Relative Illumination Fields: Learning Medium and Light Independent Underwater Scenes

We address the challenge of constructing a consistent and photorealistic Neural Radiance Field (NeRF) in inhomogeneously illuminated, scattering environments with unknown, co-moving light sources. While most existing works on underwater scene representation focus on homogeneous, globally illuminated scattering mediums, limited attention has been given to such scenarios-such as when a robot explores water deeper than a few tens of meters, where sunlight becomes insufficient. To address this, we propose a novel illumination field that is locally attached to the camera, enabling the capture of uneven lighting effects within the viewing frustum. We combine this with a volumetric representation of the medium to an overall method which effectively handles the interaction between the dynamic illumination field and the static scattering medium. Evaluation results demonstrate the effectiveness and flexibility of our approach. We release our code and dataset at link.