Principles of Appearance Acquisition and Representation
Foundations and Trends® in Computer Graphics and Vision
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
ACM SIGGRAPH 2011 papers
Lightness Recovery for Pictorial Surfaces
International Journal of Computer Vision
Accurate fitting of measured reflectances using a Shifted Gamma micro-facet distribution
Computer Graphics Forum
Interactive editing of lighting and materials using a bivariate BRDF representation
EGSR'10 Proceedings of the 21st Eurographics conference on Rendering
Base materials for photometric stereo
ECCV'12 Proceedings of the 12th international conference on Computer Vision - Volume 2
Toward efficient acquisition of BRDFs with fewer samples
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part IV
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Different materials reflect light in different ways, so reflectance is a useful surface descriptor. Existing systems for measuring reflectance are cumbersome, however, and although the process can be streamlined using cameras, projectors and clever catadioptrics, it generally requires complex infrastructure. In this paper we propose a simpler method for inferring reflectance from images, one that eliminates the need for active lighting and exploits natural illumination instead. The method's distinguishing property is its ability to handle a broad class of isotropic reflectance functions, including those that are neither radially-symmetric nor well-represented by low-parameter reflectance models. The key to the approach is a bi-variate representation of isotropic reflectance that enables a tractable inference algorithm while maintaining generality. The resulting method requires only a camera, a light probe, and as little as one HDR image of a known, curved, homogeneous surface.