\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
M2S\_CSPN & & 1.51 \% & 2.88 \% & 1.74 \% & 100.00 \% & 0.5 s / GPU & X. Cheng, P. Wang and R. Yang: Learning Depth with Convolutional Spatial Propagation Network. arXiv preprint arXiv:1810.02695 2018.\\
MS\_CSPN & & 1.56 \% & 3.78 \% & 1.93 \% & 100.00 \% & 0.5 s / GPU & X. Cheng, P. Wang and R. Yang: Learning Depth with Convolutional Spatial Propagation Network. arXiv preprint arXiv:1810.02695 2018.\\
NCA-Net & & 1.68 \% & 3.28 \% & 1.94 \% & 100.00 \% & 0.5 s / GPU & \\
Samsung\_System\_LSI & & 1.60 \% & 3.88 \% & 1.98 \% & 100.00 \% & 0.4 s / GPU & \\
PSMNet\_R & & 1.62 \% & 3.79 \% & 1.98 \% & 100.00 \% & 0.5 s / GPU & \\
DSHNet & & 1.65 \% & 4.29 \% & 2.09 \% & 100.00 \% & 0.7 s / & \\
FUA-Net & & 1.66 \% & 4.27 \% & 2.09 \% & 100.00 \% & 0.9 s / 1 core & \\
KesonStereo\_V1 & & 1.77 \% & 3.74 \% & 2.09 \% & 100.00 \% & 0.4 s / GPU & \\
open-depth & & 1.76 \% & 3.84 \% & 2.10 \% & 100.00 \% & 0.51 s / & \\
HuaNet & & 1.69 \% & 4.29 \% & 2.12 \% & 100.00 \% & 0.9s / 1 core & \\
HuaNet-Init & & 1.69 \% & 4.38 \% & 2.14 \% & 100.00 \% & 0.40s / & \\
HSM & & 1.80 \% & 3.85 \% & 2.14 \% & 100.00 \% & 0.15 s / GPU & \\
Stereo-fusion-SJTU & & 1.87 \% & 3.61 \% & 2.16 \% & 100.00 \% & 0.7 s / & X. Song, X. Zhao, H. Hu and L. Fang: EdgeStereo: A Context Integrated Residual Pyramid Network for Stereo Matching. Asian Conference on Computer Vision (ACCV) 2018.\\
TinyStereo\_V2 & & 1.93 \% & 3.76 \% & 2.24 \% & 100.00 \% & 0.4 s / GPU & \\
SegStereo & & 1.88 \% & 4.07 \% & 2.25 \% & 100.00 \% & 0.6 s / & G. Yang, H. Zhao, J. Shi, Z. Deng and J. Jia: SegStereo: Exploiting Semantic Information for Disparity Estimation. arXiv preprint arXiv:1807.11699 2018.\\
HDU-LJJ-Group & & 1.82 \% & 4.42 \% & 2.25 \% & 100.00 \% & 0.47 s / GPU & \\
Stereo-DRNet & & 1.72 \% & 4.95 \% & 2.26 \% & 100.00 \% & 0.23 s / & \\
PASM & & 1.78 \% & 4.64 \% & 2.26 \% & 100.00 \% & 0.52 s / 1 core & \\
MPSMNet & & 1.78 \% & 4.63 \% & 2.26 \% & 100.00 \% & 1.0 s / GPU & \\
MSDC-Net & & 1.96 \% & 3.77 \% & 2.26 \% & 100.00 \% & 0.6 s / GPU & \\
TinyStereo & & 1.92 \% & 4.13 \% & 2.28 \% & 100.00 \% & 0.39 s / 1 core & \\
PSMNet\_ROB & & 1.79 \% & 4.92 \% & 2.31 \% & 100.00 \% & 0.41 s / 1 core & \\
MeituNet & & 1.88 \% & 4.48 \% & 2.31 \% & 100.00 \% & 0.51 s / GPU & \\
CFP-Net & & 1.90 \% & 4.39 \% & 2.31 \% & 100.00 \% & 0.9 s / 8 cores & \\
PSMNet & & 1.86 \% & 4.62 \% & 2.32 \% & 100.00 \% & 0.41 s / & J. Chang and Y. Chen: Pyramid Stereo Matching Network. arXiv preprint arXiv:1803.08669 2018.\\
MSDC-Net(v1) & & 2.00 \% & 4.05 \% & 2.34 \% & 100.00 \% & 0.8 s / 1 core & \\
SE-PSM & & 1.90 \% & 4.59 \% & 2.34 \% & 100.00 \% & 0.85 s / GPU & \\
disparity stereo & & 1.85 \% & 4.86 \% & 2.35 \% & 100.00 \% & 0.5 s / GPU & \\
DeepStereo\_V2 & & 2.00 \% & 4.21 \% & 2.37 \% & 100.00 \% & 0.4 s / 1 core & \\
UMS-GANs & & 1.95 \% & 4.57 \% & 2.38 \% & 100.00 \% & 0.7 s / GPU & \\
L2-method & & 1.91 \% & 4.90 \% & 2.40 \% & 100.00 \% & 0.35 s / GPU & \\
PSM+NN & & 1.95 \% & 4.85 \% & 2.43 \% & 100.00 \% & 1 s / GPU & \\
RAP & & 2.00 \% & 4.83 \% & 2.47 \% & 100.00 \% & 0.54 s / 1 core & \\
NNet & & 1.95 \% & 5.32 \% & 2.51 \% & 100.00 \% & 0.69 s / GPU & \\
PSMNet-test & & 2.02 \% & 5.02 \% & 2.52 \% & 100.00 \% & 2 s / GPU & J. Chang and Y. Chen: Pyramid Stereo Matching Network. arXiv preprint arXiv:1803.08669 2018.\\
X\_ASPP & & 2.13 \% & 4.57 \% & 2.54 \% & 100.00 \% & 0.88 s / GPU & \\
DSSF & fl & 2.16 \% & 4.49 \% & 2.55 \% & 100.00 \% & 0.75 s / CPU+GPU & \\
FBW-Net & & 2.08 \% & 4.98 \% & 2.56 \% & 100.00 \% & 2 s / GPU & \\
PDSNet & & 2.29 \% & 4.05 \% & 2.58 \% & 100.00 \% & 0.5 s / 1 core & 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.\\
DeepStereo & & 2.16 \% & 4.72 \% & 2.59 \% & 100.00 \% & 0.9 s / & \\
EdgeStereo & & 2.27 \% & 4.18 \% & 2.59 \% & 100.00 \% & 0.27 s / & X. Song, X. Zhao, H. Hu and L. Fang: EdgeStereo: A Context Integrated Residual Pyramid Network for Stereo Matching. Asian Conference on Computer Vision (ACCV) 2018.\\
SCV & & 2.22 \% & 4.53 \% & 2.61 \% & 100.00 \% & 0.36 s / & \\
MRFnet & & 1.97 \% & 5.81 \% & 2.61 \% & 100.00 \% & 0.24 s / GPU & \\
RESC & & 2.44 \% & 3.63 \% & 2.64 \% & 100.00 \% & 0.1 s / 1 core & \\
CooperativeStereo & & 2.09 \% & 5.38 \% & 2.64 \% & 100.00 \% & 0.9 s / GPU & \\
SANet & & 2.13 \% & 5.24 \% & 2.65 \% & 100.00 \% & 0.8 s / 1 core & \\
HTC & fl & 2.12 \% & 5.40 \% & 2.67 \% & 100.00 \% & 0.03 s / 1 core & \\
CRL & & 2.48 \% & 3.59 \% & 2.67 \% & 100.00 \% & 0.47 s / & 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.\\
CCFP-Net & & 2.11 \% & 5.53 \% & 2.68 \% & 100.00 \% & 0.5 s / 8 cores & \\
NCCL2 & & 2.11 \% & 5.59 \% & 2.69 \% & 100.00 \% & 0.61 s / GPU & \\
MFS-NET & & 2.22 \% & 5.09 \% & 2.70 \% & 100.00 \% & 0.5 s / GPU & \\
iResNet\_ROB & & 2.27 \% & 4.89 \% & 2.71 \% & 100.00 \% & 0.35 s / & \\
LALA\_ROB & & 2.22 \% & 5.30 \% & 2.73 \% & 100.00 \% & 0.5 s / 1 core & \\
ETE\_ROB & & 2.17 \% & 5.54 \% & 2.73 \% & 100.00 \% & 0.4 s / GPU & \\
NaN\_ROB & & 2.30 \% & 5.09 \% & 2.77 \% & 100.00 \% & 0.1 s / 1 core & \\
DLCB\_ROB & & 2.16 \% & 5.85 \% & 2.77 \% & 100.00 \% & 140 s / GPU & \\
ResV2\_ASPP & & 2.32 \% & 5.47 \% & 2.84 \% & 100.00 \% & 0.56 s / GPU & \\
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. Proceedings of the International Conference on Computer Vision (ICCV) 2017.\\
CAR & & 2.35 \% & 5.53 \% & 2.88 \% & 100.00 \% & 0.5 s / 1 core & \\
SemanStereo & & 2.36 \% & 5.72 \% & 2.92 \% & 100.00 \% & 60 s / 1 core & \\
DN-CSS\_ROB & & 2.39 \% & 5.71 \% & 2.94 \% & 100.00 \% & 0.07 s / 1 core & \\
FBW\_ROB & & 2.35 \% & 6.20 \% & 2.99 \% & 100.00 \% & 2 s / GPU & \\
SPF-Net & & 2.60 \% & 4.97 \% & 2.99 \% & 100.00 \% & 0.16 s / GPU & \\
XPNet\_ROB & & 2.41 \% & 6.06 \% & 3.01 \% & 100.00 \% & 0.42 s / GPU & \\
LRCR & & 2.55 \% & 5.42 \% & 3.03 \% & 100.00 \% & 49.2 s / & 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.\\
CFCNet & & 2.47 \% & 5.90 \% & 3.04 \% & 100.00 \% & 0.47 s / GPU & \\
NVStereoNet & & 2.62 \% & 5.69 \% & 3.13 \% & 100.00 \% & 0.6 s / & 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.\\
NVStereoNet\_ROB & & 2.62 \% & 5.69 \% & 3.13 \% & 100.00 \% & 0.6 s / & \\
MSFNet & & 2.70 \% & 5.36 \% & 3.15 \% & 100.00 \% & 0.15 s / & \\
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.\\
MFMNet\_s & & 2.97 \% & 4.20 \% & 3.17 \% & 100.00 \% & 0.36 s / GPU & \\
MFMNert & & 3.05 \% & 4.48 \% & 3.29 \% & 100.00 \% & 0.36 s / GPU & \\
SsSMnet & & 2.70 \% & 6.92 \% & 3.40 \% & 100.00 \% & 0.8 s / & Y. Zhong, Y. Dai and H. Li: Self-Supervised Learning for Stereo Matching with Self-Improving Ability. arXiv:1709.00930 2017.\\
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 / & F. Zhang and B. Wah: Fundamental Principles on Learning New Features for Effective Dense Matching. IEEE Transactions on Image Processing 2018.\\
DH-SF & fl & 2.70 \% & 8.07 \% & 3.60 \% & 100.00 \% & 350 s / 1 core & \\
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.\\
SGM-Net & & 2.66 \% & 8.64 \% & 3.66 \% & 100.00 \% & 67 s / & A. Seki and M. Pollefeys: SGM-Nets: Semi-Global Matching With Neural Networks. CVPR 2017.\\
Dense-CNN & & 2.90 \% & 8.79 \% & 3.88 \% & 100.00 \% & 53 s / 1 core & \\
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 .\\
ESM & & 3.33 \% & 6.73 \% & 3.90 \% & 100.00 \% & 0.03 s / 1 core & \\
MSMD\_ROB & & 3.14 \% & 9.28 \% & 4.16 \% & 100.00 \% & 1.2 s / GPU & \\
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.\\
PDISCO\_ROB & & 3.79 \% & 7.00 \% & 4.33 \% & 100.00 \% & 0.2 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.\\
SGM-Forest & & 3.11 \% & 10.74 \% & 4.38 \% & 99.92 \% & 6 seconds / 1 core & 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.\\
SSF & fl & 3.55 \% & 8.75 \% & 4.42 \% & 100.00 \% & 5 min / 1 core & Z. Ren, D. Sun, J. Kautz and E. Sudderth: Cascaded Scene Flow Prediction using Semantic Segmentation. International Conference on 3D Vision (3DV) 2017.\\
ISF & fl & 4.12 \% & 6.17 \% & 4.46 \% & 100.00 \% & 10 min / 1 core & 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.\\
MSFG-Net & & 3.62 \% & 8.90 \% & 4.50 \% & 100.00 \% & 0.6 s / 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.\\
MADnet & & 3.75 \% & 9.20 \% & 4.66 \% & 100.00 \% & 0.02 s / GPU & \\
MSFG-Net & & 3.81 \% & 9.62 \% & 4.77 \% & 100.00 \% & 0.6 s / GPU & \\
StereoNet & & 4.30 \% & 7.45 \% & 4.83 \% & 100.00 \% & 0.02 s / & \\
MC-CNN-WS & & 3.78 \% & 10.93 \% & 4.97 \% & 100.00 \% & 1.35 s / & S. Tulyakov, A. Ivanov and F. Fleuret: Weakly supervised learning of deep metrics for stereo reconstruction. ICCV 2017.\\
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 .\\
CBMV\_ROB & & 3.55 \% & 12.09 \% & 4.97 \% & 100.00 \% & 300 s / >8 cores & \\
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.\\
CBMV & & 4.17 \% & 9.53 \% & 5.06 \% & 100.00 \% & 250 s / 6 cores & K. Batsos, C. Cai and P. Mordohai: CBMV: A Coalesced Bidirectional Matching Volume for Disparity Estimation. 2018.\\
SS-SF & fl & 3.59 \% & 13.11 \% & 5.18 \% & 100.00 \% & 3 min / 1 core & \\
OSF 2018 & fl & 4.11 \% & 11.12 \% & 5.28 \% & 100.00 \% & 390 s / 1 core & M. Menze, C. Heipke and A. Geiger: Object Scene Flow. ISPRS Journal of Photogrammetry and Remote Sensing (JPRS) 2018.\\
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.\\
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.\\
PWCDC\_ROB & & 5.11 \% & 7.03 \% & 5.43 \% & 100.00 \% & 0.02 s / 1 core & \\
DC-NET & & 4.31 \% & 11.52 \% & 5.51 \% & 100.00 \% & 0.53 s / >8 cores & \\
WDMC & & 4.64 \% & 10.33 \% & 5.59 \% & 100.00 \% & 1 min / 8 cores & \\
RIMM-SF & fl mv & 4.31 \% & 12.23 \% & 5.63 \% & 100.00 \% & 150 s / 4 cores & \\
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.\\
VN & & 4.88 \% & 10.52 \% & 5.82 \% & 100.00 \% & 0.5 s / GPU & \\
pSGM & & 4.84 \% & 11.64 \% & 5.97 \% & 100.00 \% & 7.77 s / 4 cores & Y. Lee, M. Park, Y. Hwang, Y. Shin and C. Kyung: Memory-Efficient Parametric Semiglobal Matching. IEEE Signal Processing Letters 2018.\\
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 & \\
TBA & & 4.20 \% & 16.97 \% & 6.33 \% & 100.00 \% & 0.9 s / GPU & \\
DeepCostAggr & & 5.34 \% & 11.35 \% & 6.34 \% & 99.98 \% & 0.03 s / GPU & 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.\\
SGM\_ROB & & 5.06 \% & 13.00 \% & 6.38 \% & 100.00 \% & 0.11 s / & H. Hirschm\"uller: Stereo Processing by Semi-Global Matching and Mutual Information. IEEE Transactions on Pattern Analysis and Machine Intelligence 2008.\\
SceneFFields & fl & 5.12 \% & 13.83 \% & 6.57 \% & 100.00 \% & 65 s / 4 cores & 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.\\
SPS+FF++ & fl & 5.47 \% & 12.19 \% & 6.59 \% & 100.00 \% & 36 s / 1 core & R. Schuster, O. Wasenmüller and D. Stricker: Dense Scene Flow from Stereo Disparity and Optical Flow. ACM Computer Science in Cars Symposium (CSCS) 2018.\\
unscene & fl & 5.10 \% & 14.55 \% & 6.67 \% & 100.00 \% & 0.08 s / 1 core & \\
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.\\
DLM-Net & & 5.04 \% & 15.76 \% & 6.83 \% & 100.00 \% & 0.68 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
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.\\
DSimNet & & 6.15 \% & 13.20 \% & 7.32 \% & 100.00 \% & 0.57 s / GPU & \\
WCMA\_ROB & & 5.68 \% & 16.36 \% & 7.45 \% & 100.00 \% & 40 s / 1 core & \\
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.\\
OASM-Net & & 6.89 \% & 19.42 \% & 8.98 \% & 100.00 \% & 0.73 s / GPU & \\
ELAS\_ROB & & 7.38 \% & 21.15 \% & 9.67 \% & 100.00 \% & 0.19 s / 4 cores & A. Geiger, M. Roser and R. Urtasun: Efficient Large-Scale Stereo Matching. ACCV 2010.\\
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.\\
DPSimNet\_ROB & & 8.92 \% & 15.63 \% & 10.04 \% & 100.00 \% & 0.67 s / GPU & \\
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.\\
SGM&FlowFie+ & fl & 11.93 \% & 20.57 \% & 13.37 \% & 81.24 \% & 29 s / 1 core & 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.\\
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.\\
NOSS\_ROB & & 14.81 \% & 12.36 \% & 14.40 \% & 99.59 \% & 240 s / 4 cores & \\
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.\\
mRM & & 22.10 \% & 19.81 \% & 21.72 \% & 100.00 \% & 0.04 s / GPU & \\
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 & 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.\\
DispCC & & 30.99 \% & 37.37 \% & 32.05 \% & 100.00 \% & 0.2 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.\\
seg\_stereo & & 60.71 \% & 72.46 \% & 62.67 \% & 100.00 \% & 20 min / 8 cores & ERROR: Wrong syntax in BIBTEX file.
\end{tabular}