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DC poleHodnotaJazyk
dc.contributor.authorFriedrich, Markus
dc.contributor.authorCuevas, Felip Guimerà
dc.contributor.authorSedlmeier, Andreas
dc.contributor.authorEbert, André
dc.contributor.editorSkala, Václav
dc.date.accessioned2019-10-21T09:36:28Z
dc.date.available2019-10-21T09:36:28Z
dc.date.issued2019
dc.identifier.citationJournal of WSCG. 2018, vol. 26, no. 1, p. 17-26.en
dc.identifier.issn1213-6964 (on-line)
dc.identifier.issn1213-6972 (print)
dc.identifier.issn1213-6980 (CD-ROM)
dc.identifier.urihttp://hdl.handle.net/11025/35550
dc.format10 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.rights© Václav Skala - UNION Agencycs
dc.subjectevoluční algoritmycs
dc.subjectzpracování geometriecs
dc.subjectComputer Aided Designcs
dc.subjectCSGcs
dc.subjecthluboké učenícs
dc.titleEvolutionary Generation of Primitive-Based Mesh Abstractionsen
dc.typečlánekcs
dc.typearticleen
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedThe procedural generation of data sets for empirical algorithm validation and deep learning tasks in the area of primitive-based geometry is cumbersome and time-consuming while ready-to-use data sets are rare. We propose a new and highly flexible framework based on Evolutionary Computing that is able to create primitive-based abstractions of existing triangle meshes favoring fast running times and high geometric variation over reconstruction precision. These abstractions are represented as CSG trees to widen the scope of possible applications. As part of the evaluation, we show how we successfully used the generator to create a data set for the evaluation of neural point cloud segmentation pipelines and additionally explain how to use the system to create artistic abstractions of meshes provided by publicly available triangle mesh databases.en
dc.subject.translatedevolutionary algorithmsen
dc.subject.translatedgeometry processingen
dc.subject.translatedCADen
dc.subject.translatedCSGen
dc.subject.translateddeep learningen
dc.identifier.doihttps://doi.org/10.24132/JWSCG.2019.27.1.3
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:Volume 27, Number 1 (2019)

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  1. DSpace at University of West Bohemia
  2. Publikace ZČU / UWB Publications
  3. Časopisy ZČU / UWB Journals
  4. Journal of WSCG
  5. Volume 27 (2019)
  6. Volume 27, Number 1 (2019)