Population Monte Carlo Samplers for Rendering
File(s)
Date
2007Author
Fan, ShaoHua
Lai, Yu-Chi
Chenney, Stephen
Dyer, Charles
Publisher
University of Wisconsin-Madison Department of Computer Sciences
Metadata
Show full item recordAbstract
We present novel samplers and algorithms for Monte Carlo rendering.
The adaptive image-plane sampler selects pixels for
refinement according to a perceptually-weighted variance criteria.
The hemispheric integrals sampler learns an importance sampling function
for computing common rendering integrals. Both algorithms, which are
unbiased, are derived in the generic Population
Monte Carlo statistical framework, which works on a population of samples
that is iterated through distributions that are modified over time.
Information found in one iteration can be used to guide subsequent iterations.
Our results improve rendering efficiency by a factor of between 2 to 5 over
existing techniques. We also show how both samplers can be easily incorporated
into a global rendering system.
Permanent Link
http://digital.library.wisc.edu/1793/60590Citation
TR1613