A Data-Driven Reflectance Model

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A Data-Driven Reflectance Model

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Title: A Data-Driven Reflectance Model
Author: Matusik, Wojciech; Pfister, Hanspeter; Brand, Matt; McMillan, Leonard

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Citation: Matusik, Wojciech, Hanspeter Pfister, Matt Brand, and Leonard McMillan. 2003. A data-driven reflectance model. In Proceedings International Conference on Computer Graphics and Interactive Techniques, ACM SIGGRAPH 2003 Papers: July 27-31, 2003, San Diego, California, 759-769. New York, N.Y.: ACM Press. Also published in ACM Transactions on Graphics 22(3): 759-769.
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Abstract: We present a generative model for isotropic bidirectional reflectance distribution functions (BRDFs) based on acquired reflectance data. Instead of using analytical reflectance models, we represent each BRDF as a dense set of measurements. This allows us to interpolate and extrapolate in the space of acquired BRDFs to create new BRDFs. We treat each acquired BRDF as a single high-dimensional vector taken from a space of all possible BRDFs. We apply both linear (subspace) and non-linear (manifold) dimensionality reduction tools in an effort to discover a lower-dimensional representation that characterizes our measurements. We let users define perceptually meaningful parametrization directions to navigate in the reduced-dimension BRDF space. On the low-dimensional manifold, movement along these directions produces novel but valid BRDFs.
Published Version: doi:10.1145/882262.882343
Other Sources: http://gvi.seas.harvard.edu/sites/all/files/SIG2003_0.pdf
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:4135447
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