# PointSwin: Modeling Self-Attention with Shifted Window on Point Cloud
## 1 Introduction  
Point cloud data are the most popular data representation in the current 3D data representation domain, and it naturally possesses properties such as arrangement invariance and rigid transformation invariance [1]. The early point cloud data are usually converted into voxel data [2, 3] and processed by deep learning methods such as Convolutional Neural Networks (CNN) [4, 5] because of the non-standardization problem that its data storage form does not match the physical meaning. However, starting from PointNet [6] pioneering the use of deep learning methods to directly process raw point cloud data, more and more new methods have been extended and developed on the basis of this Pointwise MLP type of method [7] to pursue a better understanding of point cloud data. The main body of PointNet [6] is the alignment network, the MultiLayer Perceptron (MLP) layer, and the MaxPooling operator. After correcting the input data through the alignment network, the MLP layer will combine the MaxPooling operator to approximate and fit the data distribution law. Finally, the MLP classifier is used to output the category score to predict the category to complete the classification work. PointNet has excellent performance in learning global features, with high accuracy and fast computation speed. However, it has certain limitations in the ability to learn local features [8]. There is actually an overfitting phenomenon in the process of model training. That is to say, after the PointNet model reaches a certain accuracy rate in training, the accuracy rate on the test set will no longer improve. If the training continues, the networkwill continue to fit the training set strongly, but such fitting does not learn the more general data distribution in local details. Therefore, in order to better utilize the natural spatial location information of raw point cloud data, we hope to improve the feature extraction ability but control the computational cost of the model. The Self-Attention mechanism, which originated from the Transformer model [9], establishes the connection between elements by calculating the correlation between each element in the receptive field. This mechanism is known for its good perception of context information [10], and it just can complete the task goal of point cloud processing, which intends to establish connections between independent points. Considering the excellent gain effect of the Self-Attention mechanism on feature expression [11], the method in SwinTransformer [12] to achieve better performance with less computational overhead is more in line with our needs. SwinTransformer uses an advanced version of Self-Attention, MultiHead Self-Attention, which is calculated in the divided window to strengthen the connection between features. We use the Shifted-Window [12; 13; 14] method to move the calculation window to achieve the effect of window overlapping to reduce the amount of calculation. We hope to use the new Self-Attention method proposed by SwinTransformer to help improve the problem of insufficient local detail learning ability of PointNet so as to achieve higher accuracy of point cloud classification task. Thus, we apply the Self-Attention with Shifted-Window mechanism to the point cloud feature map to form our model PointSwin. The highlight of our method is the adoption of the Self-Attention with Shifted-Window mechanism to promote the interaction of local features with each other, enhance the feature learning ability, and maximize the advantages and benefits of the global features of PointNet. The core of PointSwin is the backbone network and Self-Attention with Shifted-Window module. The whole PointSwin is constructed by the backbone network and Self-Attention with Shifted-Window part. In the backbone network, we follow the backbone network part of PointNet. That is, we continue to use the Joint Align Network to calibrate the input data, use MLP as the feature extraction operator, and use the MaxPooling operator as the global feature aggregation tool.