\begin{tabular}{c | c | c | c | c | c | c | c | c | c | c | c | c | c | c | c | c}
{\bf Method} & {\bf Setting} & {\bf D1-bg} & {\bf D1-fg} & {\bf D1-all} & {\bf D2-bg} & {\bf D2-fg} & {\bf D2-all} & {\bf Fl-bg} & {\bf Fl-fg} & {\bf Fl-all} & {\bf SF-bg} & {\bf SF-fg} & {\bf SF-all} & {\bf Density} & {\bf Runtime} & {\bf Environment}\\ \hline
ISF & & 4.12 \% & 6.17 \% & 4.46 \% & 4.88 \% & 11.34 \% & 5.95 \% & 5.40 \% & 10.29 \% & 6.22 \% & 6.58 \% & 15.63 \% & 8.08 \% & 100.00 \% & 10 min / 1 core & \\
PRSM & mv & 3.02 \% & 10.52 \% & 4.27 \% & 5.13 \% & 15.11 \% & 6.79 \% & 5.33 \% & 13.40 \% & 6.68 \% & 6.61 \% & 20.79 \% & 8.97 \% & 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.\\
OSF+TC & mv & 4.11 \% & 9.64 \% & 5.03 \% & 5.18 \% & 15.12 \% & 6.84 \% & 5.76 \% & 13.31 \% & 7.02 \% & 7.08 \% & 20.03 \% & 9.23 \% & 100.00 \% & 50 min / 1 core & M. Neoral and J. Šochman: Object Scene Flow with Temporal Consistency. 22nd Computer Vision Winter Workshop (CVWW) 2017.\\
SSF & & 3.55 \% & 8.75 \% & 4.42 \% & 4.94 \% & 17.48 \% & 7.02 \% & 5.63 \% & 14.71 \% & 7.14 \% & 7.18 \% & 24.58 \% & 10.07 \% & 100.00 \% & 5 min / 1 core & \\
OSF & & 4.54 \% & 12.03 \% & 5.79 \% & 5.45 \% & 19.41 \% & 7.77 \% & 5.62 \% & 18.92 \% & 7.83 \% & 7.01 \% & 26.34 \% & 10.23 \% & 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.\\
SOSF & & 4.30 \% & 8.72 \% & 5.03 \% & 5.13 \% & 15.27 \% & 6.82 \% & 5.42 \% & 22.59 \% & 8.28 \% & 6.95 \% & 29.94 \% & 10.77 \% & 100.00 \% & 55 min / 1 core & \\
FSF+MS & ms mv & 5.72 \% & 11.84 \% & 6.74 \% & 7.57 \% & 21.28 \% & 9.85 \% & 8.48 \% & 25.43 \% & 11.30 \% & 11.17 \% & 33.91 \% & 14.96 \% & 100.00 \% & 2.7 s / 4 cores & \\
CSF & & 4.57 \% & 13.04 \% & 5.98 \% & 7.92 \% & 20.76 \% & 10.06 \% & 10.40 \% & 25.78 \% & 12.96 \% & 12.21 \% & 33.21 \% & 15.71 \% & 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.\\
PR-Sceneflow & & 4.74 \% & 13.74 \% & 6.24 \% & 11.14 \% & 20.47 \% & 12.69 \% & 11.73 \% & 24.33 \% & 13.83 \% & 13.49 \% & 31.22 \% & 16.44 \% & 100.00 \% & 150 s / 4 core & C. Vogel, K. Schindler and S. Roth: Piecewise Rigid Scene Flow. ICCV 2013.\\
SGM+SF & & 5.15 \% & 15.29 \% & 6.84 \% & 14.10 \% & 23.13 \% & 15.60 \% & 20.91 \% & 25.50 \% & 21.67 \% & 23.09 \% & 34.46 \% & 24.98 \% & 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.\\
PCOF-LDOF & & 6.31 \% & 19.24 \% & 8.46 \% & 19.09 \% & 30.54 \% & 20.99 \% & 14.34 \% & 38.32 \% & 18.33 \% & 25.26 \% & 49.39 \% & 29.27 \% & 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.\\
PCOF + ACTF & & 6.31 \% & 19.24 \% & 8.46 \% & 19.15 \% & 36.27 \% & 22.00 \% & 14.89 \% & 60.15 \% & 22.43 \% & 25.77 \% & 67.75 \% & 32.76 \% & 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.\\
SGM+C+NL & & 5.15 \% & 15.29 \% & 6.84 \% & 28.77 \% & 25.65 \% & 28.25 \% & 34.24 \% & 42.46 \% & 35.61 \% & 38.21 \% & 50.95 \% & 40.33 \% & 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 & & 5.15 \% & 15.29 \% & 6.84 \% & 29.58 \% & 23.48 \% & 28.56 \% & 40.81 \% & 31.92 \% & 39.33 \% & 43.99 \% & 42.09 \% & 43.67 \% & 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.\\
DWBSF & & 19.61 \% & 22.69 \% & 20.12 \% & 35.72 \% & 28.15 \% & 34.46 \% & 40.74 \% & 31.16 \% & 39.14 \% & 46.42 \% & 40.76 \% & 45.48 \% & 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.\\
GCSF & & 11.64 \% & 27.11 \% & 14.21 \% & 32.94 \% & 35.77 \% & 33.41 \% & 47.38 \% & 41.50 \% & 46.40 \% & 52.92 \% & 56.68 \% & 53.54 \% & 100.00 \% & 2.4 s / 1 core & J. Cech, J. Sanchez-Riera and R. Horaud: Scene Flow Estimation by growing Correspondence Seeds. CVPR 2011.\\
VSF & & 27.31 \% & 21.72 \% & 26.38 \% & 59.51 \% & 44.93 \% & 57.08 \% & 50.06 \% & 45.40 \% & 49.28 \% & 67.69 \% & 62.93 \% & 66.90 \% & 100.00 \% & 125 min / 1 core & F. Huguet and F. Devernay: A Variational Method for Scene Flow Estimation from Stereo Sequences. ICCV 2007.
\end{tabular}