  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
