Use this url to cite publication: https://hdl.handle.net/20.500.12259/108240
Options
3D object reconstruction from imperfect depth data using extended YOLOv3 network
Type of publication
Straipsnis Web of Science ir Scopus duomenų bazėje / Article in Web of Science and Scopus database (S1)
Author(s)
Kulikajevas, Audrius | Kauno technologijos universitetas |
Kauno technologijos universitetas | |
Ho, Edmond S. L. | Northumbria University |
Title
3D object reconstruction from imperfect depth data using extended YOLOv3 network
Is part of
Sensors. Basel : MDPI AG, 2020, vol. 20, iss. 7
Date Issued
Date Issued |
---|
2020 |
Publisher
Basel : MDPI AG
Extent
p. 1-27
Field of Science
Abstract
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in the scenarios of intelligent remote control and communications, where virtual and augmented reality will soon become outdated and are forecasted to be replaced by point cloud streams providing explorable 3D environments of communication and industrial data.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.
Type of document
type::text::journal::journal article::research article
Language
Anglų / English (en)
Coverage Spatial
Šveicarija / Switzerland (CH)