Proceedings: GI 2017

Real-time Rendering with Compressed Animated Light Fields

Babis Koniaris (Disney Research), Maggie Kosek (Edinburgh Napier University and Disney Research), David Sinclair (Disney Research), Kenny Mitchell (Edinburgh Napier University and Disney Research)

Proceedings of Graphics Interface 2017: Edmonton, Alberta, 16-19 May 2017, 33 - 40

DOI 10.20380/GI2017.05

  • Bibtex

    author = {Koniaris, Babis and Kosek, Maggie and Sinclair, David and Mitchell, Kenny},
    title = {Real-time Rendering with Compressed Animated Light Fields},
    booktitle = {Proceedings of Graphics Interface 2017},
    series = {GI 2017},
    year = {2017},
    issn = {0713-5424},
    isbn = {978-0-9947868-2-1},
    location = {Edmonton, Alberta},
    pages = {33 -- 40},
    numpages = {8},
    doi = {10.20380/GI2017.05},
    publisher = {Canadian Human-Computer Communications Society / Soci{\'e}t{\'e} canadienne du dialogue humain-machine},
    keywords = {image-based rendering, compression, light field rendering, multi-view},


We propose an end-to-end solution for presenting movie quality animated graphics to the user while still allowing the sense of presence afforded by free viewpoint head motion. By transforming offline rendered movie content into a novel immersive representation, we display the content in real-time according to the tracked head pose. For each frame, we generate a set of cubemap images per frame (colors and depths) using a sparse set of of cameras placed in the vicinity of the potential viewer locations. The cameras are placed with an optimization process so that the rendered data maximise coverage with minimum redundancy, depending on the lighting environment complexity. We compress the colors and depths separately, introducing an integrated spatial and temporal scheme tailored to high performance on GPUs for Virtual Reality applications. We detail a real-time rendering algorithm using multi-view ray casting and view dependent decompression. Compression rates of 150:1 and greater are demonstrated with quantitative analysis of image reconstruction quality and performance.