Authors: Kenneth Weiss, Peter Lindstrom
Abstract: Multilinear interpolants are at the foundation of many key visualization techniques, including isosurfacing, direct volume rendering and texture mapping, which assume piecewise multilinear interpolants over the cells of a mesh. However, despite their importance, there has not been much focus within the visualization community on techniques that efficiently generate and encode globally continuous functions defined by the union of piecewise multilinear cells. Wavelets provide a rich context for analyzing and processing complicated datasets. In this paper, we exploit adaptive regular refinement as a means of representing and evaluating functions described by a subset of their nonzero wavelet coefficients. We analyze the dependencies involved in the transform and describe how to generate the coarsest adaptive mesh with nodal function values such that the inverse wavelet transform is exactly reproduced via simple interpolation (subdivision) over the mesh elements. This allows for an adaptive, sparse representation of the function with on-demand evaluation at any point in the domain. We focus on the popular wavelets formed by tensor products of linear B-splines, resulting in an adaptive, nonconforming but crack-free quad- or octree mesh that allows reproducing globally continuous functions via multilinear interpolation over elements.