WaterClear-GS: 用于水下重建和修复的光学感知高斯点云
WaterClear-GS: Optical-Aware Gaussian Splatting for Underwater Reconstruction and Restoration
AI总结：
WaterClear-GS通过整合水的光学特性，提出了一种纯3DGS框架，实现高效的水下3D重建和修复，同时保持实时渲染能力。
摘要：
水下3D重建和外观修复受到水的复杂光学特性阻碍，如波长依赖性衰减和散射。现有的基于神经辐射场（NeRF）的方法在渲染速度慢和颜色修复不理想方面存在困难，而3D高斯点云（3DGS）本质上缺乏建模复杂体积散射效应的能力。为了解决这些问题，我们引入了WaterClear-GS，这是首个纯基于3DGS的框架，它明确将水的局部衰减和散射光学特性整合到高斯原始体中，消除了对辅助介质网络的需求。我们的方法采用双分支优化策略，以确保水下的光度一致性，同时自然地恢复无水外观。该策略通过深度引导的几何正则化和感知驱动的图像损失，以及曝光约束、空间自适应正则化和物理引导的光谱正则化，共同强制局部3D一致性并保持自然的视觉感知。在标准基准和我们新收集的数据集上的实验表明，WaterClear-GS在新型视角合成（NVS）和水下图像修复（UIR）任务上取得了卓越的性能，同时保持实时渲染。代码将在https://buaaxrzhang.github.io/WaterClear-GS/上提供。
英文摘要：
Underwater 3D reconstruction and appearance restoration remain challenging due to the complex optical properties of water, such as wavelength-dependent attenuation and scattering. Existing Neural Radiance Fields (NeRF)-based approaches often suffer from slow rendering and limited practicality, while vanilla 3D Gaussian Splatting (3DGS) lacks an effective mechanism to account for underwater degradation. To address this, we propose WaterClear-GS, a physics-informed reformulation of underwater Gaussian splatting that models degradation as intrinsic Gaussian attributes rather than external medium fields. Furthermore, we adopt a dual-branch optimization strategy that preserves underwater photometric consistency while encouraging a practical separation between restoration-oriented latent clean appearance and degradation. This strategy is enhanced by depth-guided geometry regularization and perception-driven image supervision, together with exposure constraints, spatially adaptive regularization, and physics-informed spectral regularization, which collectively promote spatial coherence and plausible visual appearance. Extensive experiments on standard benchmarks and our collected dataset demonstrate that WaterClear-GS achieves strong performance on both novel view synthesis (NVS) and underwater image restoration (UIR) tasks, while maintaining 160+ FPS real-time rendering. The code will be available at https://sherii888.github.io/WaterClear-GS/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
