  Volumetric video allows viewers to experience highly-realistic 3D content
with six degrees of freedom in mixed reality (MR) environments. Rendering
complex volumetric videos can require a prohibitively high amount of
computational power for mobile devices. A promising technique to reduce the
computational burden on mobile devices is to perform the rendering at a cloud
server. However, cloud-based rendering systems suffer from an increased
interaction (motion-to-photon) latency that may cause registration errors in MR
environments. One way of reducing the effective latency is to predict the
viewer's head pose and render the corresponding view from the volumetric video
in advance. In this paper, we design a Kalman filter for head motion prediction
in our cloud-based volumetric video streaming system. We analyze the
performance of our approach using recorded head motion traces and compare its
performance to an autoregression model for different prediction intervals
(look-ahead times). Our results show that the Kalman filter can predict head
orientations 0.5 degrees more accurately than the autoregression model for a
look-ahead time of 60 ms.
