Agostino Bozzo, Daniele Panozzo, E. Puppo, N. Pietroni, Luigi Rocca
{"title":"Adaptive Quad Mesh Simplification","authors":"Agostino Bozzo, Daniele Panozzo, E. Puppo, N. Pietroni, Luigi Rocca","doi":"10.2312/LocalChapterEvents/ItalChap/ItalianChapConf2010/095-102","DOIUrl":null,"url":null,"abstract":"We present an improved algorithm for the progressive simplification of quad meshes, which adapts the resolution of the mesh to details of the modeled shape. We extend previous work [TPC∗10], by simplifying the approach and combining it with the concept of Fitmaps introduced in [PPT∗10]. The new algorithm has several advantages: it is simpler and more robust; it does not need a parametrization of the input shape; it is adaptive; and it preserves projectability of the output mesh to the input shape, thus supporting displacement mapping. We present experimental results on a variety of datasets, showing relevant improvement over previous results under several aspects.","PeriodicalId":405486,"journal":{"name":"European Interdisciplinary Cybersecurity Conference","volume":"2 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Interdisciplinary Cybersecurity Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2312/LocalChapterEvents/ItalChap/ItalianChapConf2010/095-102","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8
Abstract
We present an improved algorithm for the progressive simplification of quad meshes, which adapts the resolution of the mesh to details of the modeled shape. We extend previous work [TPC∗10], by simplifying the approach and combining it with the concept of Fitmaps introduced in [PPT∗10]. The new algorithm has several advantages: it is simpler and more robust; it does not need a parametrization of the input shape; it is adaptive; and it preserves projectability of the output mesh to the input shape, thus supporting displacement mapping. We present experimental results on a variety of datasets, showing relevant improvement over previous results under several aspects.