A Unified Approach to Data Analysis
and Modeling of the Appearance
of Materials for Computer Graphics
and Multidimensional Reflectometry
Mikhail Langovoy
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 appear-
ance of materials for computer graphics and reflectometry. We discuss the differ-
ences 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 proce-
dures 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.
Keywords BRDF
Computer graphics
Data analysis
Light reflection
Machine learning
Metrology
Perception
Realistic image representation
Reflectometry
Statistics of manifolds
1 Introduction
Characterizing the appearance of real-world surfaces is a fundamental problem in
multidimensional reflectometry, computer vision and computer graphics. For many
applications, appearance is suf ficiently well characterized by the bidirectional
reflectance distribution function (BRDF).
M. Langovoy (&)
The Physikalisch-Technische Bundesanstalt, Abbestrasse 2–12, 10587 Berlin, Germany
e-mail: mikhail.langovoy@ptb.de
© Springer Science+Business Media Dordrecht 2015
H.K. Kim et al. (eds.), Transactions on Engineering Technologies,
DOI 10.1007/978-94-017-7236-5_2
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