An adaptive subdivision method for surface-fitting from sampled data

Abstract

A method is developed for surface-fitting from sampled data. Surface-fitting is the process of constructing a compact representation to model the surface of an object based on a fairly large number of given data points. In our case, the data is obtained from a real object using an automatic three-dimensional digitizing system. The method is based on an adaptive subdivision approach, a technique previously used for the design and display of free-form curved surface objects. Our approach begins with a rough approximating surface and progressively refines it in successive steps in regions where the data is poorly approximated. The method has been implemented using a parametric piecewise bicubic Bernstein-Bézier surface possessing G 1 geometric continuity. An advantage of this approach is that the refinement is essentially local reducing the computational requirements which permits the processing of large databases. Furthermore, the method is simple in concept, yet realizes efficient data compression. Some experimental results are given which show that the representation constructed by this method is faithful to the original database.