## 5 Conclusions  
In this article, we design WGA-SWIN, a novel approach for multi-view 3D reconstruction that uses Window Grouping Attention (WGA) to efficiently capture intra- and inter-view dependencies using a swin transformer integrated into our encoder. In conjunction with a progressive hierarchical decoder that integrates swin transformer blocks with 3D convolutional layers, our method provides efficient voxel-level and high-resolution 3D reconstruction. According to the results of the experiments conducted on the benchmark ShapeNet dataset, the proposed method outperforms existing SOTA approaches in 3D reconstruction. We lead by 0.95% and 1.07% in both IoU and F-Scores, respectively, which demonstrates the effectiveness of our method. Our approach enhances multi-view feature aggregation and spatial consistency, making it ideal for high-resolution 3D object reconstruction tasks. In addition to providing an efficient and scalable solution with a variety of  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **3 Views** & **8 Views** & **12 Views** & **16 Views** & **20 Views** \\ \hline UMIFormer [13] & 0.0328 (s) & 0.0494 (s) & 0.0652 (s) & 0.0818 (s) & 0.0995 (s) \\ LRGT [11] & 0.0319 (s) & 0.0480 (s) & 0.0639 (s) & 0.0796 (s) & 0.0983 (s) \\
**WGA-SWIN (Ours)** & **0.0302 (s)** & **0.0469 (s)** & **0.0627 (s)** & **0.0784 (s)** & **0.0973 (s)** \\ \hline \hline \end{tabular}  
* The best results are highlighted in bold.  
\end{table}
Table 7: The average inference time (s) comparison between our WGA-SWIN and other previous methods, such as UMIFormer and LRGT, on the ShapeNet test set. To handle the variables, the same decoder as WGA-SWIN is used in all experiments.  
potential applications, multi-view 3D reconstruction is developed by WGA-SWIN. Even though our method simplifies computation using static grouping in WGA, it limits the flexibility of dynamically modeling relationships between views. Additionally, the method may struggle with extreme occlusions or highly detailed objects with complex geometries, where local window-based attention has limitations. Despite these challenges, WGA-SWIN provides a scalable and efficient solution for multi-view 3D reconstruction. We will investigate adaptive grouping techniques in the future to improve the performance of 3D object reconstruction on a variety of datasets and real-time applications.  
Conceptualization, S.S.M. and S.R.; methodology, S.S.M. and S.R.; software, S.S.M.; validation, S.S.M., M.Y.K.R. and S.R.; formal analysis, S.S.M. and S.R.; investigation, S.R.; resources, S.S.M. and S.R.; data curation, S.S.M. and G.F.A.; writing--original draft preparation, S.S.M.; writing--review and editing, S.R., M.Y.K.R. and G.F.A.; visualization, S.S.M., M.Y.K.R. and G.F.A.; supervision, S.R.; project administration, S.S.M. and S.R.; funding acquisition, S.R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data will be made available upon request.  
The authors would like to thank all individuals and institutions that contributed to the success of this work.  
The authors declare no conflicts of interest.