Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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,  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.
['Weize Quan', 'Dong-Ming Yan', 'Jianwei Guo', 'Weiliang Meng', 'Xiaopeng Zhang']
I am writing a research paper on a idea Maximal poisson-disk sampling via sampling radius optimization
[{"from": "human", "value": "I am writing a research paper on a idea Maximal poisson-disk sampling via sampling radius optimization"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: 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,  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."}]