Aquatic-GS: A Hybrid 3D Representation for Underwater Scenes

TL;DR

Aquatic-GS introduces a hybrid 3D representation combining neural water modeling and Gaussian splatting to effectively depict underwater scenes, enabling high-quality rendering and restoration with improved speed.

Contribution

The paper presents Aquatic-GS, a novel hybrid 3D underwater scene representation integrating neural water fields and Gaussian splatting, with a physics-based model and depth-guided optimization for enhanced accuracy.

Findings

Outperforms state-of-the-art methods in rendering quality.

Achieves 410x faster rendering speed.

Improves underwater image restoration results.

Abstract

Representing underwater 3D scenes is a valuable yet complex task, as attenuation and scattering effects during underwater imaging significantly couple the information of the objects and the water. This coupling presents a significant challenge for existing methods in effectively representing both the objects and the water medium simultaneously. To address this challenge, we propose Aquatic-GS, a hybrid 3D representation approach for underwater scenes that effectively represents both the objects and the water medium. Specifically, we construct a Neural Water Field (NWF) to implicitly model the water parameters, while extending the latest 3D Gaussian Splatting (3DGS) to model the objects explicitly. Both components are integrated through a physics-based underwater image formation model to represent complex underwater scenes. Moreover, to construct more precise scene geometry and details, we design a Depth-Guided Optimization (DGO) mechanism that uses a pseudo-depth map as auxiliary guidance. After optimization, Aquatic-GS enables the rendering of novel underwater viewpoints and supports restoring the true appearance of underwater scenes, as if the water medium were absent. Extensive experiments on both simulated and real-world datasets demonstrate that Aquatic-GS surpasses state-of-the-art underwater 3D representation methods, achieving better rendering quality and real-time rendering performance with a 410x increase in speed. Furthermore, regarding underwater image restoration, Aquatic-GS outperforms representative dewatering methods in color correction, detail recovery, and stability. Our models, code, and datasets can be accessed at https://aquaticgs.github.io.