Gaussian Blue Noise
This addresses the need for efficient and high-quality sampling methods in computer graphics and related fields, representing a significant advance rather than an incremental improvement.
The paper tackles the problem of generating high-quality blue noise point distributions by proposing an optimization framework using Gaussian kernels, achieving unprecedented quality that provably surpasses the current state-of-the-art BNOT approach and scaling smoothly to high dimensions.
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.