Abstract
Underwater 3D reconstruction and appearance restoration are hindered by the complex optical properties of water, such as wavelength-dependent attenuation and scattering. Existing Neural Radiance Fields (NeRF)-based methods struggle with slow rendering speeds and suboptimal color restoration, while 3D Gaussian Splatting (3DGS) inherently lacks the capability to model volumetric scattering effects.
To address these issues, we introduce WaterClear-GS, the first pure 3DGS-based framework that explicitly integrates underwater optical properties of local attenuation and scattering into Gaussian primitives, eliminating the need for an auxiliary medium network. Our method employs a dual-branch optimization strategy to ensure underwater photometric consistency while naturally recovering water-free appearances. This strategy is enhanced by depth-guided geometry regularization and perception-driven image loss, together with exposure constraints, spatially-adaptive regularization and physical prior regularization, which collectively enforce local 3D coherence and maintain natural visual perception.
Experiments on standard benchmarks and our newly collected dataset demonstrate that WaterClear-GS achieves outstanding performance on both novel view synthesis (NVS) and underwater image restoration (UIR) tasks, while maintaining real-time rendering.
Method
The pipeline of WaterClear-GS. Our method extends each Gaussian with water optical parameters. The dual-branch design simultaneously renders underwater images by applying these parameters and clear images by zeroing them out. Depth-guided enhancement guides the geometry optimization, while exposure constraint balances the dynamic range of restored color and spatially-adaptive regularization, together with spectral regularization, ensures physical plausibility of medium properties. Our framework ensures high-quality reconstruction and realistic color restoration.
