When generating large procedural worlds, placing objects believably is harder than it looks.

Trees shouldnt overlap. Rocks shouldnt clump unnaturally. Buildings need breathing room. And ideally, all of this should be controllable, performant, and compatible with procedural generation.

A few years ago, while working on procedural world generation for my game, I ran into exactly this problem i.e. placing objects of varying sizes, driven by noise, with zero overlap.

This post is a write-up of how I approached that problem, what didnt work, and the solution I ended up shipping.

The Problem

At a high level, I wanted to:

• Place objects procedurally across a 2D surface

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• Allow each object to have a different radius

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• Guarantee no overlap

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• Retain a natural, non-grid-like distribution

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• Be able to bias density using noise or gameplay logic

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Classic Poisson disk sampling gives you a nice \"even but random\" distribution but it assumes a fixed minimum distance between points. That assumption breaks down immediately when object sizes vary.

Why Standard Poisson Disk Sampling Falls Short

Traditional Poisson disk sampling works by enforcing a single global minimum distance between samples. Thats great if every object is the same size.

But once you introduce variable radii:

• A small object can sit comfortably near another small object

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• A large object needs more space

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• The minimum distance is no longer constant

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You can try to cheat by inflating everything to the maximum radius but that leads to:

• wasted space

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• overly sparse distributions

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• loss of detail where small objects should be dense

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In short: the distance constraint needs to be local, not global.

Idea: The Radius Is the Constraint

Instead of thinking in terms of \"points with a minimum distance\" I reframed the problem as:

Each object has a radius, and no two objects\' influence circles may overlap.

That seems obvious in hindsight, but it changes how you structure the algorithm.

Instead of asking:

\"Is this point at least D away from others?\"

You ask:

\"Does this objects radius overlap with any existing objects radius?\"

That means overlap tests become:

Once you accept that, the rest of the system can be built around it.

Adding Noise-Driven Density

Uniform distributions are fine but procedural worlds benefit from variation.

To control where objects want to appear, I introduced procedural noise as a placement bias, not as a hard rule.

The workflow became: