An Adaptive Point Sampler on a Regular Latice

Abdalla G.M. Ahmed, Till Niese, Hui Huang, Oliver Deussen

Research output: Contribution to conferencePaperpeer-review

19 Scopus citations


We present a framework to distribute point samples with controlled spectral properties using a regular lattice of tiles with a single sample per tile. We employ a word-based identification scheme to identify individual tiles in the lattice. Our scheme is recursive, permitting tiles to be subdivided into smaller tiles that use the same set of IDs. The corresponding framework offers a very simple setup for optimization towards different spectral properties. Small lookup tables are suficient to store all the information needed to produce different point sets. For blue noise with varying densities, we employ the bit-reversal principle to recursively traverse sub-tiles. Our framework is also capable of delivering multi-class blue noise samples. It is well-suited for different sampling scenarios in rendering, including area-light sampling (uniform and adaptive), and importance sampling. Other applications include stippling and distributing objects.

Original languageEnglish (US)
StatePublished - 2017
EventACM SIGGRAPH 2017 - Los Angeles, United States
Duration: Jul 30 2017Aug 3 2017


ConferenceACM SIGGRAPH 2017
Country/TerritoryUnited States
CityLos Angeles


  • Blue noise
  • Monte Carlo
  • Multi-class blue noise
  • Quasi-Monte Carlo
  • Sampling
  • Self-similarity
  • Thue-Morse word
  • Tiling

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design


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