3D Shape Partition via Multi-class Spectral Graph Clustering

Abstract

This paper proposes a new 3D shape partition algorithm. The main idea of our method is to represent 3D shape as a weighted graph whose nodes denote each face in the shape and the weights on the edges encode the similarities between faces. We then extract a set of geometric features from shape to build an affinity matrix for weighted graph and use multi-class spectral clustering method to find an optimal partition. We compute the generalized eigenvalue problems of the affinity matrix to group the most similar faces into the same semantic part and apply the graph cut method to refine the boundaries between the partition parts. Finally we evaluate our approach on the Princeton shape benchmark. The experiment shows that our method can successfully segment different classes of 3D shapes and can achieve more meaningful segmentation results compared to other leading single shape partition algorithms.