Rendering Along the Hilbert Curve

Alexander Keller (), Carsten Wächter () and Nikolaus Binder ()
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Alexander Keller: NVIDIA
Carsten Wächter: NVIDIA
Nikolaus Binder: NVIDIA

A chapter in Advances in Modeling and Simulation, 2022, pp 319-332 from Springer

Abstract: Abstract Based on the seminal work on Array-RQMC methods and rank-1 lattice sequences by Pierre L’Ecuyer and collaborators, we introduce efficient deterministic algorithms for image synthesis. Enumerating a low discrepancy sequence along the Hilbert curve superimposed on the raster of pixels of an image, we achieve noise characteristics that are desirable with respect to the human visual system, especially at very low sampling rates. As compared to the state of the art, our simple algorithms neither require randomization, nor costly optimization, nor lookup tables. We analyze correlations of space-filling curves and low discrepancy sequences, and demonstrate the benefits of the new algorithms in a professional, massively parallel light transport simulation and rendering system.

Keywords: Quasi-Monte Carlo methods; Hilbert curve; Array-RQMC; Low discrepancy sequences; Rank-1 lattice sequences; Image synthesis (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-10193-9_16

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DOI: 10.1007/978-3-031-10193-9_16

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