Improving Curl Noise
We introduce a divergence-free nD vector noise defined as the n-dimensional cross product of the gradients of n − 1 noise functions. We show that this vector noise function is divergence-free and hence volume preserving for any dimension n. Our method enables precise integration and extends to new settings by substituting noise functions with implicit surfaces, (hyper)surfaces, or custom functions. We demonstrate applications including image warping, surface texturing, noise bounded by implicit surfaces, anisotropic curl-noise, and high-dimensional point jittering up to 7D.
Authors
- Jeppe Revall Frisvad (ORCID: https://orcid.org/0000-0002-0603-3669)
- Jakob Andreas Bærentzen (ORCID: https://orcid.org/0000-0003-2583-0660)
- Jonàs Martínez (ORCID: https://orcid.org/0000-0001-8443-9624)
- Sylvain Lefebvre (ORCID: https://orcid.org/0000-0003-4045-3779)
Institutions
- Centre National de la Recherche Scientifique (FR)
- Laboratoire Lorrain de Recherche en Informatique et ses Applications (FR)
- Centre Inria de l'Université de Lorraine (FR)
- Université de Lorraine (FR)
- Technical University of Denmark (DK)
Publication Details
- Published
- 2025-12-08
- DOI
- https://doi.org/10.1145/3757377.3763980
- Citations
- 1
- Primary Topic
- 3D Shape Modeling and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 1.33