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Harmonising Observations and Underlying Principles for Visual Data Association

Project description

Optimising principles for visual data association

Applications for visual data association include the mapping of physics models to complex scenarios in autonomous driving or matching collections of 3D shapes for medical analysis. However, current visual data association methods are ill-equipped to deal with recent advances in deep learning. Consequently, they cannot guarantee global optimality, scalability or geometric consistency in critical applications, because they are unable to interpret complex interconnections across different observable entities. To address this, the EU-funded Harmony project proposes to conceptually and algorithmically harmonise the complex interconnections between observable entities and underlying fundamental principles such as geometry and physics. Outcomes will benefit researchers and practitioners, providing them with optimised solutions to complex tasks in relevant applications.

Objective

Visual data association aims to find task-specific mappings involving visual data. Two significant examples are the mapping of physics models to complex scenes for planning overtaking manoeuvrers in autonomous driving, or matching collections of 3D shapes for medical analysis. Despite the high relevance of visual data association, its progress has not kept pace with the revolutionary developments fuelled by recent deep learning advances: existing data association machinery lacks theoretical guarantees (e.g. global optimality, or structure such as geometric consistency in 3D shape matching) that are critical for high-stakes settings, or suffers from poor scalability. Moreover, current procedures fall short of understanding complex interconnections across different observable entities (collections of e.g. objects or scenes). The vision of Harmony is to tackle these shortcomings by harmonising the complex interconnections between observable entities and underlying fundamental principles (e.g. geometry, or physics). This research direction is challenging, largely unexplored and will require to break substantially new ground at conceptual, algorithmic and practical levels simultaneously. Harmony is organised into four complementary challenges:
Challenge A addresses global optimality and scalability for 3D shape matching;
Challenge B addresses structure and dynamics inference from static images;
Challenge C addresses non-linear synchronisation in data collections defined over graphs;
Challenge D will exploit synergies and cross-fertilise insights across Harmony.
Overall, Harmony will benefit both researchers and practitioners by providing solutions to more complex tasks in practically relevant settings (e.g. geometrically consistent medical shape analysis, or physics-based scene understanding).

Fields of science (EuroSciVoc)

Keywords

Programme(s)

Topic(s)

Funding Scheme

HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

(opens in new window) ERC-2024-STG

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Host institution

RHEINISCHE FRIEDRICH-WILHELMS-UNIVERSITAT BONN
Net EU contribution
€ 1 624 911,00
Address
REGINA PACIS WEG 3
53113 Bonn
Germany
Region
Nordrhein-Westfalen Köln Bonn, Kreisfreie Stadt
Activity type
Higher or Secondary Education Establishments
Links
Total cost
€ 1 624 911,00

Beneficiaries (1)