  We propose a new technique for estimating spatially varying parametric
materials from a single image of an object with unknown shape in unknown
illumination. Our method uses a low-order parametric reflectance model, and
incorporates strong assumptions about lighting and shape. We develop new priors
about how materials mix over space, and jointly infer all of these properties
from a single image. This produces a decomposition of an image which
corresponds, in one sense, to microscopic features (material reflectance) and
macroscopic features (weights defining the mixing properties of materials over
space). We have built a large dataset of real objects rendered with different
material models under different illumination fields for training and ground
truth evaluation. Extensive experiments on both our synthetic dataset images as
well as real images show that (a) our method recovers parameters with
reasonable accuracy; (b) material parameters recovered by our method give
accurate predictions of new renderings of the object; and (c) our low-order
reflectance model still provides a good fit to many real-world reflectances.
