Modeling Bidirectional
Texture Functions with Multivariate Spherical Radial Basis Functions
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Raw image |
Tensor approximation |
Fixed parameterization |
Optimized parameterization |
Overview
This paper presents a novel parametric representation for bidirectional texture
functions. Our method mainly relies on two original techniques, namely, multivariate spherical radial basis
functions (SRBFs) and optimized parameterization. First, since the surface appearance of a real-world object is frequently a mixed effect
of different physical factors, the proposed sum-of-products model based on multivariate SRBFs especially provides an intrinsic and
efficient representation for heterogenous materials. Second, optimized parameterization particularly aims at overcoming the major
disadvantage of traditional fixed parameterization. By using a parametric model to account for variable transformations, the
parameterization process can be tightly integrated with multivariate SRBFs into a unified framework. Finally, a hierarchical fitting
algorithm for bidirectional texture functions is developed to exploit spatial coherence and reduce computational cost. Our experimental results
further reveal that the proposed representation can easily achieve high-quality approximation and real-time rendering performance.
Publications
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Yu-Ting Tsai, Kuei-Li
Fang, Wen-Chieh Lin, and Zen-Chung Shih, “Modeling
Bidirectional Texture Functions with Multivariate Spherical Radial Basis
Functions,” IEEE Transactions on Pattern Analysis and Machine Intelligence,
Vol. 33, No. 7, 2011. pp. 1356-1369.
Real-time rendering results
