\begin{tabular}{c | c | c | c | c | c}
{\bf Method} & {\bf D1-bg} & {\bf D1-fg} & {\bf D1-all} & {\bf Density} & {\bf Runtime}\\ \hline
CSPN \cite{8869936} & 1.51 \% & 2.88 \% & 1.74 \% & 100.00 \% & 1.0 s / GPU \\
SUW-Stereo \cite{ren2020suw} & 1.47 \% & 3.45 \% & 1.80 \% & 100.00 \% & 1.8 s / 1 core \\
GANet-deep \cite{zhang2019GANet} & 1.48 \% & 3.46 \% & 1.81 \% & 100.00 \% & 1.8 s / GPU \\
Stereo expansion \cite{yang2020upgrading} & 1.48 \% & 3.46 \% & 1.81 \% & 100.00 \% & 2 s / GPU \\
CMF \cite{ERROR: Wrong syntax in BIBTEX file.} & 1.44 \% & 3.76 \% & 1.83 \% & 100.00 \% & 0.5 s / GPU \\
NLCA-Net-3 \cite{rao2020nlca} & 1.45 \% & 3.78 \% & 1.83 \% & 100.00 \% & 0.44 s / >8 cores \\
AMNet \cite{1904.09099} & 1.53 \% & 3.43 \% & 1.84 \% & 100.00 \% & 0.9 s / GPU \\
AcfNet \cite{zhang2019adaptive} & 1.51 \% & 3.80 \% & 1.89 \% & 100.00 \% & 0.48 s / GPU \\
NLCA\_NET\_v2\_RVC \cite{rao2020nlca} & 1.51 \% & 3.97 \% & 1.92 \% & 100.00 \% & 0.67 s / GPU \\
GANet-15 \cite{zhang2019GANet} & 1.55 \% & 3.82 \% & 1.93 \% & 100.00 \% & 0.36 s / \\
NLCA-Net \cite{rao2020nlca} & 1.53 \% & 4.09 \% & 1.96 \% & 100.00 \% & 0.6 s / 1 core \\
HITNet \cite{tankovich2020hitnet} & 1.74 \% & 3.20 \% & 1.98 \% & 100.00 \% & 0.015 s / \\
CSN \cite{gu2020cascade} & 1.59 \% & 4.03 \% & 2.00 \% & 100.00 \% & 0.6 s / 1 core \\
HD^3-Stereo \cite{yin2019hd3} & 1.70 \% & 3.63 \% & 2.02 \% & 100.00 \% & 0.14 s / \\
AANet+ \cite{xu2020aanet} & 1.65 \% & 3.96 \% & 2.03 \% & 100.00 \% & 0.06 s / \\
EdgeStereo-V2 \cite{song2019edgestereo} & 1.84 \% & 3.30 \% & 2.08 \% & 100.00 \% & 0.32s / \\
GwcNet-g \cite{guo2019group} & 1.74 \% & 3.93 \% & 2.11 \% & 100.00 \% & 0.32 s / GPU \\
SSPCVNet \cite{Wu2019ICCV} & 1.75 \% & 3.89 \% & 2.11 \% & 100.00 \% & 0.9 s / 1 core \\
WSMCnet \cite{wang2019WSMCnet} & 1.72 \% & 4.19 \% & 2.13 \% & 100.00 \% & 0.39s / \\
HSM-1.8x \cite{yang2019hsm} & 1.80 \% & 3.85 \% & 2.14 \% & 100.00 \% & 0.14 s / \\
DeepPruner (best) \cite{Duggal2019ICCV} & 1.87 \% & 3.56 \% & 2.15 \% & 100.00 \% & 0.18 s / 1 core \\
Stereo-fusion-SJTU \cite{song2018stereo} & 1.87 \% & 3.61 \% & 2.16 \% & 100.00 \% & 0.7 s / \\
AutoDispNet-CSS \cite{iccv19autodispnet} & 1.94 \% & 3.37 \% & 2.18 \% & 100.00 \% & 0.9 s / 1 core \\
Bi3D \cite{badki2020Bi3D} & 1.95 \% & 3.48 \% & 2.21 \% & 100.00 \% & 0.48 s / GPU \\
dh \cite{zhang2019GANet} & 1.86 \% & 4.01 \% & 2.22 \% & 100.00 \% & 1.9 s / 1 core \\
SENSE \cite{Jiang2019ICCV} & 2.07 \% & 3.01 \% & 2.22 \% & 100.00 \% & 0.32s / \\
SegStereo \cite{yang2018SegStereo} & 1.88 \% & 4.07 \% & 2.25 \% & 100.00 \% & 0.6 s / \\
MCV-MFC \cite{liang2019stereo} & 1.95 \% & 3.84 \% & 2.27 \% & 100.00 \% & 0.35 s / 1 core \\
HSM-1.5x \cite{yang2019hsm} & 1.95 \% & 3.93 \% & 2.28 \% & 100.00 \% & 0.085 s / \\
DWA \cite{ERROR: Wrong syntax in BIBTEX file.} & 1.99 \% & 3.92 \% & 2.31 \% & 100.00 \% & 0.1 s / 1 core \\
CFP-Net \cite{Zhu2019Multi} & 1.90 \% & 4.39 \% & 2.31 \% & 100.00 \% & 0.9 s / 8 cores \\
PSMNet \cite{chang2018pyramid} & 1.86 \% & 4.62 \% & 2.32 \% & 100.00 \% & 0.41 s / \\
GANetREF\_RVC \cite{Zhang2019GANet} & 1.88 \% & 4.58 \% & 2.33 \% & 100.00 \% & 1.62 s / GPU \\
ERSCNet \cite{ERSCNet2020} & 2.11 \% & 4.46 \% & 2.50 \% & 100.00 \% & 0.28 s / GPU \\
UberATG-DRISF \cite{Ma2019CVPR} & 2.16 \% & 4.49 \% & 2.55 \% & 100.00 \% & 0.75 s / CPU+GPU \\
AANet \cite{xu2020aanet} & 1.99 \% & 5.39 \% & 2.55 \% & 100.00 \% & 0.062 s / \\
PDSNet \cite{tulyakovetal2018b} & 2.29 \% & 4.05 \% & 2.58 \% & 100.00 \% & 0.5 s / 1 core \\
DeepPruner (fast) \cite{Duggal2019ICCV} & 2.32 \% & 3.91 \% & 2.59 \% & 100.00 \% & 0.06 s / 1 core \\
SCV \cite{lu2018sparse} & 2.22 \% & 4.53 \% & 2.61 \% & 100.00 \% & 0.36 s / \\
WaveletStereo: \cite{waveletstereo} & 2.24 \% & 4.62 \% & 2.63 \% & 100.00 \% & 0.27 s / 1 core \\
AANet\_RVC \cite{xu2020aanet} & 2.23 \% & 4.89 \% & 2.67 \% & 100.00 \% & 0.1 s / GPU \\
CRL \cite{pang2017cascade} & 2.48 \% & 3.59 \% & 2.67 \% & 100.00 \% & 0.47 s / \\
LFENet \cite{ERROR: Wrong syntax in BIBTEX file.} & 2.42 \% & 3.97 \% & 2.67 \% & 100.00 \% & 0.09 s / GPU \\
FADNet \cite{wang2020fadnet} & 2.68 \% & 3.50 \% & 2.82 \% & 100.00 \% & 0.05 s / \\
GC-NET \cite{kendall2017end} & 2.21 \% & 6.16 \% & 2.87 \% & 100.00 \% & 0.9 s / \\
LRCR \cite{Jie2018CVPR} & 2.55 \% & 5.42 \% & 3.03 \% & 100.00 \% & 49.2 s / \\
Fast DS-CS \cite{yee2019fast} & 2.83 \% & 4.31 \% & 3.08 \% & 100.00 \% & 0.02 s / GPU \\
RecResNet \cite{batsos2018recresnet} & 2.46 \% & 6.30 \% & 3.10 \% & 100.00 \% & 0.3 s / GPU \\
NVStereoNet \cite{smolyanskiy2018nvstereo} & 2.62 \% & 5.69 \% & 3.13 \% & 100.00 \% & 0.6 s / \\
AdaStereo \cite{song2020adastereo} & 2.64 \% & 5.75 \% & 3.16 \% & 100.00 \% & 0.41 s / GPU \\
DRR \cite{gidaris2016detect} & 2.58 \% & 6.04 \% & 3.16 \% & 100.00 \% & 0.4 s / \\
DWARF \cite{AleottiAAAI2020} & 3.20 \% & 3.94 \% & 3.33 \% & 100.00 \% & 0.14s - 1.43s / \\
SsSMnet \cite{SsSMnet2017} & 2.70 \% & 6.92 \% & 3.40 \% & 100.00 \% & 0.8 s / \\
L-ResMatch \cite{shaked2016stereo} & 2.72 \% & 6.95 \% & 3.42 \% & 100.00 \% & 48 s / 1 core \\
Displets v2 \cite{Guney2015CVPR} & 3.00 \% & 5.56 \% & 3.43 \% & 100.00 \% & 265 s / >8 cores \\
LBPS \cite{knoebelreitercvpr2020} & 2.85 \% & 6.35 \% & 3.44 \% & 100.00 \% & 0.39 s / GPU \\
ACOSF \cite{Cong2020ICPR} & 2.79 \% & 7.56 \% & 3.58 \% & 100.00 \% & 5 min / 1 core \\
CNNF+SGM \cite{PrincipleZhang} & 2.78 \% & 7.69 \% & 3.60 \% & 100.00 \% & 71 s / \\
PBCP \cite{Seki2016BMVC} & 2.58 \% & 8.74 \% & 3.61 \% & 100.00 \% & 68 s / \\
SGM-Net \cite{Seki2017CVPR} & 2.66 \% & 8.64 \% & 3.66 \% & 100.00 \% & 67 s / \\
HSM-Net\_RVC \cite{yang2019hierarchical} & 2.74 \% & 8.73 \% & 3.74 \% & 100.00 \% & 0.97 s / GPU \\
MC-CNN-acrt \cite{Zbontar2016JMLR} & 2.89 \% & 8.88 \% & 3.89 \% & 100.00 \% & 67 s / \\
Reversing-PSMNet \cite{AleottiECCV2020} & 3.13 \% & 8.70 \% & 4.06 \% & 100.00 \% & 0.41 s / 1 core \\
PRSM \cite{Vogel2015IJCV} & 3.02 \% & 10.52 \% & 4.27 \% & 99.99 \% & 300 s / 1 core \\
DispNetC \cite{Mayer2016CVPR} & 4.32 \% & 4.41 \% & 4.34 \% & 100.00 \% & 0.06 s / \\
SGM-Forest \cite{schoenberger2018sgm} & 3.11 \% & 10.74 \% & 4.38 \% & 99.92 \% & 6 seconds / 1 core \\
SSF \cite{Ren20173DV} & 3.55 \% & 8.75 \% & 4.42 \% & 100.00 \% & 5 min / 1 core \\
ISF \cite{Behl2017ICCV} & 4.12 \% & 6.17 \% & 4.46 \% & 100.00 \% & 10 min / 1 core \\
Content-CNN \cite{Vogel2015IJCV} & 3.73 \% & 8.58 \% & 4.54 \% & 100.00 \% & 1 s / \\
MADnet \cite{Tonioni2019CVPR} & 3.75 \% & 9.20 \% & 4.66 \% & 100.00 \% & 0.02 s / GPU \\
VN \cite{knoebelreitergcpr19} & 4.29 \% & 7.65 \% & 4.85 \% & 100.00 \% & 0.5 s / GPU \\
MC-CNN-WS \cite{Tulyakov2017} & 3.78 \% & 10.93 \% & 4.97 \% & 100.00 \% & 1.35 s / \\
3DMST \cite{li20173DMST} & 3.36 \% & 13.03 \% & 4.97 \% & 100.00 \% & 93 s / 1 core \\
CBMV\_ROB \cite{batsos2018cbmv} & 3.55 \% & 12.09 \% & 4.97 \% & 100.00 \% & 250 s / 6 core \\
OSF+TC \cite{Neoral2017CVWW} & 4.11 \% & 9.64 \% & 5.03 \% & 100.00 \% & 50 min / 1 core \\
CBMV \cite{1804.01967} & 4.17 \% & 9.53 \% & 5.06 \% & 100.00 \% & 250 s / 6 cores \\
PWOC-3D \cite{saxena2019pwoc} & 4.19 \% & 9.82 \% & 5.13 \% & 100.00 \% & 0.13 s / \\
OSF 2018 \cite{Menze2018JPRS} & 4.11 \% & 11.12 \% & 5.28 \% & 100.00 \% & 390 s / 1 core \\
SPS-St \cite{Yamaguchi2014ECCV} & 3.84 \% & 12.67 \% & 5.31 \% & 100.00 \% & 2 s / 1 core \\
MDP \cite{Li2016CVPR} & 4.19 \% & 11.25 \% & 5.36 \% & 100.00 \% & 11.4 s / 4 cores \\
SFF++ \cite{schuster2019sffpp} & 4.27 \% & 12.38 \% & 5.62 \% & 100.00 \% & 78 s / 4 cores \\
OSF \cite{Menze2015CVPR} & 4.54 \% & 12.03 \% & 5.79 \% & 100.00 \% & 50 min / 1 core \\
pSGM \cite{lee2018memory} & 4.84 \% & 11.64 \% & 5.97 \% & 100.00 \% & 7.77 s / 4 cores \\
CSF \cite{Lv2016ECCV} & 4.57 \% & 13.04 \% & 5.98 \% & 99.99 \% & 80 s / 1 core \\
MBM \cite{Einecke2014IV} & 4.69 \% & 13.05 \% & 6.08 \% & 100.00 \% & 0.13 s / 1 core \\
PR-Sceneflow \cite{Vogel2013ICCV} & 4.74 \% & 13.74 \% & 6.24 \% & 100.00 \% & 150 s / 4 core \\
DispSegNet \cite{dissegnet} & 4.20 \% & 16.97 \% & 6.33 \% & 100.00 \% & 0.9 s / GPU \\
DeepCostAggr \cite{kuzmin2017end} & 5.34 \% & 11.35 \% & 6.34 \% & 99.98 \% & 0.03 s / GPU \\
SGM\_RVC \cite{Hirschmueller2008} & 5.06 \% & 13.00 \% & 6.38 \% & 100.00 \% & 0.11 s / \\
SceneFFields \cite{schuster2018sceneflowfields} & 5.12 \% & 13.83 \% & 6.57 \% & 100.00 \% & 65 s / 4 cores \\
SPS+FF++ \cite{schuster2018dense} & 5.47 \% & 12.19 \% & 6.59 \% & 100.00 \% & 36 s / 1 core \\
Flow2Stereo \cite{Liu2020Flow2Stereo} & 5.01 \% & 14.62 \% & 6.61 \% & 99.97 \% & 0.05 s / GPU \\
FSF+MS \cite{Taniai2017} & 5.72 \% & 11.84 \% & 6.74 \% & 100.00 \% & 2.7 s / 4 cores \\
AABM \cite{Einecke2013IV} & 4.88 \% & 16.07 \% & 6.74 \% & 100.00 \% & 0.08 s / 1 core \\
SGM+C+NL \cite{Hirschmueller2008PAMI} & 5.15 \% & 15.29 \% & 6.84 \% & 100.00 \% & 4.5 min / 1 core \\
SGM+LDOF \cite{Hirschmueller2008PAMI} & 5.15 \% & 15.29 \% & 6.84 \% & 100.00 \% & 86 s / 1 core \\
SGM+SF \cite{Hirschmueller2008PAMI} & 5.15 \% & 15.29 \% & 6.84 \% & 100.00 \% & 45 min / 16 core \\
SNCC \cite{Einecke2010DICTA} & 5.36 \% & 16.05 \% & 7.14 \% & 100.00 \% & 0.08 s / 1 core \\
PASMnet \cite{PAM} & 5.41 \% & 16.36 \% & 7.23 \% & 100.00 \% & 0.5 s / GPU \\
CSCT+SGM+MF \cite{chacon2013n} & 6.91 \% & 14.87 \% & 8.24 \% & 100.00 \% & 0.0064 s / Nvidia GTX Titan X \\
MeshStereo \cite{Zhang2015ICCV} & 5.82 \% & 21.21 \% & 8.38 \% & 100.00 \% & 87 s / 1 core \\
PCOF + ACTF \cite{Derome2016GCPR} & 6.31 \% & 19.24 \% & 8.46 \% & 100.00 \% & 0.08 s / GPU \\
PCOF-LDOF \cite{Derome2016GCPR} & 6.31 \% & 19.24 \% & 8.46 \% & 100.00 \% & 50 s / 1 core \\
OASM-Net \cite{OASMaccv18} & 6.89 \% & 19.42 \% & 8.98 \% & 100.00 \% & 0.73 s / GPU \\
ELAS\_RVC \cite{Geiger2010ACCV} & 7.38 \% & 21.15 \% & 9.67 \% & 100.00 \% & 0.19 s / 4 cores \\
ELAS \cite{Geiger2010ACCV} & 7.86 \% & 19.04 \% & 9.72 \% & 92.35 \% & 0.3 s / 1 core \\
REAF \cite{Cigla2015CVPRWorkshops} & 8.43 \% & 18.51 \% & 10.11 \% & 100.00 \% & 1.1 s / 1 core \\
iGF \cite{hamzah2016stereo} & 8.64 \% & 21.85 \% & 10.84 \% & 100.00 \% & 220 s / 1 core \\
OCV-SGBM \cite{Hirschmueller08} & 8.92 \% & 20.59 \% & 10.86 \% & 90.41 \% & 1.1 s / 1 core \\
PPEP-GF \cite{ERROR: Wrong syntax in BIBTEX file.} & 9.87 \% & 19.01 \% & 11.39 \% & 100.00 \% & 3.41 s / 2 cores \\
TW-SMNet \cite{mostafa2019TWSMNet} & 11.92 \% & 12.16 \% & 11.96 \% & 100.00 \% & 0.7 s / GPU \\
SDM \cite{Kostkova2003BMVC} & 9.41 \% & 24.75 \% & 11.96 \% & 62.56 \% & 1 min / 1 core \\
SGM&FlowFie+ \cite{Schuster2018Combining} & 11.93 \% & 20.57 \% & 13.37 \% & 81.24 \% & 29 s / 1 core \\
GCSF \cite{Cech2011CVPR} & 11.64 \% & 27.11 \% & 14.21 \% & 100.00 \% & 2.4 s / 1 core \\
MT-TW-SMNet \cite{Elkhamy2019} & 15.47 \% & 16.25 \% & 15.60 \% & 100.00 \% & 0.4s / GPU \\
Mono-SF \cite{brickwedde2019monosf} & 14.21 \% & 26.94 \% & 16.32 \% & 100.00 \% & 41 s / 1 core \\
CostFilter \cite{Rhemann2011CVPR} & 17.53 \% & 22.88 \% & 18.42 \% & 100.00 \% & 4 min / 1 core \\
DWBSF \cite{Richardt2016THREEDV} & 19.61 \% & 22.69 \% & 20.12 \% & 100.00 \% & 7 min / 4 cores \\
monoResMatch \cite{Tosi2019CVPR} & 22.10 \% & 19.81 \% & 21.72 \% & 100.00 \% & 0.16 s / \\
Self-Mono-SF-ft \cite{Hur2020CVPR} & 20.72 \% & 29.41 \% & 22.16 \% & 100.00 \% & 0.09 s / \\
OCV-BM \cite{Bradski2000} & 24.29 \% & 30.13 \% & 25.27 \% & 58.54 \% & 0.1 s / 1 core \\
VSF \cite{Huguet2007ICCV} & 27.31 \% & 21.72 \% & 26.38 \% & 100.00 \% & 125 min / 1 core \\
SED \cite{Peña2017} & 25.01 \% & 40.43 \% & 27.58 \% & 4.02 \% & 0.68 s / 1 core \\
mts1 \cite{BRANDT2020} & 28.03 \% & 46.55 \% & 31.11 \% & 2.52 \% & 0.18 s / 4 cores \\
Self-Mono-SF \cite{Hur2020CVPR} & 31.22 \% & 48.04 \% & 34.02 \% & 100.00 \% & 0.09 s / \\
MST \cite{Yang2012CVPR} & 45.83 \% & 38.22 \% & 44.57 \% & 100.00 \% & 7 s / 1 core
\end{tabular}