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Learning Neural Light Fields with Ray-Space Embedding Networks. (arXiv:2112.01523v3 [cs.CV] UPDATED)
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 …
More from arxiv.org / cs.CV updates on arXiv.org
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