Overview
Poisson disk object placement uses distance constraints to scatter objects across a domain while avoiding overlaps or dense clusters.
CoreSource checked
Poisson Disk Object Placement
Places objects with a minimum spacing so distributions look natural but not clumped.
Quick Facts
Compact metadata for implementation and discovery.
- Category
- Procedural Generation
- Difficulty
- Intermediate
- Family
- Spatial Distribution
- Tier
- Core
- Status
- Verified
- Review
- Source Checked
- Sources
- 1
- Formulas
- 1
- Relationships
- 3
- Code example
- Ready
- Visualization
- Ready
Core Concepts
minimum distancespatial gridcandidate sampleblue-noise spacing
Quick facts
Complexity
Runtime and memory behavior at a glance.
Best
O(n)
Average
O(n)
Worst
O(n^2)
Space
O(n)
When to Use
- Use when generating terrain needs minimum distance.
- Use when the input matches the assumptions behind spatial Distribution.
When Not to Use
- Avoid when the design needs strict authored control over every generated detail.
- Avoid when a simpler algorithm gives the required result with less setup.
What It Does
- Places objects with a minimum spacing so distributions look natural but not clumped
- Uses minimum distance and spatial grid to guide the work.
- Produces generated map, texture, layout, or pattern.
Use Cases
- Generating terrain
- Creating textures
- Building levels or natural patterns
Inputs
- Seed
- Generation parameters
- Constraint rules
Outputs
- Generated map, texture, layout, or pattern
Key Formulas (1)
Minimum distance rule
distance(samplei, samplej) ≥ r
distance(sample_i, sample_j) >= rAccepts samples only if they are far enough from existing samples.
Pseudocode
- 1.Initialize the seed, grid, rule set, or generator state
- 2.Choose the next cell, feature, or parameter to update
- 3.Apply constraints, noise, or stochastic choices
- 4.Repeat until the generated structure satisfies the stopping rule
Canonical Example
- Input
- seed = 7 with a small tile set, rule set, terrain grid, or generation parameter
- Output
- Generated map, texture, layout, or pattern
- Explanation
- Poisson Disk Object Placement applies minimum distance to produce generated map, texture, layout, or pattern.
Implementation Notes
- Keep the seed and parameters stored with generated output for reproducibility.
- Keep the minimum distance step separate enough to test independently.
- Store seed, parameters, and validation results with generated output so artifacts can be reproduced.
Common Pitfalls
- Small parameter changes can create large visual or structural differences.
- Test edge cases, not only clean examples.
- Changing a seed or constraint weight can create artifacts that are hard to reproduce without recorded parameters.
Interactive Mechanism Walkthrough
Spatial Primitive & Sweep-Line CanvasShow the generation state evolving from seed values to the final artifact.
Step 1 of 4: Initialize the seed, grid, rule set, or generator state
procedural-generation-mechanismNotes
Poisson Disk Object Placement should be used with the assumptions and tradeoffs of spatial Distribution in mind.
Sources (1)
What each reference supports for this entry.
Bridson Poisson Disk Sampling
PaperReference for fast Poisson disk sampling used for blue-noise object placement.
Open sourceExplore Next
Good next jumps from the relationship graph, ranked for useful discovery.
Relationships
View full relationship pageObject placement uses Poisson disk sampling to keep points well separated.
Alternative toStratified Sampling
Both distribute samples with coverage constraints.
Related Algorithms
Tags
Procedural GenerationSpatial SamplingPoisson DiskSampling