Implementation of the weighted sample elimination method.
This file includes an implementation of the weighted sample elimination method for generating Poisson disk sample sets.
Blue noise (Poisson disk) sample sets produce high-quality sampling. They often lead to lower noise and better convergence with Monte Carlo sampling. They provide a uniform sample distribution over a sampling domain. Unlike regular random sampling, Poisson disk sample sets avoid placing any two samples too close together (determined by a Poisson disk radius).
The weighted sample elimination method implemented in this file generates a subset of samples with blue noise (Poisson disk) characteristics from a given input sample set. The weighted sample elimination method is simple, computationally efficient, and suitable for any sampling domain. It produces high-quality blue noise sample sets with a relatively large average Poisson disk radius without the need for specifying a Poisson disk radius. It also allows progressive (adaptive) sampling and it is efficient for high- dimensional sampling. However, it does not guarantee maximal coverage.
More details can be found in the original publication:
Cem Yuksel. 2015. Sample Elimination for Generating Poisson Disk Sample Sets. Computer Graphics Forum 34, 2 (May 2015), 25-32. http://www.cemyuksel.com/research/sampleelimination/
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