Fitting Parameters for Procedural Plant Generation

Abstract

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.