  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.
