Viewpoint-based simplification using f-divergences

We propose a new viewpoint-based simplification method for polygonal meshes, driven by several f-divergences such as Kullback–Leibler, Hellinger and Chi-Square. These distances are a measure of discrimination between probability distributions. The Kullback–Leibler distance between the projected and the actual area distributions of the polygons in the scene already has been used as a measure of viewpoint quality. In this paper, we use the variation in those viewpoint distances to determine the error introduced by an edge collapse. We apply the best half-edge collapse as a decimation criterion. The approximations produced by our method are close to the original model in terms of both visual and geometric criteria. Unlike many pure visibility-driven methods, our new approach does not completely remove hidden interiors in order to increase the visual quality of the simplified models. This makes our approach more suitable for applications which require exact geometry tolerance but also require high visual quality.