# PointSwin: Modeling Self-Attention with Shifted Window on Point Cloud
## 3 Overall Architecture
### The Efficient Architecture (PointSwin-E)  
In the course of our research, we also constructed an efficient version of the PointSwin model and named it PointSwin-E. PointSwin-E, like PointSwin, includes the backbone network and SASW modules. However, unlike PointSwin, PointSwin-E does not stack multiple SASW modules to form a hierarchical structure, and it does not combine SASW modules with downsampling operations.  
PointSwin-E consists of two parallel SASW modules which, respectively, operate on the output features of the two hidden layers in the early stage of the backbone network. As shown in Figure 5, only one SW operation is performed in each SASW module. The output features of the two parallel SASW modules will be transmitted back to the intermediate stage of the backbone network for splicing with the output features of the previous MLP operator. The concatenated features are used as the global feature candidate pool to continue the MaxPooling operation and subsequent MLP operations until the feature aggregation work is completed. Therefore, it can be seen from the organization of the SASW module that PointSwin-E pays more attention to the time overhead and GPU Memory-Usage overhead.  
Figure 4: Illustration of the class of relative position bias. For block **A**, the self of block **A** is class SPSF, block **B** in the same row is class DPSF, block **C** in the same column is class SPDF and block **D** in the rest of window is class DPDF.  
PointSwin-E still focuses on global features in feature extraction, and it only performs intra-feature interaction operations on a small number of feature maps. Its significance is mainly to influence the model's decision to construct global features by adding a small number of intra-feature interactions, and the influence is a positive effect. This significance proves the feasibility and effectiveness of the Sel-Attention with Shifted-Window mechanism in point cloud processing tasks. This feasibility shows that using the Self-Attention mechanism limited in the shifting window to calculate the correlation between point cloud features will not only bring higher prediction accuracy to the model but also reduce the high computational overhead brought by the Self-Attention mechanism to ensure the availability of the model.  
PointSwin-E only performs feature interaction operations on the small-scale output feature maps of low-order functions of the backbone network, ensuring the efficient use of computing resources. PointSwin-E retains the concate link mode between the SASW output data and the backbone network in PointSwin, which ensures that local features can participate in the construction of global features and affect the final prediction decision of the model. Benefiting from such a delicate and compact network structure, the computational time overhead of PointSwin-E is much smaller than that of PointSwin. However, just as the old saying goes, every coin has two sides. The delicate and small network structure also means a lack of learning ability. This problem will become more apparent as the solution problem becomes more complex.