Graph-Based Descriptor Learning for Non-Rigid 3D Shapes

Abstract

In this paper, we propose a deformable 3D shape descriptor learning approach that takes into account the spatial correlations among local shape descriptors. By constructing a weighted graph that connects salient points on a shape surface, local feature descriptors on salient points are considered as signals defined on graph vertices, incorporating local surface information into the global graph structure. We then learn a graph structure-aware dictionary for each category of shapes with the multi-graph dictionary learning strategy, capturing similar spectral properties among graph signals generated from different shapes of the same category. Experiments conducted on representative 3D shape benchmark datasets demonstrate that our method improves over the state-of-the-art.