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          <dc:title>Minimizing Absolute Gaussian Curvature Locally</dc:title>
          <dc:creator>Giesen, Joachim</dc:creator>
          <dc:creator>Madhusudan, Manjunath</dc:creator>
          <dc:subject>Absolute Gaussian curvature</dc:subject>
          <dc:subject>surface reconstruction</dc:subject>
          <dc:subject>mesh smoothing</dc:subject>
          <dc:description>One of the remaining challenges when reconstructing a surface from a&#13;
  finite sample is recovering non-smooth surface features like sharp&#13;
  edges. There is practical evidence showing that a two step approach&#13;
  could be an aid to this problem, namely, first computing a&#13;
  polyhedral reconstruction isotopic to the sampled surface, and&#13;
  secondly minimizing the absolute Gaussian curvature of this&#13;
  reconstruction globally. The first step ensures topological&#13;
  correctness and the second step improves the geometric accuracy of&#13;
  the reconstruction in the presence of sharp features without&#13;
  changing its topology. Unfortunately it is computationally hard to&#13;
  minimize the absolute Gaussian curvature globally. Hence we study a&#13;
  local variant of absolute Gaussian curvature minimization problem&#13;
  which is still meaningful in the context of surface&#13;
  fairing. Absolute Gaussian curvature like Gaussian curvature is&#13;
  concentrated at the vertices of a polyhedral surface embedded into&#13;
  $mathbb{R}^3$. Local optimization tries to move a single vertex in&#13;
  space such that the absolute Gaussian curvature at this vertex is&#13;
  minimized. We show that in general it is algebraically hard to find&#13;
  the optimal position of a vertex. By algebraically hard we mean that&#13;
  in general an optimal solution is not constructible, i.e., there&#13;
  exist no finite sequence of expressions starting with rational numbers, &#13;
where each expression is either the sum, difference,&#13;
  product, quotient or $k$'th root of preceding expressions and the&#13;
  last expressions give the coordinates of an optimal solution. Hence&#13;
  the only option left is to approximate the optimal position.  We&#13;
  provide an approximation scheme for the minimum possible value of&#13;
  the absolute Gaussian curvature at a vertex.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Joachim Giesen and Manjunath Madhusudan</dc:contributor>
          <dc:date>2009</dc:date>
          <dc:relation>Is Part Of Dagstuhl Seminar Proceedings, Volume 9111, Computational Geometry (2009)</dc:relation>
          <dc:type>InProceedings</dc:type>
          <dc:type>Text</dc:type>
          <dc:type>doc-type:ResearchArticle</dc:type>
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          <dc:identifier>doi:10.4230/DagSemProc.09111.3</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-20311</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.09111.3</dc:identifier>
          <dc:language>eng</dc:language>
          <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
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