EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Hacker News·Sep 2·5 min read
Poisson Disk Sampling
TL;DR
Robert Bridson's 2007 paper introduces Poisson disk sampling, a method for placing objects randomly while maintaining a minimum distance between them. This efficient algorithm is widely used in computer graphics and simulations.
✦ Why It Matters
Implement Poisson disk sampling in your next graphics project to enhance object placement and visual realism.
Key Takeaways
- Use Bridson's algorithm for efficient procedural generation of natural environments.
- Implement parental optimization to reduce iterations in point generation.
- Experiment with dynamic minimum distances for creative stippling effects.
- Explore Scott A. Mitchell's algorithm for guaranteed maximality and uniformity.
How It Works
Bridson's algorithm partitions the space into a grid, ensuring each cell can hold only one point. It initializes with a random point and iteratively samples annuli around existing points to find valid placements while checking for collisions efficiently using the grid structure.
The algorithm can be enhanced by storing parent-child relationships between points to avoid unnecessary angle calculations during sampling.