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

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dc.contributor.authorLeitão, Gonçalo N. V.en_US
dc.contributor.authorGomes, Abel J. P.en_US
dc.contributor.editorJakob Andreas Bærentzen and Klaus Hildebrandten_US
dc.date.accessioned2017-07-02T17:44:42Z
dc.date.available2017-07-02T17:44:42Z
dc.date.issued2017
dc.description.abstractReconstructing 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.description.sectionheadersPosters
dc.description.seriesinformationSymposium on Geometry Processing 2017- Posters
dc.identifier.doi10.2312/sgp.20171206
dc.identifier.isbn978-3-03868-047-5
dc.identifier.issn1727-8384
dc.identifier.pages11-12
dc.identifier.urihttps://doi.org/10.2312/sgp.20171206
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/sgp20171206
dc.publisherThe Eurographics Associationen_US
dc.subjectComputing methodologies
dc.subject
dc.subject> Mesh models
dc.subjectPoint
dc.subjectbased models
dc.titlePCR: A Geometric Cocktail for Triangulating Point Clouds Beautifully Without Angle Boundsen_US
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