\begin{tabular}{c | c | c | c | c | c | c | c}
{\bf Method} & {\bf Setting} & {\bf Fl-bg} & {\bf Fl-fg} & {\bf Fl-all} & {\bf Density} & {\bf Runtime} & {\bf Environment}\\ \hline
ISF & st & 5.40 \% & 10.29 \% & 6.22 \% & 100.00 \% & 10 min / 1 core & \\
PRSM & st mv & 5.33 \% & 13.40 \% & 6.68 \% & 100.00 \% & 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 & st mv & 5.76 \% & 13.31 \% & 7.02 \% & 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 & st & 5.63 \% & 14.71 \% & 7.14 \% & 100.00 \% & 5 min / 1 core & \\
OSF & st & 5.62 \% & 18.92 \% & 7.83 \% & 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 & st & 5.42 \% & 22.59 \% & 8.28 \% & 100.00 \% & 55 min / 1 core & \\
FlowNet2 & & 10.75 \% & 8.75 \% & 10.41 \% & 100.00 \% & 0.12 s / & \\
MirrorFlow & & 9.05 \% & 18.27 \% & 10.58 \% & 100.00 \% & 1.8 h / 1 core & \\
SDF & & 8.61 \% & 23.01 \% & 11.01 \% & 100.00 \% & TBA / 1 core & M. Bai*, W. Luo*, K. Kundu and R. Urtasun: Exploiting Semantic Information and Deep Matching for Optical Flow. ECCV 2016.\\
FSF+MS & st ms mv & 8.48 \% & 25.43 \% & 11.30 \% & 100.00 \% & 2.7 s / 4 cores & \\
CNNF+PMBP & & 10.08 \% & 18.56 \% & 11.49 \% & 100.00 \% & 45 min / 1 cores & \\
CSF & st & 10.40 \% & 25.78 \% & 12.96 \% & 100.00 \% & 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.\\
MR-Flow & mv & 11.44 \% & 21.65 \% & 13.14 \% & 100.00 \% & 8 min / 1 core & \\
PR-Sceneflow & st & 11.73 \% & 24.33 \% & 13.83 \% & 100.00 \% & 150 s / 4 core & C. Vogel, K. Schindler and S. Roth: Piecewise Rigid Scene Flow. ICCV 2013.\\
DCFlow & & 13.10 \% & 23.70 \% & 14.86 \% & 100.00 \% & 8.6 s / GPU & J. Xu, R. Ranftl and V. Koltun: Accurate Optical Flow via Direct Cost Volume Processing. CVPR 2017.\\
SOF & & 14.63 \% & 22.83 \% & 15.99 \% & 100.00 \% & 6 min / 1 core & L. Sevilla-Lara, D. Sun, V. Jampani and M. Black: Optical Flow with Semantic Segmentation and Localized Layers. CVPR 2016.\\
JFS & ms & 15.90 \% & 19.31 \% & 16.47 \% & 100.00 \% & 13 min / 1 core & J. Hur and S. Roth: Joint Optical Flow and Temporally Consistent Semantic Segmentation. ECCV Workshops 2016.\\
ImpPB+SPCI & & 17.25 \% & 20.44 \% & 17.78 \% & 100.00 \% & 60 s / GPU & T. Schuster, L. Wolf and D. Gadot: Optical Flow Requires Multiple Strategies (but only one network). CVPR 2017.\\
PCOF-LDOF & st & 14.34 \% & 38.32 \% & 18.33 \% & 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.\\
CNN-HPM & & 18.33 \% & 20.42 \% & 18.68 \% & 100.00 \% & 23 s / GPU/CPU 4 core & C. Bailer, K. Varanasi and D. Stricker: CNN-based Patch Matching for Optical Flow with Thresholded Hinge Embedding Loss. CVPR 2017.\\
RicFlow & & 18.73 \% & 19.09 \% & 18.79 \% & 100.00 \% & 5 s / 1 core & Y. Hu, Y. Li and R. Song: Robust Interpolation of Correspondences for Large Displacement Optical Flow. CVPR 2017.\\
PGM-G & & 18.90 \% & 23.43 \% & 19.66 \% & 100.00 \% & 5.05 s / 1 core & \\
FlowFields+ & & 19.51 \% & 21.26 \% & 19.80 \% & 100.00 \% & 28s / 1 core & \\
PatchBatch & & 19.98 \% & 26.50 \% & 21.07 \% & 100.00 \% & 50 s / GPU & D. Gadot and L. Wolf: PatchBatch: a Batch Augmented Loss for Optical Flow. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2016.\\
DDF & & 20.36 \% & 25.19 \% & 21.17 \% & 100.00 \% & ~1 min / GPU & F. G\"uney and A. Geiger: Deep Discrete Flow. Asian Conference on Computer Vision (ACCV) 2016.\\
SODA-Flow & & 20.01 \% & 29.14 \% & 21.53 \% & 100.00 \% & 96 s / 2 cores & \\
DiscreteFlow & & 21.53 \% & 21.76 \% & 21.57 \% & 100.00 \% & 3 min / 1 core & M. Menze, C. Heipke and A. Geiger: Discrete Optimization for Optical Flow. German Conference on Pattern Recognition (GCPR) 2015.\\
SGM+SF & st & 20.91 \% & 25.50 \% & 21.67 \% & 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.\\
OAR-Flow & & 20.62 \% & 27.67 \% & 21.79 \% & 100.00 \% & 100 s / 2 cores & \\
CPM-Flow & & 22.32 \% & 22.81 \% & 22.40 \% & 100.00 \% & 4.2 s / 1 core & Y. Hu, R. Song and Y. Li: Efficient Coarse-to-Fine PatchMatch for Large Displacement Optical Flow. CVPR 2016.\\
PCOF + ACTF & st & 14.89 \% & 60.15 \% & 22.43 \% & 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.\\
IntrpNt-df & & 22.15 \% & 26.03 \% & 22.80 \% & 100.00 \% & 3 min / GPU & \\
MotionSLIC & ms & 14.86 \% & 64.44 \% & 23.11 \% & 100.00 \% & 30 s / 4 cores & K. Yamaguchi, D. McAllester and R. Urtasun: Robust Monocular Epipolar Flow Estimation. CVPR 2013.\\
IntrpNt-cpm & & 22.51 \% & 26.54 \% & 23.18 \% & 100.00 \% & 5.6 s / GPU & \\
FullFlow & & 23.09 \% & 24.79 \% & 23.37 \% & 100.00 \% & 4 min / 4 cores & Q. Chen and V. Koltun: Full Flow: Optical Flow Estimation By Global Optimization over Regular Grids. CVPR 2016.\\
IntrpNt-dm & & 23.46 \% & 26.27 \% & 23.93 \% & 100.00 \% & 15 s / GPU & \\
SPM-BP & & 24.06 \% & 24.97 \% & 24.21 \% & 100.00 \% & 10 s / 2 cores & Y. Li, D. Min, M. Brown, M. Do and J. Lu: SPM-BP: Sped-up PatchMatch Belief Propagation for Continuous MRFs. Proceedings of the IEEE International Conference on Computer Vision 2015.\\
FFlow & & 25.56 \% & 29.33 \% & 26.19 \% & 100.00 \% & 16 s / 4 cores & \\
EpicFlow & & 25.81 \% & 28.69 \% & 26.29 \% & 100.00 \% & 15 s / 1 core & J. Revaud, P. Weinzaepfel, Z. Harchaoui and C. Schmid: EpicFlow: Edge-Preserving Interpolation of Correspondences for Optical Flow. CVPR 2015 - IEEE Conference on Computer Vision \& Pattern Recognition 2015.\\
faldoi & & 27.08 \% & 27.09 \% & 27.08 \% & 100.00 \% & 130 s / 1 core & \\
SBFlow & & 27.13 \% & 30.83 \% & 27.75 \% & 100.00 \% & 14.8 s / 4 cores & \\
DeepFlow & & 27.96 \% & 31.06 \% & 28.48 \% & 100.00 \% & 17 s / 1 core & P. Weinzaepfel, J. Revaud, Z. Harchaoui and C. Schmid: DeepFlow: Large displacement optical flow with deep matching. IEEE Intenational Conference on Computer Vision (ICCV) 2013.\\
GPC & & 30.60 \% & 28.87 \% & 30.31 \% & 100.00 \% & 4 s / GPU & \\
SPyNet & & 33.36 \% & 43.62 \% & 35.07 \% & 100.00 \% & 0.16 s / 1 core & \\
SGM+C+NL & st & 34.24 \% & 42.46 \% & 35.61 \% & 93.83 \% & 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.\\
RDENSE & & 35.38 \% & 36.85 \% & 35.62 \% & 100.00 \% & 0.5 s / 4 cores & \\
DSTflow & st mv & 38.84 \% & 36.82 \% & 38.50 \% & 100.00 \% & 0.07 s / GPU & \\
DWBSF & st & 40.74 \% & 31.16 \% & 39.14 \% & 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.\\
SGM+LDOF & st & 40.81 \% & 31.92 \% & 39.33 \% & 95.89 \% & 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.\\
HS & & 39.90 \% & 51.39 \% & 41.81 \% & 100.00 \% & 2.6 min / 1 core & D. Sun, S. Roth and M. Black: A Quantitative Analysis of Current Practices in Optical Flow Estimation and The Principles Behind Them. 2014.\\
GCSF & st & 47.38 \% & 41.50 \% & 46.40 \% & 100.00 \% & 2.4 s / 1 core & J. Cech, J. Sanchez-Riera and R. Horaud: Scene Flow Estimation by growing Correspondence Seeds. CVPR 2011.\\
DB-TV-L1 & & 47.52 \% & 48.27 \% & 47.64 \% & 100.00 \% & 16 s / 1 core & C. Zach, T. Pock and H. Bischof: A Duality Based Approach for Realtime TV- L1 Optical Flow. DAGM 2007.\\
VSF & st & 50.06 \% & 45.40 \% & 49.28 \% & 100.00 \% & 125 min / 1 core & F. Huguet and F. Devernay: A Variational Method for Scene Flow Estimation from Stereo Sequences. ICCV 2007.\\
HAOF & & 49.89 \% & 50.74 \% & 50.04 \% & 100.00 \% & 16.2 s / 1 core & T. Brox, A. Bruhn, N. Papenberg and J. Weickert: High accuracy optical flow estimation based on a theory for warping. ECCV 2004.\\
SSF & & 51.61 \% & 48.39 \% & 51.07 \% & 100.00 \% & 200 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
PolyExpand & & 52.00 \% & 58.56 \% & 53.09 \% & 100.00 \% & 1 s / 1 core & G. Farneback: Two-Frame Motion Estimation Based on Polynomial Expansion. SCIA 2003.\\
Pyramid-LK & & 71.84 \% & 76.82 \% & 72.67 \% & 100.00 \% & 1.5 min / 1 core & J. Bouguet: Pyramidal implementation of the Lucas Kanade feature tracker. Intel 2000.\\
MEDIAN & & 87.37 \% & 92.80 \% & 88.27 \% & 99.86 \% & 0.01 s / 1 core & \\
AVERAGE & & 88.47 \% & 92.08 \% & 89.07 \% & 99.86 \% & 0.01 s / 1 core &
\end{tabular}