Interactive demo
Worley Noise
Also known as cell noise: Steven Worley's cellular texture function from 1996, computed pixel by pixel in your browser. It is a classic building block for stone, scales, biological cells or water caustics.
The dots are the feature points, one per grid cell. Switch the output to F2 − F1 to see the borders between the cells.
Controls
Legend
- Feature point
Noise types compared
| Noise | Output | Built from | Work per sample | Smooth |
|---|---|---|---|---|
| Value Noise | Scalar field | Random values on a grid | 4 grid points | Yes |
| Perlin Noise | Scalar field | Random gradients on a grid | 4 grid points | Yes |
| Simplex Noise | Scalar field | Gradients on a triangle grid | 3 corners | Yes |
| Worley Noise this page | Scalar field | Random feature points | 9 to 25 cells | No |
| Blue Noise | Point set | Random candidates around existing points | Up to 30 tries per point | – |
| Fractal Noise | Scalar field | Octaves of a base noise | 1 base noise per octave | Yes |
| Diamond-Square | Heightmap | Displaced midpoints of a grid | 4 neighbors per point | No |
| Colors of Noise | Signal or image | White noise filtered by frequency | 1 FFT for all samples | – |
| Domain Warping | Scalar field | Fractal noise at warped positions | Up to 5 fractal noises | Yes |
| Curl Noise | Vector field | Derivatives of a noise field | 4 noise samples | Yes |
| Noise Terrain Generator | Heightmap | Stacked noise layers | Layers × octaves | – |
1. One point per cell
The image is divided into a grid, and every cell gets one feature point at a random position. The jitter sets how far it may move away from the center of its cell: at 0 the points form a regular grid.
2. Distances F1 and F2
Every pixel measures the distance to the nearest feature point (F1) and to the second nearest one (F2). Only the points of the surrounding cells have to be checked, which keeps it fast.
3. Outputs and metrics
F1 forms dark spots around the points, F2 − F1 draws the borders between the cells, like cobblestones or cracked mud. The Manhattan and Chebyshev distances turn the round shapes into diamonds and squares.