Ray-decomposed and gradient-constrained NeRF for few-shot view synthesis under low-light conditions

Abstract

Neural Radiance Fields (NeRF) have shown impressive performance in novel view synthesis, providing high-quality visual results for 3D reconstruction. However, existing NeRF-based methods often fail under extreme low-light conditions with sparse-view inputs, suffering from color distortion and degraded visual quality due to inaccurate illumination modeling and overfitting to limited views. To address these challenges, we propose R-GNeRF, a novel framework that leverages ray decomposition and gradient constraint. Specifically, we decompose sampled rays into reflective and illumination components, each modeled by an independent MLP in an unsupervised manner. A gradient constraint guides the network to learn physically plausible illumination fields, allowing the synthesis of novel views under normal lighting using only the reflective component. In addition, we introduce a view-consistency annealing strategy that adaptively adjusts the sampling sphere radius based on projection consistency across views, mitigating overfitting and improving reconstruction of fine details in few-shot synthesis. To evaluate performance under extreme low-light, we construct the 3L-P dataset using a multi-pixel photon counter (MPPC) at illuminance levels of ${10}^{-3}$ and ${10}^{-4}$ lux, providing challenging low-light images. Extensive experiments demonstrate that R-GNeRF consistently outperforms existing methods in low-light few-shot novel view synthesis, achieving higher visual fidelity and accurate depth reconstruction while maintaining efficient rendering.