Multi-view data capture using edge-synchronised mobiles

• URL: http://arxiv.org/abs/2005.03286v1
• Date: Thu, 7 May 2020 07:13:20 GMT
• Title: Multi-view data capture using edge-synchronised mobiles
• Authors: Matteo Bortolon, Paul Chippendale, Stefano Messelodi and Fabio Poiesi
• Abstract summary: New-generation network architectures (e.g. 5G) promise lower latency and larger bandwidth connections supported by powerful edge computing. We propose a novel and scalable data capture architecture that exploits edge resources to synchronise and harvest frame captures. We empirically show the benefits of our edge computing unit by analysing latencies and show the quality of 3D reconstruction outputs against an alternative and popular centralised solution.
• Score: 0.17205106391379021
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-view data capture permits free-viewpoint video (FVV) content creation. To this end, several users must capture video streams, calibrated in both time and pose, framing the same object/scene, from different viewpoints. New-generation network architectures (e.g. 5G) promise lower latency and larger bandwidth connections supported by powerful edge computing, properties that seem ideal for reliable FVV capture. We have explored this possibility, aiming to remove the need for bespoke synchronisation hardware when capturing a scene from multiple viewpoints, making it possible through off-the-shelf mobiles. We propose a novel and scalable data capture architecture that exploits edge resources to synchronise and harvest frame captures. We have designed an edge computing unit that supervises the relaying of timing triggers to and from multiple mobiles, in addition to synchronising frame harvesting. We empirically show the benefits of our edge computing unit by analysing latencies and show the quality of 3D reconstruction outputs against an alternative and popular centralised solution based on Unity3D.

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