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Fitting Parameters for Procedural Plant Generationстатья
- Авторы: Garifullin A., Shcherbakov A., Frolov V.
- Сборник: Computer Science Research Notes 3201
- Серия: CSRN 3201
- Год издания: 2022
- Место издания: Pilsen, Czech Republic
- Первая страница: 282
- Последняя страница: 288
- DOI: 10.24132/CSRN.3201.35
- Аннотация: 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.
- Добавил в систему: Фролов Владимир Александрович