\begin{tabular}{c | c | c | c | c | c}
{\bf Method} & {\bf IoU class} & {\bf iIoU class} & {\bf IoU category} & {\bf iIoU category} & {\bf Runtime}\\ \hline
WRP \cite{Ganeshan2021ICCV} & 76.44 \% & 50.92 \% & 89.63 \% & 73.69 \% & 1 s / GPU \\
UJS\_model \cite{Yingfeng Cai2021TIP} & 75.11 \% & 47.71 \% & 89.53 \% & 75.75 \% & 0.26 s / 1 core \\
RoadFormer+ \cite{huang2024roadformer+} & 73.13 \% & 45.88 \% & 88.75 \% & 73.46 \% & 0.04 s / 1 core \\
VideoProp-LabelRelax \cite{semanticcvpr19} & 72.82 \% & 48.68 \% & 88.99 \% & 75.26 \% & n s / GPU \\
SN\_DN161\_fat\_pyrx8 \cite{bevandic22wacv} & 68.89 \% & 40.45 \% & 87.06 \% & 67.93 \% & 1 s / \\
MSeg1080\_RVC \cite{MSeg2020CVPR} & 62.64 \% & 31.62 \% & 86.59 \% & 68.05 \% & 0.49 s / 1 core \\
Chroma UDA \cite{erkenthal02502457} & 60.36 \% & 31.70 \% & 80.73 \% & 61.91 \% & 0.4 s / GPU \\
IfN-DomAdap-Seg \cite{Bolte2019CVPRWorkshops} & 59.50 \% & 30.28 \% & 81.57 \% & 61.91 \% & 1 s / GPU \\
SegStereo \cite{yang2018SegStereo} & 59.10 \% & 28.00 \% & 81.31 \% & 60.26 \% & 0.6 s / \\
MCANet \cite{10164083} & 58.52 \% & 24.00 \% & 83.04 \% & 54.06 \% & 0.003 s / \\
SDBNetV2 \cite{singha2023improved} & 56.77 \% & 23.11 \% & 81.08 \% & 50.77 \% & 0.004 s / \\
SGDepth \cite{klingner2020selfsupervised} & 53.04 \% & 24.36 \% & 78.65 \% & 55.95 \% & 0.1 s / GPU \\
SDBNet \cite{singha2022sdbnet} & 51.80 \% & 18.72 \% & 78.00 \% & 44.46 \% & 0.01 s / \\
SDNet \cite{OchsKretzMester2019} & 51.14 \% & 17.74 \% & 79.62 \% & 50.45 \% & 0.2 s / GPU \\
SFRSeg \cite{SINGHA2023109557} & 49.27 \% & 17.43 \% & 77.91 \% & 46.88 \% & 0.005 s / \\
APMoE\_seg\_ROB \cite{kong2018pag} & 47.96 \% & 17.86 \% & 78.11 \% & 49.17 \% & 0.2 s / GPU \\
LIISIESS \cite{2023ssegtim} & 46.73 \% & 19.82 \% & 76.04 \% & 49.91 \% & NA s / 1 core
\end{tabular}