Segmenting meshes into natural regions is useful for model understanding
and many practical applications. In this paper, we present
a novel, automatic algorithm for segmenting meshes into meaningful
pieces. Our approach is a clustering-based top-down hierarchical
segmentation algorithm. We extend recent work on feature sensitive
isotropic remeshing to generate a mesh hierarchy especially
suitable for segmentation of large models with regions at multiple
scales. Using integral invariants for estimation of local characteristics,
our method is robust and efficient. Moreover, statistical quantities
can be incorporated, allowing our approach to segment regions
with different geometric characteristics or textures.
Yu-Kun Lai, Qian-Yi Zhou, Shi-Min Hu, Ralph R. Mar