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Bidirectional importance sampling for illumination from environment maps

University of British Columbia

Image-based representations for illumination are able to capture complex real-world lighting that is difficult to represent in other forms. Current importance sampling strategies for image-based illumination have difficulties in the case where both the environment map and the surface BRDF contain important high-frequency detail, for example, when a specular surface is illuminated by an environment map containing small light sources. We introduce the notion of bidirectional importance sampling, in which samples are drawn from the product distribution of both the surface reflectance and the energy in the environment map. Although this makes the sample selection process more expensive, we show significant quality improvements over traditional importance sampling strategies for the same compute time.

Thesis/Dissertation

application/pdf

2009-12-03

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http://hdl.handle.net/2429/16236

Computer Science

University of British Columbia

UBCV

DSpace