PCR: A Geometric Cocktail for Triangulating Point Clouds Beautifully Without Angle Bounds

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dc.contributor.author Leitão, Gonçalo N. V. en_US
dc.contributor.author Gomes, Abel J. P. en_US
dc.contributor.editor Jakob Andreas Bærentzen and Klaus Hildebrandt en_US
dc.date.accessioned 2017-07-02T17:44:42Z
dc.date.available 2017-07-02T17:44:42Z
dc.date.issued 2017
dc.identifier.isbn 978-3-03868-047-5
dc.identifier.issn 1727-8384
dc.identifier.uri http://dx.doi.org/10.2312/sgp.20171206
dc.identifier.uri https://diglib.eg.org:443/handle/10.2312/sgp20171206
dc.description.abstract Reconstructing a triangulated surface from a point cloud through a mesh growing algorithm is a difficult problem, in largely because they use bounds for the admissible dihedral angle to decide on the next triangle to be attached to the mesh front. This paper proposes a solution to this problem by combining three geometric properties: proximity, co-planarity, and regularity; hence, the PCR cocktail. The PCR cocktail-based algorithm works well even for point clouds with non-uniform point density, holes, high curvature regions, creases, apices, and noise. en_US
dc.publisher The Eurographics Association en_US
dc.subject Computing methodologies
dc.subject > Mesh models
dc.subject Point
dc.subject based models
dc.title PCR: A Geometric Cocktail for Triangulating Point Clouds Beautifully Without Angle Bounds en_US
dc.description.seriesinformation Symposium on Geometry Processing 2017- Posters
dc.description.sectionheaders Posters
dc.identifier.doi 10.2312/sgp.20171206
dc.identifier.pages 11-12

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