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dc.contributor.authorNicolet, Baptisteen_US
dc.contributor.authorEcormier-Nocca, Pierreen_US
dc.contributor.authorMemari, Pooranen_US
dc.contributor.authorCani, Marie-Pauleen_US
dc.contributor.editorWilkie, Alexander and Banterle, Francescoen_US
dc.date.accessioned2020-05-24T13:43:15Z
dc.date.available2020-05-24T13:43:15Z
dc.date.issued2020
dc.identifier.isbn978-3-03868-101-4
dc.identifier.issn1017-4656
dc.identifier.urihttps://doi.org/10.2312/egs.20201025
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/egs20201025
dc.description.abstractPair Correlation Functions (PCF) have been recently spreading as a reliable representation for distributions, enabling the efficient synthesis of point-sets, vector textures and object placement from examples. In this work we introduce a triangulationbased local filtering method to extend PCF-based analysis to exemplars with free-form boundaries. This makes PCF applicable to new problems such as the inpainting of missing parts in an input distribution, or the decomposition of complex, non-homogeneous distributions into a set of coherent classes, in which each category of points can be studied together with their intra and inter-class correlations.en_US
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/]
dc.subjectI.3.5 [Computer Graphics]
dc.subjectComputational Geometry and Object Modeling
dc.subjectBoundary representations
dc.titlePair Correlation Functions with Free-Form Boundaries for Distribution Inpainting and Decompositionen_US
dc.description.seriesinformationEurographics 2020 - Short Papers
dc.description.sectionheadersVisualisation / NPR
dc.identifier.doi10.2312/egs.20201025
dc.identifier.pages89-92


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Attribution 4.0 International License
Except where otherwise noted, this item's license is described as Attribution 4.0 International License