# 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 and return.

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

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

- Smooth F1
Compute and return a smooth version of F1.

- Distance To Edge
Compute and return the distance to the edges of the Voronoi cells.

- N-Sphere Radius
Compute and return 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.