Atlas Shrugged: Device-agnostic Radiance Megatextures

Mark Magro, Keith Bugeja, Sandro Spina, Kevin Napoli, Adrian De Barro

2020

Abstract

This paper proposes a novel distributed rendering pipeline for highly responsive high-fidelity graphics based on the concept of device-agnostic radiance megatextures (DARM), a network-based out-of-core algorithm that circumvents VRAM limitations without sacrificing texture variety. After an automatic precomputation stage generates the sparse virtual texture layout for rigid bodies in the scene, the server end of the pipeline populates and updates surface radiance in the texture. On demand, connected clients receive geometry and texture information selectively, completing the pipeline by asynchronously reconstituting these data into a frame using GPUs with minimal functionality. A client-side caching system makes DARM robust to network fluctuations. Furthermore, users can immediately start consuming the service without the need for lengthy downloads or installation processes. DARM was evaluated on its effectiveness as a vehicle for bringing hardware-accelerated ray tracing to various device classes, including smartphones and single board computers. Results show that DARM is effective at allowing these devices to visualise high quality ray traced output at high frame rates and low response times.

Paper Citation

in Harvard Style

Magro M., Bugeja K., Spina S., Napoli K. and De Barro A. (2020). Atlas Shrugged: Device-agnostic Radiance Megatextures. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 1: GRAPP; ISBN 978-989-758-402-2, SciTePress, pages 255-262. DOI: 10.5220/0008954902550262

in Bibtex Style

@conference{grapp20,
author={Mark Magro and Keith Bugeja and Sandro Spina and Kevin Napoli and Adrian De Barro},
title={Atlas Shrugged: Device-agnostic Radiance Megatextures},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 1: GRAPP},
year={2020},
pages={255-262},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008954902550262},
isbn={978-989-758-402-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 1: GRAPP
TI - Atlas Shrugged: Device-agnostic Radiance Megatextures
SN - 978-989-758-402-2
AU - Magro M.
AU - Bugeja K.
AU - Spina S.
AU - Napoli K.
AU - De Barro A.
PY - 2020
SP - 255
EP - 262
DO - 10.5220/0008954902550262
PB - SciTePress