Abstract

Different materials reflect light in different ways, so re- flectance is a useful surface descriptor. Existing systems for measuring reflectance are cumbersome, however, and although the process can be streamlined using cameras, pro jectors and clever catadioptrics, it gener- ally 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 re- flectance 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.