Large-Scale 3D Shape Retrieval from ShapeNet Core55

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dc.contributor.authorSavva, Manolisen_US
dc.contributor.authorYu, Fisheren_US
dc.contributor.authorSu, Haoen_US
dc.contributor.authorKanezaki, Asakoen_US
dc.contributor.authorFuruya, Takahikoen_US
dc.contributor.authorOhbuchi, Ryutarouen_US
dc.contributor.authorZhou, Zhichaoen_US
dc.contributor.authorYu, Ruien_US
dc.contributor.authorBai, Songen_US
dc.contributor.authorBai, Xiangen_US
dc.contributor.authorAono, Masakien_US
dc.contributor.authorTatsuma, Atsushien_US
dc.contributor.authorThermos, S.en_US
dc.contributor.authorAxenopoulos, A.en_US
dc.contributor.authorPapadopoulos, G. Th.en_US
dc.contributor.authorDaras, P.en_US
dc.contributor.authorDeng, Xiaoen_US
dc.contributor.authorLian, Zhouhuien_US
dc.contributor.authorLi, Boen_US
dc.contributor.authorJohan, Henryen_US
dc.contributor.authorLu, Yijuanen_US
dc.contributor.authorMk, Sanjeeven_US
dc.contributor.editorIoannis Pratikakis and Florent Dupont and Maks Ovsjanikoven_US
dc.date.accessioned2017-04-22T17:17:41Z
dc.date.available2017-04-22T17:17:41Z
dc.date.issued2017
dc.description.abstractWith the advent of commodity 3D capturing devices and better 3D modeling tools, 3D shape content is becoming increasingly prevalent. Therefore, the need for shape retrieval algorithms to handle large-scale shape repositories is more and more important. This track provides a benchmark to evaluate large-scale 3D shape retrieval based on the ShapeNet dataset. It is a continuation of the SHREC 2016 large-scale shape retrieval challenge with a goal of measuring progress with recent developments in deep learning methods for shape retrieval. We use ShapeNet Core55, which provides more than 50 thousands models over 55 common categories in total for training and evaluating several algorithms. Eight participating teams have submitted a variety of retrieval methods which were evaluated on several standard information retrieval performance metrics. The approaches vary in terms of the 3D representation, using multi-view projections, point sets, volumetric grids, or traditional 3D shape descriptors. Overall performance on the shape retrieval task has improved significantly compared to the iteration of this competition in SHREC 2016. We release all data, results, and evaluation code for the benefit of the community and to catalyze future research into large-scale 3D shape retrieval (website: https://www.shapenet.org/shrec17).en_US
dc.description.sectionheadersSHREC Session I
dc.description.seriesinformationEurographics Workshop on 3D Object Retrieval
dc.identifier.doi10.2312/3dor.20171050
dc.identifier.isbn978-3-03868-030-7
dc.identifier.issn1997-0471
dc.identifier.pages39-50
dc.identifier.urihttps://doi.org/10.2312/3dor.20171050
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/3dor20171050
dc.publisherThe Eurographics Associationen_US
dc.subjectCategories and Subject Descriptors
dc.subjectH.3.3 [Computer Graphics]
dc.subjectInformation Systems
dc.subjectInformation Search and Retrieval
dc.titleLarge-Scale 3D Shape Retrieval from ShapeNet Core55en_US
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