Shape from sensors: Curve networks on surfaces from 3D orientations

Tibor Stanko 1, 2 Stefanie Hahmann 1 Georges-Pierre Bonneau 3 Nathalie Saguin-Sprynski 2
1 IMAGINE - Intuitive Modeling and Animation for Interactive Graphics & Narrative Environments
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
3 MAVERICK - Models and Algorithms for Visualization and Rendering
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : We present a novel framework for acquisition and reconstruction of 3D curves using orientations provided by inertial sensors. While the idea of sensor shape reconstruction is not new, we present the first method for creating well-connected networks with cell complex topology using only orientation and distance measurements and a set of user- defined constraints. By working directly with orientations, our method robustly resolves problems arising from data inconsistency and sensor noise. Although originally designed for reconstruction of physical shapes, the framework can be used for “sketching” new shapes directly in 3D space. We test the performance of the method using two types of acquisition devices: a standard smartphone, and a custom-made device.
Type de document :
Article dans une revue
Computers and Graphics, Elsevier, 2017, Special Issue on SMI 2017, 66, pp.74-84. <http://www.sciencedirect.com/science/article/pii/S0097849317300626>. <10.1016/j.cag.2017.05.013>
Liste complète des métadonnées


https://hal.inria.fr/hal-01524740
Contributeur : Tibor Stanko <>
Soumis le : vendredi 19 mai 2017 - 23:25:40
Dernière modification le : samedi 15 juillet 2017 - 13:05:01

Fichier

shape-sensors.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

Citation

Tibor Stanko, Stefanie Hahmann, Georges-Pierre Bonneau, Nathalie Saguin-Sprynski. Shape from sensors: Curve networks on surfaces from 3D orientations. Computers and Graphics, Elsevier, 2017, Special Issue on SMI 2017, 66, pp.74-84. <http://www.sciencedirect.com/science/article/pii/S0097849317300626>. <10.1016/j.cag.2017.05.013>. <hal-01524740v2>

Partager

Métriques

Consultations de
la notice

263

Téléchargements du document

91