Global Illumination Using Local Linear Density Estimation
This paper is Copyright © 1997 ACM, inc. See paper
for copyright conditions.
This paper presents the density estimation framework for generating view-independent
global illumination solutions. It works by probabilisticly simulating
the light flow in an environment with light particles that trace random walks
originating at luminaires and then using statistical density estimation techniques
to reconstruct the lighting on each surface. By splitting the computation
into separate transport and reconstruction stages, we gain many advantages
including reduced memory usage, the ability to simulate non-diffuse transport,
and natural parallelism.
This paper also describes how several theoretical and practical difficulties
can be overcome in implementing this framework. Light sources that vary
spectrally and directionally are integrated into a spectral particle tracer
using non-uniform rejection. A new local linear density estimation technique
eliminates boundary bias and extends to arbitrary polygons. A mesh
decimation algorithm with perceptual calibration is introduced to simplify
the Gouraud-shaded representation of the solution for interactive display.
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