Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/8150
Full metadata record
DC FieldValueLanguage
dc.contributor.authorAğca, A.-
dc.contributor.authorAtalay, V.F.B.-
dc.date.accessioned2022-01-15T12:58:46Z-
dc.date.available2022-01-15T12:58:46Z-
dc.date.issued2021-
dc.identifier.isbn9781665436496-
dc.identifier.urihttps://doi.org/10.1109/SIU53274.2021.9477782-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/8150-
dc.description29th IEEE Conference on Signal Processing and Communications Applications, SIU 2021 -- 9 June 2021 through 11 June 2021 -- 170536en_US
dc.description.abstract3D shape completion plays a crucial role in transforming the distorted real world data to the digital data which represents the original data accurately. In recent times, there has been several works on 3D shape completion with deep learning models. Due to the input requirements of deep learning models, it is necessary to form the input data into a specific format before feeding into the network. In this work, Multilayer Spherical Depth Parameters used with a specific deep learning model for 3D shape completion and its highly accurate results will be presented. © 2021 IEEE.en_US
dc.language.isotren_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofSIU 2021 - 29th IEEE Conference on Signal Processing and Communications Applications, Proceedingsen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectConvolutional neural networksen_US
dc.subjectShape completionen_US
dc.subjectSpherical depth parametersen_US
dc.subject3D modelingen_US
dc.subjectLearning systemsen_US
dc.subjectMetadataen_US
dc.subjectMultilayersen_US
dc.subjectSignal processingen_US
dc.subject3-D shapeen_US
dc.subjectDepth parametersen_US
dc.subjectDigital datasen_US
dc.subjectHighly accurateen_US
dc.subjectInput datasen_US
dc.subjectLearning modelsen_US
dc.subjectReal-worlden_US
dc.subjectDeep learningen_US
dc.title3D shape completion using multilayer spherical depth parametersen_US
dc.title.alternativeÇok katmanli küresel derinlik parametreleri ile 3B Şekil Tamamlamaen_US
dc.typeConference Objecten_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.identifier.wosWOS:000808100700026en_US
dc.identifier.scopus2-s2.0-85111435399en_US
dc.institutionauthorAğca, Abdüllatif-
dc.identifier.doi10.1109/SIU53274.2021.9477782-
dc.authorscopusid57226402533-
dc.authorscopusid23110410300-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
item.cerifentitytypePublications-
item.fulltextNo Fulltext-
item.grantfulltextnone-
item.openairetypeConference Object-
item.languageiso639-1tr-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Show simple item record



CORE Recommender

Page view(s)

128
checked on Nov 11, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.