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.

本文提出了一种紧凑且直接预测射线上积分辐射度的新型神经光场表示方法，支持小基线光场数据集每像素一次网络评估的渲染，并可应用于大基线场景。