A Low-Discrepancy Sampler that Distributes Monte Carlo Errors as a Blue Noise in Screen Space - Archive ouverte HAL Access content directly
Conference Papers Year : 2019

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

Eric Heitz
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Laurent Belcour
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Abstract

We introduce a sampler that generates per-pixel samples achieving 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 magnitudes. 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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Dates and versions

hal-02150657 , version 1 (12-06-2019)

Identifiers

  • HAL Id : hal-02150657 , version 1

Cite

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. SIGGRAPH'19 Talks, Jul 2019, Los Angeles, United States. ⟨hal-02150657⟩
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