. Wasenmüller and D. Stricker: PWOC-3D: Deep Occlusion-Aware End-to-End Scene Flow Estimation. Intelligent Vehicles Symposium (IV) 2019.
235 naive-stereo-v1 4.99 % 6.56 % 5.25 % 100.00 % 0.05 s 1 core @ 2.5 Ghz (Python)
236 OSF 2018
This method uses optical flow information.
code 4.11 % 11.12 % 5.28 % 100.00 % 390 s 1 core @ 2.5 Ghz (Matlab + C/C++)
M. Menze, C. Heipke and A. Geiger: Object Scene Flow. ISPRS Journal of Photogrammetry and Remote Sensing (JPRS) 2018.
237 SPS-St code 3.84 % 12.67 % 5.31 % 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.
238 MDP
This method uses stereo information.
4.19 % 11.25 % 5.36 % 100.00 % 11.4 s 4 cores @ 3.5 Ghz (Matlab + C/C++)
A. Li, D. Chen, Y. Liu and Z. Yuan: Coordinating Multiple Disparity Proposals for Stereo Computation. IEEE Conference on Computer Vision and Pattern Recognition 2016.
239 SFF++
This method uses optical flow information.
This method makes use of multiple (>2) views.
4.27 % 12.38 % 5.62 % 100.00 % 78 s 4 cores @ 3.5 Ghz (C/C++)
R. Schuster, O. Wasenmüller, C. Unger, G. Kuschk and D. Stricker: SceneFlowFields++: Multi-frame Matching, Visibility Prediction, and Robust Interpolation for Scene Flow Estimation. International Journal of Computer Vision (IJCV) 2019.
240 OSF
This method uses optical flow information.
code 4.54 % 12.03 % 5.79 % 100.00 % 50 min 1 core @ 2.5 Ghz (C/C++)
M. Menze and A. Geiger: Object Scene Flow for Autonomous Vehicles. Conference on Computer Vision and Pattern Recognition (CVPR) 2015.
241 pSGM 4.84 % 11.64 % 5.97 % 100.00 % 7.77 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.
242 CSF
This method uses optical flow information.
4.57 % 13.04 % 5.98 % 99.99 % 80 s 1 core @ 2.5 Ghz (C/C++)
Z. Lv, C. Beall, P. Alcantarilla, F. Li, Z. Kira and F. Dellaert: A Continuous Optimization Approach for Efficient and Accurate Scene Flow. European Conf. on Computer Vision (ECCV) 2016.
243 MBM 4.69 % 13.05 % 6.08 % 100.00 % 0.13 s 1 core @ 3.0 Ghz (C/C++)
N. Einecke and J. Eggert: A Multi-Block-Matching Approach for Stereo. IV 2015.
244 PR-Sceneflow
This method uses optical flow information.
code 4.74 % 13.74 % 6.24 % 100.00 % 150 s 4 core @ 3.0 Ghz (Matlab + C/C++)
C. Vogel, K. Schindler and S. Roth: Piecewise Rigid Scene Flow. ICCV 2013.
245 DispSegNet 4.20 % 16.97 % 6.33 % 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.
246 DeepCostAggr code 5.34 % 11.35 % 6.34 % 99.98 % 0.03 s GPU @ 2.5 Ghz (C/C++)
A. Kuzmin, D. Mikushin and V. Lempitsky: End-to-end Learning of Cost-Volume Aggregation for Real-time Dense Stereo. 2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) 2017.
247 SGM_RVC 5.06 % 13.00 % 6.38 % 100.00 % 0.11 s Nvidia GTX 980
H. Hirschm\"uller: Stereo Processing by Semi-Global Matching and Mutual Information. IEEE Transactions on Pattern Analysis and Machine Intelligence 2008.
248 SceneFFields
This method uses optical flow information.
5.12 % 13.83 % 6.57 % 100.00 % 65 s 4 cores @ 3.7 Ghz (C/C++)
R. Schuster, O. Wasenmüller, G. Kuschk, C. Bailer and D. Stricker: SceneFlowFields: Dense Interpolation of Sparse Scene Flow Correspondences. IEEE Winter Conference on Applications of Computer Vision (WACV) 2018.
249 SPS+FF++
This method uses optical flow information.
code 5.47 % 12.19 % 6.59 % 100.00 % 36 s 1 core @ 3.5 Ghz (C/C++)
R. Schuster, O. Wasenmüller and D. Stricker: Dense Scene Flow from Stereo Disparity and Optical Flow. ACM Computer Science in Cars Symposium (CSCS) 2018.
250 Flow2Stereo 5.01 % 14.62 % 6.61 % 99.97 % 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.
251 FSF+MS
This method uses optical flow information.
This method makes use of the epipolar geometry.
This method makes use of multiple (>2) views.
5.72 % 11.84 % 6.74 % 100.00 % 2.7 s 4 cores @ 3.5 Ghz (C/C++)
T. Taniai, S. Sinha and Y. Sato: Fast Multi-frame Stereo Scene Flow with Motion Segmentation. IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017) 2017.
252 AABM 4.88 % 16.07 % 6.74 % 100.00 % 0.08 s 1 core @ 3.0 Ghz (C/C++)
N. Einecke and J. Eggert: Stereo Image Warping for Improved Depth Estimation of Road Surfaces. IV 2013.
253 DistillFlow
This method uses optical flow information.
5.00 % 15.88 % 6.81 % 100.00 % 0.03 s 1 core @ 2.5 Ghz (C/C++)
254 SGM+C+NL
This method uses optical flow information.
code 5.15 % 15.29 % 6.84 % 100.00 % 4.5 min 1 core @ 2.5 Ghz (C/C++)
H. Hirschmüller: Stereo Processing by Semiglobal Matching and Mutual Information. PAMI 2008.
D. Sun, S. Roth and M. Black: A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles Behind Them. IJCV 2013.
255 SGM+LDOF
This method uses optical flow information.
code 5.15 % 15.29 % 6.84 % 100.00 % 86 s 1 core @ 2.5 Ghz (C/C++)
H. Hirschmüller: Stereo Processing by Semiglobal Matching and Mutual Information. PAMI 2008.
T. Brox and J. Malik: Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation. PAMI 2011.
256 SGM+SF
This method uses optical flow information.
5.15 % 15.29 % 6.84 % 100.00 % 45 min 16 core @ 3.2 Ghz (C/C++)
H. Hirschmüller: Stereo Processing by Semiglobal Matching and Mutual Information. PAMI 2008.
M. Hornacek, A. Fitzgibbon and C. Rother: SphereFlow: 6 DoF Scene Flow from RGB-D Pairs. CVPR 2014.
257 FC-DCNN code 5.21 % 15.16 % 6.87 % 100.00 % 5 s GPU @ >3.5 Ghz (Python)
258 SNCC 5.36 % 16.05 % 7.14 % 100.00 % 0.08 s 1 core @ 3.0 Ghz (C/C++)
N. Einecke and J. Eggert: A Two-Stage Correlation Method for Stereoscopic Depth Estimation. DICTA 2010.
259 MBSF
This method uses optical flow information.
5.63 % 15.04 % 7.20 % 100.00 % 1 min GPU @ 2.5 Ghz (C/C++)
260 PASMnet code 5.41 % 16.36 % 7.23 % 100.00 % 0.5 s GPU @ 2.5 Ghz (Python)
L. Wang, Y. Guo, Y. Wang, Z. Liang, Z. Lin, J. Yang and W. An: Parallax Attention for Unsupervised Stereo Correspondence Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence(T-PAMI) 2020.
261 AAFS 6.27 % 13.95 % 7.54 % 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.
262 FCU-Net
This method uses optical flow information.
6.04 % 17.48 % 7.95 % 100.00 % 0.05 s 1 core @ 2.5 Ghz (Python)
263 UUF-Net
This method uses optical flow information.
6.35 % 16.60 % 8.06 % 100.00 % 0.02 s 1 core @ 2.5 Ghz (C/C++)
264 GOUSM 6.42 % 17.24 % 8.22 % 100.00 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
265 CSCT+SGM+MF 6.91 % 14.87 % 8.24 % 100.00 % 0.0064 s Nvidia GTX Titan X @ 1.0 Ghz (CUDA)
D. Hernandez-Juarez, A. Chacon, A. Espinosa, D. Vazquez, J. Moure and A. Lopez: Embedded real-time stereo estimation via Semi-Global Matching on the GPU. Procedia Computer Science 2016.
266 MBMGPU 6.61 % 16.70 % 8.29 % 100.00 % 0.0019 s GPU @ 1.0 Ghz (CUDA)
Q. Chang and T. Maruyama: Real-Time Stereo Vision System: A Multi-Block Matching on GPU. IEEE Access 2018.
267 MeshStereo code 5.82 % 21.21 % 8.38 % 100.00 % 87 s 1 core @ 2.5 Ghz (C/C++)
C. Zhang, Z. Li, Y. Cheng, R. Cai, H. Chao and Y. Rui: MeshStereo: A Global Stereo Model With Mesh Alignment Regularization for View Interpolation. The IEEE International Conference on Computer Vision (ICCV) 2015.
268 PCOF + ACTF
This method uses optical flow information.
6.31 % 19.24 % 8.46 % 100.00 % 0.08 s GPU @ 2.0 Ghz (C/C++)
M. Derome, A. Plyer, M. Sanfourche and G. Le Besnerais: A Prediction-Correction Approach for Real-Time Optical Flow Computation Using Stereo. German Conference on Pattern Recognition 2016.
269 PCOF-LDOF
This method uses optical flow information.
6.31 % 19.24 % 8.46 % 100.00 % 50 s 1 core @ 3.0 Ghz (C/C++)
M. Derome, A. Plyer, M. Sanfourche and G. Le Besnerais: A Prediction-Correction Approach for Real-Time Optical Flow Computation Using Stereo. German Conference on Pattern Recognition 2016.
270 OASM-Net 6.89 % 19.42 % 8.98 % 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.
271 ELAS_RVC code 7.38 % 21.15 % 9.67 % 100.00 % 0.19 s 4 cores @ >3.5 Ghz (C/C++)
A. Geiger, M. Roser and R. Urtasun: Efficient Large-Scale Stereo Matching. ACCV 2010.
272 DDP_out_ELAS_params1 7.45 % 21.11 % 9.72 % 100.00 % 30 min GPU @ 2.5 Ghz (Python)
273 ELAS code 7.86 % 19.04 % 9.72 % 92.35 % 0.3 s 1 core @ 2.5 Ghz (C/C++)
A. Geiger, M. Roser and R. Urtasun: Efficient Large-Scale Stereo Matching. ACCV 2010.
274 DDP_in_ELAS_params1 7.48 % 21.14 % 9.76 % 99.97 % 1 s 1 core @ 2.5 Ghz (Matlab + C/C++)
275 ACMC 8.50 % 17.87 % 10.06 % 98.53 % 0.05 s GPU @ 1.5 Ghz (C/C++)
276 REAF code 8.43 % 18.51 % 10.11 % 100.00 % 1.1 s 1 core @ 2.5 Ghz (C/C++)
C. Cigla: Recursive Edge-Aware Filters for Stereo Matching. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2015.
277 iGF
This method makes use of multiple (>2) views.
8.64 % 21.85 % 10.84 % 100.00 % 220 s 1 core @ 3.0 Ghz (C/C++)
R. Hamzah, H. Ibrahim and A. Hassan: Stereo matching algorithm based on per pixel difference adjustment, iterative guided filter and graph segmentation. Journal of Visual Communication and Image Representation 2016.
278 OCV-SGBM code 8.92 % 20.59 % 10.86 % 90.41 % 1.1 s 1 core @ 2.5 Ghz (C/C++)
H. Hirschmueller: Stereo processing by semiglobal matching and mutual information. PAMI 2008.
279 TW-SMNet 11.92 % 12.16 % 11.96 % 100.00 % 0.7 s GPU @ 2.5 Ghz (Python)
M. El-Khamy, H. Ren, X. Du and J. Lee: TW-SMNet: Deep Multitask Learning of Tele-Wide Stereo Matching. arXiv:1906.04463 2019.
280 SDM 9.41 % 24.75 % 11.96 % 62.56 % 1 min 1 core @ 2.5 Ghz (C/C++)
J. Kostkova: Stratified dense matching for stereopsis in complex scenes. BMVC 2003.
281 SGM&FlowFie+
This method uses optical flow information.
11.93 % 20.57 % 13.37 % 81.24 % 29 s 1 core @ 3.5 Ghz (C/C++)
R. Schuster, C. Bailer, O. Wasenmüller and D. Stricker: Combining Stereo Disparity and Optical Flow for Basic Scene Flow. Commercial Vehicle Technology Symposium (CVTS) 2018.
282 DDP 12.32 % 20.20 % 13.63 % 100.00 % 10 min 1 core @ 2.5 Ghz (Python)
283 DDP_out_ELAS_params2 12.32 % 20.20 % 13.63 % 100.00 % 10 min 1 core @ 2.5 Ghz (Python)
284 GCSF
This method uses optical flow information.
code 11.64 % 27.11 % 14.21 % 100.00 % 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.
285 MT-TW-SMNet 15.47 % 16.25 % 15.60 % 100.00 % 0.4s GPU @ 2.5 Ghz (Python)
M. El-Khamy, X. Du, H. Ren and J. Lee: Multi-Task Learning of Depth from Tele and Wide Stereo Image Pairs. Proceedings of the IEEE Conference on Image Processing 2019.
286 Mono-SF
This method uses optical flow information.
14.21 % 26.94 % 16.32 % 100.00 % 41 s 1 core @ 3.5 Ghz (Matlab + C/C++)
F. Brickwedde, S. Abraham and R. Mester: Mono-SF: Multi-View Geometry meets Single-View Depth for Monocular Scene Flow Estimation of Dynamic Traffic Scenes. Proc. of International Conference on Computer Vision (ICCV) 2019.
287 AbNet2 14.71 % 29.88 % 17.23 % 100.00 % 0.01 s 1 core @ 2.5 Ghz (C/C++)
288 CostFilter code 17.53 % 22.88 % 18.42 % 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.
289 MonoComb
This method uses optical flow information.
17.89 % 21.16 % 18.44 % 100.00 % 0.58 s RTX 2080 Ti
R. Schuster, C. Unger and D. Stricker: MonoComb: A Sparse-to-Dense Combination Approach for Monocular Scene Flow. ACM Computer Science in Cars Symposium (CSCS) 2020.
290 DWBSF
This method uses optical flow information.
19.61 % 22.69 % 20.12 % 100.00 % 7 min 4 cores @ 3.5 Ghz (C/C++)
C. Richardt, H. Kim, L. Valgaerts and C. Theobalt: Dense Wide-Baseline Scene Flow From Two Handheld Video Cameras. 3DV 2016.
291 monoResMatch code 22.10 % 19.81 % 21.72 % 100.00 % 0.16 s Titan X GPU
F. Tosi, F. Aleotti, M. Poggi and S. Mattoccia: Learning monocular depth estimation infusing traditional stereo knowledge. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2019.
292 DDP_in_ELAS_params2 20.63 % 28.68 % 21.97 % 85.92 % 1 s 1 core @ 2.5 Ghz (Matlab + C/C++)
293 Self-Mono-SF-ft
This method uses optical flow information.
code 20.72 % 29.41 % 22.16 % 100.00 % 0.09 s NVIDIA GTX 1080 Ti
J. Hur and S. Roth: Self-Supervised Monocular Scene Flow Estimation. CVPR 2020.
294 Multi-Mono-SF-ft
This method uses optical flow information.
This method makes use of multiple (>2) views.
21.60 % 28.22 % 22.71 % 100.00 % 0.06 s NVIDIA GTX 1080 Ti
295 OCV-BM code 24.29 % 30.13 % 25.27 % 58.54 % 0.1 s 1 core @ 2.5 Ghz (C/C++)
G. Bradski: The OpenCV Library. Dr. Dobb's Journal of Software Tools 2000.
296 VSF
This method uses optical flow information.
code 27.31 % 21.72 % 26.38 % 100.00 % 125 min 1 core @ 2.5 Ghz (C/C++)
F. Huguet and F. Devernay: A Variational Method for Scene Flow Estimation from Stereo Sequences. ICCV 2007.
297 YunYang-Monodepth 26.06 % 28.92 % 26.53 % 100.00 % 0.01 s 1 core @ 2.5 Ghz (Python)
298 SED code 25.01 % 40.43 % 27.58 % 4.02 % 0.68 s 1 core @ 2.0 Ghz (C/C++)
D. Pe\~{n}a and A. Sutherland: Disparity Estimation by Simultaneous Edge Drawing. Computer Vision -- ACCV 2016 Workshops: ACCV 2016 International Workshops, Taipei, Taiwan, November 20-24, 2016, Revised Selected Papers, Part II 2017.
299 Multi-Mono-SF
This method uses optical flow information.
This method makes use of multiple (>2) views.
27.48 % 47.30 % 30.78 % 100.00 % 0.06 s NVIDIA GTX 1080 Ti
300 mts1 code 28.03 % 46.55 % 31.11 % 2.52 % 0.18 s 4 cores @ 3.5 Ghz (C/C++)
R. Brandt, N. Strisciuglio, N. Petkov and M. Wilkinson: Efficient binocular stereo correspondence matching with 1-D Max-Trees. Pattern Recognition Letters 2020.
301 Self-Mono-SF
This method uses optical flow information.
code 31.22 % 48.04 % 34.02 % 100.00 % 0.09 s NVIDIA GTX 1080 Ti
J. Hur and S. Roth: Self-Supervised Monocular Scene Flow Estimation. CVPR 2020.
302 MST code 45.83 % 38.22 % 44.57 % 100.00 % 7 s 1 core @ 2.5 Ghz (Matlab + C/C++)
Q. Yang: A Non-Local Cost Aggregation Method for Stereo Matching. CVPR 2012.
Table as LaTeX | Only published Methods




Related Datasets

  • 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.
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  • 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.
  • Middlebury Optical Flow Evaluation: The classic optical flow evaluation benchmark, featuring eight test images, with very accurate ground truth from a shape from UV light pattern system. 24 image pairs are provided in total.

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