RIDEL, Brett; GUENNEBAUD, Gael; REUTER, Patrick; GRANIER, Xavier

doi:10.1016/j.cag.2015.05.006

Métadonnées

Licence d’utilisation du document

RIDEL, Brett
Melting the frontiers between Light, Shape and Matter [MANAO]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]

GUENNEBAUD, Gael
Melting the frontiers between Light, Shape and Matter [MANAO]
Laboratoire Bordelais de Recherche en Informatique [LaBRI]

REUTER, Patrick
Laboratoire Bordelais de Recherche en Informatique [LaBRI]
Melting the frontiers between Light, Shape and Matter [MANAO]

Langue

Article de revue

Ce document a été publié dans

Computers and Graphics. 2015-06-26, vol. 51, p. 60-66

Elsevier

Résumé en anglais

Moving least squares (MLS) surface approximation is a popular tool for the processing and reconstruction of non-structured and noisy point clouds. This paper introduces a new variant improving the approximation quality when the underlying surface is assumed to be locally developable, which is often the case in point clouds coming from the acquisition of manufactured objects. Our approach follows Levin's classical MLS procedure: the point cloud is locally approximated by a bivariate quadratic polynomial height-field defined in a local tangent frame. The a priori developability knowledge is introduced by constraining the fitted poly-nomials to have a zero-Gaussian curvature leading to the actual fit of so-called parabolic cylinders. When the local developability assumption cannot be made unambiguously, our fitted parabolic cylinders seamlessly degenerate to linear approximations. We show that our novel MLS kernel reconstructs more locally-developable surfaces than previous MLS methods while being faithful to the data.< Réduire

Mots clés en anglais

reconstruction

squares

least

moving

mls

parabolic-cylinder

developable

cylinder

parabolic

URI

https://oskar-bordeaux.fr/handle/20.500.12278/182027

DOI

http://dx.doi.org/10.1016/j.cag.2015.05.006

Projet Européen

Virtual Museum Transnational Network

Origine

Importé de hal

Unités de recherche

Laboratoire Photonique, Numérique et Nanosciences (LP2N) - UMR 5298