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Reconstructing the Past: Artificial Intelligence and Robotics Meet Cultural Heritage

Leistungen

Setup of integration and sharing repository

Describes the setup of the repositories to be created to facilitate interaction during the development phase

Acquisition and 3D modelling of fragments

Describes the work done in WP21 which will focus on deriving an accurate 3D model for each archaeological fragment together with a mapped texture so as to obtain appearance and geometrybased descriptors that will be used by the puzzlesolving and grasping modules

Hand pose selection for grasp planning using vision sensors

Describes the work done in WP51 which aims to learn by explorationimitation the parameters of the visual servoing controllers for softhands

Quality Management plan

Sets the quality standards to be pursued in the whole project

Exploitation plan (including IP provisions)

Describes the exploitation plan of the project including IP provisions

Manipulation platform requirements

Describes the work done in WP41 and outlines the desired specifications of the robot and vision systems based on the requirements of the manipulation tasks with the fresco pieces

Style priors and their formal description for appearance prediction

Describes the work done in WP31 where highlevel knowledge of fresco styles will be formalised in ways that allow its integration into the puzzle solving algorithm

Strategic communication and dissemination plan

Describes the strategic communication and dissemination plan of the project

Data Management Plan

It provides indications as to the management of data in relation to the H2020 Open Research Data Pilot Data Management Plan might be updated during the project implementation

Veröffentlichungen

3DSGrasp: 3D shape-completion for robotic grasp

Autoren: S. S. Mohammadi, N. F. Duarte, D. Dimou, Y. Wang, M. Taiana, P. Morerio, A. Dehban, P. Moreno, A. Bernardino, A. Del Bue, and J. Santos-Victor
Veröffentlicht in: Proceedings of the IEEE International Conference on Robotics and Automation, 2023
Herausgeber: IEEE

Learning goal-directed non-prehensile pushing in cluttered scenes

Autoren: N. Dengler, D Großklaus, and M. Bennewitz
Veröffentlicht in: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2022
Herausgeber: IEEE

Relaxation labeling meets GANs: Solving jigsaw puzzles with missing borders

Autoren: M. Khoroshiltseva, A. Traviglia, M. Pelillo and S. Vascon
Veröffentlicht in: Proceedings of the 21st International Conference on Image Analysis and Processing, 2022
Herausgeber: Springer
DOI: 10.1007/978-3-031-06433-3_3

Multi-phase relaxation labeling for square jigsaw puzzle solving

Autoren: B. Vardi, M. Khoroshiltseva, A. Torcinovich, M. Pelillo, and O. Ben-Shahar
Veröffentlicht in: Proceedings of the 18th International Joint Conference on Computer Vision, Imaging, and Computer Graphics: Theory and Applications, 2023
Herausgeber: SCITEPRESS
DOI: 10.5220/0011622800003417

GANzzle: Reframing jigsaw puzzle solving as a retrieval task using a generative mental image

Autoren: D. Talon, A. Del Bue, S. James
Veröffentlicht in: Proceedings of IEEE International Conference on Image Processing, 2022
Herausgeber: IEEE
DOI: 10.1109/icip46576.2022.9897553

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