Title: Approximate Program Smoothing Using Mean-Variance Statistics, with Application to Procedural Shader Bandlimiting,
Abstract: This paper introduces a general method to approximate the convolution of an
arbitrary program with a Gaussian kernel. This process has the effect of
smoothing out a program. Our compiler framework models intermediate values in
the program as random variables, by using mean and variance statistics. Our
approach breaks the input program into parts and relates the statistics of the
different parts, under the smoothing process. We give several approximations
that can be used for the different parts of the program. These include the
approximation of Dorn et al., a novel adaptive Gaussian approximation, Monte
Carlo sampling, and compactly supported kernels. Our adaptive Gaussian
approximation is accurate up to the second order in the standard deviation of
the smoothing kernel, and mathematically smooth. We show how to construct a
compiler that applies chosen approximations to given parts of the input
program. Because each expression can have multiple approximation choices, we
use a genetic search to automatically select the best approximations. We apply
this framework to the problem of automatically bandlimiting procedural shader
programs. We evaluate our method on a variety of complex shaders, including
shaders with parallax mapping, animation, and spatially varying statistics. The
resulting smoothed shader programs outperform previous approaches both
numerically, and aesthetically, due to the smoothing properties of our
approximations.