## 4 Experiments and Analysis
### Main Results  
We first evaluated the experiments of the MSSAViT on the ModelNet10 dataset; by taking advantage of the high complexity of the dataset, we can further discuss the performance of our MSSAViT compared with other state-of-the-art methods. In Table 2, because of the varying view capturing schemes, our method with the input of top-view sequential images is denoted as MSSAViT-TV, while MSSAViT-SV takes in surround-view sequential images as the input of the network. Table 2 compares the performances of the proposed MSSAViT models with those of state-of-the-art methods across point-based, voxel-based, and view-based representations for 3D object classification. Our models, MSSAViT-TV and MSSAViT-SV, achieve 97.41% and 98.40% average class accuracy, respectively, with the sequential view configuration (MSSAViT-SV) reaching a 98.46% instance accuracy, rivaling or surpassing all other approaches. Point-based methods like Point2 Sequence (95.10% class accuracy) and SO-Net (93.90%) require dense point clouds (1024 \(\times\) 3 or 2048 \(\times\) 3), resulting in significant computational costs. Voxel-based methods such as VoxNet (92.00%) and LightNet (93.39%) suffer from spatial resolution loss during voxelization, limiting their performance. Hybrid approaches, such as FusionNet (93.11%), also fall short of view-based methods. View-based models consistently outperform other representations. MVMSAN achieves a 98.42% class accuracy with 20 views but at a high computational cost, while VCGR-Net offers a better balance with a 97.25% accuracy using 12 views. Our MSSAViT-SV model matches the performance of MVMSAN but requires only 12 views, demonstrating a superior trade-off between accuracy and efficiency. The confusion matrices of both our methods are shown in Figure 6.  
The success of MSSAViT-SV is attributed to its Structural Self-Attention (SSSA) and Temporal Self-Attention (TSSA) mechanisms, which effectively capture spatial and sequential dependencies across views. Additionally, its lightweight architecture ensures high accuracy with significantly lower computational demands compared to multi-view methods like MVMSAN. To further validate the performance of MSSAViT, we conducted more comparative experiments on the larger ModelNet40 dataset. Table 3 highlights the performance comparison between our proposed models, MSSAViT-TV and MSSAViT-SV, and existing state-of-the-art methods across mesh-, point-, voxel-, and view-based representations for 3D object classification. Our models achieve \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & **Representation** & \multirow{2}{*}{**Points/View Number**} & **Average Class** & **Average Instance** \\  & & & **Accuracy (\%)** & **Accuracy (\%)** \\ \hline Point2 Sequence [16] & Points & 1024 \(\times\) 3 & 95.10 & 95.30 \\ FoldingNet [35] & Points & - & - & 94.40 \\ VoxNet [36] & Voxel & - & 92.00 & - \\ LightNet [37] & Voxel & - & 93.39 & - \\ Geometry image [38] & View & 1 & 88.40 & - \\ MVMSAN [39] & View & 20 & 98.42 & 98.57 \\ VCGR-Net [40] & View & 12 & 97.24 & 97.79 \\ SV2 SL [22] & SeqView & 12 & 94.56 & 94.71 \\
3D2 SV [23] & SeqView & 12 & 94.68 & 94.71 \\ FusionNet [41] & Voxel + View & -/20 & - & 93.11 \\ NormalNet [42] & Voxel + Normal & - & 93.10 & - \\ SO-Net [15] & Points & 2048 \(\times\) 3 & 93.90 & 94.10 \\ MVPointNet [43] & Points + View & -/5 & 95.10 & 95.20 \\ MSSAViT-TV (ours) & View & 12 & 97.41 & 97.58 \\ MSSAViT-SV (ours) & View & 12 & 98.40 & 98.46 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparative results on ModelNet10. Figure 6: Confusion matrices of **(a)** MSSAViT-TV and **(b)** MSSAViT-SV on Modelnet10. 94.91% and 95.31% in average class accuracy, with 96.07% and 96.11% in average instance accuracy, respectively. These results demonstrate competitive or superior performance to existing methods, especially within view-based approaches.