Web: http://arxiv.org/abs/2112.01523

May 11, 2022, 1:10 a.m. | Benjamin Attal, Jia-Bin Huang, Michael Zollhoefer, Johannes Kopf, Changil Kim

cs.CV updates on arXiv.org arxiv.org

Neural radiance fields (NeRFs) produce state-of-the-art view synthesis
results. However, they are slow to render, requiring hundreds of network
evaluations per pixel to approximate a volume rendering integral. Baking NeRFs
into explicit data structures enables efficient rendering, but results in a
large increase in memory footprint and, in many cases, a quality reduction. In
this paper, we propose a novel neural light field representation that, in
contrast, is compact and directly predicts integrated radiance along rays. Our
method supports rendering …

arxiv cv embedding learning light networks neural space

More from arxiv.org / cs.CV updates on arXiv.org

Director, Applied Mathematics & Computational Research Division

@ Lawrence Berkeley National Lab | Berkeley, Ca

Business Data Analyst

@ MainStreet Family Care | Birmingham, AL

Assistant/Associate Professor of the Practice in Business Analytics

@ Georgetown University McDonough School of Business | Washington DC

Senior Data Science Writer

@ NannyML | Remote

Director of AI/ML Engineering

@ Armis Industries | Remote (US only), St. Louis, California

Digital Analytics Manager

@ Patagonia | Ventura, California