Houdini 20.0 Nodes VOP nodes

Turbulent Noise VOP node

Can compute three types of 1D and 3D noise with the ability to compute turbulence with roughness and attenuation.

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This operator can compute three types of 1D and 3D noise with the ability to compute turbulence with roughness and attenuation:

Perlin noise              (string value "pnoise")
Original Perlin noise     (string value "onoise")
Sparse Convolution noise  (string value "snoise")
Alligator noise           (string value "anoise")
Simplex noise             (string value "xnoise")
Zero Centered Perlin      (string value "correctnoise")

Original Perlin noise is similar to Perlin noise, but marginally less efficient in computation and with different characteristics. The bounds on the noise are roughly (-1, 1) when turbulence is 0.

Perlin noise sums octaves of a noise with range about (0, 1), resulting in a non-zero centered result. Thus the zero centered perlin better matches the ranges of the other noise fields.

Sparse Convolution noise is similar to Worley noise. The noise returned is based on the weights of all of the closest points, with each point’s contribution based on a meta-ball like rolloff curve. That is, if the sample point is close to the sphere, its contribution will be greater. The bounds on the noise are roughly (-1.7, 1.7) when turbulence is 0.

Alligator noise is similar to Worley noise. It is currently not possible to simulate Alligator noise using the Worley functions, but it is possible to get a very similar 'look'. The bounds on the noise are roughly (0, 0.5) when turbulence is 0.

Simplex noise is very close to Perlin noise, except with the samples on a simplex mesh rather than a grid. This results in less grid artifacts. It also uses a higher order bspline to provide better derivatives.

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
 1.8 | Worley Noise             (see Worley Noise operator)
 1.8 | Periodic Worley Noise    (see Periodic Worley Noise operator)
 2.1 | Sparse Convolution Noise
 2.3 | Alligator Noise

Inputs

Make sure at least the Position (pos) input is connected in order to notice any noise. The easiest way to accomplish this is to pipe the global variable P into the pos input. All global variables are accessible from the Global Variables operator.

Outputs

noise

The output of the noise function. The data type of this output is controlled by the Signature parameter.

Parameters

Noise type

The type of noise to generate. Different algorithms give noise with different characteristics.

Fast

The default. A faster and more interesting variant of Perlin noise.

Sparse Convolution

Sparse Convolution noise is similar to Worley noise. Does not have artifacts at grid points.

Alligator

Produces a bumpy output. Named for its alleged resemblance to alligator skin.

Perlin

A noise where the visual details are the same size. Wikipedia article

Perlin Flow

A noise that’s stable over time, like a rotated Perlin noise, useful to create noise that seems to swirl and flow smoothly across time. Use the Flow rotation parameter below to control the rotation.

Simplex

A noise similar to Perlin but the noise lattice is on a tetrahedral mesh rather than a grid. This can avoid the grid patterns often visible in Perlin noise.

Worley Cellular F1

Produces cellular features similar to plant cells, ocean waves, honeycombs, cratered landscapes, and so on. Wikipedia article

Worley Cellular F2-F1

A variant of Worley noise that produces blunted and cornered features.

Manhattan Cellular F1

A variant of Worley F1 noise that uses Manhattan distance calculation. Useful when you want unusual-looking noise.

Manhattan Cellular F2-F1

A variant of Worley F2-F1 noise that uses Manhattan distance calculation. Useful when you want unusual-looking noise.

Chebyshev Cellular F1

A variant of Worley F1 noise that uses Chebyshev distance calculation. Useful when you want unusual-looking noise.

Chebyshev Cellular F2-F1

A variant of Worley F2-F1 noise that uses Chebyshev distance calculation. Useful when you want unusual-looking noise.

Frequency

The frequency of the noise. Higher values give smaller scaled details in the noise.

Offset

The offset of the input into the noise function. If you visualize the noise as a 2D graph or 3D height field, this has the effect of “panning” across the space of possible noise outputs. If you have the general noise effect you want but just want to get a different set of values for a different look, try changing the offset.

Amplitude

Controls the noise’s minimum and maximum values. When you think of noise as a curve, the parameter controls the height of the peaks and valleys.

Roughness

The scale increment between iterations of fractal noise added to the basic output. The higher the value the larger the “jaggies” added to the output. You can use a negative value for roughness.

Attenuation

Flattens the noise to prevent extreme spikes by damping the values. Higher values create a smoother look.

Turbulence

Controls the degree of noise. Higher values create a more chaotic and less smooth look, with more peaks and valleys.

See also

VOP nodes