Team geometrica

Overall Objectives
Scientific Foundations
Application Domains
New Results
Contracts and Grants with Industry
Other Grants and Activities

Section: Other Grants and Activities

Actions Funded by the EC

Coordination action FOCUS K3D

Participants : Pierre Alliez, Jean-Daniel Boissonnat, Mariette Yvinec.

Web page: .

FOCUS K3D (ICT-2007-214993) is a Coordination Action of the European Union's 7th Framework Programme. The other consortium members are:

– Istituto di Matematica Applicata e Tecnologie Informatiche - Unità Organizzativa di Genova - Consiglio Nazionale delle Ricerche (CNR-IMATI-GE), Italy.

– Center for Research and Technology - Thessaly - Laboratory for Information Technology Systems and Services (CERETETH), Greece.

– École Polytechnique Federale de Lausanne - VRlab (EPFL), Switzerland.

– Fraunhofer-Institut für Graphische Datenverarbeitung, Germany.

– Université de Genève - MIRALab, Switzerland.

– SINTEF, Norway.

– Utrecht University, The Netherlands.

The aim of FOCUS K3D was to foster the comprehension, adoption and use of knowledge intensive technologies for coding and sharing 3D media content in application communities by: (i) exploiting the scientific and technological advances in the representation of the semantics of 3D media to increase awareness of the new technologies for intelligent 3D content creation and management; (ii) building user-driven scenarios to evaluate and adapt the technologies so far developed to the requirements of application environments; and (iii) fostering a shift of role of 3D content users, from passive consumers of technologies to active creators.

- Dates: March 2008 - March 2010.

- Duration: 2 years.

CG Learning

Participants : Jean-Daniel Boissonnat, Frédéric Chazal, David Cohen-Steiner, Olivier Devillers, Marc Glisse, Steve Oudot, Mariette Yvinec.

Web page: .

Computational Geometric Learning (ICT-2007-255827) is FET Open project of the European Union's 7th Framework Programme. The consortium members are:

– Friedrich-Schiller Universität Jena

– National and Kapodestrian University of Athens

– Technische Universität Dortmund

– Institut National de Recherche en Informatique

– Tel Aviv University

– Eidgenössische Technische Hochschule Zürich

– Rijksuniversität Groningen

– Freie Universität Berlin

High dimensional geometric data are ubiquitous in science and engineering, and thus processing and analyzing them is a core task in these disciplines. The Computational Geometric Learning project (CG Learning) aims at extending the success story of geometric algorithms with guarantees, as achieved in the CGAL library and the related EU funded research projects, to spaces of high dimensions. This is not a straightforward task. For many problems, no efficient algorithms exist that compute the exact solution in high dimensions. This behavior is commonly called the curse of dimensionality. We plan to address the curse of dimensionality by focusing on inherent structure in the data like sparsity or low intrinsic dimension, and by resorting to fast approximation algorithms. The following two kinds of approximation guarantee are particularly desirable: first, the solution approximates an objective better if more time and memory resources are employed (algorithmic guarantee), and second, the approximation gets better when the data become more dense and/or more accurate (learning theoretic guarantee). To lay the foundation of a new field—computational geometric learning—we will follow an approach integrating both theoretical and practical developments, the latter in the form of the construction of a high quality software library and application software.

- Dates : November 2010, November 2013.

- Duration: 3 years.


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