\begin{tabular}{c | c | c | c | c | c}
{\bf Method} & {\bf Fl-bg} & {\bf Fl-fg} & {\bf Fl-all} & {\bf Density} & {\bf Runtime}\\ \hline
CCMR+ \cite{jahedi2023ccmr} & 1.89 \% & 2.88 \% & 2.07 \% & 100.00 \% & 1.5 s / GPU \\
CamLiRAFT \cite{liu2023learning} & 1.64 \% & 4.57 \% & 2.18 \% & 100.00 \% & 1 s / GPU \\
MS\_RAFT+\_corr\_RVC \cite{jahedi2022high} & 2.07 \% & 2.69 \% & 2.18 \% & 100.00 \% & 0.65 s / GPU \\
DDVM \cite{saxena2023surprising} & 1.97 \% & 3.46 \% & 2.24 \% & 100.00 \% & / \\
CamLiRAFT-NR \cite{liu2023learning} & 2.03 \% & 3.98 \% & 2.38 \% & 100.00 \% & 1 s / GPU \\
CamLiFlow \cite{liu2021camliflow} & 1.79 \% & 5.12 \% & 2.40 \% & 100.00 \% & 1.2 s / GPU \\
GMFlow+ \cite{xu2022unifying} & 2.29 \% & 2.89 \% & 2.40 \% & 100.00 \% & 0.2 s / \\
CroCo-Flow \cite{crocov2} & 2.05 \% & 3.98 \% & 2.40 \% & 100.00 \% & 3s / \\
DIP \cite{zheng2022dip} & 2.31 \% & 3.00 \% & 2.43 \% & 100.00 \% & 0.15 s / 1 core \\
MemFlow-T \cite{dong2024memflow} & 2.28 \% & 3.22 \% & 2.45 \% & 100.00 \% & / \\
RAFT-it+\_RVC \cite{sun2022disentangling} & 2.36 \% & 2.87 \% & 2.46 \% & 100.00 \% & 0.14 s / 1 core \\
M-FUSE \cite{Mehl2023} & 1.98 \% & 4.74 \% & 2.48 \% & 100.00 \% & 1.3 s / \\
RigidMask+ISF \cite{yang2021rigidmask} & 2.03 \% & 4.85 \% & 2.54 \% & 100.00 \% & 3.3 s / GPU \\
MemFlow \cite{dong2024memflow} & 2.37 \% & 3.40 \% & 2.56 \% & 100.00 \% & / \\
RAFT-OCTC \cite{jeong2022imposing} & 2.54 \% & 2.80 \% & 2.58 \% & 100.00 \% & 0.2 s / GPU \\
GMFlow\_RVC \cite{xu2022unifying} & 2.55 \% & 2.98 \% & 2.63 \% & 100.00 \% & 0.2 s / \\
FlowFormer \cite{huang2022flowformer} & 2.54 \% & 3.38 \% & 2.69 \% & 100.00 \% & 0.3 s / \\
AnyFlow \cite{jung2023anyflow} & 2.60 \% & 3.10 \% & 2.69 \% & 100.00 \% & 0.1 s / 1 core \\
RPKNet \cite{Morimitsu2024RecurrentPartialKernel} & 2.70 \% & 2.74 \% & 2.71 \% & 100.00 \% & 0.6 s / GPU \\
GMFlowNet \cite{gmflownet} & 2.56 \% & 3.64 \% & 2.75 \% & 100.00 \% & 0.5 s / GPU \\
MatchFlow(R) \cite{dong2023rethinking} & 2.59 \% & 3.54 \% & 2.76 \% & 100.00 \% & 0.26 s / \\
MatchFlow(G) \cite{dong2023rethinking} & 2.61 \% & 3.48 \% & 2.77 \% & 100.00 \% & 0.3 s / \\
SeparableFlow \cite{zhang2021SeparableFlow} & 2.56 \% & 3.75 \% & 2.78 \% & 100.00 \% & 0.5 s / \\
MS\_RAFT \cite{jahedi2022multi} & 2.66 \% & 3.41 \% & 2.80 \% & 100.00 \% & 0.3 s / \\
KPA-Flow \cite{luo2022learning} & 2.59 \% & 3.83 \% & 2.82 \% & 100.00 \% & 0.2 s / GPU \\
SSTM\_T [MV] \cite{FEREDE2023126705} & 2.75 \% & 3.43 \% & 2.87 \% & 100.00 \% & 0.4 s / GPU \\
SSTM++\_ttt [mv] \cite{FEREDE2023126705} & 2.71 \% & 3.66 \% & 2.89 \% & 100.00 \% & 0.3 s / GPU \\
SF2SE3 \cite{sommer2022sf2se3} & 2.23 \% & 6.05 \% & 2.93 \% & 100.00 \% & 2.7 s / GPU \\
SSTMT++-tt-main [mv] \cite{ferede2023sstm} & 2.78 \% & 3.64 \% & 2.94 \% & 100.00 \% & 0.4 s / GPU \\
OPM(C) \cite{ERROR: Wrong syntax in BIBTEX file.} & 2.82 \% & 3.55 \% & 2.95 \% & 100.00 \% & ** s / 1 core \\
SplatFlow \cite{wang2024splatflow} & 2.81 \% & 3.60 \% & 2.96 \% & 100.00 \% & 0.1 s / GPU \\
DEQ-Flow-H \cite{deqflow} & 2.82 \% & 3.59 \% & 2.96 \% & 100.00 \% & 0.5 s / GPU \\
AGFlow \cite{luo2022learning} & 2.79 \% & 3.77 \% & 2.97 \% & 100.00 \% & 0.2 s / 8 cores \\
CSFlow \cite{shi2022csflow} & 2.90 \% & 3.40 \% & 3.00 \% & 100.00 \% & 0.2 s / GPU \\
CRAFT \cite{craft} & 2.87 \% & 3.68 \% & 3.02 \% & 100.00 \% & 0.2 s / GPU \\
RAFT-A \cite{sun2021autoflow} & 3.01 \% & 3.17 \% & 3.04 \% & 100.00 \% & 0.7 s / GPU \\
RAFT+AOIR \cite{MehlSSVM2021} & 2.80 \% & 4.13 \% & 3.04 \% & 100.00 \% & 10 s / GPU \\
RAFT \cite{ECCV2020teedraft} & 2.87 \% & 3.98 \% & 3.07 \% & 100.00 \% & 0.2 s / GPU \\
SSTM++\_thes\_[mv] \cite{ferede2022multi} & 2.83 \% & 4.16 \% & 3.08 \% & 100.00 \% & 0.4 s / GPU \\
PRAFlow\_RVC \cite{wan2020praflowrvc} & 2.82 \% & 4.40 \% & 3.11 \% & 100.00 \% & 0.5 s / GPU \\
DPCTF-F \cite{9459444} & 3.01 \% & 3.58 \% & 3.11 \% & 100.00 \% & 0.07 s / GPU \\
SSTM\_thes\_[mv] \cite{ferede2022multi} & 2.93 \% & 4.02 \% & 3.13 \% & 100.00 \% & 0.3 s / GPU \\
RAFT-3D \cite{teed2020raft} & 2.69 \% & 5.70 \% & 3.23 \% & 100.00 \% & 2 s / GPU \\
PPAC-HD3 \cite{Wannenwetsch2020PPA} & 3.09 \% & 4.50 \% & 3.35 \% & 100.00 \% & 0.19 s / \\
Scale-flow \cite{ling2022scale} & 3.26 \% & 3.79 \% & 3.36 \% & 100.00 \% & 0.8 s / GPU \\
RAFT-TF\_RVC \cite{sun2020tfraft} & 3.45 \% & 3.81 \% & 3.52 \% & 100.00 \% & 0.7 s / GPU \\
UberATG-DRISF \cite{Ma2019CVPR} & 2.67 \% & 7.66 \% & 3.58 \% & 100.00 \% & 0.75 s / CPU+GPU \\
RAPIDFlow \cite{Morimitsu2024RAPIDFlowRecurrentAdap table} & 3.67 \% & 3.51 \% & 3.64 \% & 100.00 \% & 0.04 s / GPU \\
GMFlow \cite{xu2022gmflow} & 3.65 \% & 4.46 \% & 3.80 \% & 100.00 \% & 0.071 s / \\
Stereo expansion \cite{yang2020upgrading} & 3.50 \% & 5.62 \% & 3.89 \% & 100.00 \% & 2 s / GPU \\
VCN \cite{yang2019vcn} & 3.50 \% & 5.62 \% & 3.89 \% & 100.00 \% & 0.18 s / \\
VCN+LCV \cite{Xiao2018ECCV} & 3.47 \% & 5.78 \% & 3.89 \% & 100.00 \% & 0.26 s / 1 core \\
Binary TTC \cite{badki2021BiTTC} & 3.51 \% & 5.63 \% & 3.89 \% & 100.00 \% & 2 s / GPU \\
MonoComb \cite{schuster2020mono} & 3.51 \% & 5.63 \% & 3.89 \% & 100.00 \% & 0.58 s / \\
MaskFlownet \cite{zhao2020maskflownet} & 3.70 \% & 4.93 \% & 3.92 \% & 100.00 \% & 0.06 s / \\
PRichFlow \cite{wangricher} & 3.92 \% & 3.94 \% & 3.93 \% & 100.00 \% & 0.1 s / \\
HD^3-Flow \cite{yin2019hd3} & 3.39 \% & 6.39 \% & 3.93 \% & 100.00 \% & 0.10 s / \\
RAFT+LCT-Flow \cite{RAFT+LCTFlow} & 3.42 \% & 6.45 \% & 3.97 \% & 100.00 \% & 0.65 s / GPU \\
LiteFlowNet3-S \cite{hui20liteflownet3} & 4.21 \% & 3.93 \% & 4.15 \% & 100.00 \% & 0.07s / \\
MaskFlownet-S \cite{zhao2020maskflownet} & 4.03 \% & 5.39 \% & 4.27 \% & 100.00 \% & 0.03 s / \\
LiteFlowNet3 \cite{hui20liteflownet3} & 4.23 \% & 4.59 \% & 4.29 \% & 100.00 \% & 0.07s / \\
SwiftFlow \cite{wang2020atg} & 3.92 \% & 6.22 \% & 4.34 \% & 100.00 \% & 0.03 s / GPU \\
LiteFlowNet2 \cite{hui19liteflownet2} & 4.38 \% & 4.59 \% & 4.42 \% & 100.00 \% & 0.0486 s / \\
RAFT+LCV \cite{Xiao2018ECCV} & 4.04 \% & 6.16 \% & 4.43 \% & 100.00 \% & 0.1 s / 1 core \\
ScopeFlow \cite{BarHaim2020CVPR} & 4.44 \% & 4.49 \% & 4.45 \% & 100.00 \% & -1 s / \\
MFF \cite{ren2018fusion} & 4.52 \% & 4.25 \% & 4.47 \% & 100.00 \% & 0.05 s / \\
PMC-PWC \cite{ZHANG2022116560} & 4.58 \% & 4.12 \% & 4.50 \% & 100.00 \% & TBD s / GPU \\
ACOSF \cite{Cong2020ICPR} & 3.40 \% & 9.52 \% & 4.51 \% & 100.00 \% & 5 min / 1 core \\
ISF \cite{Behl2017ICCV} & 4.21 \% & 6.83 \% & 4.69 \% & 100.00 \% & 10 min / 1 core \\
IRR-PWC \cite{Hur2019CVPR} & 4.92 \% & 4.62 \% & 4.86 \% & 100.00 \% & 0.18 s / \\
PWC-Net+ \cite{sun2018models} & 4.91 \% & 4.88 \% & 4.91 \% & 100.00 \% & 0.03 s / \\
Separable-Sim2real \cite{zhang2021SeparableFlow} & 4.46 \% & 7.78 \% & 5.06 \% & 100.00 \% & 0.25 s / \\
STaRFlow \cite{godet2020starflow} & 5.07 \% & 5.23 \% & 5.10 \% & 100.00 \% & 0.24 s / GPU \\
SMURF \cite{Stone2021CVPR} & 4.46 \% & 8.86 \% & 5.26 \% & 100.00 \% & .2 s / 1 core \\
OSF+TC \cite{Neoral2017CVWW} & 4.34 \% & 9.67 \% & 5.31 \% & 100.00 \% & 50 min / 1 core \\
SSF \cite{Ren20173DV} & 4.20 \% & 10.81 \% & 5.40 \% & 100.00 \% & 5 min / 1 core \\
SemARFlow \cite{yuan2023semarflow} & 4.58 \% & 9.30 \% & 5.43 \% & 100.00 \% & 0.0168s / GPU \\
LiteFlowNet \cite{hui18liteflownet} & 5.58 \% & 5.09 \% & 5.49 \% & 100.00 \% & 0.0885 s / \\
PRSM \cite{Vogel2015IJCV} & 4.33 \% & 10.80 \% & 5.50 \% & 100.00 \% & 300 s / 1 core \\
FDFlowNet \cite{kong2020fdflownet} & 5.35 \% & 6.62 \% & 5.58 \% & 100.00 \% & 0.02 s / \\
AL-OF\_r0.2 \cite{10.1007978303120047224} & 4.67 \% & 10.29 \% & 5.69 \% & 100.00 \% & 0.1 s / 1 core \\
LSM\_FLOW\_RVC \cite{Tang2020CVPR} & 4.67 \% & 10.40 \% & 5.70 \% & 100.00 \% & 0.2 s / 1 core \\
OSF 2018 \cite{Menze2018JPRS} & 4.02 \% & 14.14 \% & 5.86 \% & 100.00 \% & 390 s / 1 core \\
IRR-PWC\_RVC \cite{Hur2019CVPR} & 5.19 \% & 8.92 \% & 5.87 \% & 100.00 \% & 0.18 s / \\
SelFlow \cite{Liu2019SelFlow} & 5.12 \% & 9.41 \% & 5.90 \% & 100.00 \% & 0.09 s / GPU \\
SENSE \cite{Jiang2019ICCV} & 5.90 \% & 6.37 \% & 5.98 \% & 100.00 \% & 0.32s / \\
DTF\_SENSE \cite{schuster2021dtf} & 5.90 \% & 6.41 \% & 5.99 \% & 100.00 \% & 0.76 s / 1 core \\
PWC-Net \cite{Sun2018PWCNet} & 6.14 \% & 5.98 \% & 6.12 \% & 100.00 \% & 0.03 s / \\
OSF \cite{Menze2015CVPR} & 4.21 \% & 15.49 \% & 6.26 \% & 100.00 \% & 50 min / 1 core \\
NeuFlow \cite{ERROR: Wrong syntax in BIBTEX file.} & 5.73 \% & 8.63 \% & 6.26 \% & 100.00 \% & 0.01 s / GPU \\
CoT-AMFlow \cite{wang2020cot} & 5.83 \% & 8.30 \% & 6.28 \% & 100.00 \% & 0.06 s / GPU \\
MDFlow \cite{Kong2022TCSVT} & 5.72 \% & 10.34 \% & 6.56 \% & 100.00 \% & 0.03 s / \\
FastFlowNet \cite{Kong2021ICRA} & 6.29 \% & 7.78 \% & 6.56 \% & 100.00 \% & 0.01 s / \\
FlowNet2 \cite{IMSKDB17} & 7.24 \% & 5.60 \% & 6.94 \% & 100.00 \% & 0.1 s / GPU \\
VCN\_RVC \cite{yang2019vcn} & 5.33 \% & 14.97 \% & 7.08 \% & 100.00 \% & 0.36 s / GPU \\
DWARF \cite{AleottiAAAI2020} & 6.67 \% & 10.11 \% & 7.29 \% & 100.00 \% & 0.14s - 1.43s / \\
CNNF+PMBP \cite{PrincipleZhang} & 5.64 \% & 14.96 \% & 7.33 \% & 100.00 \% & 45 min / 1 cores \\
MDFlow-Fast \cite{Kong2022TCSVT} & 6.42 \% & 11.90 \% & 7.42 \% & 100.00 \% & 0.01 s / \\
MirrorFlow \cite{Hur2017ICCV} & 6.24 \% & 12.95 \% & 7.46 \% & 100.00 \% & 11 min / 4 core \\
UnFlow \cite{Meister2018UUL} & 6.38 \% & 12.36 \% & 7.46 \% & 100.00 \% & 0.12 s / GPU \\
ContinualFlow\_ROB \cite{Neoral2018ACCV} & 5.90 \% & 14.99 \% & 7.55 \% & 100.00 \% & 0.15 s / \\
PWC-Net\_RVC \cite{Sun2018PWCNet} & 7.12 \% & 10.29 \% & 7.69 \% & 100.00 \% & 0.03 s / \\
NccFLow \cite{wang2021nccflow} & 6.43 \% & 14.67 \% & 7.93 \% & 100.00 \% & 0.04 s / 1 core \\
SDF \cite{Bai2016ECCV} & 5.75 \% & 18.38 \% & 8.04 \% & 100.00 \% & TBA / 1 core \\
Flow2Stereo \cite{Liu2020Flow2Stereo} & 6.84 \% & 13.46 \% & 8.04 \% & 100.00 \% & 0.05 s / GPU \\
UnsupSimFlow \cite{Im2020ECCV} & 7.06 \% & 13.36 \% & 8.21 \% & 100.00 \% & 0.03 s / 8 cores \\
Self-SuperFlow-ft \cite{bendig2022selfsuperflow} & 6.82 \% & 15.27 \% & 8.36 \% & 100.00 \% & 0.13 s / \\
UFlow \cite{jonschkowski2020matters} & 7.01 \% & 14.75 \% & 8.41 \% & 100.00 \% & 0.04 s / 1 core \\
Mono-SF \cite{brickwedde2019monosf} & 6.67 \% & 16.48 \% & 8.45 \% & 100.00 \% & 41 s / 1 core \\
PWOC-3D \cite{saxena2019pwoc} & 7.70 \% & 11.96 \% & 8.47 \% & 100.00 \% & 0.13 s / \\
FlowFields++ \cite{schuster2018flowfields++} & 7.31 \% & 13.83 \% & 8.49 \% & 100.00 \% & 29 s / 1 core \\
Multi-Mono-SF-ft \cite{Hur2021CVPR} & 7.54 \% & 14.05 \% & 8.72 \% & 100.00 \% & 0.06 s / \\
MR-Flow \cite{WulffCVPR2017} & 6.86 \% & 17.91 \% & 8.86 \% & 100.00 \% & 8 min / 1 core \\
DTF\_PWOC \cite{schuster2021dtf} & 7.37 \% & 16.42 \% & 9.01 \% & 100.00 \% & 0.38 s / \\
SFF++ \cite{schuster2019sffpp} & 7.97 \% & 14.10 \% & 9.08 \% & 100.00 \% & 78 s / 4 cores \\
FSF+MS \cite{Taniai2017} & 6.53 \% & 20.72 \% & 9.11 \% & 100.00 \% & 2.7 s / 4 cores \\
JFS \cite{Hur2016ECCVWORK} & 7.85 \% & 14.97 \% & 9.14 \% & 100.00 \% & 13 min / 1 core \\
FF++\_ROB \cite{schuster2018flowfields++} & 7.82 \% & 15.33 \% & 9.18 \% & 100.00 \% & 29 s / 1 core \\
ImpPB+SPCI \cite{Schuster2017Multiple} & 7.70 \% & 16.25 \% & 9.25 \% & 100.00 \% & 60 s / GPU \\
SfM-PM \cite{MaurerECCV2018} & 6.94 \% & 19.94 \% & 9.30 \% & 100.00 \% & 69 s / 3 cores \\
PR-Sceneflow \cite{Vogel2013ICCV} & 6.94 \% & 20.24 \% & 9.36 \% & 100.00 \% & 150 s / 4 core \\
DDFlow+LCV \cite{Xiao2018ECCV} & 7.83 \% & 16.31 \% & 9.37 \% & 100.00 \% & 0.1 s / GPU \\
DDFlow \cite{Liu2019DDFlow} & 7.95 \% & 16.77 \% & 9.55 \% & 100.00 \% & 0.06 s / GPU \\
SelFlow \cite{Liu2019SelFlow} & 7.73 \% & 18.34 \% & 9.65 \% & 100.00 \% & 0.09 s / GPU \\
SOF \cite{Sevilla2016CVPR} & 8.11 \% & 18.16 \% & 9.93 \% & 100.00 \% & 6 min / 1 core \\
ProFlow \cite{MaurerBMVC2018Proflow} & 8.44 \% & 17.90 \% & 10.15 \% & 100.00 \% & 112 s / GPU+CPU \\
DCFlow \cite{xu2017dcflow} & 8.04 \% & 19.84 \% & 10.18 \% & 100.00 \% & 8.6 s / GPU \\
FlowFieldCNN \cite{Bailer2017CNN} & 8.91 \% & 16.06 \% & 10.21 \% & 100.00 \% & 23 s / GPU/CPU 4 core \\
RicFlow \cite{Hu2017CVPR} & 9.27 \% & 14.88 \% & 10.29 \% & 100.00 \% & 5 s / 1 core \\
SPS+FF++ \cite{schuster2018dense} & 9.09 \% & 15.91 \% & 10.33 \% & 100.00 \% & 36 s / 1 core \\
SceneFFields \cite{schuster2018sceneflowfields} & 8.29 \% & 19.85 \% & 10.39 \% & 100.00 \% & 65 s / 4 cores \\
ProFlow\_ROB \cite{MaurerBMVC2018Proflow} & 8.56 \% & 18.71 \% & 10.40 \% & 100.00 \% & 112 s / GPU+CPU \\
DIP-Flow-DF \cite{MaurerBMVC2018DIPFlow} & 8.61 \% & 19.25 \% & 10.54 \% & 100.00 \% & 104s / 2 cores \\
DF+OIR \cite{MaurerBMVC2017} & 8.73 \% & 19.18 \% & 10.63 \% & 100.00 \% & 3 min / 1 core \\
Self-Mono-SF-ft \cite{Hur2020CVPR} & 10.13 \% & 14.04 \% & 10.84 \% & 100.00 \% & 0.09 s / \\
FlowFields+ \cite{1508.05151} & 9.69 \% & 16.82 \% & 10.98 \% & 100.00 \% & 28s / 1 core \\
PGM-G \cite{DBLPjournalscorrLi17n} & 9.24 \% & 19.06 \% & 11.02 \% & 100.00 \% & 5.05 s / 1 core \\
CSF \cite{Lv2016ECCV} & 8.72 \% & 22.38 \% & 11.20 \% & 100.00 \% & 80 s / 1 core \\
DiscreteFlow \cite{Menze2015GCPR} & 9.96 \% & 17.03 \% & 11.25 \% & 100.00 \% & 3 min / 1 core \\
DIP-Flow-CPM \cite{MaurerBMVC2018DIPFlow} & 9.35 \% & 19.89 \% & 11.26 \% & 100.00 \% & 52 s / 2 core \\
RAFT-MSF \cite{ERROR: Wrong syntax in BIBTEX file.} & 10.73 \% & 15.85 \% & 11.66 \% & 100.00 \% & 0.18 s / GPU \\
HCSH \cite{FAN20181} & 9.39 \% & 22.05 \% & 11.69 \% & 100.00 \% & 3.5 s / 1 core \\
SGM&FlowFie+ \cite{Schuster2018Combining} & 10.38 \% & 17.97 \% & 11.75 \% & 91.79 \% & 29 s / 1 core \\
PatchBatch \cite{Gadot2016CVPR} & 10.06 \% & 22.29 \% & 12.28 \% & 100.00 \% & 50 s / GPU \\
DDF \cite{Guney2016ACCV} & 10.44 \% & 21.32 \% & 12.41 \% & 100.00 \% & ~1 min / GPU \\
UJG \cite{li2021unsupervised} & 11.21 \% & 20.08 \% & 12.82 \% & 100.00 \% & 0.03 s / GPU \\
Self-SuperFlow \cite{bendig2022selfsuperflow} & 11.18 \% & 24.17 \% & 13.54 \% & 100.00 \% & 0.13 s / \\
IntrpNt-df \cite{Zweig2017CVPR} & 11.67 \% & 22.09 \% & 13.56 \% & 100.00 \% & 3 min / GPU \\
SegFlow(d0=3) \cite{3DFlow} & 12.41 \% & 19.39 \% & 13.68 \% & 100.00 \% & 6.6 s / 1 core \\
Multi-Mono-SF \cite{Hur2021CVPR} & 11.72 \% & 22.83 \% & 13.74 \% & 100.00 \% & 0.06 s / \\
PCOF-LDOF \cite{Derome2016GCPR} & 9.24 \% & 34.40 \% & 13.80 \% & 100.00 \% & 50 s / 1 core \\
CPM-Flow \cite{Hu2016CVPR} & 12.77 \% & 18.71 \% & 13.85 \% & 100.00 \% & 4.2 s / 1 core \\
Back2FutureFlow(UFO) \cite{Janai2018ECCV} & 12.49 \% & 20.00 \% & 13.85 \% & 100.00 \% & 0.12 s / GPU \\
OmegaNet \cite{tosi2020distilled} & 11.14 \% & 26.10 \% & 13.86 \% & 100.00 \% & 0.01 s / GPU \\
IntrpNt-cpm \cite{Zweig2017CVPR} & 12.10 \% & 22.73 \% & 14.03 \% & 100.00 \% & 5.6 s / GPU \\
HiLM \cite{Fathy2018ECCV} & 13.32 \% & 17.45 \% & 14.07 \% & 100.00 \% & 8 sec / \\
SPM-BP \cite{Li2015ICCV} & 12.86 \% & 20.33 \% & 14.22 \% & 100.00 \% & 10 s / 2 cores \\
FullFlow \cite{Chen2016CVPR} & 12.97 \% & 20.58 \% & 14.35 \% & 100.00 \% & 4 min / 4 cores \\
IntrpNt-dm \cite{Zweig2017CVPR} & 12.88 \% & 22.41 \% & 14.61 \% & 100.00 \% & 15 s / GPU \\
SGM+SF \cite{Hirschmueller2008PAMI} & 13.36 \% & 21.78 \% & 14.89 \% & 100.00 \% & 45 min / 16 core \\
EPC++ (stereo) \cite{Luo2019EveryPC} & 13.24 \% & 22.70 \% & 14.96 \% & 100.00 \% & 0.05 s / GPU \\
PPM \cite{kuang} & 15.09 \% & 18.91 \% & 15.78 \% & 100.00 \% & 17.3 s / 1 core \\
SODA-Flow \cite{MaurerSSVM2017} & 13.93 \% & 25.45 \% & 16.02 \% & 100.00 \% & 96 s / 2 cores \\
OAR-Flow \cite{MaurerSSVM20172} & 14.33 \% & 24.03 \% & 16.09 \% & 100.00 \% & 100 s / 2 cores \\
MotionSLIC \cite{Yamaguchi2013CVPR} & 6.19 \% & 63.03 \% & 16.50 \% & 100.00 \% & 30 s / 4 cores \\
EpicFlow \cite{Revaud2015CVPR} & 15.00 \% & 24.34 \% & 16.69 \% & 100.00 \% & 15 s / 1 core \\
Self-Mono-SF \cite{Hur2020CVPR} & 15.98 \% & 20.85 \% & 16.86 \% & 100.00 \% & 0.09 s / \\
3DFlow \cite{3DFlow} & 15.13 \% & 25.02 \% & 16.92 \% & 100.00 \% & 448s / \\
DeepFlow \cite{Weinzaepfel2013ICCV} & 16.47 \% & 26.80 \% & 18.35 \% & 100.00 \% & 17 s / 1 core \\
PCOF + ACTF \cite{Derome2016GCPR} & 9.77 \% & 57.63 \% & 18.45 \% & 100.00 \% & 0.08 s / GPU \\
SegFlow(d0=11) \cite{3DFlow} & 19.02 \% & 18.05 \% & 18.84 \% & 100.00 \% & 4.5 s / 1 core \\
DMF\_ROB \cite{weinzaepfelhal00873592} & 19.32 \% & 25.60 \% & 20.46 \% & 100.00 \% & 150 s / 1 core \\
IIOF-NLDP \cite{TBDicip2017} & 19.40 \% & 28.20 \% & 20.99 \% & 100.00 \% & 350 s / 4 cores \\
CPNFlow \cite{yang2018conditional} & 23.41 \% & 23.39 \% & 23.40 \% & 100.00 \% & 0.1 s / GPU \\
SGM+C+NL \cite{Hirschmueller2008PAMI} & 23.03 \% & 38.80 \% & 25.89 \% & 99.90 \% & 4.5 min / 1 core \\
SPyNet \cite{spynet2017} & 23.64 \% & 40.58 \% & 26.71 \% & 100.00 \% & 0.16 s / 1 core \\
DWBSF \cite{Richardt2016THREEDV} & 30.13 \% & 26.68 \% & 29.50 \% & 100.00 \% & 7 min / 4 cores \\
SGM+LDOF \cite{Hirschmueller2008PAMI} & 30.41 \% & 27.62 \% & 29.90 \% & 99.94 \% & 86 s / 1 core \\
HS \cite{Sun2014IJCV} & 30.49 \% & 48.25 \% & 33.71 \% & 100.00 \% & 2.6 min / 1 core \\
GCSF \cite{Cech2011CVPR} & 38.12 \% & 37.77 \% & 38.05 \% & 100.00 \% & 2.4 s / 1 core \\
DB-TV-L1 \cite{Zach2007GCPR} & 38.67 \% & 44.94 \% & 39.81 \% & 100.00 \% & 16 s / 1 core \\
VSF \cite{Huguet2007ICCV} & 41.15 \% & 41.85 \% & 41.28 \% & 100.00 \% & 125 min / 1 core \\
HAOF \cite{Brox2004ECCV} & 41.52 \% & 47.66 \% & 42.63 \% & 100.00 \% & 16.2 s / 1 core \\
TVL1\_ROB \cite{ipol.2013.26} & 42.85 \% & 47.99 \% & 43.79 \% & 100.00 \% & 3 s / 4 cores \\
PolyExpand \cite{Farneback2003SCIA} & 43.77 \% & 55.90 \% & 45.97 \% & 100.00 \% & 1 s / 1 core \\
H+S\_ROB \cite{ipol.2013.20} & 62.55 \% & 74.96 \% & 64.80 \% & 100.00 \% & 8 s / 4 cores \\
Stereo-RSSF \cite{salehi2023stereo} & 65.47 \% & 71.92 \% & 66.64 \% & 10.75 \% & 2.5 s / 8 core \\
Pyramid-LK \cite{Bouguet2000} & 66.72 \% & 75.32 \% & 68.28 \% & 100.00 \% & 1.5 min / 1 core
\end{tabular}