# CAPDepth: 360 Monocular Depth Estimation by Content-Aware Projection
## 5 Conclusions  
In this study, we propose a new monocular 360\({}^{\circ}\) depth estimation method. To address the spherical distortion present in 360\({}^{\circ}\) images, CAPDepth employs a content-aware projection module that optimizes the projection and segmentation of ERP images. To mitigate the geometric differences in the overlapping regions between patches, improve prediction consistency, and better estimate global depth, CAPDepth uses an improved geometric-aware fusion mechanism. This mechanism integrates three-dimensional geometric features \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**FoV** & **Number/Patch Size** & **Abs Rel\(\downarrow\)** & **Sq Rel\(\downarrow\)** & **RMSE\(\downarrow\)** \\ \hline
80 & 18/256 \(\times\) 256 & 0.0382 & 0.0092 & 0.1599 \\
90 & 18/256 \(\times\) 256 & 0.0361 & 0.0087 & 0.1565 \\
**100** & **18/256 \(\times\) 256** & **0.0348** & **0.0084** & **0.1531** \\
110 & 18/256 \(\times\) 256 & 0.0355 & 0.0086 & 0.1546 \\
120 & 18/256 \(\times\) 256 & 0.0370 & 0.0088 & 0.1564 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Ablation experiments at different FoVs with a fixed patch size and number. Bold numbers indicate that CAPDepth achieves the best performance. \(\downarrow\) means better performance with smaller values. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Methods** & **FPS** & **Params** & **Abs Rel\(\downarrow\)** & **Sq Rel\(\downarrow\)** & **RMSE\(\downarrow\)** \\ \hline Baseline & 42.3 M & 4.15 & 0.0430 & 0.0114 & 0.1808 \\ Baseline + GAF & 42.3 M & 9.26 & 0.0414 & 0.0103 & 0.1669 \\ Baseline + CAP & 44.8 M (+2.5 M) & 3.59 & 0.0375 & 0.0090 & 0.1580 \\ Baseline + CAP + GAF & 44.8 M (+2.5 M) & **5.01** & **0.0348** & **0.0084** & **0.1531** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ablation experiment of a single component, which takes OmniFusion as the baseline, adding one component at a time, to compare the inference speed and performance. CAP is the adaptive content-aware projection module, and the geometry-aware fusion (GAF) is the geometry-aware fusion module. \(\downarrow\) means better performance with smaller values. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number** & **FoV/Patch Size** & **Abs Rel\(\downarrow\)** & **Sq Rel\(\downarrow\)** & **RMSE\(\downarrow\)** \\ \hline
10 & 100\({}^{\circ}\)/128 \(\times\) 128 & 0.0392 & 0.0093 & 0.1584 \\
18 & 100\({}^{\circ}\)/128 \(\times\) 128 & 0.0363 & 0.0086 & 0.1539 \\
26 & 100\({}^{\circ}\)/128 \(\times\) 128 & 0.0385 & 0.0090 & 0.1570 \\ \hline
10 & 100\({}^{\circ}\)/256 \(\times\) 256 & 0.0389 & 0.0092 & 0.1677 \\
**18** & **100\({}^{\circ}\)/256 \(\times\) 256** & **0.0348** & **0.0084** & **0.1531** \\
26 & 100\({}^{\circ}\)/256 \(\times\) 256 & 0.0369 & 0.0089 & 0.1568 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ablation experiments with a fixed FoV and different patch sizes and numbers. Bold numbers indicate that CAPDepth achieves the best performance. \(\downarrow\) means better performance with smaller values. with image features and uses a Transformer for global aggregation. We demonstrate that CAPDepth effectively reduces distortion and significantly improves depth estimation performance, indicating its potential for future application. In the future, we will focus on geometric information-fusion completion to further address the boundary-discontinuity issues caused by geometric fusion. Additionally, our task is closely related to 360\({}^{\circ}\) semantic segmentation as both are dense scene-understanding tasks. Therefore, 360\({}^{\circ}\) monocular semantic segmentation, which combines distortion reduction and geometric fusion, represents another promising research direction. Conceptualization, X.G. and Y.S. ; investigation, Y.S., Y.Z., J.W., and G.W. ; data curation, Y.S., Y.Z., and Y.W. ; writing--original draft preparation, X.G. and Y.S. ; writing--review and editing, Y.S., Y.Z., Y.W., J.W., and G.W. ; visualization, Y.S. ; supervision, X.G. ; funding acquisition, X.G.