Gaussian Blue Noise

TL;DR

This paper introduces a Gaussian kernel-based optimization framework for generating high-quality blue noise point distributions, outperforming existing methods like BNOT, and scalable to high dimensions with adaptive sampling capabilities.

Contribution

It presents a novel optimization approach using Gaussian kernels that surpasses current state-of-the-art blue noise generation methods and scales efficiently to high dimensions.

Findings

Achieves higher quality blue noise distributions than BNOT.

Scales effectively to high-dimensional spaces.

Supports adaptive sampling extensions.

Abstract

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