# CasOmniMVS: Cascade Omnidirectional Depth Estimation with Dynamic Spherical Sweeping
## 4 Experiments
### Quantitative Evaluation  
To evaluate our method, we conducted a comparative analysis with several publicly available methods. The resource usage of the different models during the inference process and the datasets used for training are summarized in Table 2. The results clearly  
Figure 5: Cascade spherical sweeping in different stages. In the three stages, our method’s prediction becomes closer to the ground truth.  
demonstrate that our method exhibited the lowest GPU memory utilization and the fastest inference time. All models used training weights provided by the authors.  
For quantitative evaluation, we calculated the error by converting the predicted sphere indices into absolute depth values using Equation (7). We used metrics such as MAE (mean absolute error), RMSE (root mean square error), AbsRel (absolute relative error), and SqRel (squared relative error), as presented in Table 3. As shown in Table 4, the error was also measured using the percent error [8] of the estimated sphere index compared to all possible spheres, which is defined as  
\[Error_{index}=\frac{\left|s_{3}^{pred}-s_{3}^{gt}\right|}{N}\times 100, \tag{14}\]  
The quantitative results demonstrated that our method achieved the best performance compared to the other methods. The depth range for the evaluation metric was [1.65 m, 100 m]. As shown in Table 2, we also evaluated the GPU memory usage and inference speed, and the results showed that our method was more efficient.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & **GPU Memory** & **Inference** & \multicolumn{2}{c}{**Training Data**} & \\  & **Utilization (MiB)** & **Time (Seconds)** & **OmniThings**[8] & **OmniHouse**[8] & **Urban**[8] \\ \hline Crown360 [27] & 3772 & \(\sim\)0.231 & ✓ & & \\ OmniMVS-ft [8] & 7720 & \(\sim\)0.320 & ✓ & ✓ & ✓ \\
**CasOmniMVS (ours)** & **3022** & \(\sim\)**0.085** & ✓ & & \\
**CasOmniMVS-ft (ours)** & **3022** & \(\sim\)**0.085** & ✓ & ✓ & ✓ \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of model information. ‘-ft’ represents the fine-tuned model. The best results are in bold. ‘✓’ indicates that the dataset is used for training.  
\begin{table}
\begin{tabular}{c l l l l l} \hline \hline \multirow{2}{*}{**Datasets**} & \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**Metrics**} & \\ \cline{3-5}  & & **MAE (\(\downarrow\))** & **RMSE (\(\downarrow\))** & **AbsRel (\(\downarrow\))** & **SqRel (\(\downarrow\))** \\ \hline \multirow{5}{*}{OmniThings} & Crown360 [27] & 1.788 & 5.307 & 0.161 & 5.554 \\  & OmniMVS-ft [8] & 2.363 & 7.883 & 0.283 & 15.169 \\  & **CasOmniMVS** & **0.604** & **1.420** & **0.045** & **0.227** \\  & CasOmniMVS-ft [8] & 0.949 & 2.018 & 0.060 & 0.454 \\ \hline \multirow{5}{*}{OmniHouse} & Crown360 [27] & 1.694 & 4.141 & 0.102 & 1.535 \\  & OmniMVS-ft [8] & 0.631 & 2.292 & 0.044 & 0.582 \\  & CasOmniMVS & 0.807 & 1.889 & 0.049 & 0.222 \\  & **CasOmniMVS-ft** & **0.497** & **1.321** & **0.029** & **0.118** \\ \hline \multirow{5}{*}{Sunny} & Crown360 [27] & 4.244 & 10.707 & 0.180 & 5.091 \\  & OmniMVS-ft [8] & 1.772 & 6.979 & 0.083 & 2.807 \\ \cline{1-1}  & CasOmniMVS & 2.750 & 8.042 & 0.119 & 2.577 \\ \cline{1-1}  & **CasOmniMVS-ft** & **1.471** & **5.736** & **0.051** & **1.162** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quantitative results (depth) on the synthetic datasets. The metrics refer to depth errors. The best results are in bold. ‘\(\downarrow\)’ indicates that the smaller the value, the better.