In this paper, we focus on the problem of rendering novel views from a Neural Radiance Field (NeRF) under unobserved light conditions. To this end, we introduce a novel dataset, dubbed ReNe (Relighting NeRF), framing real world objects under one-light-at-time (OLAT) conditions, annotated with accurate ground-truth camera and light poses. Our acquisition pipeline leverages two robotic arms holding, respectively, a camera and an omni-directional point-wise light source. We release a total of 20 scenes depicting a variety of objects with complex geometry and challenging materials. Each scene includes 2000 images, acquired from 50 different points of views under 40 different OLAT conditions. By leveraging the dataset, we perform an ablation study on the relighting capability of variants of the vanilla NeRF architecture and identify a lightweight architecture that can render novel views of an object under novel light conditions, which we use to establish a non-trivial baseline for the dataset. Dataset and benchmark are available at https://eyecan-ai.github.io/rene.

翻译：本文聚焦于在未观测光照条件下从神经辐射场（NeRF）渲染新视角的问题。为此，我们引入了一个名为ReNe（重光照NeRF）的新型数据集，该数据集以逐光源（OLAT）条件采集真实世界物体，并附带精确的相机与光源位姿真值标注。我们的采集流程利用两个机械臂分别搭载相机与全向点光源，共发布20个场景，包含具有复杂几何结构和挑战性材质的各类物体。每个场景包含2000张图像，分别从50个不同视角在40种不同OLAT条件下采集。基于该数据集，我们对原始NeRF架构变体的重光照能力进行了消融研究，并确定了一种轻量级架构——该架构能够在未知光照条件下渲染物体的新视角，并据此建立了该数据集的非平凡基线。数据集与基准测试平台可于https://eyecan-ai.github.io/rene获取。