Robust Low-light Scene Restoration via Illumination Transition

Synthesizing normal-light novel views from low-light multiview images remains a challenging yet practical task due to the low visibility and high ISO noise challenges. Existing low-light enhancement methods often struggle to preprocess these images effectively due to their inability to structurally correlate multiple views. While state-of-the-art approaches have advanced by manipulating illumination-related components during rendering, they often introduce color distortions and artifacts. Moreover, they rely solely on NeRF’s multi-view optimization, which offers limited denoising effectiveness. In this paper, we propose a novel Robust Low-light Scene Restoration framework termed (RoSe), which enables novel-view synthesis under normal lighting from low-light multiview images. Inspired by the 2D Retinex theory, we frame this task as an illuminance transition estimation problem in 3D space, further conceptualizing it as a specialized rendering task. This multiview-consistent illuminance transition field establishes a robust connection between low-light and normal-light conditions. By further exploiting the inherent low-rank property of illumination to constrain the transition representation, we achieve more effective denoising without complex 2D techniques or explicit noise modeling. To this end, we design a concise dual-branch architecture and propose a low-rank denoising module. Experiments demonstrate that RoSe significantly outperforms state-of-the-art models in both rendering quality and multiview consistency on standard benchmarks.