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dc.contributor.authorJae Eun Kim-
dc.contributor.authorARSHAD MUHAMMAD ZEESHAN-
dc.contributor.authorYou Seong Jong-
dc.contributor.authorJinwook Kim-
dc.contributor.authorJe Hyeong Hong-
dc.contributor.authorYoung Min Kim-
dc.date.accessioned2024-01-12T04:08:46Z-
dc.date.available2024-01-12T04:08:46Z-
dc.date.created2021-09-29-
dc.date.issued2021-01-
dc.identifier.issn1051-4651-
dc.identifier.urihttps://pubs.kist.re.kr/handle/201004/77788-
dc.description.abstractWhile potteries are common artifacts excavated in archaeological sites, the restoration process relies on manual cleaning and reassembly of shattered pieces. Since the number of possible 3D configurations is considerably large, the exhaustive manual trial may result in abrasion on fractured surfaces and even failure to find the correct matches. As a result, many recent works suggest virtual reassembly from 3D scans of the fragments. The problem is challenging in the view of the conventional 3D geometric analysis, as it is hard to extract reliable shape features from the thin break lines. We propose to optimize for the global configuration by combining geometric constraints with information from noisy shape features. Specifically, we enforce bijection and continuity of sequence of correspondences given estimates of corners and pair-wise matching scores between multiple break lines. We demonstrate that our pipeline greatly increases the accuracy of correspondences, resulting in stable restoration of 3D configurations from irregular and noisy evidence.-
dc.languageEnglish-
dc.publisherIEEE COMPUTER SOC-
dc.subject도자기-
dc.subject최적화-
dc.subject복원-
dc.subject매칭-
dc.subject접합-
dc.title3D Pots Configuration System by Optimizing over Geometric Constraints-
dc.typeConference-
dc.identifier.doi10.1109/ICPR48806.2021.9412372-
dc.description.journalClass1-
dc.identifier.bibliographicCitation25th International Conference on Pattern Recognition (ICPR), pp.2398 - 2405-
dc.citation.title25th International Conference on Pattern Recognition (ICPR)-
dc.citation.startPage2398-
dc.citation.endPage2405-
dc.citation.conferencePlaceUS-
dc.citation.conferencePlaceMilan-
dc.citation.conferenceDate2021-01-10-
dc.relation.isPartOf2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR)-
dc.identifier.wosid000678409202066-
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KIST Conference Paper > 2021
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