Shape and Spatially-Varying BRDFs from Photometric Stereo

Authors:
Dan B Goldman;Brian Curless;Aaron Hertzmann;Steven M. Seitz
Affiliations:
Adobe Systems, Inc., Seattle;University of Washington, Seattle;University of Toronto, Toronto;University of Washington, Seattle
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
2010

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Abstract

This paper describes a photometric stereo method designed for surfaces with spatially-varying BRDFs, including surfaces with both varying diffuse and specular properties. Our optimization-based method builds on the observation that most objects are composed of a small number of fundamental materials by constraining each pixel to be representable by a combination of at most two such materials. This approach recovers not only the shape but also material BRDFs and weight maps, yielding accurate rerenderings under novel lighting conditions for a wide variety of objects. We demonstrate examples of interactive editing operations made possible by our approach.