Overview
Worley Noise scatters feature points in space and evaluates each location by its nearest or nth-nearest feature distance.
CoreSource checked
Worley Noise
Generates cellular patterns from distances to nearby feature points.
Quick Facts
Compact metadata for implementation and discovery.
- Category
- Procedural Generation
- Difficulty
- Intermediate
- Family
- Cellular Noise
- Tier
- Core
- Status
- Verified
- Review
- Source Checked
- Sources
- 2
- Formulas
- 1
- Relationships
- 3
- Code example
- Ready
- Visualization
- Ready
Core Concepts
feature pointnearest distancecell hashcellular pattern
Quick facts
Complexity
Runtime and memory behavior at a glance.
Best
O(1)
Average
O(neighbor cells)
Worst
O(neighbor cells)
Space
O(1)
When to Use
- Use when cell-like procedural texture or mask patterns are needed.
- Use when cellular textures is a central requirement.
When Not to Use
- Avoid when smooth gradient noise without cellular boundaries is desired.
- Avoid when the generator cannot enforce the playability, connectivity, or style constraints required.
What It Does
- Places feature points in grid cells
- Finds nearest feature points
- Maps distances to pattern values
Use Cases
- Cellular textures
- Stone and organic patterns
- Procedural masks
Inputs
- Point position
- Feature point seed
- Distance metric
Outputs
- Noise value
Key Formulas (1)
Cellular value
value(p) = min(distance(p, featurei))
value(p) = min(distance(p, feature_i))Returns the nearest feature-point distance for a sample position.
Pseudocode
- 1.Find the grid cell containing the sample point
- 2.Generate deterministic feature points in nearby cells
- 3.Measure distances to those features
- 4.Return the nearest or nth-nearest distance
Canonical Example
- Input
- sample point near one feature point
- Output
- value = nearest distance
- Explanation
- Nearby pixels share distance structure around feature points.
Implementation Notes
- Use deterministic hashing per grid cell so the noise is repeatable without storing all points.
- Expose seed, scale, and constraint parameters so outputs can be reproduced.
- Keep a tiny example with expected output beside the implementation.
Common Pitfalls
- Checking too few neighboring cells can miss the true nearest feature.
- Random-looking output can still contain repeated patterns, dead ends, or invalid regions.
- Not defining boundary, empty, or degenerate inputs before implementation.
Interactive Mechanism Walkthrough
2D Grid Wavefront & TrajectoryShow cells around random feature points forming organic cellular boundaries.
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Step 1 of 4: Find the grid cell containing the sample point
generated-gridNotes
Different distance metrics create different cellular looks.
Sources (2)
What each reference supports for this entry.
Worley Cellular Texture Basis Functions
PaperOriginal reference for cellular/Worley noise based on nearest feature points.
Open sourceTexturing and Modeling: A Procedural Approach
BookProcedural-generation reference for Worley Noise's noise, texture synthesis, and terrain/shape generation use cases.
Explore Next
Good next jumps from the relationship graph, ranked for useful discovery.
Relationships
View full relationship pageAlternative toPerlin Noise
Worley Noise produces cellular patterns rather than smooth gradient noise.
Conceptual neighborVoronoi Diagram
Worley Noise is based on nearest feature points, similar to Voronoi regions.
Related Algorithms
Tags
Procedural GenerationNoiseGraphicsSpatial Sampling