Gaussian Blue Noise

Among the various approaches for producing point distributions with blue noise spectrum, we argue an optimization framework using Gaussian kernels. We show that a wise selection of parameters, this approach attains unprecedented quality, provably surpassing current state art attained by optimal transport (BNOT) approach. Further, our algorithm scales smoothly and feasibly to high dimensions while maintaining same realizing high-quality high-dimensional sets. Finally, extension adaptive sampling.