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：摘要：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耦合为仅从三维点出发的发射过程。这种简化渲染方式在准确建模低光或过曝等不利光照条件下采集的图像时面临挑战。受古希腊发射理论（该理论认为视觉感知源于从眼睛发射的光线）启发，我们对传统NeRF框架进行优化，使其能在恶劣光照条件下训练，并无监督地生成正常光照条件的新视角图像。我们引入"遮蔽场"概念，通过为周围空气赋予透射率来表征光照效应。在暗光场景中，我们假设物体发射保持标准光照水平，但会在渲染过程中随光线穿越空气而衰减。因此，遮蔽场迫使NeRF即使在昏暗环境中也能为物体学习合理的密度与颜色估计。类似地，遮蔽场还能在渲染阶段缓解过曝放射现象。此外，我们提供了一套在极端光照条件下采集的多视角数据集以进行评估。代码与数据集可见https://github.com/cuiziteng/Aleth-NeRF。