Kalman Filter-based Head Motion Prediction for Cloud-based Mixed Reality
This addresses latency reduction for users of cloud-based mixed reality systems, but it is incremental as it builds on existing prediction methods.
The paper tackled the problem of high motion-to-photon latency in cloud-based mixed reality systems by designing a Kalman filter for head motion prediction, showing it predicts head orientations 0.5 degrees more accurately than an autoregression model at a 60 ms look-ahead time.
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.