DocumentCode :
2289551
Title :
Directional statistics BRDF model
Author :
Nishino, Ko
Author_Institution :
Dept. of Comput. Sci., Drexel Univ., Philadelphia, PA, USA
fYear :
2009
fDate :
Sept. 29 2009-Oct. 2 2009
Firstpage :
476
Lastpage :
483
Abstract :
We introduce a novel parametric BRDF model that can accurately encode a wide variety of real-world isotropic BRDFs with a small number of parameters. The key observation we make is that a BRDF may be viewed as a statistical distribution on a unit hemisphere. We derive a novel directional statistics distribution, which we refer to as the hemispherical exponential power distribution, and model an isotropic BRDF with a mixture of it. The novel directional statistics BRDF model allows us to derive a canonical probabilistic method for estimating its parameters including the number of components. We show that the model captures the full spectrum of real-world isotropic BRDFs with accuracy comparable to non-parametric models but with a much more compact representation. We also experimentally show that the model achieves better accuracy with less measurements compared with such non-parametric models. We further demonstrate the advantages of the novel BRDF model by showing its use for reflection component separation and for exploring the space of isotropic BRDFs.
Keywords :
computational geometry; parameter estimation; statistical distributions; bidirectional reflectance distribution function; canonical probabilistic method; directional statistics BRDF model; directional statistics distribution; hemispherical exponential power distribution; parameter estimation; Brain modeling; Computer vision; Inverse problems; Lighting; Optical reflection; Parameter estimation; Parametric statistics; Solid modeling; Space exploration; Statistical distributions;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computer Vision, 2009 IEEE 12th International Conference on
Conference_Location :
Kyoto
ISSN :
1550-5499
Print_ISBN :
978-1-4244-4420-5
Electronic_ISBN :
1550-5499
Type :
conf
DOI :
10.1109/ICCV.2009.5459255
Filename :
5459255
Link To Document :
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