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Deterministic Importance Sampling with Error Diffusion
Author(s) -
SzirmayKalos László,
Szécsi László
Publication year - 2009
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/j.1467-8659.2009.01482.x
Subject(s) - computer science , curse of dimensionality , rendering (computer graphics) , sampling (signal processing) , algorithm , domain (mathematical analysis) , artificial intelligence , mathematics , computer vision , mathematical analysis , filter (signal processing)
This paper proposes a deterministic importance sampling algorithm that is based on the recognition that delta‐sigma modulation is equivalent to importance sampling. We propose a generalization for delta‐sigma modulation in arbitrary dimensions, taking care of the curse of dimensionality as well. Unlike previous sampling techniques that transform low‐discrepancy and highly stratified samples in the unit cube to the integration domain, our error diffusion sampler ensures the proper distribution and stratification directly in the integration domain. We also present applications, including environment mapping and global illumination rendering with virtual point sources.