## 4 Experiment
### Prior Knowledge of Planar Geometric Structures Optimization Ablation Experiments  
To investigate the impact of planar prior geometric structures on the generated depth results, we designed an ablation experiment for blur information transfer. Based on the three quantitative evaluation metrics outlined in Section 3.2.2, we conducted indepth quantitative experiments on three types of datasets, before and after blur information transfer. The specific results of the experiments are illustrated in Tables 5 and 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Depth Pixel Density** & **BadPixel(0.5)** & **RMSE** \\ \hline HR260-Indoor & 34.24\% & 5.4\% & 17.87 \\ R32-Indoor & 53.76\% & 5.5\% & 14.20 \\ R12-Indoor & 48.12\% & 5.0\% & 14.65 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Quantitative experiments after depth information transfer.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Depth Pixel Density**} & \multicolumn{4}{c}{**BadPixel(0.5)**} & \multicolumn{4}{c}{**RMSE**} \\ \hline  & **Our** & **Zeller** & **BLADE** & **Our** & **Zeller** & **BLADE** & **Our** & **Zeller** & **BLADE** \\ \hline  & **63.63\%** & 34.98\% & 6.88\% & **1.88\%** & 6.33\% & 75.12\% & **16.32** & 22.06 & 59.45 \\ HR260-Outdoor & **57.62\%** & 41.58\% & 6.37\% & **4.41\%** & 10.69\% & 61.30\% & **17.28** & 27.94 & 46.23 \\  & **63.70\%** & 45.06\% & 12.84\% & **5.23\%** & 8.07\% & 49.87\% & **20.01** & 29.76 & 37.62 \\ \hline  & **57.17\%** & 50.02\% & 12.88\% & **2.55\%** & 4.61\% & 19.91\% & **9.20** & 15.62 & 35.67 \\ R32-Indoor & **52.58\%** & 40.08\% & 16.84\% & **2.40\%** & 5.68\% & 72.34\% & **11.34** & 21.5 & 76.81 \\  & **40.15\%** & 34.67\% & 12.12\% & **6.21\%** & 9.19\% & 79.12\% & **22.06** & 31.91 & 77.52 \\ \hline  & 43.01\% & 41.83\% & **56.98\%** & **3.63\%** & 11.18\% & 6.01\% & **20.92** & 32.73 & 27.06 \\ R12-Indoor & 46.21\% & 37.05\% & **50.35\%** & **3.40\%** & 7.99\% & 5.72\% & **12.38** & 15.93 & 16.37 \\  & 54.96\% & 47.83\% & **66.47\%** & **3.74\%** & 5.67\% & 5.79\% & **10.66** & 16.81 & 17.76 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Accuracy indicator results of various algorithms in different scenes.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Depth Pixel Density** & **BadPixel(0.5)** & **RMSE** \\ \hline HR260-Indoor & 20.91\% & 5.5\% & 47.28 \\ R32-Indoor & 30.23\% & 6.6\% & 27.88 \\ R12-Indoor & 32.20\% & 5.4\% & 31.92 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative experiment without depth information transfer.  
After applying Prior Knowledge of Planar Geometric Structures Optimization, it can be observed that all three types of metrics have improved. Specifically, Depth Pixel Density and BadPixel(0.5) have shown significant enhancements, with minimum increases of 12.7% and 9.2%, respectively. The former improvement is primarily due to the fact that weakly textured regions often exhibit strong planarity, and thus, the depth estimation can be enhanced through planar prior optimization. Since the cost computation takes into account both photometric consistency and planar priors, the influence of planar priors in non-planar regions is reduced. The latter improvement, in terms of BadPixel(0.5), is mainly achieved by utilizing geometric consistency for outlier rejection, which decreases the probability of erroneous matches and results in a larger BadPixel(0.5) value that reflects fewer bad pixels.