## 5 Conclusions  
This paper proposes an uncertainty-epipolar Transformer multi-view stereo network (U-ETMVSNet) for object stereo reconstruction. Initially, an enhanced Cascaded U-Net is employed to bolster both feature extraction and query construction within the epipolar Transformer. The epipolar Transformer, along with the cross-scale information exchange module, enhances the correlation of cross-dimensional information during cost volume  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{3}{c}{**Settings**} & \multirow{2}{*}{**Overall (mm)**} & \multirow{2}{*}{**Runtime (s)**} & \multirow{2}{*}{**Param (G)**} \\ \cline{1-1}
**ET** & & & & \\ \hline \(\surd\) & & 0.331 & 0.21 & 1.12 \\  & \(\surd\) & 0.324 & 1.53 & 3.76 \\ \(\surd\) & \(\surd\) & 0.315 & 1.17 & 4.53 \\ \hline \hline \end{tabular}
\end{table}
Table 7: DTU dataset ablation study results’ comparison. Ablation study on the components used in the cost volume aggregation process on the DTU dataset.  
Figure 8: Qualitative comparison of depth map predictions by different methods. (**a**) Real image. (**b**) Real depth map. (**c**) CasMVSNet predicted depth map. (**d**) CasMVSNet + ET. (**e**) Depth map predicted by our algorithm.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Acc. (mm)** & **Comp. (mm)** & **Overall (mm)** & **Depth Error** \\ \hline \(L_{1}\) loss & 0.373 & 0.281 & 0.327 & 8.53 \\ CE loss & 0.351 & 0.279 & 0.315 & 6.79 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Ablation study of \(L_{1}\) loss and cross-entropy loss on the DTU dataset.  
aggregation, ensuring 3D consistency in depth space. The dynamic adjustment of depth sampling range based on the uncertainty of the probability volume also enhance stability in reconstructing regions with weak texture, and the reconstruction performance remains excellent even at lower depth sampling rates. Finally, the multi-stage joint learning method based on multi-depth value classification solution also effectively improves the reconstruction accuracy. The proposed method in this paper exhibits excellent performance in terms of completeness, accuracy, and generalization ability on the DTU and Tanks&Temples datasets, comparable to existing mainstream CNN-based MVS networks. However, the algorithm retains common 3D CNN regularization modules, resulting in no significant advantage in terms of memory usage. Future work aims to explore the role of Transformers in dense feature matching to replace CNN regularization, enhancing the practicality of deploying the model on mobile devices.  
Conceptualization, L.W. and N.Z.; Methodology, N.Z.; Software, Q.C.; Validation, N.Z.; Investigation, H.W.; Resources, L.W.; Data curation, N.Z.; Writing--original draft, N.Z.; Writing--review and editing, L.W. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant No. 61973103).  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to legal restrictions.  
The authors declare no conflicts of interest.