Simplex noise

Simplex noise

Simplex noise is a method for constructing an n-dimensional noise function comparable to Perlin noise ("classic" noise) but with a lower computational overhead, especially in larger dimensions. Ken Perlin designed the algorithm in 2001 [Ken Perlin, Noise hardware. In Real-Time Shading SIGGRAPH Course Notes (2001), Olano M., (Ed.). [ (pdf)] ] to address the limitations of his classic noise function, especially in higher dimensions.

The advantages of simplex noise over Perlin noise:
* Simplex noise has a lower computational complexity and requires fewer multiplications.
* Simplex noise scales to higher dimensions (4D, 5D and up) with much less computational cost, the complexity is O(n^2) for n dimensions instead of the O(2^n) of classic Noise [Ken Perlin, Making noise. Based on a talk presented at GDCHardcore (Dec 9, 1999). [ (url)] ] .
* Simplex noise has no noticeable directional artifacts (is isotropic).
* Simplex noise has a well-defined and continuous gradient everywhere that can be computed quite cheaply.
* Simplex noise is easy to implement in hardware.

Whereas classical noise interpolates between the values from the surrounding hypergrid end points (ie: North South East West in 2D), Simplex noise divides the space into simplexes (ie: n dimensional equilateral triangles) to interpolate between. This reduces the number of data points. While a hypercube in N dimensions has 2^N corners, a simplex in N dimensions has only N+1 corners.

See also

*Perlin noise


External links

* [ Short technical article with source code by Stefan Gustavson] (PDF)
* [ Perlin's animated "rubber sheet" simplex noise demo]
* [ Another implementation of Simplex Noise in C++ (SimplexNoise1234)]

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