CAD-driven pattern recognition in reverse engineered models - CNRS - Centre national de la recherche scientifique Accéder directement au contenu
Communication Dans Un Congrès Année : 2019

CAD-driven pattern recognition in reverse engineered models

William Puech
Gérard Subsol

Résumé

Today, it has become frequent and relatively easy to digitize the surface of 3D objects and then to reconstruct a combination of geometric primitives such as planes, cylinders, spheres or cones. However, the given reconstruction contains only geometry, no information of a semantic nature used during the design process is included. In this paper, we present a robust method to recognize specific geometric structures which are not explicitly present in an object, such as features and repetitions. These are known as patterns, which are used in the CAD modeling process. Moreover, the digitization of an object often leads to various inaccuracies, and therefore inaccurate extracted primitives. We also demonstrate how recognized patterns can be useful as an application in beautification, which consists of the adjustment of primitive parameters to satisfy geometrical relations such as parallelism and concentricity. Our objective is to design a fast and automatic method, which is seldom seen in reverse engineering. We show the efficiency and robustness of our method through experimental results applied on reverse engineered 3D meshes.
Fichier principal
Vignette du fichier
GRAPP_2019_19.pdf (2.42 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

lirmm-02084850 , version 1 (17-01-2020)

Licence

Paternité - Pas d'utilisation commerciale - Pas de modification

Identifiants

Citer

Silvère Gauthier, William Puech, Roseline Bénière, Gérard Subsol. CAD-driven pattern recognition in reverse engineered models. GRAPP 2019 - 14th International Conference on Computer Graphics Theory and Applications, Feb 2019, Prague, Czech Republic. pp.244-254, ⟨10.5220/0007360702440254⟩. ⟨lirmm-02084850⟩
204 Consultations
142 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More