# Monocular Depth Estimation Based on Dilated Convolutions and Feature Fusion
## 4 Experiments and Results
### Experimental Results and Analysis
#### 4.4.2 Results and Analyses
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Higher Is Better**} & \multicolumn{4}{c}{**Lower Is Better**} \\ \cline{2-9}  & \(\delta<\mathbf{1.25}\) & \(\delta<\mathbf{1.25^{2}}\) & \(\delta<\mathbf{1.25^{3}}\) & **RMSE** & **RMSE\({}_{\text{Log}}\)** & **Abs Rel** & **Sq Rel** \\ \hline Eigen [8] & 0.692 & 0.899 & 0.967 & 1.156 & 0.270 & 0.190 & 1.515 \\ Make3D [24] & 0.601 & 0.820 & 0.926 & 1.734 & 0.261 & 0.280 & 3.012 \\ DORN [11] & 0.850 & 0.966 & 0.981 & 0.394 & 0.160 & 0.137 & 0.080 \\ BTS [16] & 0.866 & 0.972 & **0.992** & 0.398 & 0.153 & 0.121 & 0.075 \\ DDP [15] & **0.920** & 0.980 & 0.989 & 0.369 & 0.140 & 0.129 & 0.077 \\ Ours & 0.912 & **0.983** & 0.990 & **0.355** & **0.132** & **0.120** & **0.066** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance on NYU Depth-v2 Dataset. \(\delta\): threshold. Bold numbers: best performance under the respective conditions. Numbers with an underscore: the second-best performance under the respective condition. The DDP algorithm, as a recent model, is included in the table. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Higher Is Better**} & \multicolumn{4}{c}{**Lower Is Better**} \\ \cline{2-9}  & \(\delta<\mathbf{1.25}\) & \(\delta<\mathbf{1.25^{2}}\) & \(\delta<\mathbf{1.25^{3}}\) & **RMSE** & **RMSE\({}_{\text{Log}}\)** & **Abs Rel** & **Sq Rel** \\ \hline Eigen [8] & 0.800 & 0.889 & 0.922 & 2.755 & 0.144 & 0.137 & 0.355 \\ Make3D [24] & 0.851 & 0.903 & 0.944 & 2.502 & 0.131 & 0.107 & 0.309 \\ BTS [16] & **0.945** & **0.988** & 0.990 & 2.355 & 0.120 & 0.091 & 0.288 \\ Ours & 0.930 & 0.985 & **0.998** & **1.951** & **0.095** & **0.072** & **0.156** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance on KITTI Dataset. \(\delta\): threshold. Bold numbers: best performance under the respective conditions. Numbers with an underscore: the second-best performance under the respective condition. achieving a 20% improvement in both metrics. Figure 14 illustrates the depth estimation results of different approaches on the KITTI Dataset. In Scene 15 and Scene 16, our algorithm effectively discerns the disparity in depth between the sky and the adjacent houses and trees, whereas other algorithms fail to retain the boundary details of the sky area. In Scene 17, the billboards flanking the road exhibit relatively small dimensions and are characterized by a distinct depth in contrast to the background sky. In contrast to the subpar performance exhibited by other algorithms, our algorithm adeptly captures the contour information of small targets. In Scene 18, there are several billboards and traffic lights. The accuracy of the BTS algorithm in estimating the overall boundary depth is compromised by the influence of background trees when processing the traffic light. Our algorithm effectively estimates the depth information of the traffic light by accurately extracting precise boundary depths. In Scene 20, our algorithm effectively estimates the depth at the boundary between the sky and the buildings, whereas the Make3D and BTS algorithms are unsuccessful in predicting the depth value at this boundary. Figure 13: Depth estimation results of different approaches on the NYU dataset.The ground truth is interpolated for visualization. The DDP algorithm, as a recent model, is included in the comparison results.