Frequency-Guided 3D Gaussian Splatting for Challenging Low-Light View Synthesis

Abstract

Robust 3D scene understanding is crucial for autonomous robots, but degrades sharply in low-light environments where sensor noise and illumination inconsistencies corrupt visual inputs. Even 3D Gaussian Splatting (3DGS), while efficient for real-time reconstruction, produces unstable and artifact-prone results under such conditions, limiting its reliability for navigation and mapping. To address these challenges, we propose a 3DGS-based framework for reconstructing clear scenes under low-light conditions. Firstly, We employ a frequency-aware modulator that operates on spectral components to decouple and suppress sensor noise from structural signals, providing a clean input for reconstruction. To refine the 3D model and ensure its compactness for onboard deployment, we introduce an adaptive denoising mask guided by dynamically updated statistics of rendering contribution and stability, which filters transient artifacts caused by sensor noise. Finally, a multi-view frequency consistency constraint is enforced to ensure the global coherence of the reconstructed model's appearance, which is critical for consistent mapping. Experiments on challenging low-light datasets demonstrate that our method achieves state-of-the-art reconstruction quality while significantly reducing model storage by approximately 46.4% and maintaining real-time rendering speeds.