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Acquisition and analysis of bispectral bidirectional reflectance distribution functions

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Hullin,  Matthias B.
Computer Graphics, MPI for Informatics, Max Planck Society;

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Ajdin,  Boris
Computer Graphics, MPI for Informatics, Max Planck Society;

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Hanika,  Johannes
Computer Graphics, MPI for Informatics, Max Planck Society;

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Seidel,  Hans-Peter
Computer Graphics, MPI for Informatics, Max Planck Society;

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Kautz,  Jan
Computer Graphics, MPI for Informatics, Max Planck Society;

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Lensch,  Hendrik P. A.
Computer Graphics, MPI for Informatics, Max Planck Society;

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MPI-I-2009-4-001.pdf
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Citation

Hullin, M. B., Ajdin, B., Hanika, J., Seidel, H.-P., Kautz, J., & Lensch, H. P. A.(2009). Acquisition and analysis of bispectral bidirectional reflectance distribution functions (MPI-I-2009-4-001). Saarbrücken: Max-Planck-Institut für Informatik.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0014-6671-4
Abstract
In fluorescent materials, energy from a certain band of incident wavelengths is reflected or reradiated at larger wavelengths, i.e. with lower energy per photon. While fluorescent materials are common in everyday life, they have received little attention in computer graphics. Especially, no bidirectional reflectance measurements of fluorescent materials have been available so far. In this paper, we develop the concept of a bispectral BRDF, which extends the well-known concept of the bidirectional reflectance distribution function (BRDF) to account for energy transfer between wavelengths. Using a bidirectional and bispectral measurement setup, we acquire reflectance data of a variety of fluorescent materials, including vehicle paints, paper and fabric. We show bispectral renderings of the measured data and compare them with reduced versions of the bispectral BRDF, including the traditional RGB vector valued BRDF. Principal component analysis of the measured data reveals that for some materials the fluorescent reradiation spectrum changes considerably over the range of directions. We further show that bispectral BRDFs can be efficiently acquired using an acquisition strategy based on principal components.