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.

翻译：神经辐射场（NeRF）近期已成为一种强大的图像三维重建方法，但每场景的长时间优化限制了其实际应用，尤其在资源受限的场景中。现有方法通过减少输入视角数量，并借助复杂损失函数或其他模态的额外输入对学习到的体积表示进行正则化来解决这一问题。本文提出KeyNeRF，一种简单而有效的方法，通过聚焦关键信息光线在少样本场景中训练NeRF。此类光线首先通过视角选择算法在相机层级进行筛选，该算法增强基线多样性的同时确保场景覆盖；随后基于局部图像熵的概率分布在像素层级采样。我们的方法在极少改动现有NeRF代码库的前提下，性能优于当前最先进的方法。