## 4 Experimental Analysis
### Results and Discussion
#### 4.4.1 Quantitative Results  
**RQ1:** _How well does the proposed framework, WGA-SWIN, outperform the SOTA approach on ShapeNet in the task of multi-view 3D reconstruction, and how does its performance rely on different input views?_  
Compared to the state-of-the-art (SOTA) methods [6; 10; 11; 13; 14] on the ShapeNet test dataset, our proposed WGA-SWIN framework exhibits superior performance and presents the results in the form of the IoU and F-Score@1%. The test results, shown in Tables 1 and 2, show that our WGA-SWIN and WGA-SWIN+ significantly outperform the existing SOTA methods on different numbers of input views, which range from 1 to 20 views and are consistent with standard benchmarks in multi-view reconstruction tasks, where they represent the best compromise for reconstruction accuracy. Both WGA-SWIN and WGA-SWIN+ consistently outperform existing methods including LRGT [11], UMIFormer [13], and GARNet [10] for high-quality 3D voxel grid reconstruction. On the other hand, for single-view reconstruction, our WGA-SWIN achieves 0.7032 IoU, which is better than the second-best LRGT's 0.6962 IoU. For multi-view reconstruction, when trained with 20 input views, our WGA-SWIN+ achieved a SOTA performance of 0.8017 IoU, which is 0.95% better than LRGT+. Furthermore, the F-Score@1% performance improvement is even more noticeable, further highlighting the ability of WGA-SWIN and WGA-SWIN+ to reconstruct objects with superior accuracy and detail. Additionally, for the single-view reconstruction, our WGA-SWIN achieves 0.4536 F-Score@1%, which outperforms the second-best LRGT, with 0.4461 F-Score@1%. However, when trained with 20 input views, WGA-SWIN+ achieves a SOTA performance of 0.5608 F-Score@1%, which outperforms LRGT+ by 1.07%. These results demonstrate how the proposed methods enhance the model's ability to capture the multi-view dependencies with higher accuracy to reconstruct 3D voxel grids. In addition, the performance improvement after four views is due to the framework design. The WGA process groups tokens from each window to ensure efficient inter-view relationships between views. This architectural adjustment allows the proposed framework to effectively integrate information from other views, resulting in continuous performance improvements. For example, both WGA-SWIN and WGA-SWIN+ show \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Methods** & **1 View** & **2 Views** & **3 Views** & **4 Views** & **5 Views** & **8 Views** & **12 Views** & **16 Views** & **20 Views** \\ \hline
3D-R2N2 [5] & 0.560 & 0.603 & 0.617 & 0.625 & 0.634 & 0.635 & 0.636 & 0.636 & 0.636 \\ AttSets [6] & 0.642 & 0.662 & 0.670 & 0.675 & 0.677 & 0.685 & 0.688 & 0.692 & 0.693 \\ Pix2 Vox++ [9] & 0.670 & 0.695 & 0.704 & 0.708 & 0.711 & 0.715 & 0.717 & 0.718 & 0.719 \\ GARNNet [10] & 0.673 & 0.705 & 0.716 & 0.722 & 0.726 & 0.731 & 0.734 & 0.736 & 0.737 \\ GARNet++ & 0.655 & 0.696 & 0.712 & 0.719 & 0.725 & 0.733 & 0.737 & 0.740 & 0.742 \\ EVoIT [12] & - & - & - & 0.609 & - & 0.698 & 0.720 & 0.729 & 0.735 \\ LegoFormer [15] & 0.519 & 0.644 & 0.679 & 0.694 & 0.703 & 0.713 & 0.717 & 0.719 & 0.721 \\
3D-RETR [14] & 0.674 & 0.707 & 0.716 & 0.720 & 0.723 & 0.727 & 0.729 & 0.730 & 0.731 \\ UMformer [13] & 0.6802 & 0.7384 & 0.7518 & 0.7573 & 0.7612 & 0.7661 & 0.7682 & 0.7696 & 0.7702 \\ UMformer++ & 0.5672 & 0.7115 & 0.7447 & 0.7588 & 0.7681 & 0.7790 & 0.7843 & 0.7873 & 0.7886 \\ LRGT [11] & 0.6962 & 0.7462 & 0.7590 & 0.7653 & 0.7692 & 0.7744 & 0.7766 & 0.7781 & 0.7786 \\ LRGT+ & 0.5847 & 0.7145 & 0.7476 & 0.7625 & 0.7719 & 0.7833 & 0.7888 & 0.7912 & 0.7922 \\ \hline
**WGA-SWIN** & **0.7032** & **0.7487** & **0.7636** & **0.7732** & 0.7748 & 0.7786 & 0.7808 & 0.7819 & 0.7827 \\
**WGA-SWIN+** & 0.5976 & 0.7217 & 0.7563 & 0.7712 & **0.7814** & **0.7882** & **0.7955** & **0.7996** & **0.8017** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Evaluations of performance comparison on ShapeNet using IoU for the multi-view 3D reconstruction approach. The best results are highlighted in bold.