Analysis of Scalar Fields over Point Cloud Data - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Reports (Research Report) Year : 2008

Analysis of Scalar Fields over Point Cloud Data

Frédéric Chazal
Leonidas J. Guibas
  • Function : Author
  • PersonId : 850076
Steve Y. Oudot
  • Function : Correspondent author
  • PersonId : 845393

Connectez-vous pour contacter l'auteur
Primoz Skraba
  • Function : Author
  • PersonId : 850314


Given a real-valued function f defined over some metric space X, is it possible to recover some structural information about f from the sole information of its values at a finite set L of sample points, whose pairwise distances in X are given? We provide a positive answer to this question. More precisely, taking advantage of recent advances on the front of stability for persistence diagrams, we introduce a novel algebraic construction, based on a pair of nested families of simplicial complexes built on top of the point cloud L, from which the persistence diagram of f can be faithfully approximated. We derive from this construction a series of algorithms for the analysis of scalar fields from point cloud data. These algorithms are simple and easy to implement, they have reasonable complexities, and they come with theoretical guarantees. To illustrate the genericity and practicality of the approach, we also present some experimental results obtained in various applications, ranging from clustering to sensor networks.
Fichier principal
Vignette du fichier
RR-6576.pdf (6.37 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

inria-00294591 , version 1 (09-07-2008)
inria-00294591 , version 2 (18-03-2009)
inria-00294591 , version 3 (21-04-2009)


  • HAL Id : inria-00294591 , version 3


Frédéric Chazal, Leonidas J. Guibas, Steve Y. Oudot, Primoz Skraba. Analysis of Scalar Fields over Point Cloud Data. [Research Report] RR-6576, INRIA. 2008. ⟨inria-00294591v3⟩
351 View
871 Download


Gmail Facebook X LinkedIn More