Hyperspectral Modeling of Material Appearance: General Framework, Challenges and Prospects


  • Gladimir Baranoski University of Waterloo




The main purpose of this tutorial is to address theoretical
and practical issues involved in the development of predictive material appearance
models for interdisciplinary applications within and outside the visible spectral domain.
We examine the specific constraints and
pitfalls found in each of the key stages of the model development
framework, namely data collection, design and evaluation, and discuss alternatives to enhance the effectiveness
of the entire process. Although predictive material appearance models developed by computer graphics
researchers are usually aimed at realistic image synthesis applications, they
also provide valuable support for a myriad of advanced investigations in related areas,
such as computer vision, image processing and pattern recognition,
which rely on the accurate analysis and interpretation of material appearance attributes
in the hyperspectral domain. In fact, their scope of contributions goes beyond the realm of traditional computer science applications. For example, predictive light transport simulations, which are essential for the development of these models, are also regularly being
used by physical and life science researchers to understand and
predict material appearance changes prompted by mechanisms which cannot be
fully studied using standard ``wet'' experimental procedures.
For completeness, this tutorial also provides an overview of
such synergistic
research efforts and in silico investigations, which are illustrated by case studies involving the use of hyperspectral material appearance


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How to Cite

Baranoski, G. (2015). Hyperspectral Modeling of Material Appearance: General Framework, Challenges and Prospects. Revista De Informática Teórica E Aplicada, 22(2), 203–232. https://doi.org/10.22456/2175-2745.56437



Tutoriais SIBGRAPI 2015