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.
ECCV 2008 Presentation: [5.5MB PDF]
Fabiano Romeiro, Yuriy Vasilyev, Todd Zickler.
"Passive Reflectometry." Proc. European Conf. Computer Vision, October 2008.