UniOcean: Generative Gaussian Splatting for Unified Underwater 3D Reconstruction

Underwater 3D reconstruction with Gaussian splatting has recently achieved remarkable progress. However, existing methods suffer from two critical limitations: (1) These methods typically rely on particle-specific modeling paradigms, limiting their adaptability across diverse underwater scenes with static, dynamic, and uncertain scattering conditions. (2) Underwater attenuation further exacerbates the inherent low-frequency bias of 3D Gaussians induced by their kernel distribution, leading to inferior representation of local high-frequency textures. To address these limitations, we propose UniOcean, a novel generative Gaussian splatting framework for unified and high-fidelity underwater 3D reconstruction. Specifically, we propose an Adaptive Particle Modeling strategy that exploits a learnable neural attenuation function to adaptively estimate depth-dependent absorption and scattering terms, thereby uniformly modeling diverse underwater particle effects. Furthermore, we design a Progressive Gaussian Generation module that leverages optimized coarse Gaussians to generate fine-grained texture Gaussians guided by the generative prior, thus yielding refined Gaussians featuring both global appearance and local textures. Benefiting from the above collaborative strategies, our method achieves high-quality representation while exhibiting excellent adaptability. Extensive experiments demonstrate that our UniOcean significantly outperforms existing methods across diverse underwater 3D scenes with static, dynamic, and varying synthetic scattering conditions.