GRAPES — learninG, pRocessing, And oPtimising shapES

Results in brief

learninG, pRocessing, And oPtimising shapES

Geometry is everywhere. Using digital technology to automate and facilitate the use of geometric shapes is the focus of GRAPES. We focus on optimising the digital representation of 3D objects, devising efficient ways for handling and visualising them, and making them available for a wide spectrum of everyday uses ranging from maritime engineering to producing digital twins, and from drug design to virtual reality. More concrete applications include optimized geometric design in shipping, retrieval and mining of non-rigid shapes, and reconstruction of landscapes for the prevention of natural disasters. We advanced the state of the art in a variety of fields ranging from Computational Mathematics to Geometric Modelling, up to Geometric Machine Learning. We address the technological challenge by creating synergies between Universities, Research Centers, and companies. Our focus has been to train 14 PhD candidates and 4 ESRs; our structure allowed them to benefit from top-notch research as well as a strong innovation component through a nexus of intersectoral secondments and Network-wide workshops.

Data: CORDIS, © European Union

Project objective

GRAPES aims at considerably advancing the state of the art in Mathematics, Computer-Aided Design, and Machine Learning in order to promote game changing approaches for generating, optimising, and learning 3D shapes, along with a multisectoral training for young researchers. Recent advances in the above domains have solved numerous tasks concerning multimedia and 2D data. However, automation of 3D geometry processing and analysis lags severely behind, despite their importance in science, technology and everyday life, and the well-understood underlying mathematical principles. The CAD industry, although well established for more than 20 years, urgently requires advanced methods and tools for addressing new challenges. The scientific goal of GRAPES is to bridge this gap based on a multidisciplinary consortium composed of leaders in their respective fields. Top-notch research is also instrumental in forming the new generation of European scientists and engineers. Their disciplines span the spectrum from Computational Mathematics, Numerical Analysis, and Algorithm Design, up to Geometric Modelling, Shape Optimisation, and Deep Learning. This allows the 15 PhD candidates to follow either a theoretical or an applied track and to gain knowledge from both research and innovation through a nexus of intersectoral secondments and Network-wide workshops. Horizontally, our results lead to open-source, prototype implementations, software integrated into commercial libraries as well as open benchmark datasets. These are indispensable for dissemination and training but also to promote innovation and technology transfer. Innovation relies on the active participation of SMEs, either as a beneficiary hosting an ESR or as associate partners hosting secondments. Concrete applications include simulation and fabrication, hydrodynamics and marine design, manufacturing and 3D printing, retrieval and mining, reconstruction and visualisation, urban planning and autonomous driving.

Original text from CORDIS.

Participants

ATHINA-EREVNITIKO KENTRO KAINOTOMIAS STIS TECHNOLOGIES TIS PLIROFORIAS, TON EPIKOINONION KAI TIS GNOSIS · MAROUSSICoordinatorGreece
GEOMETRY FACTORY SARL · GrasseFrance
INSTITUT NATIONAL DE RECHERCHE EN INFORMATIQUE ET AUTOMATIQUE · Le Chesnay CedexFrance
RHEINISCH-WESTFAELISCHE TECHNISCHE HOCHSCHULE AACHEN · AachenGermany
SINTEF AS · TrondheimNorway
UNIVERSITA DEGLI STUDI DI ROMA TOR VERGATA · RomaItaly
UNIVERSITA DELLA SVIZZERA ITALIANA · LuganoSwitzerland
UNIVERSITAT DE BARCELONA · BarcelonaSpain
UNIVERSITAT LINZ · LinzAustria
UNIVERSITY OF STRATHCLYDE · GlasgowUnited Kingdom
VILNIAUS UNIVERSITETAS · VilniusLithuania

GRAPES · learninG, pRocessing, And oPtimising shapES

Results in brief

learninG, pRocessing, And oPtimising shapES

Project objective

Participants

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