License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.SoCG.2020.85
URN: urn:nbn:de:0030-drops-122438
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2020/12243/
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Eder, Günther ; Held, Martin ; de Lorenzo, Stefan ; Palfrader, Peter

Computing Low-Cost Convex Partitions for Planar Point Sets Based on Tailored Decompositions (CG Challenge)

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LIPIcs-SoCG-2020-85.pdf (1 MB)

Abstract

Our work on minimum convex decompositions is based on two key components: (1) different strategies for computing initial decompositions, partly adapted to the characteristics of the input data, and (2) local optimizations for reducing the number of convex faces of a decomposition. We discuss our main heuristics and show how they helped to reduce the face count.

BibTeX - Entry

@InProceedings{eder_et_al:LIPIcs:2020:12243,
  author =	{G{\"u}nther Eder and Martin Held and Stefan de Lorenzo and Peter Palfrader},
  title =	{{Computing Low-Cost Convex Partitions for Planar Point Sets Based on Tailored Decompositions (CG Challenge)}},
  booktitle =	{36th International Symposium on Computational Geometry (SoCG 2020)},
  pages =	{85:1--85:11},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-143-6},
  ISSN =	{1868-8969},
  year =	{2020},
  volume =	{164},
  editor =	{Sergio Cabello and Danny Z. Chen},
  publisher =	{Schloss Dagstuhl--Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2020/12243},
  URN =		{urn:nbn:de:0030-drops-122438},
  doi =		{10.4230/LIPIcs.SoCG.2020.85},
  annote =	{Keywords: Computational Geometry, geometric optimization, algorithm engineering, convex decomposition}
}

Keywords: Computational Geometry, geometric optimization, algorithm engineering, convex decomposition
Collection: 36th International Symposium on Computational Geometry (SoCG 2020)
Issue Date: 2020
Date of publication: 08.06.2020
Supplementary Material: The source code of our tools and heuristics is available at GitHub and can be used freely under the https://www.gnu.org/licenses/gpl-3.0.html: https://github.com/cgalab.

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