R-Simp: Model simplification in reverse, a vector quantization approach

Just as colour image quantization can be viewed as simplification of three dimensional colours in a two dimensional image, model simplification can be viewed as quantization of three dimensional normal vectors on a two dimensional surface. Thus many of the approaches used in quantization can be applied to the problem of model simplification.;A model simplification algorithm is presented that is based on the splitting algorithm from quantization literature. The algorithm works in reverse by expanding from a coarse to a fine model. Traditionally, curvature is defined in an infinitely small local area. This algorithm measures orientation change in larger patches with techniques inspired by definitions of cur curvature from differential geometry. With these measures, the algorithm determines which portion of the model to partition next, and how to partition it. The algorithm can accept non-manifold input models and is capable of simplifying topology. It produces good quality simplifications and is faster than most other simplification algorithms.