# CAPDepth: 360 Monocular Depth Estimation by Content-Aware Projection
## 4 Experiments
### Comparisons with Previous Methods  
In Table 1, we present the performance of our model on three benchmark datasets and compare it with existing methods. For a fair comparison, this research excludes multimodal and self-supervised methods. Additionally, OmniFusion did not provide a pretrained model on the Matterport3D dataset, and PanoFormer did not provide experimental details. Therefore, this research retrains their models using the official hyperparameters and settings provided in [17]. As shown in Table 1, our model outperforms existing methods on all three benchmark datasets. On the Stanford2D3D dataset, our model outperforms UniFuse by 18.22% in Abs Rel, 13.19% in RMSE, and 4.95% in \(\delta_{1}\uparrow\). It also improves upon OmniFusion (2-iter) by 4.10% in Abs Rel and 7.77% in RMSE. On the Matterport3D dataset, our model surpasses UniFuse by 19% in Abs Rel, 16.51% in RMSE, and 3.92% in \(\delta_{1}\uparrow\). Compared with HRDFuse, it achieves improvements of 11% in Abs Rel, 41.23% in Sq Rel, and 6.94% in RMSE. For the 3D60 dataset, which includes more samples and higher quality data, our model shows even greater advantages. It outperforms UniFuse by 25.32% in Abs Rel and 22.20% in RMSE. Compared with OmniFusion (2-iter), it shows improvements of 19.06% in Abs Rel, 26.31% in Sq Rel, and 15.32% in RMSE. In Figure 8, a radar chart is used to provide a more intuitive visual comparison of the data in Table 1.  
The qualitative comparison is shown in Figure 9. As shown there, our CAPDepth captures more depth details and is less affected by the artifacts and alignment issues caused by discontinuities between tangential images. CAPDepth also improves upon the baseline, producing depth maps with fewer errors and clearer, smoother surfaces. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dataset** & **Methods** & **Abs Rel\(\downarrow\)** & **Sq Rel\(\downarrow\)** & **RMSE\(\downarrow\)** & **RMSE(log)\(\downarrow\)** & \(\delta_{1}\uparrow\) & \(\delta_{2}\uparrow\) & \(\delta_{3}\uparrow\) \\ \hline \multirow{8}{*}{Matterport3D} & BiFuse with fusion [12] & 0.2048 & - & 0.6259 & - & 0.8452 & 0.9319 & 0.9632 \\  & UniFuse with fusion [13] & 0.1063 & - & 0.4941 & - & 0.8897 & 0.9623 & 0.9831 \\  & OmniFusion (2-iter) [16] & 0.0900 & 0.0552 & 0.4261 & 0.1483 & 0.9189 & 0.9797 & 0.9931 \\  & PanoFormer [22] & 0.0904 & 0.0764 & 0.4470 & 0.1650 & 0.8816 & 0.9661 & 0.9878 \\  & HRDFuse [17] & 0.0967 & 0.0936 & 0.4433 & 0.1642 & 0.9162 & 0.9669 & 0.9844 \\ \cline{2-10}  & CAPDepth, Ours & **0.0861** & **0.0550** & **0.4125** & **0.1463** & **0.9246** & **0.9819** & **0.9935** \\ \hline \multirow{8}{*}{Stanford2D3D} & BiFuse with fusion [12] & 0.1209 & - & 0.4142 & - & 0.8660 & 0.9580 & 0.9860 \\  & UniFuse with fusion [13] & 0.1114 & - & 0.3691 & - & 0.8711 & 0.9664 & 0.9882 \\ \cline{1-1}  & OmniFusion (2-iter) [16] & 0.0950 & 0.0491 & 0.3474 & 0.1599 & 0.8988 & 0.9769 & 0.9924 \\ \cline{1-1}  & PanFormer [22] & 0.1131 & 0.0723 & 0.3557 & 0.2454 & 0.8808 & 0.9623 & 0.9855 \\ \cline{1-1}  & HRDFuse [17] & 0.0935 & 0.0508 & 0.3106 & 0.1422 & 0.9140 & 0.9798 & 0.9927 \\ \cline{1-1} \cline{2-10}  & CAPDepth, Ours & **0.0911** & **0.0491** & 0.3204 & 0.1458 & **0.9143** & **0.9799** & **0.9928** \\ \hline \multirow{8}{*}{3D60} & BiFuse with fusion [12] & 0.0615 & - & 0.2440 & - & 0.9699 & 0.9927 & 0.9969 \\  & UniFuse with fusion [13] & 0.0466 & - & 0.1968 & - & 0.9835 & 0.9965 & 0.9987 \\ \cline{1-1}  & ODE-CNN [43] & 0.0467 & 0.0124 & 0.1728 & 0.0793 & 0.9814 & 0.9967 & 0.9989 \\ \cline{1-1}  & OmniFusion (2-iter) [16] & 0.0430 & 0.0114 & 0.1808 & 0.0735 & 0.9859 & 0.9969 & 0.9989 \\ \cline{1-1}  & HRDFuse [17] & 0.0358 & 0.0100 & 0.1555 & 0.0592 & 0.9894 & 0.9973 & 0.9990 \\ \cline{1-1} \cline{2-10}  & CAPDepth, Ours & **0.0348** & **0.0084** & **0.1531** & 0.0612 & **0.9898** & **0.9974** & **0.9991** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Quantitative comparison with state-of-the-art methods on three benchmark datasets. Bold numbers indicate that CAPDepth achieves the best performance.