Abstract
Characterizing the appearance of real-world surfaces is a fundamental problem in multidimensional , computer vision and computer graphics. In this paper, we outline a unified perception-based approach to modeling of the appearance of materials for computer graphics and reflectometry. We discuss the differences and the common points of data analysis and modeling for BRDFs in both physical and in virtual application domains. We outline a mathematical framework that captures important problems in both types of application domains, and allows for application and performance comparisons of statistical and machine learning methods. For comparisons between methods, we use criteria that are relevant to both statistics and machine learning, as well as to both virtual and physical application domains. Additionally, we propose a class of multiple testing procedures to test a hypothesis that a material has diffuse reflection in a generalized sense. We treat a general case where the number of hypotheses can potentially grow with the number of measurements. Our approach leads to tests that are more powerful than the generic multiple testing procedures.
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Acknowledgments
The author would like to thank Gerd Wübbeler, Clemens Elster and Franko Schmähling for useful discussions and Anna Langovaya for her suggestions that led to improvement of results in this paper.
This work has been carried out within EMRP project IND 52 ‘Multidimensional reflectometry for industry’. The EMRP is jointly funded by the EMRP participating countries within EURAMET and the European Union.
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Langovoy, M. (2015). A Unified Approach to Data Analysis and Modeling of the Appearance of Materials for Computer Graphics and Multidimensional Reflectometry. In: Kim, H., Amouzegar, M., Ao, Sl. (eds) Transactions on Engineering Technologies. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-7236-5_2
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