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Abstract:
A novel view-independent technique for
progressive global illumination computations
has been developed that uses prediction of visible differences
to improve both efficiency and effectiveness
of physically-sound lighting solutions.
The technique is a mixture of
stochastic (density estimation) and
deterministic (adaptive mesh refinement) algorithms
that are used in a sequence optimized to reduce the
differences between the intermediate and final images as perceived
by the human observer in the course of lighting computations.
The quantitative measurements of visibility were obtained
using the model of human vision captured in the Visible Differences
Predictor (VDP) developed by Daly \cite{Daly93}.
The VDP responses were used to support selection of the best
component algorithms from a pool of global illumination solutions,
and to enhance the selected algorithms for even better progressive
refinement of the image quality. Also, the VDP was used
to determine the optimal sequential order of component-algorithm
execution, and to choose the points at which switch-over between
algorithms should take place.
As the VDP is computationally expensive, it was applied
exclusively at the stage of design and tuning of the composite
technique, and so perceptual considerations are
embedded into the resulting solution,
though no VDP calculations are performed
during the lighting simulation.
The proposed global illumination technique is also novel,
providing at unprecedented speeds intermediate image solutions
of high quality even for complex scenes.
One advantage of the technique
is that local estimates of global illumination are
readily available at early stages of computations.
This makes possible the development of more robust
adaptive mesh subdivision, which is guided by local
contrast information. Also, based on
stochastically-derived estimates of the local illumination
error, an efficient object space filtering is applied to
substantially reduce the visible noise inherent in
stochastic solutions.
