\begin{tabular}{c | c | c | c | c | c}
{\bf Method} & {\bf iRMSE} & {\bf iMAE} & {\bf RMSE} & {\bf MAE} & {\bf Runtime}\\ \hline
GuideNet \cite{tang2019learning} & 2.25 & 0.99 & 736.24 & 218.83 & 0.14 s / GPU \\
NLSPN \cite{park2020nonlocal} & 1.99 & 0.84 & 741.68 & 199.59 & 0.22 s / GPU \\
CSPN++ \cite{cheng2019cspn} & 2.07 & 0.90 & 743.69 & 209.28 & 0.2 s / 1 core \\
UberATG-FuseNet \cite{learning2019yun} & 2.34 & 1.14 & 752.88 & 221.19 & 0.09 s / GPU \\
DeepLiDAR \cite{Qiu2019CVPR} & 2.56 & 1.15 & 758.38 & 226.50 & 0.07s / GPU \\
MSG-CHN \cite{li2020multi} & 2.30 & 0.98 & 762.19 & 220.41 & 0.01 s / GPU \\
DSPN \cite{xu2020deformable} & 2.47 & 1.03 & 766.74 & 220.36 & 0.34 s / 1 core \\
RGB\_guide&certainty \cite{vangansbeke2019} & 2.19 & 0.93 & 772.87 & 215.02 & 0.02 s / GPU \\
PwP \cite{yan2019completion} & 2.42 & 1.13 & 777.05 & 235.17 & 0.1 s / GPU \\
Revisiting \cite{9138427} & 2.42 & 0.99 & 792.80 & 225.81 & 0.05 s / GPU \\
CrossGuidance \cite{lee2020deep} & 2.73 & 1.33 & 807.42 & 253.98 & 0.2 s / 1 core \\
WPI\_ResNet18 \cite{ERROR: Wrong syntax in BIBTEX file.} & 2.69 & 1.07 & 808.80 & 228.07 & 0.04 s / 1 core \\
Sparse-to-Dense (gd) \cite{ma2018self} & 2.80 & 1.21 & 814.73 & 249.95 & 0.08 s / GPU \\
NConv-CNN-L2 (gd) \cite{eldesokey2019confidence} & 2.60 & 1.03 & 829.98 & 233.26 & 0.02 s / GPU \\
DDP \cite{yang2019dense} & 2.10 & 0.85 & 832.94 & 203.96 & 0.08 s / GPU \\
NConv-CNN-L1 (gd) \cite{eldesokey2019confidence} & 2.52 & 0.92 & 859.22 & 207.77 & 0.02 s / GPU \\
IR\_L2 \cite{lu2020} & 4.92 & 1.35 & 901.43 & 292.36 & 0.05 s / GPU \\
Spade-RGBsD \cite{Jaritz2018} & 2.17 & 0.95 & 917.64 & 234.81 & 0.07 s / GPU \\
glob\_guide&certainty \cite{vangansbeke2019} & 2.80 & 1.07 & 922.93 & 249.11 & 0.02 s / GPU \\
DFineNet \cite{Zhang2019DFineNetEE} & 3.21 & 1.39 & 943.89 & 304.17 & 0.02 s / GPU \\
Sparse-to-Dense (d) \cite{ma2018self} & 3.21 & 1.35 & 954.36 & 288.64 & 0.04 s / GPU \\
pNCNN (d) \cite{Eldesokey2020CVPR} & 3.37 & 1.05 & 960.05 & 251.77 & 0.02 s / 1 core \\
Conf-Net \cite{hekmatian2019confnet} & 3.10 & 1.09 & 962.28 & 257.54 & 0.02 s / GPU \\
DT\_Physical \cite{ERROR: Wrong syntax in BIBTEX file.} & 3.09 & 1.16 & 965.65 & 261.86 & 0.04 s / \\
DCrgb\_80b\_3coef \cite{imran2019depth} & 2.43 & 0.98 & 965.87 & 215.75 & 0.15 s / 1 core \\
DCd\_all \cite{imran2019depth} & 2.87 & 1.13 & 988.38 & 252.21 & 0.1 s / 1 core \\
CSPN \cite{cheng2018depth} & 2.93 & 1.15 & 1019.64 & 279.46 & 1 s / GPU \\
Spade-sD \cite{Jaritz2018} & 2.60 & 0.98 & 1035.29 & 248.32 & 0.04 s / GPU \\
Morph-Net \cite{8569539} & 3.84 & 1.57 & 1045.45 & 310.49 & 0.17 s / GPU \\
DCd\_3 \cite{imran2019depth} & 2.95 & 1.07 & 1109.04 & 234.01 & 0.1 s / 1 core \\
IMat \cite{ERROR: Wrong syntax in BIBTEX file.} & 3.59 & 1.24 & 1111.39 & 284.25 & 0.1 s / 1 core \\
ScaffFusion \cite{ERROR: Wrong syntax in BIBTEX file.} & 3.30 & 1.15 & 1121.93 & 280.76 & 0.2s / 1 core \\
VOICED \cite{wong2020unsupervised} & 3.56 & 1.20 & 1169.97 & 299.41 & 0.02 s / 1 core \\
DFuseNet \cite{shivakumar2018deepfuse} & 3.62 & 1.79 & 1206.66 & 429.93 & 0.08 s / GPU \\
NG\_Depth \cite{an2020} & 14.93 & 1.38 & 1266.22 & 305.98 & 0.8 s / 1 core \\
NConv-CNN (d) \cite{Eldesokey2018} & 4.67 & 1.52 & 1268.22 & 360.28 & 0.01 s / GPU \\
IP-Basic \cite{ku2018defense} & 3.78 & 1.29 & 1288.46 & 302.60 & 0.011 s / 1 core \\
Sparse2Dense(w/o gt) \cite{ma2018self} & 4.07 & 1.57 & 1299.85 & 350.32 & 0.08 s / GPU \\
ADNN \cite{chodosh18} & 59.39 & 3.19 & 1325.37 & 439.48 & .04 s / GPU \\
NN+CNN \cite{Uhrig2017THREEDV} & 3.25 & 1.29 & 1419.75 & 416.14 & 0.02 s / \\
B-ADT \cite{9130033} & 4.16 & 1.23 & 1480.36 & 298.72 & 0.120 sec. / \\
SparseConvs \cite{Uhrig2017THREEDV} & 4.94 & 1.78 & 1601.33 & 481.27 & 0.01 s / \\
NadarayaW \cite{Uhrig2017THREEDV} & 6.34 & 1.84 & 1852.60 & 416.77 & 0.05 s / 1 core \\
SGDU \cite{schneider2016semantically} & 7.38 & 2.05 & 2312.57 & 605.47 & 0.2 s / 4 cores
\end{tabular}