Graph regularized sparse coding for 3D shape clustering

Feature descriptors have become an increasingly important tool in shape analysis. Features can be extracted and subsequently used to design robust signatures for shape retrieval, correspondence, classification and clustering. In this paper, we present a graph-theoretic framework for 3D shape clustering using the biharmonic distance map and graph regularized sparse coding. While this work focuses primarily on clustering, our approach is fairly general and can be used to tackle other 3D shape analysis problems. In order to seamlessly capture the similarity between feature descriptors, we perform shape clustering on mid-level features that are generated via graph regularized sparse coding. Extensive experiments are carried out on three standard 3D shape benchmarks to demonstrate the much better performance of the proposed clustering approach in comparison with recent state-of-the-art methods.