Learning Semantic Part-Based Graph Structure for 3D Point Cloud Domain Generalization

In 3D data analysis, point clouds provide detailed geometric insights for applications like computer vision and geospatial analysis. However, their irregularity and diversity make classification challenging, especially in domain generalization, where models must generalize to new data distributions. Our research introduces a novel 3D Domain Generalization (3DDG) method using Unsupervised Part Decomposition (UPD) and Graph Structure Induction (GSI). The UPD module employs spectral clustering and a modified Shannon entropy method to segment point clouds into meaningful parts. The GSI module constructs a graph of these parts' spatial relationships, processed by a Graph Neural Network (GNN) to understand complex geometries. Our approach enhances part-based analysis, improving classification accuracy on the PointDA-10 and GraspNetPC-10 datasets by 1.25% and 2.6%, respectively. These results highlight our advancements in 3D domain generalization, enabling more robust classification models for diverse point cloud data.