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.

针对如何从神经光辐射场（NeRF）中渲染新视角的问题，本文介绍了ReNe(重新照明NeRF)数据集，细分了现实世界中的物体以及光与相机姿态，通过该数据集，可以表明NeRF varients的光照能力，进而确定了一种轻量级体系结构，以获得一种能够在新的光照条件下呈现对象新视角的方案，并用于该数据集的非平凡基线。

ReLight My NeRF:用于真实世界物体的新视角综合和重光数据集