Deep Learning based frameworks for 3D registration of differential and multimodal data
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In recent decades, Visual Computing methodologies such as image processing and computer vision have addressed problems in the field of Cultural Heritage (CH)resulting in significant benefits. Specifically, accurate scanning methods have proved invaluable for documenting cultural heritage assets. However, such scans can also be used to track changes over time and to create holistic models of CH artefacts, resulting from multiple scan modalities. This in turn necessitates solving specific challenges in the task of registration, a classic problem in Visual Computing. Informally, registration is the action of placing two geometric datasets with overlap(e.g. point clouds) in a common reference frame so that the areas of overlap match as closely as possible. This thesis focuses on two special cases of 3D registration: cross-time and multimodal. The first research area concerns the registration of differential 3D data, where the object of interest may have changed over time. The second research area concerns the registration of data from different modalities; specifically 3D point clouds and micro-CT volumes have been addressed. As both problems are too complex to address with direct algorithms while training instances exist or can be generated, it was chosen to apply deep learning methodologies to solve them and the results have been very encouraging. Additionally, the cross-time registration solution has been extended into an auto-mated framework for change monitoring and difference detection for CH objects, while the multimodal method was combined with the cross-time method in order to monitor changes on both the surface and inner structure of CH objects.
Has partsPaper A: Saiti, Evdokia; Danelakis, Antonios; Theoharis, Theoharis. Cross-time registration of 3D point clouds. Computers & graphics 2021 ;Volum 99. s. 139-152 https://doi.org/10.1016/j.cag.2021.07.005 This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )
Paper B: Saiti, Evdokia; Saha, Sunita; Bunsch, Eryk; Sitnik, Robert; Theoharis, Theoharis. An automated approach for change and difference detection on cultural heritage applications
Paper C: Saiti, Evdokia; Theoharis, Theoharis. An application independent review of multimodal 3D registration methods. Computers & graphics 2020 ;Volum 91. s. 153-178 https://doi.org/10.1016/j.cag.2020.07.012 This is an open access article under the CC BY license. ( http://creativecommons.org/licenses/by/4.0/ )
Paper D: Saiti, Evdokia; Theoharis, Theoharis. Multimodal registration across 3D point clouds and CT-volumes. Computers & graphics 2022 ;Volum 106. s. 259-266 https://doi.org/10.1016/j.cag.2022.06.012 This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Paper E: Saiti, Evdokia; Theoharis, Theoharis. A pipeline for monitoring the external and inner structure of cultural heritage objects. Proceedings of Conference Archiving, 2023
Paper F: Siatou, Amalia; Papanikolaou, Athanasia; Saiti, Evdokia. Adaption of Imaging Techniques for Monitoring Cultural Heritage Objects. Springer Proceedings in Materials 2022 ;Volum 16. s. 38-47 https://doi.org/10.1007/978-3-031-03795-5_6
Paper G: Saiti, Evdokia; Theoharis, Theoharis. Automated 3D registration techniques for applications in cultural heritage monitoring