Improved surface extraction via parameter space voting techniques

Abstract

A set of methods is presented for detecting complex surfaces, using collections of simple, uniform processes. The methods are designed to detect surfaces in range data with parameters that can be estimated from local regions (for example, natural quadrics such as spheres). The system uses combinations of local estimates of zeroth and first derivative properties, to produce votes for specific parameterizations. Accumulations of votes lead to hypothesized surfaces. A conflict resolution strategy is used to separate the true surface hypotheses from the false ones. The overall approach is based on the ideas of the Hough transform and parameter space methods, but is designed to explicitly address shortcomings of these techniques, while maintaining their modularity and efficiency. Examples of these techniques, used to detect natural quadrics in real, low resolution range data scenes, are presented.