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Title: 3D shape completion using multilayer spherical depth parameters
Other Titles: Çok katmanli küresel derinlik parametreleri ile 3B Şekil Tamamlama
Authors: Ağca, A.
Atalay, V.F.B.
Keywords: Convolutional neural networks
Shape completion
Spherical depth parameters
3D modeling
Learning systems
Signal processing
3-D shape
Depth parameters
Digital datas
Highly accurate
Input datas
Learning models
Deep learning
Issue Date: 2021
Publisher: Institute of Electrical and Electronics Engineers Inc.
Abstract: 3D 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.
Description: 29th IEEE Conference on Signal Processing and Communications Applications, SIU 2021 -- 9 June 2021 through 11 June 2021 -- 170536
ISBN: 9781665436496
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

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