PointSGLN: a Novel Point Cloud Classification Network Based on Sampling Grouping and Local Point Normalization

The point cloud data structure is characterized by disorder and spatial irregularity, which makes it impossible to apply 2D convolutional neural networks directly to extract features like regular data such as images and text, so the point cloud classification task faces a significant challenge. This study aims to classify the inferior mesenteric artery (IMA) in the form of point clouds, the key of which is to analyze the correlation between its branch origin and morphological changes. Therefore, we propose PointSGLN, a point cloud classification network based on normalization after sampling and grouping operations. Unlike previous point cloud classification networks, normalization is performed after point cloud sampling and grouping operations, which reduces the complexity of the point cloud representation while preserving the geometric properties of the original point cloud to ensure that the branching structure of the inferior mesenteric artery (IMA) is adequately preserved and improves the classification accuracy by optimizing the training strategy to ensure the inference speed while improving the classification accuracy. The proposed PointSGLN is tested on the ModelNet40, ScanObjectNN and IMA. From the experimental results, it is shown that the advanced performance of this model on the point cloud classification task and its good robustness to point sparsity are verified.