![](bga.png)
(1a) Unity3D Relay
(1b) Edge Relay
(2a) Unity3D Relay
(2b) Edge Relay
Figure 9: Examples of a moving person reconstructed using
(1a,2a) the Unity3D Relay (Unity3D Multiplayer Service,
2019) and (1b,2b) our Edge Relay. Case 1: When the object
is still we can see that results (i.e. density of triangulated 3D
points) using Unity3D Relay and Edge Relay are comparable.
Case 2: When the object moves, synchronisation is key to
achieve accurate 3D triangulation, and using the Unity3D
Relay leads to sparser reconstructions.
to receive capture frames via HTTP requests. Synchro-
nisation triggers are generated by a host, rather than
by a system timer, to enable a motion-based, adaptive
sampling-rate, fostering reduced data throughput. Al-
though the creation of high-quality FVVs was not the
scope of this work, we succeeded to show the benefit of
our decentralised data capturing system using a state-
of-the-art 3D reconstruction algorithm (i.e. COLMAP)
and by implementing the assessment of end-to-end
capture delays though OCR.
Future research directions include the integration
of a volumetric 4D reconstruction algorithm that can
be executed in real-time on the edge to providing tele-
presence functionality together with the integration
of temporal filtering of 3D reconstructed points to
provide more stable volumetric videos. We also aim
to improve reconstruction accuracy by postprocessing
ARCore’s pose estimates. By the end of this year we
will deploy our system on a 5G network and carry out
the first FVV production in uncontrolled environments
using off-the-shelf mobiles.
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