Automated construction of low-resolution, texture-mapped, class-optimal meshes
In this paper, we present a framework for the groupwise processing of a set of meshes in dense correspondence. Such sets arise when modeling 3D shape variation or tracking surface motion over time. We extend a number of mesh processing tools to operate in a groupwise manner. Specifically, we present...
| Veröffentlicht in: | IEEE transactions on visualization and computer graphics. - 1996. - 18(2012), 3 vom: 18. März, Seite 434-46 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
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2012
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on visualization and computer graphics |
| Schlagworte: | Journal Article |
| Zusammenfassung: | In this paper, we present a framework for the groupwise processing of a set of meshes in dense correspondence. Such sets arise when modeling 3D shape variation or tracking surface motion over time. We extend a number of mesh processing tools to operate in a groupwise manner. Specifically, we present a geodesic-based surface flattening and spectral clustering algorithm which estimates a single class-optimal flattening. We also show how to modify an iterative edge collapse algorithm to perform groupwise simplification while retaining the correspondence of the data. Finally, we show how to compute class-optimal texture coordinates for the simplified meshes. We present alternative algorithms for topologically symmetric data which yield a symmetric flattening and low-resolution mesh topology. We present flattening, simplification, and texture mapping results on three different data sets and show that our approach allows the construction of low-resolution 3D morphable models |
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| Beschreibung: | Date Completed 28.06.2012 Date Revised 13.01.2012 published: Print Citation Status PubMed-not-MEDLINE |
| ISSN: | 1941-0506 |
| DOI: | 10.1109/TVCG.2011.101 |