\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
LEAStereo & & 1.40 \% & 2.91 \% & 1.65 \% & 100.00 \% & 0.30 s / GPU & \\
MSMD-Net(only MS) & & 1.41 \% & 3.13 \% & 1.69 \% & 100.00 \% & 0.32 s / 1 core & \\
Dahua\_SF & fl & 1.48 \% & 2.83 \% & 1.71 \% & 100.00 \% & 0.5 s / 1 core & \\
Dahua\_Stereo & & 1.48 \% & 2.83 \% & 1.71 \% & 100.00 \% & 1.52 s / GPU & \\
CSPN & & 1.51 \% & 2.88 \% & 1.74 \% & 100.00 \% & 1.0 s / GPU & 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.\\
DJIStereo & & 1.46 \% & 3.20 \% & 1.75 \% & 100.00 \% & 1.5 s / 1 core & \\
HorizonStereo & & 1.46 \% & 3.26 \% & 1.76 \% & 100.00 \% & 1.8 s / GPU & \\
DHSM & & 1.54 \% & 2.92 \% & 1.77 \% & 100.00 \% & 2 s / 1 core & \\
NLCA-Net\_V2 & & 1.41 \% & 3.56 \% & 1.77 \% & 100.00 \% & 0.67 s / 1 core & \\
DSMNet+GANet & & 1.48 \% & 3.23 \% & 1.77 \% & 100.00 \% & 1.9 s / 1 core & \\
GANet++ & & 1.55 \% & 2.96 \% & 1.78 \% & 100.00 \% & 1.52 s / GPU & \\
SUW-Stereo & & 1.47 \% & 3.45 \% & 1.80 \% & 100.00 \% & 1.8 s / 1 core & 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.\\
PVStereo & & 1.48 \% & 3.46 \% & 1.81 \% & 100.00 \% & 1.8 s / GPU & \\
GANet-deep & & 1.48 \% & 3.46 \% & 1.81 \% & 100.00 \% & 1.8 s / GPU & 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.\\
StereoExp-v2 & fl & 1.48 \% & 3.46 \% & 1.81 \% & 100.00 \% & 2 s / GPU & \\
Stereo expansion & fl & 1.48 \% & 3.46 \% & 1.81 \% & 100.00 \% & 2 s / GPU & G. Yang and D. Ramanan: Upgrading Optical Flow to 3D Scene Flow through Optical Expansion. CVPR 2020.\\
GA-Net+G & & 1.49 \% & 3.47 \% & 1.82 \% & 100.00 \% & 0.5 s / & \\
PSG-GANet & & 1.49 \% & 3.46 \% & 1.82 \% & 100.00 \% & 1.8 s / GPU & \\
HDU-FCC & & 1.50 \% & 3.45 \% & 1.82 \% & 100.00 \% & 0.70 s / 1 core & \\
CMF & & 1.44 \% & 3.76 \% & 1.83 \% & 100.00 \% & 0.5 s / GPU & ERROR: Wrong syntax in BIBTEX file.\\
NLCA-Net-3 & & 1.45 \% & 3.78 \% & 1.83 \% & 100.00 \% & 0.44 s / >8 cores & 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.\\
HD3+\_Flow & fl & 1.63 \% & 2.89 \% & 1.84 \% & 100.00 \% & 0.04 s / GPU & \\
UnDAF-GANet & & 1.51 \% & 3.51 \% & 1.84 \% & 100.00 \% & 1.8 s / GPU & \\
AMNet & & 1.53 \% & 3.43 \% & 1.84 \% & 100.00 \% & 0.9 s / GPU & X. Du, M. El-Khamy and J. Lee: AMNet: Deep Atrous Multiscale Stereo Disparity Estimation Networks. 2019.\\
CVF-BPNet & & 1.57 \% & 3.29 \% & 1.85 \% & 100.00 \% & 0.75 s / GPU & \\
NVstereo3D & & 1.52 \% & 3.54 \% & 1.86 \% & 100.00 \% & 0.15 s / GPU & \\
MFM-Net & & 1.51 \% & 3.67 \% & 1.87 \% & 100.00 \% & 0.47 s / GPU & \\
RAS-Net & & 1.61 \% & 3.16 \% & 1.87 \% & 100.00 \% & 0.6 s / 1 core & \\
AcfNet & & 1.51 \% & 3.80 \% & 1.89 \% & 100.00 \% & 0.48 s / GPU & 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.\\
LISAStereo & & 1.62 \% & 3.24 \% & 1.89 \% & 100.00 \% & 0.09 s / 4 cores & \\
DSMNet-finetune & & 1.65 \% & 3.16 \% & 1.90 \% & 100.00 \% & 1.5 s / GPU & \\
CAIS+PSMNet & & 1.57 \% & 3.62 \% & 1.91 \% & 100.00 \% & 0.38 s / GPU & \\
GAN & & 1.62 \% & 3.34 \% & 1.91 \% & 100.00 \% & 1.8 s / GPU & \\
NLCA\_NET\_v2\_RVC & & 1.51 \% & 3.97 \% & 1.92 \% & 100.00 \% & 0.67 s / GPU & 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.\\
CDN & & 1.66 \% & 3.20 \% & 1.92 \% & 100.00 \% & 0.4 s / GPU & \\
HCGANet & & 1.64 \% & 3.38 \% & 1.93 \% & 100.00 \% & 0.064 s / GPU & \\
GANet-15 & & 1.55 \% & 3.82 \% & 1.93 \% & 100.00 \% & 0.36 s / & 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.\\
NLCA-Net & & 1.53 \% & 4.09 \% & 1.96 \% & 100.00 \% & 0.6 s / 1 core & 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.\\
CFNet\_RVC & & 1.65 \% & 3.53 \% & 1.96 \% & 100.00 \% & 0.22 s / GPU & \\
FASM & & 1.69 \% & 3.37 \% & 1.97 \% & 100.00 \% & 0.42 s / 1 core & \\
MonoStereo & & 1.63 \% & 3.73 \% & 1.98 \% & 100.00 \% & 0.05 s / GPU & \\
DPCTF-S & & 1.71 \% & 3.34 \% & 1.98 \% & 100.00 \% & 0.11 s / GPU & \\
WTHNet & & 1.63 \% & 3.75 \% & 1.98 \% & 100.00 \% & 0.5 s / 1 core & \\
HITNet & & 1.74 \% & 3.20 \% & 1.98 \% & 100.00 \% & 0.015 s / & V. Tankovich, C. Häne, S. Fanello, Y. Zhang, S. Izadi and S. Bouaziz: HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching. 2020.\\
TS\_FAD & & 1.85 \% & 2.69 \% & 1.99 \% & 100.00 \% & 0.05 s / 1 core & \\
SGNet & & 1.63 \% & 3.76 \% & 1.99 \% & 100.00 \% & 0.6 s / 1 core & \\
CSN & & 1.59 \% & 4.03 \% & 2.00 \% & 100.00 \% & 0.6 s / 1 core & 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.\\
GwcNet-cmd & & 1.57 \% & 4.14 \% & 2.00 \% & 100.00 \% & 0.3 s / 1 core & \\
MANet-Selected & & 1.58 \% & 4.13 \% & 2.00 \% & 100.00 \% & 0.88 s / GPU & \\
DHSM & & 1.76 \% & 3.33 \% & 2.02 \% & 100.00 \% & 2 s / 1 core & \\
HD^3-Stereo & & 1.70 \% & 3.63 \% & 2.02 \% & 100.00 \% & 0.14 s / & Z. Yin, T. Darrell and F. Yu: Hierarchical Discrete Distribution Decomposition for Match Density Estimation. CVPR 2019.\\
CVL & & 1.71 \% & 3.59 \% & 2.02 \% & 100.00 \% & 0.36 s / 1 core & \\
AANet+ & & 1.65 \% & 3.96 \% & 2.03 \% & 100.00 \% & 0.06 s / & H. Xu and J. Zhang: AANet: Adaptive Aggregation Network for Efficient Stereo Matching. CVPR 2020.\\
GwcNet\_CSA & & 1.73 \% & 3.57 \% & 2.03 \% & 100.00 \% & 0.37 s / 1 core & \\
DHSM & & 1.78 \% & 3.34 \% & 2.04 \% & 100.00 \% & 1.9 s / 1 core & \\
MANet-Medium & & 1.63 \% & 4.15 \% & 2.05 \% & 100.00 \% & 0.88 s / GPU & \\
MANet-Selected & & 1.61 \% & 4.29 \% & 2.06 \% & 100.00 \% & 0.88 s / GPU & \\
LR-PSMNet & & 1.65 \% & 4.13 \% & 2.06 \% & 100.00 \% & 0.5 s / GPU & \\
PSMNet++ & & 1.63 \% & 4.27 \% & 2.07 \% & 100.00 \% & 0.36 s / GPU & \\
ISSGA-Net & & 1.73 \% & 3.78 \% & 2.07 \% & 100.00 \% & 0.38 s / GPU & \\
DeepStereo & & 1.71 \% & 3.87 \% & 2.07 \% & 100.00 \% & 0.3 s / 1 core & \\
UnDAF-SENSE & fl & 1.75 \% & 3.70 \% & 2.07 \% & 100.00 \% & 0.32 s / GPU & \\
MANet-Large & & 1.61 \% & 4.41 \% & 2.08 \% & 100.00 \% & 0.88 s / GPU & \\
Gwc-MSDRNet & & 1.73 \% & 3.84 \% & 2.08 \% & 100.00 \% & 0.25 s / 1 core & \\
MGSNet & & 1.68 \% & 4.06 \% & 2.08 \% & 100.00 \% & 0.65 s / GPU & \\
EdgeStereo-V2 & & 1.84 \% & 3.30 \% & 2.08 \% & 100.00 \% & 0.32s / & 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.\\
Gwc-MSRef & & 1.73 \% & 3.87 \% & 2.08 \% & 100.00 \% & 0.2 s / GPU & \\
DDP\_in\_HD3 & & 1.78 \% & 3.64 \% & 2.09 \% & 100.00 \% & 1 s / 1 core & \\
DDP\_out\_HD3 & & 1.78 \% & 3.66 \% & 2.09 \% & 100.00 \% & 10 min / & \\
MDA-Net(New) & & 1.76 \% & 3.77 \% & 2.10 \% & 100.00 \% & 0.4 s / 1 core & \\
PSMNet\_CSA & & 1.76 \% & 3.79 \% & 2.10 \% & 100.00 \% & 0.47 s / 1 core & \\
GwcNet-g & & 1.74 \% & 3.93 \% & 2.11 \% & 100.00 \% & 0.32 s / GPU & X. Guo, K. Yang, W. Yang, X. Wang and H. Li: Group-wise correlation stereo network. CVPR 2019.\\
False & & 1.75 \% & 3.93 \% & 2.11 \% & 100.00 \% & 0.4 s / 1 core & \\
SPOSF & & 1.77 \% & 3.81 \% & 2.11 \% & 100.00 \% & 10 min / 1 core & \\
SSPCVNet & & 1.75 \% & 3.89 \% & 2.11 \% & 100.00 \% & 0.9 s / 1 core & 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.\\
DHSM\_atte & & 1.79 \% & 3.74 \% & 2.11 \% & 100.00 \% & 1.9 s / 1 core & \\
SDEA & & 1.71 \% & 4.17 \% & 2.12 \% & 100.00 \% & 0.40 s / 1 core & \\
WSMCnet & & 1.72 \% & 4.19 \% & 2.13 \% & 100.00 \% & 0.39s / & 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.\\
DEINet+ft & & 1.72 \% & 4.26 \% & 2.14 \% & 100.00 \% & 0.23 s / GPU & \\
HSM-1.8x & & 1.80 \% & 3.85 \% & 2.14 \% & 100.00 \% & 0.14 s / & 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.\\
DSMNet & & 1.78 \% & 3.97 \% & 2.14 \% & 100.00 \% & 0.67 s / 1 core & \\
DFNet & & 1.78 \% & 4.03 \% & 2.15 \% & 100.00 \% & 0.7 s / 1 core & \\
DeepPruner (best) & & 1.87 \% & 3.56 \% & 2.15 \% & 100.00 \% & 0.18 s / 1 core & S. Duggal, S. Wang, W. Ma, R. Hu and R. Urtasun: DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch. ICCV 2019.\\
GANet++ & & 1.88 \% & 3.53 \% & 2.16 \% & 100.00 \% & 0.04 s / GPU & \\
PSM+CRF & & 1.86 \% & 3.66 \% & 2.16 \% & 100.00 \% & 0.32 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 2018.\\
AutoDispNet-CSS & & 1.94 \% & 3.37 \% & 2.18 \% & 100.00 \% & 0.9 s / 1 core & 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.\\
MAN & & 1.74 \% & 4.44 \% & 2.19 \% & 100.00 \% & 1.65 s / 1 core & \\
Bi3D & & 1.95 \% & 3.48 \% & 2.21 \% & 100.00 \% & 0.48 s / GPU & 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.\\
MFRANet & & 1.78 \% & 4.35 \% & 2.21 \% & 100.00 \% & 0.32 s / GPU & \\
ICANet & & 1.81 \% & 4.23 \% & 2.21 \% & 100.00 \% & 0.47 s / GPU & \\
dh & & 1.86 \% & 4.01 \% & 2.22 \% & 100.00 \% & 1.9 s / 1 core & 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.\\
MPA-Net & & 1.78 \% & 4.43 \% & 2.22 \% & 100.00 \% & 0.86 s / 1 core & \\
SENSE & fl & 2.07 \% & 3.01 \% & 2.22 \% & 100.00 \% & 0.32s / & 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.\\
CTFNet-v2 & & 1.80 \% & 4.46 \% & 2.24 \% & 100.00 \% & 0.7 s / 8 cores & \\
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. ECCV 2018.\\
PSMNet+GLR & & 1.85 \% & 4.25 \% & 2.25 \% & 100.00 \% & 0.3 s / & \\
NWS & & 1.88 \% & 4.13 \% & 2.26 \% & 100.00 \% & 0.41 s / 1 core & \\
CTFNet & & 1.81 \% & 4.56 \% & 2.27 \% & 100.00 \% & 0.7 s / 8 cores & \\
MCV-MFC & & 1.95 \% & 3.84 \% & 2.27 \% & 100.00 \% & 0.35 s / 1 core & 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.\\
CTFNet-v1 & & 1.85 \% & 4.35 \% & 2.27 \% & 100.00 \% & 0.6 s / 8 cores & \\
NS-PSM & & 1.86 \% & 4.35 \% & 2.27 \% & 100.00 \% & 0.5 s / 1 core & \\
HSM-1.5x & & 1.95 \% & 3.93 \% & 2.28 \% & 100.00 \% & 0.085 s / & 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.\\
CCNet & & 1.74 \% & 4.98 \% & 2.28 \% & 100.00 \% & 0.8 s / 1 core & \\
MSFGNet & & 1.79 \% & 4.73 \% & 2.28 \% & 100.00 \% & 0.14 s / GPU & \\
CVANet\_RVC & & 1.76 \% & 4.91 \% & 2.28 \% & 100.00 \% & 0.8 s / 1 core & \\
PSMNet-Naifan & & 1.82 \% & 4.64 \% & 2.29 \% & 100.00 \% & 0.4 s / GPU & \\
SGNet & & 1.82 \% & 4.69 \% & 2.30 \% & 100.00 \% & 0.3 s / 1 core & \\
DWA & & 1.99 \% & 3.92 \% & 2.31 \% & 100.00 \% & 0.1 s / 1 core & ERROR: Wrong syntax in BIBTEX file.\\
CFP-Net & & 1.90 \% & 4.39 \% & 2.31 \% & 100.00 \% & 0.9 s / 8 cores & Z. Zhu, M. He, Y. Dai, Z. Rao and B. Li: Multi-scale Cross-form Pyramid Network for Stereo Matching. arXiv preprint 2019.\\
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.\\
GANetREF\_RVC & & 1.88 \% & 4.58 \% & 2.33 \% & 100.00 \% & 1.62 s / GPU & 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.\\
DEINet+ & & 1.82 \% & 4.88 \% & 2.33 \% & 100.00 \% & 0.21 s / GPU & \\
JPSMNet & & 1.93 \% & 4.40 \% & 2.34 \% & 100.00 \% & 0.47 s / GPU & \\
PSM+LGF55 & & 1.89 \% & 4.76 \% & 2.37 \% & 100.00 \% & 0.05 s / 1 core & \\
VH-Net & & 2.11 \% & 3.71 \% & 2.38 \% & 100.00 \% & 0.4 s / GPU & \\
PSM+LGF551 & & 1.88 \% & 4.91 \% & 2.39 \% & 100.00 \% & 0.4 s / 1 core & \\
MTLnet & & 2.07 \% & 4.01 \% & 2.39 \% & 100.00 \% & 0.09 s / & \\
MABNet\_origin & & 1.89 \% & 5.02 \% & 2.41 \% & 100.00 \% & 0.38 s / & \\
HybridNet & & 1.93 \% & 4.90 \% & 2.42 \% & 100.00 \% & 0.12 s / GPU & \\
FBNet & & 1.96 \% & 4.86 \% & 2.45 \% & 100.00 \% & 0.6 s / 8 cores & \\
MSN & & 1.97 \% & 5.00 \% & 2.47 \% & 100.00 \% & 1.3 s / 8 cores & \\
ANM3 & & 1.95 \% & 5.19 \% & 2.49 \% & 100.00 \% & 0.4 s / 1 core & \\
ANM1 & & 1.99 \% & 5.05 \% & 2.50 \% & 100.00 \% & 0.41 s / 1 core & \\
ERSCNet & & 2.11 \% & 4.46 \% & 2.50 \% & 100.00 \% & 0.28 s / GPU & Anonymous: ERSCNet. Proceedings of the European Conference on Computer Vision (ECCV) 2020.\\
MDA-Net & & 2.12 \% & 4.63 \% & 2.54 \% & 100.00 \% & 0.7 s / 1 core & \\
UberATG-DRISF & fl & 2.16 \% & 4.49 \% & 2.55 \% & 100.00 \% & 0.75 s / CPU+GPU & W. Ma, S. Wang, R. Hu, Y. Xiong and R. Urtasun: Deep Rigid Instance Scene Flow. CVPR 2019.\\
AANet & & 1.99 \% & 5.39 \% & 2.55 \% & 100.00 \% & 0.062 s / & H. Xu and J. Zhang: AANet: Adaptive Aggregation Network for Efficient Stereo Matching. CVPR 2020.\\
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.\\
SPP & & 2.10 \% & 5.02 \% & 2.59 \% & 100.00 \% & 0.41 s / 4 cores & \\
DeepPruner (fast) & & 2.32 \% & 3.91 \% & 2.59 \% & 100.00 \% & 0.06 s / 1 core & S. Duggal, S. Wang, W. Ma, R. Hu and R. Urtasun: DeepPruner: Learning Efficient Stereo Matching via Differentiable PatchMatch. ICCV 2019.\\
SCV & & 2.22 \% & 4.53 \% & 2.61 \% & 100.00 \% & 0.36 s / & C. Lu, H. Uchiyama, D. Thomas, A. Shimada and R. Taniguchi: Sparse Cost Volume for Efficient Stereo Matching. Remote Sensing 2018.\\
MS-PSMNet & & 2.15 \% & 5.01 \% & 2.63 \% & 100.00 \% & 0.9 s / GPU & \\
WaveletStereo: & & 2.24 \% & 4.62 \% & 2.63 \% & 100.00 \% & 0.27 s / 1 core & Anonymous: WaveletStereo: Learning wavelet coefficients for stereo matching. arXiv: Computer Vision and Pattern Recognition 2019.\\
AANet\_RVC & & 2.23 \% & 4.89 \% & 2.67 \% & 100.00 \% & 0.1 s / GPU & H. Xu and J. Zhang: AANet: Adaptive Aggregation Network for Efficient Stereo Matching. CVPR 2020.\\
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.\\
GC+CRF & & 2.11 \% & 5.71 \% & 2.71 \% & 100.00 \% & 0.27 s / GPU & \\
FADNet & & 2.68 \% & 3.50 \% & 2.82 \% & 100.00 \% & 0.05 s / & 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.\\
PS-Net & & 2.39 \% & 4.98 \% & 2.82 \% & 100.00 \% & 0.2 s / GPU & \\
NVstereo2D & & 2.57 \% & 4.20 \% & 2.84 \% & 100.00 \% & 0.01 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.\\
BSDCNet & & 2.49 \% & 4.98 \% & 2.90 \% & 100.00 \% & 0.025s / 1 core & \\
MBFnet & & 2.59 \% & 4.80 \% & 2.96 \% & 100.00 \% & 0.05 s / GPU & \\
CRAR & & 2.48 \% & 5.78 \% & 3.03 \% & 100.00 \% & 0.028 s / & \\
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.\\
DCNet & & 2.70 \% & 4.70 \% & 3.04 \% & 100.00 \% & 0.025s / GPU & \\
Stereo Object & & 1.88 \% & 8.91 \% & 3.05 \% & 100.00 \% & 0.9 s / 8 cores & \\
LFENet & & 2.68 \% & 4.95 \% & 3.06 \% & 100.00 \% & 0.09 s / GPU & ERROR: Wrong syntax in BIBTEX file.\\
Fast DS-CS & & 2.83 \% & 4.31 \% & 3.08 \% & 100.00 \% & 0.02 s / GPU & K. Yee and A. Chakrabarti: Fast Deep Stereo with 2D Convolutional Processing of Cost Signatures. WACV 2020 (to appear).\\
FDNet & & 2.83 \% & 4.31 \% & 3.08 \% & 100.00 \% & 0.7 s / 1 core & \\
RecResNet & & 2.46 \% & 6.30 \% & 3.10 \% & 100.00 \% & 0.3 s / GPU & K. Batsos and P. Mordohai: RecResNet: A Recurrent Residual CNN Architecture for Disparity Map Enhancement. In International Conference on 3D Vision (3DV) 2018.\\
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.\\
NineNet2 & & 2.83 \% & 4.64 \% & 3.13 \% & 100.00 \% & 0.07 s / 1 core & \\
PMY-net & & 2.63 \% & 5.72 \% & 3.15 \% & 100.00 \% & 1 s / 1 core & \\
LANet & & 2.69 \% & 5.43 \% & 3.15 \% & 100.00 \% & 0.08 s / GPU & \\
Net3\_2015 & & 2.69 \% & 5.44 \% & 3.15 \% & 100.00 \% & 0.03 s / 1 core & \\
AdaStereo & & 2.64 \% & 5.75 \% & 3.16 \% & 100.00 \% & 0.41 s / GPU & 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.\\
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.\\
NineNet3 & & 2.69 \% & 5.57 \% & 3.17 \% & 100.00 \% & 0.03 s / 1 core & \\
Net3\_2015 & & 2.71 \% & 5.55 \% & 3.18 \% & 100.00 \% & 0.3 s / 1 core & \\
MA-Net & & 2.67 \% & 5.99 \% & 3.22 \% & 100.00 \% & 0.5 s / GPU & \\
NineNet & & 2.70 \% & 5.94 \% & 3.24 \% & 100.00 \% & 0.07 s / 1 core & \\
DMSNet & & 2.81 \% & 5.39 \% & 3.24 \% & 100.00 \% & 0.015625 s / 1 core & \\
FPN & & 2.85 \% & 5.37 \% & 3.27 \% & 100.00 \% & 1 s / 1 core & \\
SFFNet & & 2.69 \% & 6.23 \% & 3.28 \% & 100.00 \% & 0.07 s / GPU & \\
MS-GCNet & & 2.58 \% & 6.83 \% & 3.29 \% & 100.00 \% & 3 s / GTX 1080Ti & \\
DMSNetv2 & & 2.80 \% & 5.85 \% & 3.31 \% & 100.00 \% & 0.02 s / 1 core & \\
DWARF & fl & 3.20 \% & 3.94 \% & 3.33 \% & 100.00 \% & 0.14s - 1.43s / & 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.\\
AbNet1 & & 3.14 \% & 4.43 \% & 3.35 \% & 100.00 \% & 0.02 s / 1 core & \\
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.\\
LBPS & & 2.85 \% & 6.35 \% & 3.44 \% & 100.00 \% & 0.39 s / GPU & 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.\\
TBDN\_Net & & 2.86 \% & 6.92 \% & 3.53 \% & 100.00 \% & 1.0 s / GPU & \\
ACOSF & fl & 2.79 \% & 7.56 \% & 3.58 \% & 100.00 \% & 5 min / 1 core & 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.\\
ZYY & & 2.96 \% & 6.74 \% & 3.58 \% & 100.00 \% & 0.22 s / 1 core & \\
TBDN\_Net40 & & 2.89 \% & 7.15 \% & 3.60 \% & 100.00 \% & 0.95 s / GPU & \\
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.\\
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.\\
ASMNet & & 3.18 \% & 5.98 \% & 3.64 \% & 100.00 \% & 0.04 s / 4 cores & \\
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.\\
DSMNet & & 3.11 \% & 6.72 \% & 3.71 \% & 100.00 \% & 1.5 s / GPU & \\
Three3 & & 2.99 \% & 7.33 \% & 3.71 \% & 100.00 \% & 0.03 s / 1 core & \\
HSM-Net\_RVC & & 2.74 \% & 8.73 \% & 3.74 \% & 100.00 \% & 0.97 s / GPU & 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.\\
MABNet\_tiny & & 3.04 \% & 8.07 \% & 3.88 \% & 100.00 \% & 0.11 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 .\\
Net3\_3 & & 3.52 \% & 6.22 \% & 3.97 \% & 100.00 \% & 0.03 s / 1 core & \\
Reversing-PSMNet & & 3.13 \% & 8.70 \% & 4.06 \% & 100.00 \% & 0.41 s / 1 core & 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.\\
DAStereo & & 3.67 \% & 6.83 \% & 4.20 \% & 100.00 \% & 0.32 s / 1 core & \\
STTRV1\_RVC & & 3.56 \% & 7.71 \% & 4.25 \% & 99.99 \% & 0.6 s / GPU & \\
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.\\
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.\\
USegScene & & 4.12 \% & 6.58 \% & 4.53 \% & 100.00 \% & 0.3 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 & 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.\\
DTF & fl mv & 3.91 \% & 8.57 \% & 4.68 \% & 100.00 \% & 0.14 s / & \\
VN & & 4.29 \% & 7.65 \% & 4.85 \% & 100.00 \% & 0.5 s / GPU & P. Knöbelreiter and T. Pock: Learned Collaborative Stereo Refinement. German Conference on Pattern Recognition (GCPR) 2019.\\
LWANet & & 4.28 \% & 8.22 \% & 4.94 \% & 100.00 \% & 0.02 s / GPU & \\
naive-stereo & & 4.43 \% & 7.65 \% & 4.96 \% & 100.00 \% & 0.05 s / 1 core & \\
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 \% & 250 s / 6 core & 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.\\
SMV & & 3.80 \% & 10.99 \% & 5.00 \% & 100.00 \% & 0.3 s / 6 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.\\
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.\\
SimpleStereo & & 4.48 \% & 8.29 \% & 5.12 \% & 100.00 \% & 0.01 s / 1 core & \\
PWOC-3D & fl & 4.19 \% & 9.82 \% & 5.13 \% & 100.00 \% & 0.13 s / & 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.\\
naive-stereo-v1 & & 4.99 \% & 6.56 \% & 5.25 \% & 100.00 \% & 0.05 s / 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.\\
LGF & & 4.78 \% & 8.10 \% & 5.33 \% & 100.00 \% & 0.06 s / GPU & \\
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.\\
LGF\_dense & & 4.68 \% & 9.14 \% & 5.42 \% & 100.00 \% & 0.06 s / 1 core & \\
SFF++ & fl mv & 4.27 \% & 12.38 \% & 5.62 \% & 100.00 \% & 78 s / 4 cores & 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.\\
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.\\
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.\\
DispSegNet & & 4.20 \% & 16.97 \% & 6.33 \% & 100.00 \% & 0.9 s / GPU & 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.\\
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\_RVC & & 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.\\
OASM-DDS & & 4.66 \% & 15.76 \% & 6.51 \% & 100.00 \% & 0.90 s / 1 core & \\
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.\\
Flow2Stereo & & 5.01 \% & 14.62 \% & 6.61 \% & 99.97 \% & 0.05 s / GPU & P. Liu, I. King, M. Lyu and J. Xu: Flow2Stereo: Effective Self-Supervised Learning of Optical Flow and Stereo Matching. CVPR 2020.\\
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.\\
DistillFlow & fl & 5.00 \% & 15.88 \% & 6.81 \% & 100.00 \% & 0.03 s / 1 core & \\
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.\\
MBSF & fl & 5.63 \% & 15.04 \% & 7.20 \% & 100.00 \% & 1 min / GPU & \\
PASMnet & & 5.41 \% & 16.36 \% & 7.23 \% & 100.00 \% & 0.5 s / GPU & 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.\\
AAFS & & 6.27 \% & 13.95 \% & 7.54 \% & 100.00 \% & 0.01 s / 1 core & \\
NL-DL & & 6.73 \% & 13.08 \% & 7.79 \% & 100.00 \% & 0.04 s / 8 cores & \\
GOUSM & & 6.42 \% & 17.24 \% & 8.22 \% & 100.00 \% & 0.1 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 & A. Li and Z. Yuan: Occlusion Aware Stereo Matching via Cooperative Unsupervised Learning. Proceedings of the Asian Conference on Computer Vision, ACCV 2018.\\
ELAS\_RVC & & 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.\\
DDP\_out\_ELAS\_params1 & & 7.45 \% & 21.11 \% & 9.72 \% & 100.00 \% & 30 min / GPU & \\
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.\\
DDP\_in\_ELAS\_params1 & & 7.48 \% & 21.14 \% & 9.76 \% & 99.97 \% & 1 s / 1 core & \\
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.\\
PPEP-GF & & 9.87 \% & 19.01 \% & 11.39 \% & 100.00 \% & 3.41 s / 2 cores & ERROR: Wrong syntax in BIBTEX file.\\
TW-SMNet & & 11.92 \% & 12.16 \% & 11.96 \% & 100.00 \% & 0.7 s / GPU & M. El-Khamy, H. Ren, X. Du and J. Lee: TW-SMNet: Deep Multitask Learning of Tele-Wide Stereo Matching. arXiv:1906.04463 2019.\\
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.\\
DDP & & 12.32 \% & 20.20 \% & 13.63 \% & 100.00 \% & 10 min / 1 core & \\
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.\\
MT-TW-SMNet & & 15.47 \% & 16.25 \% & 15.60 \% & 100.00 \% & 0.4s / GPU & 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.\\
Mono-SF & fl & 14.21 \% & 26.94 \% & 16.32 \% & 100.00 \% & 41 s / 1 core & 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.\\
AbNet2 & & 14.71 \% & 29.88 \% & 17.23 \% & 100.00 \% & 0.01 s / 1 core & \\
DDP\_out\_ELAS\_params2 & & 16.28 \% & 26.06 \% & 17.91 \% & 100.00 \% & 10 min / 1 core & \\
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.\\
MonoComb & fl & 17.89 \% & 21.16 \% & 18.44 \% & 100.00 \% & 0.58 s / & \\
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.\\
monoResMatch & & 22.10 \% & 19.81 \% & 21.72 \% & 100.00 \% & 0.16 s / & 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.\\
DDP\_in\_ELAS\_params2 & & 20.63 \% & 28.68 \% & 21.97 \% & 85.92 \% & 1 s / 1 core & \\
Self-Mono-SF-ft & fl & 20.72 \% & 29.41 \% & 22.16 \% & 100.00 \% & 0.09 s / & J. Hur and S. Roth: Self-Supervised Monocular Scene Flow Estimation. CVPR 2020.\\
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.\\
Mono expansion & fl & 24.85 \% & 27.90 \% & 25.36 \% & 100.00 \% & 0.25 s / GPU & \\
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.\\
YunYang-Monodepth & & 26.06 \% & 28.92 \% & 26.53 \% & 100.00 \% & 0.01 s / 1 core & \\
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.\\
MTS & & 26.68 \% & 47.30 \% & 30.11 \% & 4.31 \% & 1.7 s / 4 cores & \\
mts1 & & 28.03 \% & 46.55 \% & 31.11 \% & 2.52 \% & 0.18 s / 4 cores & R. Brandt, N. Strisciuglio, N. Petkov and M. Wilkinson: Efficient binocular stereo correspondence matching with 1-D Max-Trees. Pattern Recognition Letters 2020.\\
Self-Mono-SF & fl & 31.22 \% & 48.04 \% & 34.02 \% & 100.00 \% & 0.09 s / & J. Hur and S. Roth: Self-Supervised Monocular Scene Flow Estimation. CVPR 2020.\\
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.
\end{tabular}