PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Automated Construction of Low Resolution, Texture-Mapped, Class-Optimal Meshes
Ankur Patel and Will Smith
IEEE Transactions on Visualization and Computer Graphics Volume 18, Number 3, pp. 434-446, 2012. ISSN 1077-2626

Abstract

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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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
ID Code:8564
Deposited By:Will Smith
Deposited On:12 February 2012