Inverse Procedural Modelling of Trees
Inverse Procedural Modelling of Trees
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DOI：
10.1111/cgf.12282
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发表时间：
2014-09
影响因子：
2.5
通讯作者：
O. Stava;S. Pirk;J. Kratt;Baoquan Chen;R. Mech;O. Deussen;Bedrich Benes
中科院分区：
文献类型：
--
作者：
O. Stava;S. Pirk;J. Kratt;Baoquan Chen;R. Mech;O. Deussen;Bedrich Benes
过程树模型在计算机图形学中很受欢迎，因为它们能够从一组输入参数生成各种输出树，并模拟植物与环境的相互作用，以便在虚拟场景中逼真地放置树木。然而，定义这些模型及其参数是一项艰巨的任务。我们提出了一种逆建模方法随机树，多边形树模型作为输入，并估计参数的程序模型，使其产生类似的输入树。我们的框架是基于一种新的参数模型生成树，并使用蒙特卡罗马尔可夫链，以找到最佳的参数集。我们证明了我们的方法从不同的来源，如交互式建模系统，重建扫描的真实的树木和发展模型的各种输入模型。
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.