\begin{tabular}{c | c | c | c | c | c | c | c}
{\bf Method} & {\bf Setting} & {\bf D1-bg} & {\bf D1-fg} & {\bf D1-all} & {\bf Density} & {\bf Runtime} & {\bf Environment}\\ \hline
CRL & & 2.48 \% & 3.59 \% & 2.67 \% & 100.00 \% & 0.47 s / & \\
GC-NET & & 2.21 \% & 6.16 \% & 2.87 \% & 100.00 \% & 0.9 s / & 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. arXiv preprint arxiv:1703.04309 2017.\\
DRR & & 2.58 \% & 6.04 \% & 3.16 \% & 100.00 \% & 0.4 s / & S. Gidaris and N. Komodakis: Detect, Replace, Refine: Deep Structured Prediction For Pixel Wise Labeling. arXiv preprint arXiv:1612.04770 2016.\\
L-ResMatch & & 2.72 \% & 6.95 \% & 3.42 \% & 100.00 \% & 48 s / 1 core & A. Shaked and L. Wolf: Improved Stereo Matching with Constant Highway Networks and Reflective Loss. arXiv preprint arxiv:1701.00165 2016.\\
Displets v2 & & 3.00 \% & 5.56 \% & 3.43 \% & 100.00 \% & 265 s / >8 cores & F. Guney and A. Geiger: Displets: Resolving Stereo Ambiguities using Object Knowledge. Conference on Computer Vision and Pattern Recognition (CVPR) 2015.\\
CNNF+SGM & & 2.78 \% & 7.69 \% & 3.60 \% & 100.00 \% & 71 s / & \\
PBCP & & 2.58 \% & 8.74 \% & 3.61 \% & 100.00 \% & 68 s / & A. Seki and M. Pollefeys: Patch Based Confidence Prediction for Dense Disparity Map. British Machine Vision Conference (BMVC) 2016.\\
SN & & 2.66 \% & 8.64 \% & 3.66 \% & 100.00 \% & 67 s / & \\
MC-CNN-acrt & & 2.89 \% & 8.88 \% & 3.89 \% & 100.00 \% & 67 s / & J. Zbontar and Y. LeCun: Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches. Submitted to JMLR .\\
ASTCC & & 2.94 \% & 8.95 \% & 3.94 \% & 100.00 \% & 130 s / GPU & \\
CNN-SPS & & 3.30 \% & 7.92 \% & 4.07 \% & 100.00 \% & 80 s / GPU & L. Chen, J. Chen and L. Fan: A Convolutional Neural Networks based Full Density Stereo Matching Framework. .\\
RGL & & 4.22 \% & 4.02 \% & 4.19 \% & 100.00 \% & 0.1 s / 1 core & \\
PRSM & fl mv & 3.02 \% & 10.52 \% & 4.27 \% & 99.99 \% & 300 s / 1 core & C. Vogel, K. Schindler and S. Roth: 3D Scene Flow Estimation with a Piecewise Rigid Scene Model. ijcv 2015.\\
CAL & & 4.33 \% & 4.13 \% & 4.30 \% & 100.00 \% & 0.1 s / & \\
DispNetC & & 4.32 \% & 4.41 \% & 4.34 \% & 100.00 \% & 0.06 s / & 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.\\
SSF & fl & 3.55 \% & 8.75 \% & 4.42 \% & 100.00 \% & 5 min / 1 core & \\
CGNet & & 4.39 \% & 4.59 \% & 4.43 \% & 100.00 \% & 2.3 s / 1 core & \\
ISF & fl & 4.12 \% & 6.17 \% & 4.46 \% & 100.00 \% & 10 min / 1 core & \\
Content-CNN & & 3.73 \% & 8.58 \% & 4.54 \% & 100.00 \% & 1 s / & W. Luo, A. Schwing and R. Urtasun: Efficient Deep Learning for Stereo Matching. CVPR 2016.\\
MCSC & & 3.61 \% & 10.13 \% & 4.69 \% & 100.00 \% & 1 s / & \\
MC-CNN-SS & & 3.78 \% & 10.93 \% & 4.97 \% & 100.00 \% & 1.35 s / & \\
3DMST & & 3.36 \% & 13.03 \% & 4.97 \% & 100.00 \% & 93 s / 1 core & X. Lincheng Li and L. Zhang: 3D Cost Aggregation with Multiple Minimum Spanning Trees for Stereo Matching. submitted to Applied Optics .\\
LPU & & 3.55 \% & 12.30 \% & 5.01 \% & 100.00 \% & 1650 s / 1 core & \\
OSF+TC & fl mv & 4.11 \% & 9.64 \% & 5.03 \% & 100.00 \% & 50 min / 1 core & M. Neoral and J. Šochman: Object Scene Flow with Temporal Consistency. 22nd Computer Vision Winter Workshop (CVWW) 2017.\\
SOSF & fl & 4.30 \% & 8.72 \% & 5.03 \% & 100.00 \% & 55 min / 1 core & \\
SGM+CNN & & 3.93 \% & 10.56 \% & 5.04 \% & 100.00 \% & 2 s / & \\
SPS-St & & 3.84 \% & 12.67 \% & 5.31 \% & 100.00 \% & 2 s / 1 core & K. Yamaguchi, D. McAllester and R. Urtasun: Efficient Joint Segmentation, Occlusion Labeling, Stereo and Flow Estimation. ECCV 2014.\\
MNP & & 3.92 \% & 12.37 \% & 5.33 \% & 100.00 \% & 3 min / >8 cores & \\
MDP & st & 4.19 \% & 11.25 \% & 5.36 \% & 100.00 \% & 11.4 s / 4 cores & 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.\\
CPM2 & fl & 4.13 \% & 12.03 \% & 5.44 \% & 99.95 \% & 3 s / 1 core & \\
CNN-MS & & 3.89 \% & 13.28 \% & 5.45 \% & 100.00 \% & 3 min / GPU & \\
UCNN & & 4.15 \% & 12.08 \% & 5.47 \% & 99.98 \% & 3 s / & \\
JMR & & 4.35 \% & 11.25 \% & 5.50 \% & 99.81 \% & 1.3 sec / & \\
OSF & fl & 4.54 \% & 12.03 \% & 5.79 \% & 100.00 \% & 50 min / 1 core & M. Menze and A. Geiger: Object Scene Flow for Autonomous Vehicles. Conference on Computer Vision and Pattern Recognition (CVPR) 2015.\\
CSF & fl & 4.57 \% & 13.04 \% & 5.98 \% & 99.99 \% & 80 s / 1 core & 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.\\
MBM & & 4.69 \% & 13.05 \% & 6.08 \% & 100.00 \% & 0.13 s / 1 core & N. Einecke and J. Eggert: A Multi-Block-Matching Approach for Stereo. IV 2015.\\
PR-Sceneflow & fl & 4.74 \% & 13.74 \% & 6.24 \% & 100.00 \% & 150 s / 4 core & C. Vogel, K. Schindler and S. Roth: Piecewise Rigid Scene Flow. ICCV 2013.\\
SGM+DAISY & & 4.86 \% & 13.42 \% & 6.29 \% & 95.26 \% & 5 s / 1 core & \\
DeepCostAggr & & 5.34 \% & 11.35 \% & 6.34 \% & 99.98 \% & 0.03 s / GPU & \\
FSF+MS & fl ms mv & 5.72 \% & 11.84 \% & 6.74 \% & 100.00 \% & 2.7 s / 4 cores & 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.\\
AABM & & 4.88 \% & 16.07 \% & 6.74 \% & 100.00 \% & 0.08 s / 1 core & N. Einecke and J. Eggert: Stereo Image Warping for Improved Depth Estimation of Road Surfaces. IV 2013.\\
SGM+C+NL & fl & 5.15 \% & 15.29 \% & 6.84 \% & 100.00 \% & 4.5 min / 1 core & 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.\\
SGM+LDOF & fl & 5.15 \% & 15.29 \% & 6.84 \% & 100.00 \% & 86 s / 1 core & 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.\\
SGM+SF & fl & 5.15 \% & 15.29 \% & 6.84 \% & 100.00 \% & 45 min / 16 core & 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.\\
SNCC & & 5.36 \% & 16.05 \% & 7.14 \% & 100.00 \% & 0.08 s / 1 core & N. Einecke and J. Eggert: A Two-Stage Correlation Method for Stereoscopic Depth Estimation. DICTA 2010.\\
rcam & & 6.17 \% & 14.01 \% & 7.47 \% & 100.00 \% & 12 s / 8 cores & \\
DMDE & & 6.89 \% & 12.92 \% & 7.90 \% & 100.00 \% & 7 s / 4 cores & \\
CSCT+SGM+MF & & 6.91 \% & 14.87 \% & 8.24 \% & 100.00 \% & 0.0064 s / Nvidia GTX Titan X & 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.\\
MeshStereo & & 5.82 \% & 21.21 \% & 8.38 \% & 100.00 \% & 87 s / 1 core & 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.\\
PCOF + ACTF & fl & 6.31 \% & 19.24 \% & 8.46 \% & 100.00 \% & 0.08 s / GPU & 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.\\
PCOF-LDOF & fl & 6.31 \% & 19.24 \% & 8.46 \% & 100.00 \% & 50 s / 1 core & 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.\\
BRIEF & & 7.04 \% & 18.72 \% & 8.99 \% & 100.00 \% & 3.72 s / 4 cores & \\
CPL+SP & & 7.09 \% & 19.89 \% & 9.22 \% & 99.78 \% & 5 min / 1 core & \\
ELAS & & 7.86 \% & 19.04 \% & 9.72 \% & 92.35 \% & 0.3 s / 1 core & A. Geiger, M. Roser and R. Urtasun: Efficient Large-Scale Stereo Matching. ACCV 2010.\\
REAF & & 8.43 \% & 18.51 \% & 10.11 \% & 100.00 \% & 1.1 s / 1 core & C. Cigla: Recursive Edge-Aware Filters for Stereo Matching. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops 2015.\\
iGF & mv & 8.64 \% & 21.85 \% & 10.84 \% & 100.00 \% & 220 s / 1 core & 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.\\
OCV-SGBM & & 8.92 \% & 20.59 \% & 10.86 \% & 90.41 \% & 1.1 s / 1 core & H. Hirschmueller: Stereo processing by semiglobal matching and mutual information. PAMI 2008.\\
SDM & & 9.41 \% & 24.75 \% & 11.96 \% & 62.56 \% & 1 min / 1 core & J. Kostkova: Stratified dense matching for stereopsis in complex scenes. BMVC 2003.\\
DSGCA & & 10.54 \% & 20.79 \% & 12.25 \% & 100.00 \% & 144 s / >8 cores & \\
GCSF & fl & 11.64 \% & 27.11 \% & 14.21 \% & 100.00 \% & 2.4 s / 1 core & J. Cech, J. Sanchez-Riera and R. Horaud: Scene Flow Estimation by growing Correspondence Seeds. CVPR 2011.\\
CostFilter & & 17.53 \% & 22.88 \% & 18.42 \% & 100.00 \% & 4 min / 1 core & C. Rhemann, A. Hosni, M. Bleyer, C. Rother and M. Gelautz: Fast Cost-Volume Filtering for Visual Correspondence and Beyond. CVPR 2011.\\
DWBSF & fl & 19.61 \% & 22.69 \% & 20.12 \% & 100.00 \% & 7 min / 4 cores & C. Richardt, H. Kim, L. Valgaerts and C. Theobalt: Dense Wide-Baseline Scene Flow From Two Handheld Video Cameras. 3DV 2016.\\
OCV-BM & & 24.29 \% & 30.13 \% & 25.27 \% & 58.54 \% & 0.1 s / 1 core & G. Bradski: The OpenCV Library. Dr. Dobb's Journal of Software Tools 2000.\\
VSF & fl & 27.31 \% & 21.72 \% & 26.38 \% & 100.00 \% & 125 min / 1 core & F. Huguet and F. Devernay: A Variational Method for Scene Flow Estimation from Stereo Sequences. ICCV 2007.\\
SED & & 25.01 \% & 40.43 \% & 27.58 \% & 4.02 \% & 0.68 s / 1 core & \\
MST & & 45.83 \% & 38.22 \% & 44.57 \% & 100.00 \% & 7 s / 1 core & Q. Yang: A Non-Local Cost Aggregation Method for Stereo Matching. CVPR 2012.\\
Test AD & & 58.86 \% & 57.65 \% & 58.66 \% & 100.00 \% & 181 s / 2 cores &
\end{tabular}