# Voronoi Texture Node¶

The *Voronoi Texture* node evaluates a Worley Noise at
the input texture coordinates.

## Inputs¶

The inputs are dynamic, they become available if needed depending on the node properties.

- Vector
Texture coordinate to evaluate the noise at; defaults to

*Generated*texture coordinates if the socket is left unconnected.- W
Texture coordinate to evaluate the noise at.

- Scale
Scale of the noise.

- Smoothness
The smoothness of the noise.

- Exponent
Exponent of the Minkowski distance metric.

- Randomness
The randomness of the noise.

## Properties¶

- Dimensions
The dimensions of the space to evaluate the noise in.

- 1D
Evaluate the noise in 1D space at the input W.

- 2D
Evaluate the noise in 2D space at the input Vector. The Z component is ignored.

- 3D
Evaluate the noise in 3D space at the input Vector.

- 4D
Evaluate the noise in 4D space at the input Vector and the input W as the fourth dimension.

Higher dimensions corresponds to higher render time, so lower dimensions should be used unless higher dimensions are necessary.

- Feature
The Voronoi feature that the node will compute.

- F1
The distance to the closest feature point as well as its position and color.

- F2
The distance to the second closest feature point as well as its position and color.

- Smooth F1
A smooth version of F1.

- Distance to Edge
The distance to the edges of the Voronoi cells.

- N-Sphere Radius
The radius of the n-sphere inscribed in the Voronoi cells. In other words, it is half the distance between the closest feature point and the feature point closest to it.

- Distance Metric
The distance metric used to compute the texture.

- Euclidean
Use the Euclidean distance metric.

- Manhattan
Use the Manhattan distance metric.

- Chebychev
Use the Chebychev distance metric.

- Minkowski
Use the Minkowski distance metric. The Minkowski distance is a generalization of the aforementioned metrics with an

*Exponent*as a parameter. Minkowski with an exponent of one is equivalent to the*Manhattan*distance metric. Minkowski with an exponent of two is equivalent to the*Euclidean*distance metric. Minkowski with an infinite exponent is equivalent to the*Chebychev*distance metric.

## Outputs¶

- Distance
Distance.

- Color
Cell color. The color is arbitrary.

- Position
Position of feature point.

- W
Position of feature point.

- Radius
N-Sphere radius.

## Notes¶

In some configurations of the node, especially for low values of *Randomness*,
rendering artifacts may occur. This happens due to the same reasons described
in the Notes section in the White Noise Texture page
and can be fixed in a similar manner as described there.