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Abstract: High-quality Monte Carlo image synthesis requires the
ability to importance sample realistic BRDF models. However, analytic
sampling algorithms exist only for the Phong model and its derivatives
such as Lafortune and Blinn-Phong. This paper demonstrates an
importance sampling technique for a wide range of BRDFs, including
complex analytic models such as Cook-Torrance and measured materials,
which are being increasingly used for realistic image synthesis. Our
approach is based on a compact factored representation of the BRDF
that is optimized for sampling. We show that our algorithm
consistently offers better efficiency than alternatives that involve
fitting and sampling a Lafortune or Blinn-Phong lobe, and is more
compact than sampling strategies based on tabulating the full BRDF.
We are able to efficiently create images involving multiple measured
and analytic BRDFs, under both complex direct lighting and global
illumination.
jlawrenc@cs.princeton.edu |
