Common capture low-light scenes are challenging for most computer vision techniques, including Neural Radiance Fields (NeRF). Vanilla NeRF is viewer-centred 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 the emission theory of ancient Greeks 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 scenes, we can thus render out the well-lit scene in an unsupervised manner. We introduce a surrogate concept, Concealing Fields, that reduces 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. This version is invalid, please refer to our new AAAI version: arXiv:2312.09093

翻译：常见的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。传统NeRF以观察者为中心，将渲染过程简化为仅从三维位置沿观察方向发射光线，从而无法建模弱照明导致的暗度。受古希腊人关于视觉感知通过眼睛发出的光线完成的发射理论启发，我们对原始NeRF进行轻微修改，以在低照度场景的多视角图像上训练，从而能够以无监督方式渲染出光照良好的场景。我们引入一个替代概念——隐藏场，其在体渲染阶段减少光的传输。具体而言，我们的方法Aleth-NeRF直接从暗图像学习，在先验条件下理解体对象表示和隐藏场。通过简单地消除隐藏场，我们可以渲染出单视角或多视角光照良好的图像，并获得优于其他二维低照度增强方法的性能。此外，我们收集了首个配对的低照度和正常光照多视角（LOM）数据集，以供未来研究。此版本已失效，请参考我们的新版AAAI论文：arXiv:2312.09093