A Volumetric Approach to Predictive Rendering of Fabrics
Abstract
Efficient physically accurate modeling and rendering of woven cloth at a yarn level is an inherently complicated task due to the underlying geometrical and optical complexity. In this paper, a novel and general approach to physically accurate cloth rendering is presented. By using a statistical volumetric model approximating the distribution of yarn fibers, a prohibitively costly explicit geometrical representation is avoided. As a result, accurate rendering of even large pieces of fabrics containing orders of magnitudes more fibers becomes practical without sacrifying much generality compared to fiber-based techniques. By employing the concept of local visibility and introducing the effective fiber density, limitations of existing volumetric approaches regarding self-shadowing and fiber density estimation are greatly reduced.
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Bibtex
@ARTICLE{schroeder2011_volumetric, author = {Schr{\"o}der, Kai and Klein, Reinhard and Zinke, Arno}, pages = {1277--1286}, title = {A Volumetric Approach to Predictive Rendering of Fabrics}, journal = {Computer Graphics Forum (Proceedings of EGSR 2011)}, volume = {30}, number = {4}, year = {2011}, month = jul, abstract = {Efficient physically accurate modeling and rendering of woven cloth at a yarn level is an inherently complicated task due to the underlying geometrical and optical complexity. In this paper, a novel and general approach to physically accurate cloth rendering is presented. By using a statistical volumetric model approximating the distribution of yarn fibers, a prohibitively costly explicit geometrical representation is avoided. As a result, accurate rendering of even large pieces of fabrics containing orders of magnitudes more fibers becomes practical without sacrifying much generality compared to fiber-based techniques. By employing the concept of local visibility and introducing the effective fiber density, limitations of existing volumetric approaches regarding self-shadowing and fiber density estimation are greatly reduced.} }