Segmentation of 3D Meshes through Spectral Clustering

  • Authors:
  • Rong Liu;Hao Zhang

  • Affiliations:
  • Simon Fraser University, Canada;Simon Fraser University, Canada

  • Venue:
  • PG '04 Proceedings of the Computer Graphics and Applications, 12th Pacific Conference
  • Year:
  • 2004

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Abstract

We formulate and apply spectral clustering to 3D mesh segmentation for the first time and report our preliminary findings. Given a set of mesh faces, an affinity matrix which encodes the likelihood of each pair of faces belonging to the same group is first constructed. Spectral methods then use selected eigenvectors of the affinity matrix or its closely related graph Laplacian to obtain data representations that can be more easily clustered. We develop an algorithm that favors segmentation along concave regions, which is inspired by human perception. Our algorithm is theoretically sound, efficient, simple to implement, and can achieve high-quality segmentation results on 3D meshes.