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Cascaded Sobol' Sampling

Lois Paulin 1 David Coeurjolly 1 Jean-Claude Iehl 1 Nicolas Bonneel 1 Alexander Keller 2 Victor Ostromoukhov 1
1 Origami - Origami
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : Rendering quality is largely influenced by the samplers used in Monte Carlo integration. Important factors include sample uniformity (e.g., low discrepancy) in the high-dimensional integration domain, sample uniformity in lower-dimensional projections, and lack of dominant structures that could result in aliasing artifacts. A widely used and successful construction is the Sobol' sequence that guarantees good high-dimensional uniformity and consequently results in faster convergence of quasi-Monte Carlo integration. We show that this sequence exhibits low uniformity and dominant structures in low-dimensional projections. These structures impair quality in the context of rendering, as they precisely occur in the 2-dimensional projections used for sampling light sources, reflectance functions, or the camera lens or sensor. We propose a new cascaded construction, which, despite dropping the sequential aspect of Sobol' samples, produces point sets exhibiting provably perfect dyadic partitioning (and therefore, excellent uniformity) in consecutive 2-dimensional projections, while preserving good high-dimensional uniformity. By optimizing the initialization parameters and performing Owen scrambling at finer levels of binary representations, we further improve over Sobol's integration convergence rate. Our method does not incur any overhead as compared to the generation of the Sobol' sequence, is compatible with Owen scrambling and can be used in rendering applications.
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Submitted on : Wednesday, September 29, 2021 - 5:13:23 PM
Last modification on : Friday, October 1, 2021 - 3:40:50 AM

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  • HAL Id : hal-03358957, version 1

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Lois Paulin, David Coeurjolly, Jean-Claude Iehl, Nicolas Bonneel, Alexander Keller, et al.. Cascaded Sobol' Sampling. ACM Transactions on Graphics, Association for Computing Machinery, 2021, Proceedings of SIGGRAPH ASIA 2021, 40 (6), pp.274:1--274:13. ⟨hal-03358957⟩

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