Foundations of Computational Mathematics

, Volume 18, Issue 2, pp 399–431 | Cite as

Delaunay Triangulation of Manifolds

  • Jean-Daniel Boissonnat
  • Ramsay Dyer
  • Arijit Ghosh


We present an algorithm for producing Delaunay triangulations of manifolds. The algorithm can accommodate abstract manifolds that are not presented as submanifolds of Euclidean space. Given a set of sample points and an atlas on a compact manifold, a manifold Delaunay complex is produced for a perturbed point set provided the transition functions are bi-Lipschitz with a constant close to 1, and the original sample points meet a local density requirement; no smoothness assumptions are required. If the transition functions are smooth, the output is a triangulation of the manifold. The output complex is naturally endowed with a piecewise-flat metric which, when the original manifold is Riemannian, is a close approximation of the original Riemannian metric. In this case the output complex is also a Delaunay triangulation of its vertices with respect to this piecewise-flat metric.


Delaunay complex Triangulation Manifold Protection Perturbation 

Mathematics Subject Classification

Primary 57R05 Secondary 52B70 54B15 



This research has been partially supported by the Seventh Framework Programme for Research of the European Commission, under FET-Open Grant Number 255827 (CGL Computational Geometry Learning). Partial support has also been provided by the Advanced Grant of the European Research Council GUDHI (Geometric Understanding in Higher Dimensions). Arijit Ghosh is supported by Ramanujan Fellowship Number SB/S2/RJN-064/2015. Part of this work was done when he was a researcher at the Max Planck Institute for Informatics, Germany, supported by the IndoGerman Max Planck Center for Computer Science (IMPECS). Part of this work was also done, while he was a visiting scientist at the Advanced Computing and Microelectronics Unit, Indian Statistical Institute, Kolkata, India. We gratefully profited from discussions with Mathijs Wintraeken. We also thank the reviewers for the careful reading and thoughtful comments that significantly improved the final manuscript.


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Copyright information

© SFoCM 2017

Authors and Affiliations

  • Jean-Daniel Boissonnat
    • 1
  • Ramsay Dyer
    • 1
  • Arijit Ghosh
    • 2
  1. 1.INRIA, DataShapeSophia-AntipolisFrance
  2. 2.ACM UnitIndian Statistical InstituteKolkataIndia

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