# Voronoi Texture Node

Note

This node is ported from shader nodes. The manual and images are
referencing the shader version of the node.
This node accepts field inputs and outputs.
When not connected the Vector input has an implicit `position`

attribute value.

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.