Gaussian Sphere sampling based Surface Approximation

Sampling of 3D meshes is at the foundation of any sur- face simplification technique. In this paper, we use the re- cent results on quantization and surface approximation the- ory to propose a simple, robust, linear time, output sensitive algorithm for sampling meshes with the purpose of surface approximation. Our algorithm is based on the mapping of regular sampling and triangulation of the Gaussian sphere onto a manifold surface. An interesting aspect of our al- gorithm is that we do not explicitly measure, minimize, or prioritize any error to simplify and do not explicitly clus- ter the faces to find proxies, but still achieve bounded error approximation of the shape.