Learning Neural Light Fields with Ray-Space Embedding Networks
Abstract
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 with a single network evaluation per pixel for small baseline light field datasets and can also be applied to larger baselines with only a few evaluations per pixel. At the core of our approach is a ray-space embedding network that maps the 4D ray-space manifold into an intermediate, interpolable latent space. Our method achieves state-of-the-art quality on dense forward-facing datasets such as the Stanford Light Field dataset. In addition, for forward-facing scenes with sparser inputs we achieve results that are competitive with NeRF-based approaches in terms of quality while providing a better speed/quality/memory trade-off with far fewer network evaluations.
Cite
@article{arxiv.2112.01523,
title = {Learning Neural Light Fields with Ray-Space Embedding Networks},
author = {Benjamin Attal and Jia-Bin Huang and Michael Zollhoefer and Johannes Kopf and Changil Kim},
journal= {arXiv preprint arXiv:2112.01523},
year = {2022}
}
Comments
CVPR 2022 camera ready revision. Major changes include: 1. Additional comparison to NeX on Stanford, RealFF, Shiny datasets 2. Experiment on 360 degree lego bulldozer scene in the appendix, using Pluecker parameterization 3. Moving student-teacher results to the appendix 4. Clarity edits -- in particular, making it clear that our Stanford evaluation *does not* use subdivision