Dynamic Points Agglomeration for Hierarchical Point Sets Learning

Many previous works on point sets learning achieve excellent performance with hierarchical architecture. Their strategies towards points agglomeration, however, only perform points sampling and grouping in original Euclidean space in a fixed way. These heuristic and task-irrelevant strategies severely limit their ability to adapt to more varied scenarios. To this end, we develop a novel hierarchical point sets learning architecture, with dynamic points agglomeration. By exploiting the relation of points in semantic space, a module based on graph convolution network is designed to learn a soft points cluster agglomeration. We construct a hierarchical architecture that gradually agglomerates points by stacking this learnable and lightweight module. In contrast to fixed points agglomeration strategy, our method can handle more diverse situations robustly and efficiently. Moreover, we propose a parameter sharing scheme for reducing memory usage and computational burden induced by the agglomeration module. Extensive experimental results on several point cloud analytic tasks, including classification and segmentation, well demonstrate the superior performance of our dynamic hierarchical learning framework over current state-of-the-art methods.