OpenSimplex noise

OpenSimplex noise is an n-dimensional (up to 4D) gradient noise function that was developed by Kurt Spencer 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, OpenSimplex noise instead swaps the skew and inverse-skew factors and uses a stretched hypercubic honeycomb.

Source: Wikipedia — OpenSimplex noise (CC BY-SA 4.0)

OpenSimplex noise

OpenSimplex noise is an n-dimensional (up to 4D) gradient noise function that was developed by Kurt Spencer 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, OpenSimplex noise instead swaps the skew and inverse-skew factors and uses a stretched hypercubic honeycomb.

Source: Wikipedia "OpenSimplex noise" · CC BY-SA 4.0

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