This operator computes 1D, 3D, and 4D Worley noise, which is synonymous with “cell noise”.
Worley noise works by scattering points randomly through space according to a nice Poisson distribution, generating cell-like patterns. The generated noise is not anti-aliased. For best shading results, use the anti-aliased Celluar Noise instead.
The metric option let’s users choose which distance metric to use when calculating distance between points. Euclidean (default), Manhattan, and Chebyshev are the options.
The main return values represent the distances to the N closest points, where N is 2 or 4. The distances are sorted from closest to farthest, such that:
dist1 <= dist2 <= dist3 <= dist4
If only two points are scattered, dist3 and dist4 are undefined. It is possible to combine these distances to generate noise patterns. The noise generated tends to be very cellular in nature. In fact, one attractive feature is that you can determine cell boundaries by using the expression:
if (dist2 - dist1 < tolerance) ...
which will be true if the point in space is crossing the boundary between two cells. You can look at dist1 as the amount of generated noise (see other pattern generators such as Boxes or Stripes), which can be connected to a mixing bias (see Mix), a displacement amount (see Displace Along Normal), or other float inputs.
The seed associated with the first closest point is also returned. The seed is pretty much guaranteed to be unique for every point, meaning that it is unlikely that two points close by will have the same seed associated with them.
The relative costs for computing noise of different types is roughly:
Cost | Noise Type -----+------------------------- 1.0 | Perlin Noise (see Periodic Noise operator) 1.1 | Original Perlin Noise (see Turbulent Noise operator) 1.8 | Worley Noise 1.8 | Periodic Worley Noise (see Periodic Worley Noise operator) 1.9 | Voronoi Noise (see Voronoi Noise operator) 2.1 | Sparse Convolution Noise (see Turbulent Noise operator) 2.3 | Alligator Noise (see Turbulent Noise operator)