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

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

Eric Heitz ¹ Laurent Belcour ¹ Victor Ostromoukhov ² David Coeurjolly ³ Jean-Claude Iehl ²

1 Unity Technologies

2 R3AM - Rendu Réaliste pour la Réalité Augmentée Mobile

LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information

3 M2DisCo - Geometry Processing and Constrained Optimization

LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information

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.

Document type :

Conference papers

Domain :

Computer Science [cs] / Graphics [cs.GR]

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https://hal.archives-ouvertes.fr/hal-02150657
Contributor : David Coeurjolly <>
Submitted on : Wednesday, June 12, 2019 - 2:42:17 PM
Last modification on : Friday, June 14, 2019 - 10:58:38 AM

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

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

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657

2686

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