2024-03-29T02:27:09Zhttps://repositori.uji.es/oai/requestoai:repositori.uji.es:10234/389802024-03-13T12:42:20Zcom_10234_7038com_10234_9col_10234_8634
00925njm 22002777a 4500
dc
Castelló Boscá, Pascual
author
Sbert Casasayas, Mateu
author
Chover, Miguel
author
Feixas, Miguel
author
2008
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. © 2008 Elsevier Inc. All rights reserved.
Information Sciences, 178, 11, p. 2375-2388
200255
http://hdl.handle.net/10234/38980
http://dx.doi.org/10.1016/j.ins.2008.01.011
f-divergences
Level-of-detail
Simplification
Viewpoint selection
Viewpoint-based simplification using f-divergences