  Procedural material models have been gaining traction in many applications
thanks to their flexibility, compactness, and easy editability. We explore the
inverse rendering problem of procedural material parameter estimation from
photographs, presenting a unified view of the problem in a Bayesian framework.
In addition to computing point estimates of the parameters by optimization, our
framework uses a Markov Chain Monte Carlo approach to sample the space of
plausible material parameters, providing a collection of plausible matches that
a user can choose from, and efficiently handling both discrete and continuous
model parameters. To demonstrate the effectiveness of our framework, we fit
procedural models of a range of materials---wall plaster, leather, wood,
anisotropic brushed metals and layered metallic paints---to both synthetic and
real target images.
