![[IMAGE -- three teapots]](fig5.jpg)
Metropolis Light Transport
Reference:
Images too Our algorithm is unbiased, handles general geometric and scattering
models, uses little storage, and can be orders of magnitude more
efficient than previous unbiased approaches. It performs especially
well on problems that are usually considered difficult, e.g. those
involving bright indirect light, small geometric holes, or glossy
surfaces. Furthermore, it is competitive with previous unbiased
algorithms even for relatively simple scenes.
The key advantage of the Metropolis approach is that the path space is
explored locally, by favoring mutations that make small changes to the
current path. This has several consequences. First, the average cost
per sample is small (typically only one or two rays). Second, once an
important path is found, the nearby paths are explored as well, thus
amortizing the expense of finding such paths over many samples. Third,
the mutation set is easily extended. By constructing mutations that
preserve certain properties of the path (e.g. which light source is
used) while changing others, we can exploit various kinds of coherence
in the scene. It is often possible to handle difficult lighting
problems efficiently by designing a specialized mutation in this way.
Last modified: June 17, 1997
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Abstract
We present a new Monte Carlo method for solving the light transport
problem, inspired by the Metropolis sampling method in computational
physics. To render an image, we generate a sequence of light transport
paths by randomly mutating a single current path (e.g. adding a new
vertex to the path). Each mutation is accepted or rejected with a
carefully chosen probability, to ensure that paths are sampled according
to the contribution they make to the ideal image. We then estimate this
image by sampling many paths, and recording their locations on the image
plane.
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