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
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
Complete list of metadatas

Cited literature [3 references]  Display  Hide  Download

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

Identifiers

  • HAL Id : hal-02150657, version 1

Citation

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⟩

Share

Metrics

Record views

657

Files downloads

2686