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Conference Paper

Feature Sensitive Mesh Segmentation with Mean Shift

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons45769

Yamauchi,  Hitoshi
Computer Graphics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44899

Lee,  Seungyong
Computer Graphics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44901

Lee,  Yunjin
Computer Graphics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45141

Ohtake,  Yutaka
Computer Graphics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44112

Belyaev,  Alexander
Computer Graphics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45449

Seidel,  Hans-Peter
Computer Graphics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44112

Belyaev,  Alexander
Computer Graphics, MPI for Informatics, Max Planck Society;

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Citation

Yamauchi, H., Lee, S., Lee, Y., Ohtake, Y., Belyaev, A., & Seidel, H.-P. (2005). Feature Sensitive Mesh Segmentation with Mean Shift. In Shape Modeling International 2005 (SMI 2005) (pp. 236-243). Los Alamitos, USA: IEEE.


Cite as: http://hdl.handle.net/11858/00-001M-0000-000F-2693-0
Abstract
Feature sensitive mesh segmentation is important for many computer graphics and geometric modeling applications. In this paper, we develop a mesh segmentation method which is capable of producing high-quality shape partitioning. It respects fine shape features and works well on various types of shapes, including natural shapes and mechanical parts. The method combines a procedure for clustering mesh normals with a modification of the mesh chartification technique \cite{Sander_sig03}. For clustering of mesh normals, we adapt Mean Shift, a powerful general purpose technique for clustering scattered data. We demonstrate advantages of our method by comparing it with two state-of-the-art mesh segmentation techniques.