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DC poleHodnotaJazyk
dc.contributor.authorGarifullin, Albert
dc.contributor.authorShcherbakov, Alexandr
dc.contributor.authorFrolov, Vladimir
dc.contributor.editorSkala, Václav
dc.date.accessioned2022-09-02T10:12:34Z
dc.date.available2022-09-02T10:12:34Z
dc.date.issued2022
dc.identifier.citationWSCG 2022: full papers proceedings: 30. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 282-288.en
dc.identifier.isbn978-80-86943-33-6
dc.identifier.issn2464-4617
dc.identifier.urihttp://hdl.handle.net/11025/49606
dc.format7 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencyen
dc.rights© Václav Skala - UNION Agencyen
dc.subject3D modelovánícs
dc.subjectmodelování rostlincs
dc.subjectrekonstrukce stromucs
dc.subjectgenetické algoritmycs
dc.titleFitting Parameters for Procedural Plant Generationen
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedWe propose a novel method to obtain a 3D model of a tree based on a single input image by fitting parameters for some procedural plant generator. Unlike other methods, our approach can work with any plant generator, treating it as a black-box function. It is also possible to specify the desired characteristics of the plant, such as the geometric complexity of the model or its size. We propose a similarity function between the given image and generated model, that better catches the significant differences between tree shapes. To find the appropriate parameter set, we use a specific variant of a genetic algorithm designed for this purpose to maximize similarity function. This approach can greatly simplify the artist's work. We demonstrate the results of our algorithm with several procedural generators, from a very simple to a fairly advanced one.en
dc.subject.translated3D modelingen
dc.subject.translatedplants modelingen
dc.subject.translatedtree reconstructionen
dc.subject.translatedgenetic algorithmsen
dc.identifier.doihttps://www.doi.org/10.24132/CSRN.3201.35
dc.type.statusPeer-revieweden
Vyskytuje se v kolekcích:WSCG 2022: Full Papers Proceedings

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