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Inverse Procedural Modelling of Trees
Author(s) -
Stava O.,
Pirk S.,
Kratt J.,
Chen B.,
Měch R.,
Deussen O.,
Benes B.
Publication year - 2014
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.12282
Subject(s) - computer science , procedural modeling , tree (set theory) , set (abstract data type) , computer graphics , parametric statistics , parametric model , variety (cybernetics) , algorithm , inverse , markov chain , markov chain monte carlo , graphics , theoretical computer science , artificial intelligence , machine learning , computer graphics (images) , mathematics , programming language , statistics , geometry , mathematical analysis , bayesian probability
Procedural tree models have been popular in computer graphics for their ability to generate a variety of output trees from a set of input parameters and to simulate plant interaction with the environment for a realistic placement of trees in virtual scenes. However, defining such models and their parameters is a difficult task. We propose an inverse modelling approach for stochastic trees that takes polygonal tree models as input and estimates the parameters of a procedural model so that it produces trees similar to the input. Our framework is based on a novel parametric model for tree generation and uses Monte Carlo Markov Chains to find the optimal set of parameters. We demonstrate our approach on a variety of input models obtained from different sources, such as interactive modelling systems, reconstructed scans of real trees and developmental models.

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