WaterClear-GS: Optical-Aware Gaussian Splatting for Underwater Reconstruction and Restoration

2026-01-27

AI Summary

This work addresses the challenges of underwater 3D reconstruction and appearance recovery, which are hindered by complex optical effects such as wavelength-dependent attenuation and scattering. While existing NeRF-based methods suffer from slow rendering and color distortion, and 3D Gaussian splatting struggles to model volumetric scattering, this paper presents the first pure 3D Gaussian splatting framework that explicitly integrates local underwater optical properties into Gaussian primitives. By employing a dual-branch optimization strategy, the method enforces underwater photometric consistency while naturally recovering in-air appearance—without requiring auxiliary medium networks. It directly models the underwater optical process within the splatting paradigm for the first time, incorporating depth-guided geometric regularization, perceptual-driven losses, and spectral-spatial physical constraints to jointly preserve geometric fidelity and visual realism. Experiments on both standard and custom datasets demonstrate state-of-the-art performance in novel view synthesis and underwater image restoration, all while maintaining real-time rendering capabilities.

Technology Category

• Computer Vision: 3D Computer Vision
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

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 complex 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 physically guided spectral 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. The code will be available at https://buaaxrzhang.github.io/WaterClear-GS/.

Problem

• underwater reconstruction
• appearance restoration
• optical properties
• 3D Gaussian Splatting
• volumetric scattering

Innovation

• Gaussian Splatting
• Underwater Optical Modeling
• Dual-Branch Optimization
• Real-Time Rendering
• Appearance Restoration