VR Viewport Pose Model for Quantifying and Exploiting Frame Correlations

TL;DR

This paper develops a statistical model of VR viewport poses, analyzes pixel correlations across frames, and introduces a real-time algorithm that improves VR rendering efficiency by reusing background data.

Contribution

It presents a novel viewport pose model based on experimental data and a ViS-based algorithm that enhances VR rendering performance and bandwidth efficiency.

Findings

ALG-ViS runs in real time on Oculus Quest 2

It outperforms baseline methods in frame quality

It reduces bandwidth consumption

Abstract

The importance of the dynamics of the viewport pose, i.e., the location and the orientation of users' points of view, for virtual reality (VR) experiences calls for the development of VR viewport pose models. In this paper, informed by our experimental measurements of viewport trajectories across 3 different types of VR interfaces, we first develop a statistical model of viewport poses in VR environments. Based on the developed model, we examine the correlations between pixels in VR frames that correspond to different viewport poses, and obtain an analytical expression for the visibility similarity (ViS) of the pixels across different VR frames. We then propose a lightweight ViS-based ALG-ViS algorithm that adaptively splits VR frames into the background and the foreground, reusing the background across different frames. Our implementation of ALG-ViS in two Oculus Quest 2 rendering systems demonstrates ALG-ViS running in real time, supporting the full VR frame rate, and outperforming baselines on measures of frame quality and bandwidth consumption.