Simplex noise scales to higher dimensions with much less computational cost: the complexity is for dimensions instead of the of classic noise.
Simplex noise has no noticeable directional artifacts, though noise generated for different dimensions are visually distinct.
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 gradients at the surrounding hypergrid end points, simplex noise divides the space into simplices. This reduces the number of data points. While a hypercube in dimensions has corners, a simplex in dimensions has only corners. The triangles are equilateral in 2D, but in higher dimensions the simplices are only approximately regular. For example, the tiling in the 3D case of the function is an orientation of the tetragonal disphenoid honeycomb. Simplex noise is useful for computer graphics applications, where noise is usually computed over 2, 3, 4 or possibly 5 dimensions. For higher dimensions, n-spheres around n-simplex corners are not densely enough packed, reducing the support of the function and making it zero in large portions of space.
Algorithm detail
Simplex noise is most commonly implemented as a two-, three-, or four-dimensional function, but can be defined for any number of dimensions. An implementation typically involves four steps: coordinate skewing, simplical subdivision, gradient selection, and kernel summation.
Coordinate skewing
An input coordinate is transformed using the formula where This has the effect of placing the coordinate on an A lattice, which is essentially the vertex arrangement of a hypercubic honeycomb that has been squashed along its main diagonal until the distance between the points and becomes equal to the distance between the points and. The resulting coordinate is then used in order to determine which skewed unit hypercube cell the input point lies in,, yb'=floor, and its internal coordinates.
Simplicial subdivision
Once the above is determined, the values of the internal coordinate are sorted in decreasing order, to determine which skewed Schläfli orthoscheme simplex the point lies in. Then the resulting simplex is composed of the vertices corresponding to an ordered edge traversal from to, of which there are n! possibilities each of which correspond to a single permutation of the coordinate. In other words, start with the zero coordinate, and successively add ones starting in the value corresponding to the largest internal coordinate's value, and ending with the smallest. For example, the point would lie inside the simplex with vertices,,,. The yi' coordinate is the largest so it is added first. It is then followed by the xi' coordinate, and finally zi'.
Gradient selection
Each simplex vertex is added back to the skewed hypercube's base coordinate, and hashed into a pseudo-random gradient direction. The hash can be implemented in numerous ways, though most often uses a permutation table or a bit manipulation scheme. Care should be taken in the selection of the set of gradients to include, in order to keep directional artifacts to a minimum.
Kernel summation
The contribution from each of the n+1 vertices of the simplex is factored in via a summation of radially-symmetric kernels centered around each vertex. First, the unskewed coordinate of each of the vertices is determined using the inverse formula where This point is subtracted from the input coordinate in order to obtain the unskewed displacement vector. This unskewed displacement vector is used for two purposes:
To compute the extrapolated gradient value using a dot product
To determine d2, the squared distance to the point.
From there, each vertex's summed kernel contribution is determined using the equation where r2 is usually set to either 0.5 or 0.6. 0.5 ensures no discontinuities, whereas 0.6 may increase visual quality in applications for which the discontinuities are not noticeable. 0.6 was used in Ken Perlin's original reference implementation.
Legal status
Uses of implementations in 3D and higher for textured image synthesis are covered by, if the algorithm is implemented using the specific techniques described in any of the patent claims.
