Efficient product sampling using hierarchical thresholding
Author
Summary, in Swedish
Abstract in Undetermined
We present an efficient method for importance sampling the product of multiple functions. Our algorithm computes a quick approximation of the product on the fly, based on hierarchical representations of the local maxima and averages of the individual terms. Samples are generated by exploiting the hierarchical properties of many low-discrepancy sequences, and thresholded against the estimated product. We evaluate direct illumination by sampling the triple product of environment map lighting, surface reflectance, and a visibility function estimated per pixel. Our results show considerable noise reduction compared to existing state-of-the-art methods using only the product of lighting and BRDF.
We present an efficient method for importance sampling the product of multiple functions. Our algorithm computes a quick approximation of the product on the fly, based on hierarchical representations of the local maxima and averages of the individual terms. Samples are generated by exploiting the hierarchical properties of many low-discrepancy sequences, and thresholded against the estimated product. We evaluate direct illumination by sampling the triple product of environment map lighting, surface reflectance, and a visibility function estimated per pixel. Our results show considerable noise reduction compared to existing state-of-the-art methods using only the product of lighting and BRDF.
Department/s
Publishing year
2008
Language
English
Pages
465-474
Publication/Series
Visual Computer
Volume
24
Issue
7-9
Document type
Journal article
Publisher
Springer
Topic
- Computer Science
Keywords
- importance sampling
- rejection sampling
- multiple functions
- ray tracing
- visibility
Status
Published
Research group
- Computer Graphics
ISBN/ISSN/Other
- ISSN: 0178-2789