Interpolatory Point Set Surfaces-Convexity and Hermite Data
Point set surfaces define a (typically) manifold surface from a set of scattered points. The definition involves weighted centroids and a gradient field. The data points are interpolated if singular weight functions are used to define the centroids. While this way of deriving an interpolatory scheme appears natural, we show that it has two deficiencies: Convexity of the input is not preserved and the extension to Hermite data is numerically unstable. We present a generalization of the standard scheme that we call Hermite point set surface. It allows interpolating, given normal constraints in a stable way. It also yields an intuitive parameter for shape control and preserves convexity in most situations. The analysis of derivatives also leads to a more natural way to define normals, in case they are not supplied with the point data. We conclude by comparing to similar surface definitions.
interpolation hermite data algorithms point-based modeling