Importance Resampling for Global Illumination

Author

Justin F. Talbot, Brigham Young University - ProvoFollow

Abstract

This thesis develops a generalized form of Monte Carlo integration called Resampled Importance Sampling. It is based on the importance resampling sample generation technique. Resampled Importance Sampling can lead to significant variance reduction over standard Monte Carlo integration for common rendering problems. We show how to select the importance resampling parameters for near optimal variance reduction. We also combine RIS with stratification and with Multiple Importance Sampling for further variance reduction. We demonstrate the robustness of this technique on the direct lighting problem and achieve up to a 33% variance reduction over standard techniques. We also suggest using RIS as a default BRDF sampling technique.

Degree

MS

College and Department

Physical and Mathematical Sciences; Computer Science

Rights

http://lib.byu.edu/about/copyright/

BYU ScholarsArchive Citation

Talbot, Justin F., "Importance Resampling for Global Illumination" (2005). Theses and Dissertations. 663.
https://scholarsarchive.byu.edu/etd/663

Date Submitted

2005-09-16

Document Type

Thesis

Handle

http://hdl.lib.byu.edu/1877/etd1021

Keywords

computer graphics, global illumination, importance sampling, resampling, importance resampling, sampling importance resampling, resampled importance sampling, direct lighting, variance reduction, stratification, multiple importance sampling

Language

English