AI Based Image Segmentation of Cultural Heritage Objects used for Multi-View Stereo 3D Reconstructions

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dc.contributor.authorKutlu, Hasanen_US
dc.contributor.authorBrucker, Felixen_US
dc.contributor.authorKallendrusch, Benen_US
dc.contributor.authorSantos, Pedroen_US
dc.contributor.authorFellner, Dieter W.en_US
dc.contributor.editorBucciero, Albertoen_US
dc.contributor.editorFanini, Brunoen_US
dc.contributor.editorGraf, Holgeren_US
dc.contributor.editorPescarin, Sofiaen_US
dc.contributor.editorRizvic, Selmaen_US
dc.date.accessioned2023-09-02T07:44:29Z
dc.date.available2023-09-02T07:44:29Z
dc.date.issued2023
dc.description.abstractImage segmentation (or masking) finds a very useful use case within 3D reconstruction of cultural heritage objects. The 3D reconstructions can be accelerated, reconstructing the object without any background noise. Conventional segmentation methods can calculate erroneous masks for certain objects and environments, which can lead to errors within the reconstruction: Parts of the 3D reconstruction may be missing or are incorrectly reconstructed, which contradicts adequate archiving. The automated iterative Multi-View Stereo (MVS) scanning process makes it necessary to obtain masks that reconstruct the object in the best possible way, regardless of the environment, the stabilizing mount, the color of the background and the object. In addition, it should not be necessary to tweak the best possible parameters for conventional masking procedures and to create masks manually. State-of-the-art artificial intelligence (AI) segmentation networks will be trained and applied to the MVS scans to verify the behavior of the associated 3D reconstructions and the automated iterative scanning process. In addition, a comparison between different AI segmentation networks and a comparison between conventional masking methods and AI segmentation networks is performed.en_US
dc.description.sectionheadersAI and 3D Reconstruction III
dc.description.seriesinformationEurographics Workshop on Graphics and Cultural Heritage
dc.identifier.doi10.2312/gch.20231160
dc.identifier.isbn978-3-03868-217-2
dc.identifier.issn2312-6124
dc.identifier.pages75-79
dc.identifier.pages5 pages
dc.identifier.urihttps://doi.org/10.2312/gch.20231160
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/gch20231160
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies → Image segmentation; Reconstruction; Hardware → Scanners
dc.subjectComputing methodologies → Image segmentation
dc.subjectReconstruction
dc.subjectHardware → Scanners
dc.titleAI Based Image Segmentation of Cultural Heritage Objects used for Multi-View Stereo 3D Reconstructionsen_US
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GCH 2023 - Eurographics Workshop on Graphics and Cultural Heritage