Skip to Main content Skip to Navigation

Segmentation methods for deforming meshes and its application to similarity measurement

Abstract : With an abundance of animation techniques available today, animated mesh has become a subject of various data processing techniques in Computer Graphics community, such as mesh segmentation and compression. Created from animation software or from motion capture data, a large portion of the animated meshes are deforming meshes, i.e. ordered sequences of static meshes whose topology is fixed (fixed number of vertices and fixed connectivity). Although a great deal of research on static meshes has been reported in the last two decades, the analysis, retrieval or compressions of deforming meshes remain as new research challenges. Such tasks require efficient representations of animated meshes, such as segmentation. Several spatial segmentation methods based on the movements of each vertex, or each triangle, have been presented in existing works that partition a given deforming mesh into rigid components. In this thesis, we present segmentation techniques that compute the temporal and spatio-temporal segmentation for deforming meshes, which both have not been studied before. We further extend the segmentation results towards the application of motion similarity measurement between deforming meshes. This may be significant as it solves the problem that cannot be handled by current approaches.
Complete list of metadatas

Cited literature [146 references]  Display  Hide  Download
Contributor : Abes Star :  Contact
Submitted on : Thursday, February 11, 2016 - 4:58:06 PM
Last modification on : Saturday, October 27, 2018 - 1:24:02 AM
Long-term archiving on: : Saturday, November 12, 2016 - 6:42:52 PM


Version validated by the jury (STAR)


  • HAL Id : tel-01273030, version 1


Guoliang Luo. Segmentation methods for deforming meshes and its application to similarity measurement. Computational Geometry [cs.CG]. Université de Strasbourg, 2014. English. ⟨NNT : 2014STRAD026⟩. ⟨tel-01273030⟩



Record views


Files downloads