Sample Elimination for Generating Poisson Disk Sample Sets

Abstract

In this paper we describe sample elimination for generating Poisson disk sample sets with a desired size. We introduce a greedy sample elimination algorithm that assigns a weight to each sample in a given set and eliminates the ones with greater weights in order to pick a subset of a desired size with Poisson disk property without having to specify a Poisson disk radius. This new algorithm is simple, computationally efficient, and it can work in any sampling domain, producing sample sets with more pronounced blue noise characteristics than dart throwing. Most importantly, it allows unbiased progressive (adaptive) sampling and it scales better to high dimensions than previous methods. However, it cannot guarantee maximal coverage. We provide a statistical analysis of our algorithm in 2D and higher dimensions as well as results from our tests with different example applications.