Persistent Homology Computation Using Combinatorial Map Simplification

Guillaume Damiand (1) , Rocio Gonzalez-Diaz (2)

1 M2DisCo - Geometry Processing and Constrained Optimization
2 Universidad de Sevilla = University of Seville

We propose an algorithm for persistence homology computation of orientable 2-dimensional (2D) manifolds with or without boundary (meshes) represented by 2D combinatorial maps. Having as an input a real function h on the vertices of the mesh, we first compute persistent homology of filtrations obtained by adding cells incident to each vertex of the mesh, The cells to add are controlled by both the function h and a parameter δ. The parameter δ is used to control the number of cells added to each level of the filtration. Bigger δ produces less levels in the filtration and consequently more cells in each level. We then simplify each level (cluster) by merging faces of the same cluster. Our experiments demonstrate that our method allows fast computation of persistent ho-mology of big meshes and it is persistent-homology aware in the sense that persistent homology does not change in the simplification process when fixing δ.

Mots clés: Persistent homology computation; 2D combinatorial map; mesh simplification

Domaines: Géométrie algorithmique [cs.CG]; Mathématique discrète [cs.DM]; Algorithme et structure de données [cs.DS]

Guillaume Damiand, Rocio Gonzalez-Diaz. Persistent Homology Computation Using Combinatorial Map Simplification. International Workshop on Computational Topology in Image Context, Jan 2019, Malaga, Spain. pp.26-39, ⟨10.1007/978-3-030-10828-1_3⟩. ⟨hal-02002963⟩