Fast Cubical Persistent Homology on 2D and 3D Images via Union-Find, Pruning, and Lookup Tables
2026-06-03 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors present Flash Cubical, a fast method for analyzing shapes in 2D and 3D images using cubical persistence. They use special math properties to make computations easier, prune unnecessary parts for speed, and precompute local info with lookup tables to save time during processing. Their approach works efficiently both in speed and memory usage for a specific type of input called V-filtration. The authors also note that their ideas could be adapted to other related mathematical structures.
cubical persistenceV-filtrationunion-finddualitylookup tablecubical complexestopological data analysispersistence homology2D and 3D imagesmemory optimization
Authors
Titouan Le Breton, Karol Szustakowski, Marie Piraud
Abstract
We present Flash Cubical, a highly efficient computation of cubical persistence on a V-filtration for 2D and 3D images over $\mathbb{F}_2$. The implementation is built around three core ideas. First, cubical complexes satisfy properties that allow for the computation of persistence of the highest dimension via union-find and duality. Second, pruning of certain edges allows for a fast and efficient implementation of union-find. Third, the use of a lookup table, which exploits the regularity of cubical complexes to pre-compute local information. This avoids the need to compute local information at run time. To the best of our knowledge, this is the most efficient implementation of cubical persistence with a V-filtration, both in terms of time and memory costs. Although the paper focuses on persistence for V-filtration cubical complexes, the underlying ideas generalise naturally to T-filtrations on cubical complexes and suggest promising directions for other complexes.