Effective volume sampling of solid models using distance measures

Abstract

We present several methods for converting solid models to volume models whose boundaries are defined by an iso-surface. By storing samples of a signed distance function at each point in the volume, we show that we can reconstruct the original object with greater accuracy than methods based on an inclusion function. Our methods are fast, and require no more memory than conventional point sampling. These approaches apply to all solid objects for which ray intersection and point classification methods are defined. If a space subdivision test is available, we can quickly sample complex objects. We also present enhancements that allow for sampling of the object's original colour and texture.