\begin{tabular}{c | c | c | c | c | c}
{\bf Method} & {\bf iRMSE} & {\bf iMAE} & {\bf RMSE} & {\bf MAE} & {\bf Runtime}\\ \hline
TPVD \cite{yan2024tri} & 1.82 & 0.81 & 693.97 & 188.60 & 0.01 s / GPU \\
RigNet++ \cite{yan2023rignet++} & 1.82 & 0.81 & 694.24 & 188.62 & 0.06 s / GPU \\
LRRU-Base-L2 \cite{LRRUICCV2023} & 2.18 & 0.86 & 695.67 & 198.31 & 0.12 s / 8 cores \\
LRRU-Base-L2+L1 \cite{LRRUICCV2023} & 1.87 & 0.81 & 696.51 & 189.96 & 0.12 s / GPU \\
BEV@DC \cite{zhou2023bev} & 1.83 & 0.82 & 697.44 & 189.44 & 0.1 s / 1 core \\
NDDepth \cite{shao2023NDDepth} & 1.89 & 0.83 & 698.71 & 192.75 & 0.1 s / 1 core \\
Decomposition B \cite{10173567} & 2.05 & 0.91 & 707.93 & 205.11 & 0.1 s / GPU \\
Decomposition A \cite{10173567} & 2.04 & 0.91 & 708.30 & 205.01 & 0.1 s / GPU \\
CompletionFormer \cite{Zhang2023CompletionFormer} & 2.01 & 0.88 & 708.87 & 203.45 & 0.12 s / GPU \\
DySPN \cite{lin2022dynamic} & 1.88 & 0.82 & 709.12 & 192.71 & 0.16 s / GPU \\
SemAttNet \cite{9918022} & 2.03 & 0.90 & 709.41 & 205.49 & 0.2 s / 1 core \\
RigNet \cite{yan2022rignet} & 2.08 & 0.90 & 712.66 & 203.25 & 0.20 s / GPU \\
LRRU-Small \cite{LRRUICCV2023} & 2.01 & 0.88 & 713.64 & 203.60 & 0.05 s / GPU \\
LRRU-Small-L2+L1 \cite{LRRUICCV2023} & 1.96 & 0.85 & 717.50 & 197.72 & 0.06 s / GPU \\
MFF-Net \cite{liu2023mff} & 2.21 & 0.94 & 719.85 & 208.11 & 0.05 s / GPU \\
NNNet \cite{9923926} & 1.99 & 0.88 & 724.14 & 205.57 & 0.03 s / 1 core \\
ReDC \cite{sun2023revisiting} & 2.05 & 0.89 & 728.31 & 204.60 & 0.02 s / \\
PENet \cite{hu2020PENet} & 2.17 & 0.94 & 730.08 & 210.55 & 0.032s / GPU \\
LRRU-Tiny-L2 \cite{LRRUICCV2023} & 2.09 & 0.90 & 732.43 & 209.14 & 0.04 s / GPU \\
ACMNet \cite{zhao2021adaptive} & 2.08 & 0.90 & 732.99 & 206.80 & 0.08 s / 1 core \\
SPL \cite{icprspl2022} & 2.09 & 0.93 & 733.44 & 212.49 & 0.03 s / 1 core \\
CluDe \cite{10415465} & 2.08 & 0.88 & 734.59 & 200.48 & 0.14 s / GPU \\
FCFR-Net \cite{liu2021fcfr} & 2.20 & 0.98 & 735.81 & 217.15 & 0.1 s / GPU \\
GuideNet \cite{tang2019learning} & 2.25 & 0.99 & 736.24 & 218.83 & 0.14 s / GPU \\
MDANet \cite{9561490} & 2.12 & 0.99 & 738.23 & 214.99 & 0.03 s / GPU \\
CDCNet \cite{fan2020cdcnet} & 2.18 & 0.99 & 738.26 & 216.05 & 0.06 s / GPU \\
LRRU-Tiny-L2+L1 \cite{LRRUICCV2023} & 2.04 & 0.85 & 738.86 & 200.28 & 0.04 s / GPU \\
ENet \cite{hu2020PENet} & 2.14 & 0.95 & 741.30 & 216.26 & 0.019 s / GPU \\
NLSPN \cite{park2020nonlocal} & 1.99 & 0.84 & 741.68 & 199.59 & 0.22 s / GPU \\
CluDe* \cite{10415465} & 2.02 & 0.86 & 742.26 & 197.91 & 0.14 s / GPU \\
CSPN++ \cite{cheng2019cspn} & 2.07 & 0.90 & 743.69 & 209.28 & 0.2 s / 1 core \\
ACMNet \cite{zhao2021adaptive} & 2.08 & 0.90 & 744.91 & 206.09 & 0.08 s / GPU \\
CDCNet-lite \cite{fan2020cdcnet} & 2.22 & 0.95 & 748.99 & 215.38 & 0.04 s / GPU \\
Ms\_Unc\_UARes-B \cite{zhu2021robust} & 1.98 & 0.85 & 751.59 & 198.09 & 0.1 s / GPU \\
UberATG-FuseNet \cite{learning2019yun} & 2.34 & 1.14 & 752.88 & 221.19 & 0.09 s / GPU \\
LDCNet \cite{yan2023learnable} & 2.33 & 0.98 & 753.15 & 218.02 & 0.05 s / GPU \\
DenseLiDAR \cite{9357967} & 2.25 & 0.96 & 755.41 & 214.13 & 0.02 s / 1 core \\
DeepLiDAR \cite{Qiu2019CVPR} & 2.56 & 1.15 & 758.38 & 226.50 & 0.07s / GPU \\
DANConv \cite{danconv} & 2.17 & 0.92 & 759.65 & 213.68 & 0.05 s / GPU \\
MSG-CHN \cite{li2020multi} & 2.30 & 0.98 & 762.19 & 220.41 & 0.01 s / GPU \\
ABCD \cite{jeon2021abcd} & 2.29 & 0.97 & 764.61 & 220.86 & 0.02 s / 1 core \\
CompletionFormer \cite{Zhang2023CompletionFormer} & 1.89 & 0.80 & 764.87 & 183.88 & 0.12 s / GPU \\
LRRU-Mini-L2 \cite{LRRUICCV2023} & 2.26 & 0.94 & 765.95 & 218.31 & 0.03 s / GPU \\
DSPN \cite{9191138} & 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 \\
GAENet(Full) \cite{Du2022DepthCU} & 2.29 & 1.08 & 773.90 & 231.29 & 0.05 s / GPU \\
LRRU-Mini-L2+L1 \cite{LRRUICCV2023} & 2.21 & 0.90 & 774.43 & 210.87 & 0.03 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 \\
Ms\_Unc\_UARes \cite{zhu2021robust} & 1.98 & 0.83 & 795.61 & 190.88 & 0.08 s / GPU \\
BA&GC \cite{Liu2022} & 2.44 & 1.05 & 799.31 & 232.98 & 0.05 s / GPU \\
CrossGuidance \cite{lee2020deep} & 2.73 & 1.33 & 807.42 & 253.98 & 0.2 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 \\
SSGP \cite{schuster2021ssgp} & 2.51 & 1.09 & 838.22 & 244.70 & 0.14 s / \\
TWISE \cite{Imran2021CVPR} & 2.08 & 0.82 & 840.20 & 195.58 & 0.02 s / GPU \\
ScaffFusion-SSL \cite{wong2021learning} & 3.24 & 0.88 & 847.22 & 205.75 & 0.03 s / 1 core \\
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 \\
DesNet \cite{yan2023desnet} & 2.95 & 1.13 & 938.45 & 266.24 & 0.01 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 \\
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 \\
LW-DepthNet \cite{bai2020depthnet} & 2.99 & 1.09 & 991.88 & 261.67 & 0.09 s / GPU \\
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 \\
SynthProjV \cite{lopez2020project} & 3.12 & 1.13 & 1062.48 & 268.37 & 0.1 s / 1 core \\
KBNet \cite{wong2021unsupervised} & 2.95 & 1.02 & 1069.47 & 256.76 & 0.01 s / 1 core \\
VLW-DepthNet \cite{bai2020depthnet} & 3.43 & 1.21 & 1077.22 & 282.02 & 0.09 / GPU \\
SynthProj \cite{lopez2020project} & 3.53 & 1.19 & 1095.26 & 280.42 & 0.1 s / 1 core \\
DCd\_3 \cite{imran2019depth} & 2.95 & 1.07 & 1109.04 & 234.01 & 0.1 s / 1 core \\
ScaffFusion \cite{wong2021learning} & 3.32 & 1.17 & 1121.89 & 282.86 & 0.03 s / 1 core \\
AdaFrame-VGG8 \cite{wong2021adaptive} & 3.32 & 1.16 & 1125.67 & 291.62 & 0.02 s / GPU \\
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 \\
NonLearning Complete \cite{9575867} & 3.63 & 1.23 & 1222.00 & 303.82 & 0.84 s / 1 core \\
Physical\_Surface\_Mod \cite{zhao2021surface} & 3.76 & 1.21 & 1239.84 & 298.30 & 0.06 s / 1 core \\
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}