# CasOmniMVS: Cascade Omnidirectional Depth Estimation with Dynamic Spherical Sweeping
## 5 Conclusions, Limitations, and Future Work  
In this work, we proposed an end-to-end cascade omnidirectional depth estimation algorithm with dynamic spherical sweeping. It begins with a low-resolution spherical cost volume, then progressively refines the resolution, while adaptively adjusting sweeping intervals and ranges. The experimental results demonstrated that our method significantly reduced the GPU memory usage and sped up the inference time, outperforming various existing publicly available omnidirectional depth estimation algorithms. Furthermore, without fine-tuning, our method also produced promising results in real-world scenarios, showing a high generalizability and practicality.  
Our method relies on the feature matching in the overlapping regions between camera views, leading to a less accurate performance in non-overlapping regions. When constructing the spherical cost volume, we simply calculated the variance between multi-view features, without explicitly differentiating overlapping and non-overlapping regions. In  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Datasets** & **Stage t** & **MAE (\(\downarrow\))** & **RMSE (\(\downarrow\))** & **\textgreater{1 (\(\downarrow\))** & **\textgreater{3 (\(\downarrow\))** & **\textgreater{5 (\(\downarrow\))** \\ \hline \multirow{3}{*}{OmniThings}} & 1 & 2.5987 & 5.4940 & 47.6431 & 19.3850 & 12.1928 \\  & 2 & 1.8062 & 4.2154 & 35.5856 & 13.0703 & 7.8032 \\  & 3 & 1.5157 & 3.6077 & 31.2624 & 11.3088 & 6.3731 \\ \hline \multirow{3}{*}{OmniHouse} & 1 & 0.7597 & 1.7285 & 15.4238 & 3.3810 & 1.8910 \\  & 2 & 0.5407 & 1.3086 & 8.7325 & 2.2392 & 1.2954 \\  & 3 & 0.4508 & 1.0750 & 7.1639 & 1.8408 & 1.0288 \\ \hline \multirow{3}{*}{Sunny} & 1 & 1.1544 & 2.7189 & 32.8847 & 4.9705 & 3.2113 \\  & 2 & 0.6490 & 1.9355 & 9.4358 & 2.9383 & 1.8798 \\ \cline{1-1}  & 3 & 0.4459 & 1.5696 & 6.8069 & 2.1911 & 1.2773 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Quantitative results(index) of different stages. The error was measured using Equation (14). '\(\downarrow\)’ indicates that the smaller the value, the better.  
Figure 8: Qualitative results (index) of multiple cascade stages. For each image group, from left to right, the first row displays the input image, ground truth, and colors corresponding to the different sphere indices. The second row shows the visualization of sphere indices (maximum sphere indices were set to 95 and 125, respectively), and the third row presents the error maps (maximum sphere index error was set to 5) for the three stages.  
future work, we will focus on how to effectively leverage features from overlapping versus non-overlapping areas, to optimize cost volume generation. Furthermore, optimizing the division of hypothetical spheres based on the different depth distribution of various scenes (i.e. indoor and outdoor) is also an exciting research direction. We will also explore the application of self-supervised learning, to improve our method.  
Conceptualization, P.W. and M.L.; Data curation, J.C. and Y.L.; Investigation, J.C.; Methodology, P.W. and M.L.; Project administration, Y.L. and S.D.; Software, P.W.; Supervision, Y.L. and S.D.; Validation, M.L.; Writing--original draft, P.W.; Writing--review and editing, Y.L. and S.D. All authors have read and agreed to the published version of the manuscript.  
Supported by the open research fund of Jiangsu Key Laboratory for Design and Manufacture of Micro-Nano Biomedical Instruments, Southeast University (No. KF201802).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.