论文标题
使用头部运动预测的低延迟基于云的体积视频流
Low-latency Cloud-based Volumetric Video Streaming Using Head Motion Prediction
论文作者
论文摘要
体积视频是一种用于3D空间和物体沉浸式表示的新兴关键技术。渲染体积视频需要大量的计算能力，这对于移动设备来说是具有挑战性的。为了减轻这种情况，我们开发了一个流媒体系统，该系统从云服务器上的体积视频中呈现2D视图，并将2D视频流传输到客户端。但是，由于其他网络和处理延迟，这种基于网络的处理增加了运动到光子（M2P）延迟。为了补偿增加的延迟，需要对未来用户姿势进行预测。我们开发了一个头部运动预测模型，并研究了其减少不同外观时间的M2P潜伏期的潜力。我们的结果表明，与未进行预测的基线系统相比，提出的模型减少了由M2P潜伏期引起的渲染误差。
Volumetric video is an emerging key technology for immersive representation of 3D spaces and objects. Rendering volumetric video requires lots of computational power which is challenging especially for mobile devices. To mitigate this, we developed a streaming system that renders a 2D view from the volumetric video at a cloud server and streams a 2D video stream to the client. However, such network-based processing increases the motion-to-photon (M2P) latency due to the additional network and processing delays. In order to compensate the added latency, prediction of the future user pose is necessary. We developed a head motion prediction model and investigated its potential to reduce the M2P latency for different look-ahead times. Our results show that the presented model reduces the rendering errors caused by the M2P latency compared to a baseline system in which no prediction is performed.
