Poisson Disk Points
What this grid is
Poisson disk sampling is a way to generate “blue-noise” point distributions: points look random, but they avoid clumps by enforcing a minimum distance between neighbors. The result feels natural and evenly spaced at the same time.
This generator fills a rectangular canvas with points using a Poisson disk rule. You can render the points as circles or squares, choose dot size and colors, and optionally apply relaxation to make spacing even more uniform.
Because it’s vector output, the pattern scales cleanly and is perfect for stippling, dot textures, and modern “random but controlled” design backgrounds.
Key parameters
- Width / Height (px) — Canvas size for the sampling region.
- Min Distance (px) — The Poisson radius (minimum separation between points).
- Seed — Reproducibility control; the same seed recreates the same pattern.
- Max Points — Performance cap; actual point count may be lower depending on the region and distance.
- Candidate Tries (k) — Sampling quality vs speed; higher values can pack more points but take longer.
- Relaxation Iterations — Optional repulsion pass that smooths spacing and reduces local irregularities.
- Padding (px) — Keeps points away from edges (useful when adding borders or cropping).
- Dot Shape + Dot Size — Circle (radius) or square (side length), plus the dot size.
- Fill / Border — Color, opacity, and border width for the dots.
Unique highlights
- Blue-noise distribution: looks natural without obvious clustering or grid artifacts.
- Seeded output: easy to generate repeatable variations for a consistent art direction.
- Optional relaxation produces a “designed” evenness while staying non-grid-like.
- Works as a standalone texture or as a mask/overlay layer in vector workflows.
Typical use cases
- Stippling textures and halftone-like dot fields (without strict halftone geometry).
- Grain overlays and subtle backgrounds for posters, UI, and brand assets.
- Random dot masks for clipping, reveal effects, and pattern-based shading.
- “Scatter” guides for placing icons or decorative marks with controlled spacing.
- Generative art experiments (export multiple seeds as a series).
Tips
- A good starting point is minDistance ≈ 2×dotSize (or a bit larger) to avoid overlap.
- If the result feels too sparse, reduce Min Distance first; increase Candidate Tries if packing stalls early.
- Use Padding when Border Width > 0, or when you plan to crop the canvas tightly.
- If you want a calmer look, keep dots small and lower opacity; density comes from Min Distance, not dot size.
FAQ
Why is the number of dots lower than Max Points?
Max Points is only a cap; the Poisson distance and region size determine how many points can actually fit.
How do I reproduce the exact same pattern later?
Keep Width/Height, Min Distance, and Seed the same (and avoid changing Candidate Tries/Relaxation if you need an exact match).
How can I make it denser?
Reduce Min Distance, and if needed increase Candidate Tries to improve filling quality.
What does Relaxation do?
It gently repels nearby points for a more even distribution, reducing local irregularity while keeping the pattern non-grid-like.