\begin{tabular}{c | c | c | c | c | c}
{\bf Method} & {\bf SILog} & {\bf sqErrorRel} & {\bf absErrorRel} & {\bf iRMSE} & {\bf Runtime}\\ \hline
UniDepth \cite{piccinelli2024unidepth} & 8.13 & 1.09 \% & 6.54 \% & 8.24 & 0.1 s / GPU \\
NDDepth \cite{shao2023NDDepth} & 9.62 & 1.59 \% & 7.75 \% & 10.62 & 0.1s / 1 core \\
IEBins \cite{shao2023IEBins} & 9.63 & 1.60 \% & 7.82 \% & 10.68 & 0.1s / 1 core \\
VA-DepthNet \cite{liu2023vadepthnet} & 9.84 & 1.66 \% & 7.96 \% & 10.44 & 0.1 s / 1 core \\
DiffusionDepth-I \cite{duan2023diffusiondepth} & 9.85 & 1.64 \% & 8.06 \% & 10.58 & 0.2 s / 1 core \\
iDisc \cite{piccinelli2023idisc} & 9.89 & 1.77 \% & 8.11 \% & 10.73 & 0.1 s / 1 core \\
MG \cite{liu2023mg} & 9.93 & 1.68 \% & 7.99 \% & 10.63 & 0.1 s / 1 core \\
URCDC-Depth \cite{httpsdoi.org10.48550arxiv.2302.08149} & 10.03 & 1.74 \% & 8.24 \% & 10.71 & 0.1 s / 1 core \\
BinsFormer \cite{li2022binsformer} & 10.14 & 1.69 \% & 8.23 \% & 10.90 & 0.1 s / 1 core \\
TrapNet \cite{ning2023trap} & 10.15 & 1.66 \% & 7.92 \% & 10.45 & 0.1 s / 1 core \\
PixelFormer \cite{agarwal2022attention} & 10.28 & 1.82 \% & 8.16 \% & 10.84 & 0.1 s / 1 core \\
RED-T \cite{shim2023depthrelative} & 10.36 & 1.92 \% & 8.11 \% & 10.82 & 0.1 s / GPU \\
NeWCRFs \cite{yuan2022newcrfs} & 10.39 & 1.83 \% & 8.37 \% & 11.03 & 0.1 s / 1 core \\
DepthFormer \cite{li2022depthformer} & 10.69 & 1.84 \% & 8.68 \% & 11.39 & 0.1 s / 1 core \\
ViP-DeepLab \cite{vipdeeplab} & 10.80 & 2.19 \% & 8.94 \% & 11.77 & 0.1 s / GPU \\
SideRT \cite{httpsdoi.org10.48550arxiv.2204.13892} & 11.42 & 2.25 \% & 9.28 \% & 11.88 & 0.02 s / GPU \\
PWA \cite{lee2021patch} & 11.45 & 2.30 \% & 9.05 \% & 12.32 & 0.06 s / GPU \\
BANet \cite{aich2020bidirectional} & 11.55 & 2.31 \% & 9.34 \% & 12.17 & 0.04 s / GPU \\
BTS \cite{lee2019big} & 11.67 & 2.21 \% & 9.04 \% & 12.23 & 0.06 s / GPU \\
DL\_61 (DORN) \cite{fuhal01741163} & 11.77 & 2.23 \% & 8.78 \% & 12.98 & 0.5 s / GPU \\
RefinedMPL \cite{vianney2019refinedmpl} & 11.80 & 2.31 \% & 10.09 \% & 13.39 & 0.05 s / GPU \\
DLE \cite{ce21dle} & 11.81 & 2.22 \% & 9.09 \% & 12.49 & 0.09 s / \\
PFANet \cite{9428446} & 11.84 & 2.46 \% & 9.23 \% & 12.63 & 0.1 s / GPU \\
GAC \cite{9327478} & 12.13 & 2.61 \% & 9.41 \% & 12.65 & 0.05 s / GPU \\
DL\_SORD\_SL \cite{Diaz2019CVPR} & 12.39 & 2.49 \% & 10.10 \% & 13.48 & 0.8 s / GPU \\
VNL \cite{wei2019enforcing} & 12.65 & 2.46 \% & 10.15 \% & 13.02 & 0.5 s / 1 core \\
P3Depth \cite{P3Depth} & 12.82 & 2.53 \% & 9.92 \% & 13.71 & 0.1 s / GPU \\
MS-DPT \cite{song2023knowledge} & 12.83 & 3.62 \% & 11.01 \% & 13.43 & 0.1 s / GPU \\
DS-SIDENet\_ROB \cite{Ren2019robust} & 12.86 & 2.87 \% & 10.03 \% & 14.40 & 0.35 s / GPU \\
DL\_SORD\_SQ \cite{Diaz2019CVPR} & 13.00 & 2.95 \% & 10.38 \% & 13.78 & 0.88 s / GPU \\
PAP \cite{zhang2019pattern} & 13.08 & 2.72 \% & 10.27 \% & 13.95 & 0.18 s / GPU \\
CADepth-Net \cite{yan2021channelwise} & 13.34 & 3.33 \% & 10.67 \% & 13.61 & 0.08 s / 1 core \\
VGG16-UNet \cite{guo2018learning} & 13.41 & 2.86 \% & 10.60 \% & 15.06 & 0.16 s / GPU \\
DORN\_ROB \cite{fuhal01741163} & 13.53 & 3.06 \% & 10.35 \% & 15.96 & 2 s / GPU \\
g2s \cite{chawlavarma2021multimodal} & 14.16 & 3.65 \% & 11.40 \% & 15.53 & 0.04 s / GPU \\
MT-SfMLearner \cite{mtsfmlearner} & 14.25 & 3.72 \% & 12.52 \% & 15.83 & 0.04s / GPU \\
MLDA-Net \cite{song2021mlda} & 14.42 & 3.41 \% & 11.67 \% & 16.12 & 0.2 s / 1 core \\
DABC\_ROB \cite{li2018deep} & 14.49 & 4.08 \% & 12.72 \% & 15.53 & 0.7 s / GPU \\
BTSREF\_RVC \cite{lee2019big} & 14.67 & 3.12 \% & 12.42 \% & 16.84 & 0.1 s / 1 core \\
SDNet \cite{OchsKretzMester2019} & 14.68 & 3.90 \% & 12.31 \% & 15.96 & 0.2 s / GPU \\
APMoE\_base\_ROB \cite{kong2018pag} & 14.74 & 3.88 \% & 11.74 \% & 15.63 & 0.2 s / GPU \\
DiPE \cite{jiang2020dipe} & 14.84 & 4.04 \% & 12.28 \% & 15.69 & 0.01 s / GPU \\
CSWS\_E\_ROB \cite{boli2018} & 14.85 & 3.48 \% & 11.84 \% & 16.38 & 0.2 s / 1 core \\
HBC \cite{jiang2019hbc} & 15.18 & 3.79 \% & 12.33 \% & 17.86 & 0.05 s / GPU \\
SGDepth \cite{klingner2020selfsupervised} & 15.30 & 5.00 \% & 13.29 \% & 15.80 & 0.1 s / GPU \\
DHGRL \cite{Zhang2018Deep} & 15.47 & 4.04 \% & 12.52 \% & 15.72 & 0.2 s / GPU \\
GCNDepth \cite{Masoumian2021GCNDepthSM} & 15.54 & 4.26 \% & 12.75 \% & 15.99 & 0.05 s / GPU \\
packnSFMHR\_RVC \cite{packnet} & 15.80 & 4.73 \% & 12.28 \% & 17.96 & 0.5 s / GPU \\
LSIM \cite{goldman2019lsim} & 17.92 & 6.88 \% & 14.04 \% & 17.62 & 0.08 s / GPU
\end{tabular}