Multiresolution analysis for irregular meshes

The concept of multiresolution analysis applied to irregular meshes has become more and more important. Previous contributions proposed a variety of methods using simplication and/or subdivision algorithms to build a mesh pyramid. In this paper, we propose a multiresolution analysis framework for irregular meshes with attributes. Our framework is based on simplication and subdivision algorithms to build a mesh pyramid. We introduce a surface relaxation operator that allows to build a non-uniform subdivision for a low computational cost. Furthermore, we generalize the relaxation operator to attributes such as color, texture, temperature, etc. The attribute analysis gives more information on the analysed models allowing more complete processing. We show the efcienc y of our framework through a number of applications including ltering, denoising and adaptive simplication. Multiresolution analysis2 is an efcient framework to represent a data set at different levels of resolution. The analysis decomposes an initial data set into a sequence of approximations and details. The approximations represent the initial data at different levels of resolution. The details encode the data lost by the approximations and can be seen as a frequency spectrum. The main advantage of the multiresolution analysis is its representational and computational efcicenc y. Mul- tiresolution analysis is a versatile tool to represent general functions and data sets, and gives rise to many applications such as ltering, denoising, compression, editing, etc. We propose a multiresolution analysis framework for irregular meshes containing multiple attributes (such as colors, texture, curvature, etc.). This framework is based on a popular simplication method called progressive mesh. We use a global downsampling method in order to create disjoint levels of resolution. A fast, local surface relaxation operator is introduced to build a non-uniform subdivision. We show different applications of the multiresolution analysis such as ltering, denoising and simplication of complex models.