Disney Research


Light€field video, as a high-dimensional function, is very demanding in terms of storage. As such, light€eld video data, even in a compressed form, do not typically €fit in GPU or main memory unless the capture area, resolution or duration is sufficiently small. Additionally, latency minimization–critical for viewer comfort in use-cases such as virtual reality–places further constraints in many compression schemes. In this paper, we propose a scalable method for streaming lightfi€eld video, parameterized on viewer location and time, that efficiently handles RAM-to-GPU memory transfers of light€field video in a compressed form, utilizing the GPU architecture for reduction of latency. We demonstrate the effectiveness of our method in a variety of compressed animated light€field datasets.

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