# Attention Mechanism Used in Monocular Depth Estimation: An Overview
## 4 Performance Comparison
### Comparison and Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Models** & **Methods** & **AbsRel\(\downarrow\)** & **SqRel\(\downarrow\)** & **RMSE\(\downarrow\)** & **RMSE\(\downarrow\)** & \(\boldsymbol{\delta_{1}\uparrow}\) & \(\boldsymbol{\delta_{2}\uparrow}\) & \(\boldsymbol{\delta_{3}\uparrow}\) \\ \hline \multirow{10}{*}{CNN-based} & ACAN [32], 2021 & 0.075 & - & 3.509 & 0.118 & 0.930 & 0.985 & 0.996 \\  & BANet [33], 2021 & 0.083 & 0.181 & 3.300 & - & 0.938 & 0.988 & 0.997 \\  & [PERSON] et al. [34], 2022 & 0.061 & 0.297 & 2.548 & - & 0.947 & 0.989 & 0.996 \\  & [PERSON] et al. [35], 2022 & 0.065 & 0.261 & 2.925 & 0.107 & 0.947 & 0.992 & 0.998 \\  & [PERSON] et al. [38], 2022 & 0.070 & - & 3.223 & 0.113 & 0.944 & 0.991 & 0.998 \\  & PFN [39; 202] & 0.069 & 0.302 & 2.652 & 0.112 & 0.953 & 0.989 & 0.995 \\  & [PERSON] et al. [40], 2022 & 0.038 & 0.209 & 4.771 & - & 0.952 & 0.993 & 0.998 \\ \hline \multirow{10}{*}{Transformer-based} & DPT [10], 2021 & 0.062 & - & 2.573 & 0.092 & 0.959 & 0.995 & 0.999 \\  & Swin-Depth [63], 2021 & 0.064 & 0.232 & 2.643 & 0.097 & 0.957 & 0.994 & 0.999 \\  & PixelFormer [11], 2022 & **0.051** & **0.149** & **2.081** & **0.077** & **0.976** & **0.997** & **0.999** \\  & BinsFormer [16], 2022 & 0.052 & 0.151 & 2.098 & 0.079 & 0.974 & 0.997 & 0.999 \\  & DepthFormer [17], 2022 & 0.058 & - & 2.285 & - & 0.967 & 0.996 & 0.999 \\  & RGFN [61], 2022 & 0.057 & 0.182 & 2.250 & 0.086 & 0.968 & 0.996 & 0.999 \\  & SideRT [66], 2022 & 0.054 & 0.173 & 2.249 & 0.082 & 0.972 & 0.997 & 0.999 \\ \hline \multirow{10}{*}{Hybrid} & AdaBins [73], 2020 & 0.058 & 0.190 & 2.360 & 0.088 & 0.964 & 0.995 & 0.999 \\  & DPT-hybrid [10], 2021 & 0.062- & - & 2.573 & 0.092 & 0.959 & 0.995 & 0.999 \\ \cline{1-1}  & DepthFormer [76], 2022 & 0.052 & 0.158 & 2.143 & 0.079 & 0.975 & 0.997 & 0.999 \\ \cline{1-1}  & Focal-WNet [77], 2022 & 0.082 & 0.355 & 3.076 & 0.120 & 0.926 & 0.986 & 0.997 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of supervised MDE algorithms on dataset KITTI. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Models** & **Methods** & **AbsRel\(\downarrow\)** & **SqRel\(\downarrow\)** & **RMSE\(\downarrow\)** & **RMSE\(\downarrow\)** & \(\boldsymbol{\delta_{1}\uparrow}\) & \(\boldsymbol{\delta_{2}\uparrow}\) & \(\boldsymbol{\delta_{3}\uparrow}\) \\ \hline \multirow{10}{*}{CNN-based} & [PERSON] et al. [41], 2020 & 0.135 & 0.879 & 4.051 & 0.195 & 0.822 & 0.943 & 0.981 \\  & [PERSON] et al. [42], 2020 & 0.173 & 1.153 & 4.979 & 0.249 & 0.752 & 0.916 & 0.968 \\  & [PERSON] et al. [43], 2021 & 0.127 & 0.913 & 4.970 & 0.202 & 0.846 & 0.953 & 0.980 \\  & AgFU-Net [45], 2021 & 0.119 & 0.922 & 5.033 & 0.211 & 0.851 & 0.947 & 0.977 \\  & [PERSON] et al. [46], 2020 & 0.106 & 0.861 & 4.699 & 0.185 & 0.889 & 0.962 & 0.982 \\  & TC-Depth [31], 2021 & 0.082 & 0.667 & 4.104 & - & 0.921 & - & 0.997 \\  & CADepth-Net [47], 2021 & 0.096 & 0.694 & 4.264 & 0.173 & 0.908 & 0.968 & 0.984 \\  & [PERSON] [48], 2021 & 0.120 & 0.889 & 4.329 & 0.192 & 0.865 & 0.943 & 0.989 \\  & MLDA-Net [50], 2021 & 0.097 & 0.658 & 4.278 & 0.178 & 0.889 & 0.965 & 0.984 \\  & [PERSON] et al. [51], 2021 & 0.125 & 1.553 & 5.844 & 0.202 & 0.865 & 0.946 & 0.977 \\  & SHdepth [52], 2021 & 0.108 & 0.812 & 4.634 & 0.185 & 0.887 & 0.962 & 0.982 \\  & [PERSON] et al. [53], 2022 & 0.098 & 0.810 & 4.672 & 0.177 & 0.890 & 0.964 & 0.983 \\  & [PERSON] et al. [54], 2022 & 0.096 & 0.742 & 4.450 & 0.176 & 0.901 & 0.965 & 0.983 \\  & EDNet [55], 2022 & 0.095 & - & 4.311 & - & 0.891 & 0.965 & 0.986 \\  & [PERSON] et al. [56], 2022 & 0.138 & 1.319 & 5.499 & 0.231 & 0.802 & 0.924 & 0.966 \\  & [PERSON] et al.