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OpenSimplex noise is an n-dimensional (up to 4D) gradient noise function that was developed 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,^[1] OpenSimplex noise instead swaps the skew and inverse-skew factors and uses a stretched hypercubic honeycomb. The stretched hypercubic honeycomb becomes a simplectic honeycomb after subdivision.^[2] 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.^[2]
• 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.^[2]

OpenSimplex has a variant called "SuperSimplex" (or OpenSimplex2S), which is visually smoother. "OpenSimplex2F" is identical to the original SuperSimplex.

See also

• Value noise
• Worley noise

References

External links

• 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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