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In this paper we study techniques for reducing the
sampling noise inherent in pure
Monte Carlo approaches to global illumination.
Every light energy transport path from a light source
to the eye can be generated in a number of different ways,
according to how we partition the path into an
initial portion traced from a light source,
and a final portion traced from the eye.
Each partitioning gives us a different unbiased estimator,
but some partitionings give estimators with much
lower variance than others.
We give examples of this phenomenon and describe its significance.
We also present work in progress on the problem of combining
these multiple estimators to achieve near-optimal variance,
with the goal of producing images with less noise
for a given number of samples.
There is also more
information on gamma correction.
Abstract:
Most of the research on the global illumination problem in computer graphics
has been concentrated on finite-element (radiosity) techniques.
Monte Carlo methods are an intriguing alternative
which are attractive for their ability to handle
very general scene descriptions without the need for meshing.
Additional information
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Last modified: May 22, 1995