## 3 Methodology
### Network Architectures  
#### 3.2.1 Architecture of Proposed MSSAViT Model  
In this subsection, we explore the architecture of MSSAViT for the recognition of 3D objects with sequential views. The whole architecture can be divided into two major parts: the feature extraction of single-view images based on a 2 DCNN and the slow fusion of features based on a 3 DCNN. In this paper, the sequential-view-based network is named MSSAViT, of which the whole architecture is shown in Figure 3.  
As shown in Figure 3, the view images are rendered from the 3D space in a designed trajectory and sorted by capturing orders. Following common practices, each view image is divided in the backbone model to extract single-view features, but we extract the feature maps from the middle layer of the backbone instead of the final layer. In our work, to reduce the computation, we employed MobileNetV3, which is abbreviated to MBNet in Figure 3.  
Figure 3: Architecture of proposed MSSAViT in detail. By formulating a reasonable observation method to render ordered continuous view images and then using Mobilenet as the backbone feature extraction network to extract intermediate feature maps for each view image (rather than the feature vectors), we can construct a temporal multi-channel feature maps through concatenation. This is then fed into the core Multi-head Sparse Self-Attention Network for multiple rounds of feature learning. In the SSSA module, for the S-view feature maps, feature extraction is performed only on the channel-aligned feature maps under the same view through dimension transformation operations. In the TSSA module, temporal–structural attention feature learning is carried out for the S continuous-view images with the same frequency features. Based on this, through T rounds of feature learning, the spatial–temporal feature expression ability is enhanced, and finally, the category probability is output through the fully connected layer.  
Figure 2: Proposed sequential-view capturing schemes: (**a**) top-view scheme; (**b**) surround-view scheme.  
Although the local feature correlation among view images has received attention, most research has focused on the high-level features and ignores the spatial and temporal feature richness in low-level feature maps. Thus, the low-level feature maps maintain more local spatial information than feature vectors at the final layer and could be concatenated in a specific order to build 4D pseudo-time feature maps. The SSSA and TSSA modules enhance feature extraction by leveraging structured attention mechanisms to capture spatial and temporal features, respectively. The SSSA module calculates the similarity between spatial positions to automatically perform weighted fusion of spatial information, emphasizing important regions and suppressing irrelevant ones, thereby helping the model better understand key spatial features. Meanwhile, the TSSA module extracts temporal features by computing similarities between different views, capturing dynamic variations and improving performance in multi-view scenarios. The combination of both modules enables the model to simultaneously capture spatial and temporal information, thus improving accuracy and robustness in multi-view inputs.