Riedones3D: a Celtic Coin Dataset for Registration and Fine-grained Clustering

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dc.contributor.authorHorache, Sofianeen_US
dc.contributor.authorDeschaud, Jean-Emmanuelen_US
dc.contributor.authorGoulette, Françoisen_US
dc.contributor.authorGruel, Katherineen_US
dc.contributor.authorLejars, Thierryen_US
dc.contributor.authorMasson, Olivieren_US
dc.contributor.editorHulusic, Vedad and Chalmers, Alanen_US
dc.date.accessioned2021-11-02T08:55:47Z
dc.date.available2021-11-02T08:55:47Z
dc.date.issued2021
dc.description.abstractClustering coins with respect to their die is an important component of numismatic research and crucial for understanding the economic history of tribes (especially when literary production does not exist, in celtic culture). It is a very hard task that requires a lot of times and expertise. To cluster thousands of coins, automatic methods are becoming necessary. Nevertheless, public datasets for coin die clustering evaluation are too rare, though they are very important for the development of new methods. Therefore, we propose a new 3D dataset of 2 070 scans of coins. With this dataset, we propose two benchmarks, one for point cloud registration, essential for coin die recognition, and a benchmark of coin die clustering. We show how we automatically cluster coins to help experts, and perform a preliminary evaluation for these two tasks. The code of the baseline and the dataset will be publicly available at https://www.npm3d.fr/coins-riedones3d and https: //www.chronocarto.eu/spip.php?article84&lang=fr.en_US
dc.description.sectionheadersReconstruction
dc.description.seriesinformationEurographics Workshop on Graphics and Cultural Heritage
dc.identifier.doi10.2312/gch.20211410
dc.identifier.isbn978-3-03868-141-0
dc.identifier.issn2312-6124
dc.identifier.pages83-92
dc.identifier.urihttps://doi.org/10.2312/gch.20211410
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/gch20211410
dc.publisherThe Eurographics Associationen_US
dc.titleRiedones3D: a Celtic Coin Dataset for Registration and Fine-grained Clusteringen_US
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