Two-Level Adaptive Sampling for Illumination Integrals using Bayesian Monte Carlo
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Date
2016
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
Bayesian Monte Carlo (BMC) is a promising integration technique which considerably broadens the theoretical tools that can be used to maximize and exploit the information produced by sampling, while keeping the fundamental property of data dimension independence of classical Monte Carlo (CMC). Moreover, BMC uses information that is ignored in the CMC method, such as the position of the samples and prior stochastic information about the integrand, which often leads to better integral estimates. Nevertheless, the use of BMC in computer graphics is still in an incipient phase and its application to more evolved and widely used rendering algorithms remains cumbersome. In this article we propose to apply BMC to a two-level adaptive sampling scheme for illumination integrals. We propose an efficient solution for the second level quadrature computation and show that the proposed method outperforms adaptive quasi-Monte Carlo in terms of image error and high frequency noise.
Description
@inproceedings{10.2312:egsh.20161016,
booktitle = {EG 2016 - Short Papers},
editor = {T. Bashford-Rogers and L. P. Santos},
title = {{Two-Level Adaptive Sampling for Illumination Integrals using Bayesian Monte Carlo}},
author = {Marques, Ricardo and Bouville, Christian and Santos, Luis P. and Bouatouch, Kadi},
year = {2016},
publisher = {The Eurographics Association},
ISSN = {1017-4656},
ISBN = {},
DOI = {10.2312/egsh.20161016}
}