Multiresolution mesh segmentation based on surface roughness and wavelet analysis.

Céline Roudet 1 Florent Dupont 2 Atilla Baskurt 3, 2
2 M2DisCo - Geometry Processing and Constrained Optimization
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
3 imagine - Extraction de Caractéristiques et Identification
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : During the last decades, the three-dimensional objects have begun to compete with traditional multimedia (images, sounds and videos) and have been used by more and more applications. The common model used to represent them is a surfacic mesh due to its intrinsic simplicity and efficacity. In this paper, we present a new algorithm for the segmentation of semi-regular triangle meshes, via multiresolution analysis. Our method uses several measures which reflect the roughness of the surface for all meshes resulting from the decomposition of the initial model into different fine-to-coarse multiresolution meshes. The geometric data decomposition is based on the lifting scheme. Using that formulation, we have compared various interpolant prediction operators, associated or not with an update step. For each resolution level, the resulting approximation mesh is then partitioned into classes having almost constant roughness thanks to a clustering algorithm. Resulting classes gather regions having the same visual appearance in term of roughness. The last step consists in decomposing the mesh into connex groups of triangles using region growing ang merging algorithms. These connex surface patches are of particular interest for adaptive mesh compression, visualisation, indexation or watermarking.
Type de document :
Communication dans un congrès
Visual communications and image processing 2007, Jan 2007, San Jose, United States. pp.65082E.1-65082E.12, vol. 6508 (2), 2007, <10.1117/12.704446>
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Céline Roudet, Florent Dupont, Atilla Baskurt. Multiresolution mesh segmentation based on surface roughness and wavelet analysis.. Visual communications and image processing 2007, Jan 2007, San Jose, United States. pp.65082E.1-65082E.12, vol. 6508 (2), 2007, <10.1117/12.704446>. <hal-00589408>

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