OpenSimplex noise
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OpenSimplex noise is an n-dimensional (up to 4D) gradient noise function that was developed by Kurt Spencer[1] in 2014 in order to overcome the patent-related issues surrounding simplex noise, while likewise avoiding the visually-significant directional artifacts characteristic of Perlin noise.
The algorithm shares numerous similarities with simplex noise, but has two primary differences:
- Whereas simplex noise starts with a hypercubic honeycomb and squashes it down the main diagonal in order to form its grid structure,[2] OpenSimplex noise instead swaps the skew and inverse-skew factors and uses a stretched hypercubic honeycomb. The stretched hypercubic honeycomb becomes a simplicial honeycomb after subdivision.[3] This means that 2D Simplex and 2D OpenSimplex both use different orientations of the triangular tiling, but whereas 3D Simplex uses the tetragonal disphenoid honeycomb, 3D OpenSimplex uses the tetrahedral-octahedral honeycomb.[3]
- OpenSimplex noise uses a larger kernel size than simplex noise. The result is a smoother appearance at the cost of performance, as additional vertices need to be determined and factored into each evaluation.[3]
OpenSimplex has a variant called "SuperSimplex" (or OpenSimplex2S), which is visually smoother. "OpenSimplex2F" is identical to the original SuperSimplex.
See also
[edit]References
[edit]- ^ peco, barbe (2025-04-24). "OpenSimplex Noise: The Evolution of Noise Representation". barbe_generative_diary. Retrieved 2025-09-14.
- ^ Ken Perlin, Noise hardware. In Real-Time Shading SIGGRAPH Course Notes (2001), Olano M., (Ed.). (pdf)
- ^ a b c Spirit of Iron: Simplectic Noise Michael Powell's blog
External links
[edit]- Blog post introducing OpenSimplex noise
- Author's current implementation (OpenSimplex2)
- Android library
- C implementation
- GPU implementation in OpenCL
- Heavily-optimized implementation in C#
- Noise library for the Rust programming language providing OpenSimplex noise – does not hard code gradient initial values
- Python implementation
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