The standard Neural Radiance Fields (NeRF) paradigm employs a viewer-centered methodology, entangling the aspects of illumination and material reflectance into emission solely from 3D points. This simplified rendering approach presents challenges in accurately modeling images captured under adverse lighting conditions, such as low light or over-exposure. Motivated by the ancient Greek emission theory that posits visual perception as a result of rays emanating from the eyes, we slightly refine the conventional NeRF framework to train NeRF under challenging light conditions and generate normal-light condition novel views unsupervised. We introduce the concept of a "Concealing Field," which assigns transmittance values to the surrounding air to account for illumination effects. In dark scenarios, we assume that object emissions maintain a standard lighting level but are attenuated as they traverse the air during the rendering process. Concealing Field thus compel NeRF to learn reasonable density and colour estimations for objects even in dimly lit situations. Similarly, the Concealing Field can mitigate over-exposed emissions during the rendering stage. Furthermore, we present a comprehensive multi-view dataset captured under challenging illumination conditions for evaluation. Our code and dataset available at https://github.com/cuiziteng/Aleth-NeRF

翻译：标准神经辐射场（NeRF）范式采用以观察者为中心的方法，将光照与材质反射等要素纠缠为仅由三维点发出的辐射。这种简化的渲染方式在精确建模低光、过曝等不利光照条件下拍摄的图像时存在挑战。受古希腊辐射理论（认为视觉感知源于眼睛发出的射线）启发，我们对传统NeRF框架进行细微改良，使其能在恶劣光照条件下训练，并生成无监督的正常光照新视角图像。我们引入"遮蔽场"概念，为环境空气赋予透射率值以表征光照效应。在暗光场景中，我们假设物体辐射保持标准光照水平，但在渲染过程中穿过空气时会衰减。遮蔽场由此迫使NeRF在昏暗环境中也能学习到合理的物体密度与颜色估计。类似地，遮蔽场可在渲染阶段缓解过曝辐射问题。此外，我们构建了在挑战性光照条件下拍摄的多视角综合数据集用于评估。代码与数据集见https://github.com/cuiziteng/Aleth-NeRF