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，将光照和材质反射的效应耦合为仅从3D点发射的辐射。这种简化的渲染方式在准确建模低光照或过曝光等恶劣光照条件下捕获的图像时面临挑战。受古希腊发射理论（认为视觉感知源于眼睛发射的光线）的启发，我们略微改进了传统NeRF框架，使其能够在复杂光照条件下训练，并无监督地生成正常光照条件下的新视角视图。我们引入了“隐匿场”（Concealing Field）的概念，通过为周围空气分配透射率来模拟光照效应。在暗光场景中，我们假设物体发射保持标准光照水平，但在渲染过程中穿越空气时会被衰减。因此，隐匿场迫使NeRF即使在昏暗环境中也能学习物体合理的密度与颜色估计。类似地，隐匿场可在渲染阶段缓解过曝光发射问题。此外，我们提供了一个在复杂光照条件下捕获的多视角数据集用于评估。代码与数据集详见https://github.com/cuiziteng/Aleth-NeRF