dos Anjos, RK, Pereira, J and Gaspar, J (2018) A navigation paradigm driven classification for video-based rendering techniques. Computers & Graphics, 77. pp. 205-216. ISSN 0097-8493
Abstract
The use of videos as an input for a rendering process (video-based rendering, VBR) has recently been started to be looked upon with greater interest, and has added many other challenges and also solutions to classical image-based rendering (IBR). Although the general goal of VBR is shared by different applications, approaches widely differ regarding methodology, setup, and data representation. Previous attempts on classifying VBR techniques used external aspects as classification parameters, providing little insight on the inner similarities between works, and not defining clear lines of research. We found that the chosen navigation paradigm for a VBR application is ultimately the deciding factor on several details of a VBR technique. Based on this statement, this article presents the state of art on video-based rendering and its relations and dependencies to the used data representation and image processing techniques. We present a novel taxonomy for VBR applications with the navigation paradigm being the topmost classification attribute, and methodological aspects further down in the hierarchy. Different view generation methodologies, capture baselines and data representations found in the body of work are described, and their relation to the chosen classification scheme is discussed.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 Published by Elsevier Ltd. This is an author produced version of an article published in Computers & Graphics. Uploaded in accordance with the publisher's self-archiving policy. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/. |
Keywords: | Video-based rendering; Data representation; Application; Navigation paradigm; Free viewpoint video |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 30 Mar 2023 09:59 |
Last Modified: | 30 Mar 2023 09:59 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.cag.2018.10.017 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:194581 |