Graph Convolutional Network for Point Cloud Understanding Through Homogeneity Partitioning

Effectively capturing complex point cloud information is essential for advanced functionalities in consumer electronics, such as augmented reality, virtual simulations, and 3D printing. Given the irregular and unstructured nature of point cloud data, graph-based methods have recently emerged as effective solutions. However, their uniform treatment of neighboring points often limits the ability to capture fine-grained local structural information, weakening discriminative features. To address this limitation, this paper introduces a graph convolutional network that incorporates homogeneity partitioning to enhance local structural modeling in point clouds. First, neighboring points are identified based on distance and connected to a central point to construct a local graph. Next, a graph vector is computed by summing unit edge vectors, capturing the dominant directional trend of the local neighborhood. Based on the similarity between edge vectors and this graph vector, the neighborhood is partitioned into two sets: a homogeneous point set, composed of points with consistent structural attributes, and a heterogeneous point set, composed of points with more diverse attributes. Then, a graph convolution operator is designed to capture node commonalities, suppress the influence of residual heterogeneous points within the homogeneous set, and extract shared information across different classes in the heterogeneous set. Finally, an interaction mechanism between the two sets is introduced to refine node features and incorporate richer contextual information. Extensive experiments on multiple datasets validate the effectiveness of the proposed method.