# Micro-Gear Point Cloud Segmentation Based on Multi-Scale Point Transformer
## 3 Methods
### Related Work  
As hardware and deep learning technologies continue to evolve, numerous models for processing point cloud data have been proposed. However, the inherent characteristics of point cloud data, such as being unordered, irregular, and sparse, make its processing challenging. Techniques developed for 2D data processing cannot be directly applied to point cloud data due to these differences. Some network models attempt to convert point cloud data into more structured forms like voxels, clusters, or projections before performing feature extraction. Although this approach can effectively address some issues, it involves a preliminary transformation step, which tends to be resource-intensive and can lead to lower efficiency due to the high demands on memory and computational power.  
Networks that process point cloud data directly also exist. [PERSON] and others introduced PointNet [23], which was among the first to achieve end-to-end learning directly from point cloud data, using global pooling to directly handle the unordered nature of point clouds; PointNet++ [24] built on the foundation of PointNet by adding local region processing to capture more detailed local features. These two methods provide a basic structure for processing point cloud data, yet they still fall short in handling local details of point clouds; subsequent developments, such as PointCNN [25] and DensePoint [26], began to explore how to enhance performance by learning the local structures of point cloud data; to further improve model performance, researchers proposed several innovative methods. For example, GACNet [27] utilizes a graph attention mechanism to automatically learn dependencies between points, significantly enhancing the network's ability to parse complex spatial relationships. On the other hand, KPConv [8] performs convolutions directly on point clouds by defining learnable convolution kernels, handling multi-scale information, and providing a flexible method for processing point cloud data; later, PointTransformer [18] and PCT [28] introduced self-attention mechanisms [29], further enhancing the encoding of global dependencies in point clouds and optimizing the overall flow of information.  
However, despite these advances bringing significant improvements, most networks still struggle with unclear connections between global and local features in point cloud data, leading to insufficient consideration of crucial information. Additionally, on specific applications such as custom datasets for small gears, these models often perform poorly, possibly due to not capturing fine local features adequately or lacking generalization capability under extreme conditions. For example, although HANet [30] and EPNet [31] excel in standard tasks, their applicability in specific small object recognition tasks remains limited, necessitating further consideration and integration of global and local information in the design of point cloud data processing networks.  
Based on this, the present study builds upon the PointTransformer network, incorporating multiple global feature extraction modules to extract features at various levels. Additionally, an up-and-down sampling module is utilized, which integrates multiscale contextual information, thereby enhancing the modeling capabilities for complex structured point clouds.