GS-UVCE: Gaussian Splatting-Driven Unsupervised Visual Consistency Enhancement for Underwater 3D Scene Reconstruction

Abstract

Underwater 3D scene reconstruction is critical for the operation of underwater robotics, yet remains highly challenging due to the semi-transparent water medium, which introduces optical distortions, light scattering, and severe visibility degradation. Therefore, effective underwater image enhancement is a prerequisite for reliable reconstruction. However, existing approaches typically enhance individual views with pre-trained models before reconstruction, leading to poor generalization and inconsistent multi-view results. To address these limitations, we propose GS-UVCE, an end-to-end framework for Gaussian Splatting-driven Unsupervised Visual Consistency Enhancement. GS-UVCE incorporates a Medium-MLP to model water-medium effects and a Light-MLP to adaptively correct illumination, ensuring illumination consistency. Furthermore, depth regularization is introduced to preserve geometric consistency under varying scene conditions. Extensive experiments on multiple underwater datasets show that GS-UVCE consistently outperforms SOTA methods, achieving superior reconstruction fidelity and visual consistency enhancement.