Neural Radiance Fields (NeRF) have recently emerged as a powerful method for
image-based 3D reconstruction, but the lengthy per-scene optimization limits
their practical usage, especially in resource-constrained settings. Existing
approaches solve this issue by reducing the number of input views and
regularizing the learned volumetric representation with either complex losses
or additional inputs from other modalities. In this paper, we present KeyNeRF,
a simple yet effective method for training NeRF in few-shot scenarios by
focusing on key informative rays. Such rays are first selected at camera level
by a view selection algorithm that promotes baseline diversity while
guaranteeing scene coverage, then at pixel level by sampling from a probability
distribution based on local image entropy. Our approach performs favorably
against state-of-the-art methods, while requiring minimal changes to existing
NeRF codebases.

KeyNeRF 是一种在少样本情况下训练 NeRF 的简单而有效的方法，通过关注关键信息光线，首先通过一个视图选择算法在相机级别选择这样的光线，该算法在促进基线多样性的同时保证场景全面覆盖，然后通过基于局部图像熵的概率分布进行像素级的采样。我们的方法在与最先进的方法进行比较时表现出色，同时对现有的 NeRF 代码库的改动要求较小。