On the potential of rate adaptive point cloud streaming on the point level
Autor(en): | Laniewski, D. Aschenbruck, N. |
Herausgeber: | Khoukhi, L. Oteafy, S. Bulut, E. |
Stichwörter: | Network data rates; Point cloud compression; Point cloud streaming; Point level point cloud streaming; Point-clouds; Rate adaptation; Rate-adaptation; Real-time compression; Robotics applications | Erscheinungsdatum: | 2021 | Herausgeber: | IEEE Computer Society | Journal: | Proceedings - Conference on Local Computer Networks, LCN | Volumen: | 2021-October | Startseite: | 49 | Seitenende: | 56 | Zusammenfassung: | In many robotic applications as well as in the area of immersive multimedia, large point clouds need to be transmitted over a network preferably in real-time. Typically, the point cloud is first compressed to a significantly smaller size, before it is transmitted as a simple file transfer. A drawback of this method is that it requires a point cloud to be fully available for the compression process to begin, inducing additional delay. In this paper, we explore the potential of streaming incoming points directly from an ongoing laser scan. Therefore, we propose a compression algorithm that operates on an ongoing point stream. Furthermore, we propose and compare different rate adaptation methods that dynamically adapt the compression rate to the currently available network data rate. Our evaluations show promising results in terms of rate fluctuations around the available network data rate and the achieved quality of our solution. © 2021 IEEE. |
Beschreibung: | Conference of 46th IEEE Conference on Local Computer Networks, LCN 2021 ; Conference Date: 4 October 2021 Through 7 October 2021; Conference Code:173111 |
ISBN: | 9780738124766 | DOI: | 10.1109/LCN52139.2021.9525005 | Externe URL: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118436032&doi=10.1109%2fLCN52139.2021.9525005&partnerID=40&md5=a1ac0a61e939709ce31d0f14fa6a0471 |
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geprüft am 02.06.2024