Common capture low-light scenes are challenging for most computer vision techniques, including Neural Radiance Fields (NeRF). Vanilla NeRF is viewer-centred that simplifies the rendering process only as light emission from 3D locations in the viewing direction, thus failing to model the low-illumination induced darkness. Inspired by emission theory of ancient Greek that visual perception is accomplished by rays casting from eyes, we make slight modifications on vanilla NeRF to train on multiple views of low-light scene, we can thus render out the well-lit scene in an unsupervised manner. We introduce a surrogate concept, Concealing Fields, that reduce the transport of light during the volume rendering stage. Specifically, our proposed method, Aleth-NeRF, directly learns from the dark image to understand volumetric object representation and concealing field under priors. By simply eliminating Concealing Fields, we can render a single or multi-view well-lit image(s) and gain superior performance over other 2D low light enhancement methods. Additionally, we collect the first paired LOw-light and normal-light Multi-view (LOM) datasets for future research.

翻译：常见低照度场景对包括神经辐射场（NeRF）在内的大多数计算机视觉技术构成挑战。原始NeRF以观察者为中心，将渲染过程简化为仅考虑沿视线方向三维位置的光线发射，因而无法建模低照度导致的黑暗效应。受古希腊发射理论（视觉感知通过眼睛发出的光线实现）启发，我们对原始NeRF进行轻微修改，使其能够在低照度场景的多视角图像上训练，从而以无监督方式渲染出正常光照场景。我们引入一个替代概念——遮蔽场（Concealing Fields），在体渲染阶段减少光线的传输。具体而言，所提方法Aleth-NeRF直接从暗光图像中学习，在先验条件下理解体积物体表征与遮蔽场。通过简单地消除遮蔽场，即可渲染出单视角或多视角的正常光照图像，性能优于其他二维低照度增强方法。此外，我们首次构建了配对的多视角低照度与正常光照数据集（LOM），供后续研究使用。