Scalable Remote Rendering with Depth and Motion-flow Augmented Streaming

Abstract

In this paper, we focus on efficient compression and streaming of frames rendered from a dynamic 3D model. Remote rendering and on-the-fly streaming become increasingly attractive for interactive applications. Data is kept confidential and only images are sent to the client. Even if the client's hardware resources are modest, the user can interact with state-of-the-art rendering applications executed on the server. Our solution focuses on augmented video information, e.g., by depth, which is key to increase robustness with respect to data loss, image reconstruction, and is an important feature for stereo vision and other client-side applications. Two major challenges arise in such a setup. First, the server workload has to be controlled to support many clients, second the data transfer needs to be efficient. Consequently, our contributions are twofold. First, we reduce the server-based computations by making use of sparse sampling and temporal consistency to avoid expensive pixel evaluations. Second, our data-transfer solution takes limited bandwidths into account, is robust to information loss, and compression and decompression are efficient enough to support real-time interaction. Our key insight is to tailor our method explicitly for rendered 3D content and shift some computations on client GPUs, to better balance the server/client workload. Our framework is progressive, scalable, and allows us to stream augmented high-resolution (e.g., HDready) frames with small bandwidth on standard hardware.