## 4 Results
### Other Datasets  
To demonstrate the generalization ability of our network, we use Cityscapes dataset [37] and Make3D dataset [38] for testing. Although these datasets are different in camera parameters, image resolution, etc., our network still achieves reliable results.  
\begin{table}
\begin{tabular}{l l l l l l l l} \hline \hline
**Methods** & **Strategy** & **abs\_rel \(\downarrow\)** & **sq\_rel \(\downarrow\)** & **rmse \(\downarrow\)** & **1.25 \(\uparrow\)** & **1.25\({}^{2}\)** & \(\uparrow\)** & **1.25\({}^{3}\)** \(\uparrow\)** \\ \hline [PERSON] [6] & supervised & 0.190 & 1.515 & 7.156 & 0.692 & 0.899 & 0.967 \\ [PERSON] [28] & supervised & 0.180 & & 6.311 & 0.771 & 0.917 & 0.966 \\ [PERSON] [29] & supervised & 0.122 & 0.897 & 4.677 & 0.818 & 0.954 & 0.985 \\ [PERSON] [30] & supervised & 0.101 & & 4.137 & 0.890 & 0.970 & 0.989 \\ [PERSON] [31] & supervised & 0.096 & 0.552 & 3.995 & 0.892 & 0.972 & 0.992 \\ [PERSON] [12] & unsupervised & 0.169 & & 5.104 & 0.740 & 0.904 & 0.958 \\ [PERSON] [13] & unsupervised & 0.114 & & 4.935 & 0.861 & 0.949 & 0.976 \\ [PERSON] [11] & unsupervised & 0.151 & 1.257 & 5.583 & 0.810 & 0.936 & 0.974 \\ [PERSON] [32] & unsupervised & 0.140 & 1.070 & 5.326 & 0.826 & 0.941 & 0.975 \\ [PERSON] [18] & unsupervised & 0.126 & 0.961 & 5.205 & 0.835 & 0.941 & 0.974 \\ [PERSON] [14] & unsupervised & 0.133 & 1.126 & 5.515 & 0.826 & 0.934 & 0.969 \\ [PERSON] [33] & unsupervised & 0.118 & 0.905 & 5.096 & 0.839 & 0.945 & 0.977 \\ [PERSON] [34] & unsupervised & 0.121 & 0.837 & 4.945 & 0.853 & 0.955 & 0.982 \\ [PERSON] [35] & unsupervised & 0.108 & 0.825 & 4.750 & 0.873 & 0.957 & 0.982 \\ [PERSON] [36] & unsupervised & 0.099 & **0.796** & 4.743 & 0.884 & 0.955 & 0.979 \\ [PERSON] [16] & unsupervised & 0.111 & 0.867 & 4.714 & 0.864 & 0.954 & 0.979 \\ PDANet & unsupervised & **0.084** & 0.961 & **4.701** & **0.916** & **0.970** & **0.988** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between PDANet and other models. Best results in supervised methods are in bold, and in unsupervised methods are underlined. For metrics with \(\downarrow\) the lower the better, for metrics with \(\uparrow\) the higher the better. All models are tested on KITTI 2015 [6]. Our PDANet outperforms the other models in all metrics; specifically, the abs_rel error is significantly lower than the other models.  
Figure 5: Comparison between PDANet and Monodepth on KITTI 2015 [6]. PDANet achieves much better performance than Monodepth in terms of edges, land marks, and low-texture areas.  
\begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{Abs Rel : \(\frac{1}{|D|}\sum_{d\in D}\left|d^{*}-d\right|/d^{*}\)} & \multicolumn{1}{c}{RMSE : \(\sqrt{\frac{1}{|D|}\sum_{d\in D}\left\|d^{*}-d\right\|^{2}}\)} \\ \multicolumn{1}{c}{Sq Rel : \(\frac{1}{|D|}\sum_{d\in D}\left\|d^{*}-d\right\|^{2}/d^{*}\)} & \multicolumn{1}{c}{RMSE : \(\sqrt{\frac{1}{|D|}\sum_{d\in D}\left\|\log d^{*}-\log d\right\|^{2}}\)} \\ \multicolumn{1}{c}{\(\delta_{\text{t}}:\frac{1}{|D|}\left|\left\{d\in D\mid\max\left(\frac{d^{*}}{d },\frac{d}{d^{*}}\right)<1.25^{\text{t}}\right\}\right|\times 100\%\)} \\ \hline \hline \end{tabular}
\end{table}
Table 3: Evaluation indicators for depth estimation models, where \(d^{*}\) presents the ground-truth depth, \(d\) denotes the predicted depth, and \(|D|\) is the number of predicted depth.