Ensemble Learning for Reectometry

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Ensemble Learning for Reectometry

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Title: Ensemble Learning for Reectometry
Author: Romeiro, Fabiano; Zickler, Todd

Note: Order does not necessarily reflect citation order of authors.

Citation: Romeiro, Fabiano and Todd Zickler. 2010. Ensemble Learning for Reectometry. Harvard Computer Science Group Technical Report TR-06-10.
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Abstract: In “Blind Reflectometry” (Romeiro and Zickler, 2010 [3]) we describe a variational Bayesian approach to inferring material properties (BRDF) from a single image of a known shape under unknown, real-world illumination. This technical report provides additional details of that approach. First, we detail the prior probability distribution for natural lighting environments. Second, we provide a derivation of the bilinear likelihood expression that is based on discretizing the rendering equation. Third and finally, we provide the update equations for the iterative algorithm that computes an approximation to the posterior distribution of BRDFs.
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:24829597
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