Issue No. 06 - June (2010 vol. 32)
Dan B Goldman , Adobe Systems, Inc., Seattle
Brian Curless , University of Washington, Seattle
Aaron Hertzmann , University of Toronto, Toronto
Steven M. Seitz , University of Washington, Seattle
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.
Shape/scene analysis, reflectance digitization and image capture.
S. M. Seitz, D. B. Goldman, B. Curless and A. Hertzmann, "Shape and Spatially-Varying BRDFs from Photometric Stereo," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 32, no. , pp. 1060-1071, 2009.