SHREC 2020 Track: River Gravel Characterization

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dc.contributor.authorGiachetti, Andreaen_US
dc.contributor.authorBiasotti, Silviaen_US
dc.contributor.authorMoscoso Thompson, Eliaen_US
dc.contributor.authorFraccarollo, Luigien_US
dc.contributor.authorNguyen, Quangen_US
dc.contributor.authorNguyen, Hai-Dangen_US
dc.contributor.authorTran, Minh-Trieten_US
dc.contributor.authorArvanitis, Gerasimosen_US
dc.contributor.authorRomanelis, Ioannisen_US
dc.contributor.authorFotis, Vlasisen_US
dc.contributor.authorMoustakas, Konstantinosen_US
dc.contributor.authorTortorici, Claudioen_US
dc.contributor.authorWerghi, Naoufelen_US
dc.contributor.authorBerretti, Stefanoen_US
dc.contributor.editorSchreck, Tobias and Theoharis, Theoharis and Pratikakis, Ioannis and Spagnuolo, Michela and Veltkamp, Remco C.en_US
dc.date.accessioned2020-09-03T09:50:27Z
dc.date.available2020-09-03T09:50:27Z
dc.date.issued2020
dc.description.abstractThe quantitative analysis of the distribution of the different types of sands, gravels and cobbles shaping river beds is a very important task performed by hydrologists to derive useful information on fluvial dynamics and related processes (e.g., hydraulic resistance, sediment transport and erosion, habitat suitability. As the methods currently employed in the practice to perform this evaluation are expensive and time-consuming, the development of fast and accurate methods able to provide a reasonable estimate of the gravel distribution based on images or 3D scanning data would be extremely useful to support hydrologists in their work. To evaluate the suitability of state-of-the-art geometry processing tool to estimate the distribution from digital surface data, we created, therefore, a dataset including real captures of riverbed mockups, designed a retrieval task on it and proposed them as a challenge of the 3D Shape Retrieval Contest (SHREC) 2020. In this paper, we discuss the results obtained by the methods proposed by the groups participating in the contest and baseline methods provided by the organizers. Retrieval methods have been compared using the precision-recall curves, nearest neighbor, first tier, second tier, normalized discounted cumulated gain and average dynamic recall. Results show the feasibility of gravels characterization from captured surfaces and issues in the discrimination of mixture of gravels of different size.en_US
dc.description.sectionheadersSHREC Short Papers
dc.description.seriesinformationEurographics Workshop on 3D Object Retrieval
dc.identifier.doi10.2312/3dor.20201162
dc.identifier.isbn978-3-03868-126-7
dc.identifier.issn1997-0471
dc.identifier.pages27-35
dc.identifier.urihttps://doi.org/10.2312/3dor.20201162
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/3dor20201162
dc.publisherThe Eurographics Associationen_US
dc.subjectComputing methodologies
dc.subjectShape analysis
dc.titleSHREC 2020 Track: River Gravel Characterizationen_US
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3DOR 2020 - Short Papers