基于卡尔曼滤波器的云混合现实头部运动预测
Kalman Filter-based Head Motion Prediction for Cloud-based Mixed Reality
摘要：
立体视频允许观看者在混合现实（MR）环境中体验具有六个自由度的高度逼真的3D内容。渲染复杂的体积视频对于移动设备可能需要过高的计算能力。减少移动设备上的计算负担的一种有前途的技术是在云服务器上执行渲染。然而，基于云的渲染系统遭受增加的交互（运动到光子）延迟，这可能导致MR环境中的配准错误。减少有效延迟的一种方式是预测观看者的头部姿势并提前从体积视频渲染对应的视图。在本文中，我们设计了一个卡尔曼滤波器头部运动预测在我们的基于云的体积视频流系统。我们使用记录的头部运动轨迹分析我们的方法的性能，并将其性能与不同预测间隔（前瞻时间）的自回归模型进行比较。我们的研究结果表明，卡尔曼滤波器可以预测的头部取向0.5度更准确地比自回归模型的前瞻时间为60毫秒。
英文摘要：
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.
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