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要旨:
All global illumination algorithms are based on rendering equation. The
rendering equation is solved in different ways in every algorithm. Most of
algorithms solve the equation by using Monte Carlo method. In this process many
samples are produced. These samples have different contribution to generated
image. If one hopes to get acceptable result with fewer samples, important
samples, which have more contribution for the nal image, must be considered in
the rst place. For example, in ordinary Light Tracing, millions of photons
have to be traced in order to obtain the distribution of illumination in the
whole scene. Actually only a part of scene can be observed most of the time,
and just photons hitting visible surfaces will contribute to the generated
image. If only a small part of entire scene is visible, we will spend most of
the time tracing and storing unimportant photons that have no any contribution
to the nal image. Even considering only visible photons, one can see that
their contribution to image is very different. Surfaces that are located closer
to viewpoint have larger image plane projected area and thus require more
photons to achieve the same noise level as surfaces located further away.
Orientation of surface in respect to view direction also affects viewdependent
photons importance. Depending on the application and used Monte Carlo algorithm
one can come up with many other different criteria to compute this importance,
which may dramatically affect the quality of produced images and computation
speed. Algorithm presented in the thesis takes only useful (visible) photons
into account, concentrating computation only on the surfaces visible by
currently active camera, balancing the distribution of photons on the image
plane, greatly improving the image quality. Using this concept, we can get
better result with fewer photons. In this way it is possible to save not only
rendering time, but also storage space because less photons need to be stored.
This idea also can be applied in other algorithms where millions of samples
have to be generated. Once the difference among these samples is found out, we
can pay more attention to the important samples that have more contribution to
the result image, while ignoring less important ones, thus using fewer samples
to get better result.
