Importance Resampling for Global Illumination

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.

Name thesis:degree_name

MS

Grantor dc:publisher

Brigham Young University - Provo

Author dc:creator

• Talbot, Justin F.

• computer graphics
• global illumination
• importance sampling
• resampling
• importance resampling
• sampling importance resampling
• resampled importance sampling
• direct lighting
• variance reduction
• stratification
• multiple importance sampling
• Computer Sciences

Language dc:language

English

Repository record dc:identifier

https://scholarsarchive.byu.edu/etd/663

OAI identifier oai:identifier

oai:scholarsarchive.byu.edu:etd-1662