We explore a new strategy for few-shot novel view synthesis based on a neural
light field representation. Given a target camera pose, an implicit neural
network maps each ray to its target pixel's color directly. The network is
conditioned on local ray features generated by coarse volumetric rendering from
an explicit 3D feature volume. This volume is built from the input images using
a 3D ConvNet. Our method achieves competitive performances on synthetic and
real MVS data with respect to state-of-the-art neural radiance field based
competition, while offering a 100 times faster rendering.

本文探讨了一种基于神经光场表示的少样本新视角合成策略，其使用了隐式神经网络将光线映射到目标像素的颜色，且通过由由粗略体积渲染产生的本地光线特征来确定网络的条件。该方法使用 3D ConvNet 构建的输入图像的 3D 特征体积，实现了比现有的神经辐射场方法更加快速且在合成和真实的 MVS 数据上取得了竞争性的性能。

从少量图像中学习具有泛化能力的光场网络

Learning Generalizable Light Field Networks from Few Images

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。