{"title":"Dual mesh resampling","authors":"G. Taubin","doi":"10.1109/PCCGA.2001.962871","DOIUrl":null,"url":null,"abstract":"The dual of a 2-manifold polygonal mesh without boundary is commonly defined as another mesh with the same topology (genus) but different connectivity (vertex-face incidence), in which faces and vertices occupy complementary locations and the position of each dual vertex is computed as the center of mass (barycenter or centroid) of the vertices that support the corresponding face. This barycenter dual mesh operator is connectivity idempotent but not geometrically idempotent for any choice of vertex positions, other than constants. In this paper we construct a new resampling dual mesh operator that is geometrically idempotent for the largest possible linear subspace of vertex positions. We look at the primal and dual mesh connectivities as irregular sampling spaces, and at the rules to determine dual vertex positions as the result of a resampling process that minimizes signal loss. Our formulation, motivated by the duality of Platonic solids, requires the solution of a simple least-squares problem. We introduce a simple and efficient iterative algorithm closely related to Laplacian smoothing, and with the same computational cost. We also characterize the configurations of vertex positions where signal loss does and does not occur during dual mesh resampling, and the asymptotic behavior of iterative dual mesh resampling in the general case. Finally, we describe the close relation existing with discrete fairing and variational subdivision, and define a new primal-dual interpolatory recursive subdivision scheme.","PeriodicalId":387699,"journal":{"name":"Proceedings Ninth Pacific Conference on Computer Graphics and Applications. Pacific Graphics 2001","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2001-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"39","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings Ninth Pacific Conference on Computer Graphics and Applications. Pacific Graphics 2001","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PCCGA.2001.962871","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 39

Abstract

The dual of a 2-manifold polygonal mesh without boundary is commonly defined as another mesh with the same topology (genus) but different connectivity (vertex-face incidence), in which faces and vertices occupy complementary locations and the position of each dual vertex is computed as the center of mass (barycenter or centroid) of the vertices that support the corresponding face. This barycenter dual mesh operator is connectivity idempotent but not geometrically idempotent for any choice of vertex positions, other than constants. In this paper we construct a new resampling dual mesh operator that is geometrically idempotent for the largest possible linear subspace of vertex positions. We look at the primal and dual mesh connectivities as irregular sampling spaces, and at the rules to determine dual vertex positions as the result of a resampling process that minimizes signal loss. Our formulation, motivated by the duality of Platonic solids, requires the solution of a simple least-squares problem. We introduce a simple and efficient iterative algorithm closely related to Laplacian smoothing, and with the same computational cost. We also characterize the configurations of vertex positions where signal loss does and does not occur during dual mesh resampling, and the asymptotic behavior of iterative dual mesh resampling in the general case. Finally, we describe the close relation existing with discrete fairing and variational subdivision, and define a new primal-dual interpolatory recursive subdivision scheme.
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双网格重采样
无边界的二流形多边形网格的对偶通常定义为具有相同拓扑(属)但不同连通性(顶点-面关联)的另一个网格,其中面和顶点占据互补位置,每个对偶顶点的位置被计算为支持相应面的顶点的质心(质心或质心)。这个重心对偶网格算子是连通性幂等的,但对于任何顶点位置的选择都不是几何上幂等的,除了常量。本文构造了一个新的重采样对偶网格算子,该算子对顶点位置的最大可能线性子空间几何上是幂等的。我们将原始和双网格连接视为不规则采样空间,并将确定双顶点位置的规则作为重新采样过程的结果,以最大限度地减少信号损失。我们的公式,由柏拉图立体的对偶性所激发,需要解一个简单的最小二乘问题。本文介绍了一种与拉普拉斯平滑密切相关的简单高效的迭代算法,计算量相同。我们还描述了在双网格重采样过程中信号丢失发生和不发生的顶点位置的配置,以及在一般情况下迭代双网格重采样的渐近行为。最后,我们描述了离散整流与变分细分之间的密切关系,并定义了一种新的原对偶插值递归细分方案。
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