A Low-Discrepancy Sampler that Distributes Monte Carlo Errors as a Blue Noise in Screen Space

1Unity Technologies 2Université de Lyon, CNRS, LIRIS, France

In ACM SIGGRAPH Talk, 2019

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Abstract

We introduce a sampler that generates per-pixel samples achiev- ing high visual quality thanks to two key properties related to the Monte Carlo errors that it produces. First, the sequence of each pixel is an Owen-scrambled Sobol sequence that has state-of-the-art convergence properties. The Monte Carlo errors have thus low mag- nitudes. Second, these errors are distributed as a blue noise in screen space. This makes them visually even more acceptable. Our sam- pler is lightweight and fast. We implement it with a small texture and two xor operations. Our supplemental material provides comparisons against previous work for different scenes and sample counts.

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Reference

Eric Heitz, Laurent Belcour, Victor Ostromoukhov, David Coeurjolly, Jean-Claude Iehl. A Low-Discrepancy Sampler that Distributes Monte Carlo Errors as a Blue Noise in Screen Space. ACM SIGGRAPH Talk, July 2019.

@inproceedings{heitz19,
    author = "Heitz, Eric and Belcour, Laurent and Ostromoukhov, Victor and Coeurjolly, David and Iehl, Jean-Claude",
    title = "A Low-Discrepancy Sampler that Distributes Monte Carlo Errors as a Blue Noise in Screen Space",
    booktitle = "ACM SIGGRAPH Talk",
    year = "2019",
    month = "jul",
    abstract = "We introduce a sampler that generates per-pixel samples achiev- ing high visual quality thanks to two key properties related to the Monte Carlo errors that it produces. First, the sequence of each pixel is an Owen-scrambled Sobol sequence that has state-of-the-art convergence properties. The Monte Carlo errors have thus low mag- nitudes. Second, these errors are distributed as a blue noise in screen space. This makes them visually even more acceptable. Our sam- pler is lightweight and fast. We implement it with a small texture and two xor operations. Our supplemental material provides comparisons against previous work for different scenes and sample counts.",
    doi = "10.1145/3306307.3328191"
}

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