Standards-compliant HTTP Adaptive Streaming of Static Light Fields

Abstract

Static light fields are an effective technology to precisely visualize complex inanimate objects or scenes, synthetic and real-world alike, in Augmented, Mixed and Virtual Reality contexts. Such light fields are commonly sampled as a collection of 2D images. This sampling methodology inevitably gives rise to large data volumes, which in turn hampers real-time light field streaming over best effort networks, particularly the Internet. This paper advocates the packaging of the source images of a static light field as a segmented video sequence so that the light field can then be interactively network streamed in a quality-variant fashion using MPEG-DASH, the standardized HTTP Adaptive Streaming scheme adopted by leading video streaming services like YouTube and Netflix. We explain how we appropriate MPEG-DASH for the purpose of adaptive static light field streaming and present experimental results that prove the feasibility of our approach, not only from a networking but also a rendering perspective. In particular, real-time rendering performance is achieved by leveraging video decoding hardware included in contemporary consumer-grade GPUs. Important trade-offs are investigated and reported on that impact performance, both network-wise (e.g., applied sequencing order and segmentation scheme for the source images of the static light field) and rendering-wise (e.g., disk-versus-GPU caching of source images). By adopting a standardized transmission scheme and by exclusively relying on commodity graphics hardware, the net result of our work is an interoperable and broadly deployable network streaming solution for static light fields.