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

Kulikajevas, Audrius, Maskeliūnas, Rytis, Damaševičius, Robertas and Ho, Edmond (2020) 3D Object Reconstruction from Imperfect Depth Data Using Extended YOLOv3 Network. Sensors, 20 (7). p. 2025. ISSN 1424-8220

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Official URL: https://doi.org/10.3390/s20072025


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in the scenarios of intelligent remote control and communications, where virtual and augmented reality will soon become outdated and are forecasted to be replaced by point cloud streams providing explorable 3D environments of communication and industrial data.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.

Item Type: Article
Uncontrolled Keywords: 3D scanning; 3D shape reconstruction; RGB-D sensors; imperfect data; hybrid neural networks
Subjects: G400 Computer Science
G500 Information Systems
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Elena Carlaw
Date Deposited: 03 Apr 2020 16:06
Last Modified: 31 Jul 2021 18:35
URI: http://nrl.northumbria.ac.uk/id/eprint/42688

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