Ensemble Learning for Reectometry
MetadataShow full item record
CitationRomeiro, Fabiano and Todd Zickler. 2010. Ensemble Learning for Reectometry. Harvard Computer Science Group Technical Report TR-06-10.
AbstractIn “Blind Reflectometry” (Romeiro and Zickler, 2010 ) 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.
Citable link to this pagehttp://nrs.harvard.edu/urn-3:HUL.InstRepos:24829597
- FAS Scholarly Articles 
Contact administrator regarding this item (to report mistakes or request changes)