Title:	Fitting Parameters for Procedural Plant Generation
Authors:	Garifullin, Albert Shcherbakov, Alexandr Frolov, Vladimir
Citation:	WSCG 2022: full papers proceedings: 30. International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision, p. 282-288.
Issue Date:	2022
Publisher:	Václav Skala - UNION Agency
Document type:	conferenceObject
URI:	http://hdl.handle.net/11025/49606
ISBN:	978-80-86943-33-6
ISSN:	2464-4617
Keywords:	3D modelování;modelování rostlin;rekonstrukce stromu;genetické algoritmy
Keywords in different language:	3D modeling;plants modeling;tree reconstruction;genetic algorithms
Abstract in different language:	We 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.
Rights:	© Václav Skala - UNION Agency
Appears in Collections:	WSCG 2022: Full Papers Proceedings

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