Bright-NeRF: Brightening Neural Radiance Field with Color Restoration from Low-Light RAW Images

Neural Radiance Fields (NeRF) have demonstrated prominent performance in novel view synthesis tasks. However, their input heavily relies on image acquisition under normal light conditions, making it challenging to learn accurate scene contents in low-light environments where images typically exhibit significant noise and severe color distortion. To address these challenges, we propose a novel approach, Bright-NeRF, which learns enhanced and high-quality radiance fields from multi-view low-light RAW images in an unsupervised manner. Our method simultaneously achieves color restoration, denoising, and enhanced novel view synthesis. Specifically, we leverage a physically-inspired model of the sensor's response to illumination and introduce a chromatic adaptation loss to constrain the learning of response, enabling consistent color perception of objects regardless of lighting conditions. We further utilize the RAW data's properties to expose the scene's intensity automatically. Additionally, we have collected a multi-view low-light RAW image dataset of real-world scenes to advance research in this field. Experimental results demonstrate that our proposed method significantly outperforms existing 2D and 3D approaches. Our code and dataset will be made publicly available.