Mesh Segmentation through Spectral Clustering
Mesh segmentation often serves as the first step of digital geometry processing. A proper segmentation of a model into meaningful parts facilitates subsequent tasks, such as morphing, parameterization, shape recognition, collision detection and etc. In this project, we investigate applying spectral clustering, a powerful clustering technique from machine learning, to mesh segmentation.
In order to apply spectral clustering, we first study how to measure the affinities between mesh faces that we wish to cluster. A specific spectral clustering technique is devised and various related questions are answered under the concept of Kernel Principal Component Analysis. To reduce the computational overhead associated with spectral clustering, we apply Nystrom method and study several issues on sub-sampling.