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Open AccessArticle

3D Object Reconstruction from Imperfect Depth Data Using Extended YOLOv3 Network

1
Department of Multimedia Engineering, Kaunas University of Technology, 51423 Kaunas, Lithuania
2
Department of Applied Informatics, Vytautas Magnus University, 44404 Kaunas, Lithuania
3
Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland
4
Department of Computer and Information Sciences, Northumbria University, Newcastle upon Tyne NE1 8ST, UK
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(7), 2025; https://doi.org/10.3390/s20072025
Received: 21 February 2020 / Revised: 29 March 2020 / Accepted: 2 April 2020 / Published: 3 April 2020
State-of-the-art intelligent versatile applications provoke the usage of full 3D, depth-based streams, especially in the scenarios of intelligent remote control and communications, where virtual and augmented reality will soon become outdated and are forecasted to be replaced by point cloud streams providing explorable 3D environments of communication and industrial data. One of the most novel approaches employed in modern object reconstruction methods is to use a priori knowledge of the objects that are being reconstructed. Our approach is different as we strive to reconstruct a 3D object within much more difficult scenarios of limited data availability. Data stream is often limited by insufficient depth camera coverage and, as a result, the objects are occluded and data is lost. Our proposed hybrid artificial neural network modifications have improved the reconstruction results by 8.53% which allows us for much more precise filling of occluded object sides and reduction of noise during the process. Furthermore, the addition of object segmentation masks and the individual object instance classification is a leap forward towards a general-purpose scene reconstruction as opposed to a single object reconstruction task due to the ability to mask out overlapping object instances and using only masked object area in the reconstruction process. View Full-Text
Keywords: 3D scanning; 3D shape reconstruction; RGB-D sensors; imperfect data; hybrid neural networks 3D scanning; 3D shape reconstruction; RGB-D sensors; imperfect data; hybrid neural networks
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MDPI and ACS Style

Kulikajevas, A.; Maskeliūnas, R.; Damaševičius, R.; Ho, E.S.L. 3D Object Reconstruction from Imperfect Depth Data Using Extended YOLOv3 Network. Sensors 2020, 20, 2025. https://doi.org/10.3390/s20072025

AMA Style

Kulikajevas A, Maskeliūnas R, Damaševičius R, Ho ESL. 3D Object Reconstruction from Imperfect Depth Data Using Extended YOLOv3 Network. Sensors. 2020; 20(7):2025. https://doi.org/10.3390/s20072025

Chicago/Turabian Style

Kulikajevas, Audrius; Maskeliūnas, Rytis; Damaševičius, Robertas; Ho, Edmond S.L. 2020. "3D Object Reconstruction from Imperfect Depth Data Using Extended YOLOv3 Network" Sensors 20, no. 7: 2025. https://doi.org/10.3390/s20072025

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