Opendata, web and dolomites


Exploring Relations in Structured Data with Functional Maps

Total Cost €


EC-Contrib. €






Project "EXPROTEA" data sheet

The following table provides information about the project.


Organization address
postcode: 91128

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country France [FR]
 Total cost 1˙499˙845 €
 EC max contribution 1˙499˙845 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2017-STG
 Funding Scheme ERC-STG
 Starting year 2018
 Duration (year-month-day) from 2018-01-01   to  2022-12-31


Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    ECOLE POLYTECHNIQUE FR (PALAISEAU CEDEX) coordinator 1˙499˙845.00


 Project objective

We propose to lay the theoretical foundations and design efficient computational methods for analyzing, quantifying and exploring relations and variability in structured data sets, such as collections of geometric shapes, point clouds, and large networks or graphs, among others. Unlike existing methods that are tied and often limited to the underlying data representation, our goal is to design a unified framework in which variability can be processed in a way that is largely agnostic to the underlying data type. In particular, we propose to depart from the standard representations of objects as collections of primitives, such as points or triangles, and instead to treat them as functional spaces that can be easily manipulated and analyzed. Since real-valued functions can be defined on a wide variety of data representations and as they enjoy a rich algebraic structure, such an approach can provide a completely novel unified framework for representing and processing different types of data. Key to our study will be the exploration of relations and variability between objects, which can be expressed as operators acting on functions and thus treated and analyzed as objects in their own right using the vast number of tools from functional analysis in theory and numerical linear algebra in practice. Such a unified computational framework of variability will enable entirely novel applications including accurate shape matching, efficiently tracking and highlighting most relevant changes in evolving systems, such as dynamic graphs, and analysis of shape collections. Thus, it will permit not only to compare or cluster objects, but also to reveal where and how they are different and what makes instances unique, which can be especially useful in medical imaging applications. Ultimately, we expect our study to create to a new rigorous, unified paradigm for computational variability, providing a common language and sets of tools applicable across diverse underlying domains.


year authors and title journal last update
List of publications.
2019 Jing Ren, Mikhail Panine, Peter Wonka, Maks Ovsjanikov
Structured Regularization of Functional Map Computations
published pages: 39-53, ISSN: 0167-7055, DOI: 10.1111/cgf.13788
Computer Graphics Forum 38/5 2019-08-29
2019 Jing Ren, Adrien Poulenard, Peter Wonka, Maks Ovsjanikov
Continuous and orientation-preserving correspondences via functional maps
published pages: 1-16, ISSN: 0730-0301, DOI: 10.1145/3272127.3275040
ACM Transactions on Graphics 37/6 2019-08-29
2018 Nogneng, Dorian
Correspondances non-rigides entre surfaces plongées en 3D
published pages: , ISSN: , DOI: 2 2019-08-29

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