Blue Noise as a Lattice Gibbs Ensemble

Key points

It formalized repulsion between points as a Gibbs distribution over binary lattice occupancy, controlling density, repulsion strength, interaction range, and kernel stiffness within one model.
Using finite-range interactions and backward-tracing sampling, it guaranteed that tiles with sufficient surrounding margins are bitwise identical to generation over the full domain.
Memory use depends on tile size rather than the full output, allowing a 14K stippled image to be generated with 51MiB of memory.
It extended the same distributional framework to adaptive density and multiple types of point placement, allowing large graphics workloads to be divided without ordering or communication constraints.
The lattice quantizes point positions, truncating backward history trades accuracy against cost, and stronger ordering increases the dependency radius, potentially eliminating the benefits of tiling.

External research summaries. These are not HDATF publications or measured product results.