“Shape-Curvature-Graph”: Towards a New Model of Representation for the Description of 3D Meshes

  • Arnaud PoletteEmail author
  • Jean Meunier
  • Jean-Luc Mari
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10325)


This paper presents a new shape descriptor for 3D meshes, that aims at representing an arbitrary triangular polyhedron using a graph, called SCG for “Shape-Curvature-Graph”. This entity can be used to perform self-similarity detection, or more generally to extract patterns within a shape. Our method uses discrete curvature maps and divides the meshes into eight categories of patches (peak, ridge, saddle ridge, minimal, saddle valley, valley, pit and flat). Then an adjacency graph is constructed with a node for each patch. All categories of patches cannot be neighbors in a continuous context, thus additional intermediary patches are added as boundaries to ensure a continuous consistency at the transitions between areas. To validate the relevance of this modular structure, an approach based of these shape descriptor graphs is developed in order to extract similar patterns within a surface mesh. It illustrates that these “augmented” graphs obtained using differential properties on meshes can be used to analyze shape and extract features.


Pattern Extraction Discrete Object Continuous Object Discrete Curvature Mesh Segmentation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC). The Bunny and the Happy Buddha data sets were provided courtesy of the Stanford University Computer Graphics Laboratory. The Gargoyle and the Chinese dragon data sets were provided courtesy of the AIM@SHAPE consortium.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Arts et Métiers ParisTech, CNRS, LSIS UMR 7296Aix-en-provenceFrance
  2. 2.DIROUniversity of MontrealMontrealCanada
  3. 3.Aix-Marseille Université, CNRS, LSIS UMR 7296MarseilleFrance

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