Mitigating Intra-Class Variance in Few-Shot Point Cloud Classification

Due to the significant intra-class variance of 3D point clouds, it becomes challenging to characterize prototype features with a small number of instances in few-shot classification. The significant feature discrepancies among instances also hinder category determination. In this paper, we propose a few-shot point cloud classification network based on prototype learning. We mitigate intra-class variance and enhance classification performance from three aspects of the network. Firstly, we enrich point cloud features through a multi-scale grouping and pooling strategy. Subsequently, we engage in learning compensatory information from support features to update preliminary prototype features. Finally, we enhance both prototype and query features through instance feature fusion. We conducted few-shot point cloud classification experiments on benchmark datasets, and the results indicate that our approach achieves state-of-the-art performance. The source code of our method is available at https://github.com/djzgroup/FewshotClassification.