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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) for n dimensions instead of the O(2^n) of classic Noise.

  • 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.


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