Parametric Bidirectional Scattering Distribution Functions (BSDFs) are
pervasively used because of their flexibility to represent a large variety of
material appearances by simply tuning the parameters. While efficient
evaluation of parametric BSDFs has been well-studied, high-quality importance
sampling techniques for parametric BSDFs are still scarce. Existing sampling
strategies either heavily rely on approximations, resulting in high variance,
or solely perform sampling on a portion of the whole BSDF slice. Moreover, many
of the sampling approaches are specifically paired with certain types of BSDFs.
In this paper, we seek an efficient and general way for importance sampling
parametric BSDFs. We notice that the nature of importance sampling is the
mapping between a uniform distribution and the target distribution.
Specifically, when BSDF parameters are given, the mapping that performs
importance sampling on a BSDF slice can be simply recorded as a 2D image that
we name as importance map. Following this observation, we accurately precompute
the importance maps using a mathematical tool named optimal transport. Then we
propose a lightweight neural network to efficiently compress the precomputed
importance maps. In this way, we have brought parametric BSDF important
sampling to the precomputation stage, avoiding heavy runtime computation. Since
this process is similar to light baking where a set of images are precomputed,
we name our method importance baking. Together with a BSDF evaluation network
and a PDF (probability density function) query network, our method enables full
multiple importance sampling (MIS) without any revision to the rendering
pipeline. Our method essentially performs perfect importance sampling. Compared
with previous methods, we demonstrate reduced noise levels on rendering results
with a rich set of appearances