# Gaussian Blue Noise

arXiv: https://arxiv.org/abs/2206.07798 · HTML: https://mlsanity.com/paper/2206.07798/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 69 references that are in the map.
- First posted on arXiv 15 Jun 2022.

## Builds on (most cited first, 2 of 2)

- [Spoke-Darts for High-Dimensional Blue-Noise Sampling](https://mlsanity.com/paper/1408.1118/) (2014)
- [A Comprehensive Theory and Variational Framework for Anti-aliasing Sampling Patterns](https://mlsanity.com/paper/1902.08228/) (2019)

## Built on by (most cited first, 6 of 6)

- [Blue noise for diffusion models](https://mlsanity.com/paper/2402.04930/) (2024)
- [Generative Modeling with Orbit-Space Particle Flow Matching](https://mlsanity.com/paper/2605.02222/) (2026)
- [Example-Based Sampling with Diffusion Models](https://mlsanity.com/paper/2302.05116/) (2023)
- [A Lennard-Jones Layer for Distribution Normalization](https://mlsanity.com/paper/2402.03287/) (2024)
- [DC3DO: Diffusion Classifier for 3D Objects](https://mlsanity.com/paper/2408.06693/) (2024)
- [Mat'ern Noise for Triangulation-Agnostic Flow Matching on Meshes](https://mlsanity.com/paper/2605.19305/) (2026)

## Abstract (arXiv)

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.