A Framework Based on Compressed Manifold Modes for Robust Local Spectral Analysis

Sylvain Haas 1 Atilla Baskurt 2 Florent Dupont 1 Florence Denis 1
1 M2DisCo - Geometry Processing and Constrained Optimization
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
2 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : Compressed Manifold Modes (CMM) were recently introduced as a solution to one of the drawbacks of spectral analysis on triangular meshes. The eigenfunctions of the Laplace-Beltrami operator on a mesh depend on the whole shape which makes them sensitive to local aspects. CMM are solutions of an extended problem that have a compact rather than global support and are thus suitable for a wider range of applications. In order to use CMM in real applications, an extensive test has been performed to better understand the limits of their computation (convergence and speed) according to the compactness parameter, the mesh resolution and the number of requested modes. The contribution of this paper is to propose a robust choice of parameters, the automated computation of an adequate number of modes (or eigenfunctions), stability with mutltiresolution and isometric meshes, and an example application with high potential for shape indexation.
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Communication dans un congrès
Eurographics Workshop on 3D Object Retrieval, Apr 2017, LYON, France. Eurographics Workshop on 3D Object Retrieval, 2017, <https://diglib.eg.org/handle/10.2312/2631242>. <10.2312/3dor.20171054>
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https://hal.archives-ouvertes.fr/hal-01523123
Contributeur : Sylvain Haas <>
Soumis le : mardi 16 mai 2017 - 10:00:51
Dernière modification le : mardi 13 juin 2017 - 14:41:11

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Sylvain Haas, Atilla Baskurt, Florent Dupont, Florence Denis. A Framework Based on Compressed Manifold Modes for Robust Local Spectral Analysis. Eurographics Workshop on 3D Object Retrieval, Apr 2017, LYON, France. Eurographics Workshop on 3D Object Retrieval, 2017, <https://diglib.eg.org/handle/10.2312/2631242>. <10.2312/3dor.20171054>. <hal-01523123>

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