Maximal poisson-disk sampling via sampling radius optimization
Weize Quan, Dong‐Ming Yan, Jianwei Guo, Weiliang Meng, Xiaopeng Zhang · 2016
Maximal Poisson-disk Sampling (MPS) is a fundamental research topic in computer graphics. An ideal MPS pattern should satisfy three properties: bias-free, minimal distance, maximal coverage. The classic approach for generating MPS is dart throwing, but this method is unable to precisely control the number of samples when achieving maximality [Ebeida et al. 2011]. Sample elimination [Yuksel 2015] is an recently proposed algorithm that could generate Poisson-disk sets with an exactly desired size, but it cannot guarantee the maximal coverage. In this work, we propose a simple 2D MPS algorithm that can precisely control the number of samples, while meeting all three criteria simultaneously. Unlike previous conflict-based methods, our algorithm controls the number of samples by dynamically adjusting sampling radius.