Houdini 20.0 Nodes VOP nodes

Periodic Worley Noise VOP node

Computes 1D, 3D, and 4D tileable Worley noise, which is synonymous with “cell noise”.

Since 16.5

This operator computes 1D, 3D, and 4D tileable Worley noise, which is synonymous with “cell noise”.

Periodic Worley noise works by scattering points randomly through space according to a nice Poisson distribution, generating cell-like patterns. In addition to regular Worley noise, this version smoothly repeats itself subject to given repetition period values. The generated noise is not anti-aliased. For best shading results, use the anti-aliased Celluar Noise instead.

The input period values are how long it takes for the pattern to repeat itself in a certain dimension. These values must be non negative integers. A value of 0 for the period in a given dimension means that the pattern does not repeat itself in that dimension.

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             (see Worley    Noise operator)
 1.8 | Periodic Worley Noise
 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)
See also

VOP nodes