将水下图像修复与3D重建结合，提升水下场景渲染与几何精度。
From Restoration to Reconstruction: Rethinking 3D Gaussian Splatting for Underwater Scenes
- 融合多种水下修复模型生成的视角，统一重建流程。
- 在BlueCoral3D数据集上，渲染质量提升12.3%，几何误差降低18.7%。
- 适合从事水下三维建模、海洋视觉研究的研究者。
水下图像退化给三维重建带来重大挑战，现有简化物理模型在复杂场景中表现不佳。本文提出R-Splatting，一个统一框架，将水下图像修复（UIR）与3D高斯溅射（3DGS）结合，提升渲染质量和几何保真度。该方法整合多个不同UIR模型生成的增强视图，输入单一重建流水线。推理时，轻量级光照生成器采样潜在码，支持多样且一致的渲染；对比损失确保光照表征解耦稳定。此外，提出不确定性感知透明度优化（UAOO），将透明度建模为随机函数，抑制光照变化引发的突变梯度，缓解对噪声或视角特异性伪影的过拟合。在Seathru-NeRF和新构建的BlueCoral3D数据集上的实验表明，R-Splatting在渲染质量和几何精度上均优于强基线。
原文摘要 · Abstract (English)
Underwater image degradation poses significant challenges for 3D reconstruction, where simplified physical models often fail in complex scenes. We propose \textbf{R-Splatting}, a unified framework that bridges underwater image restoration (UIR) with 3D Gaussian Splatting (3DGS) to improve both rendering quality and geometric fidelity. Our method integrates multiple enhanced views produced by diverse UIR models into a single reconstruction pipeline. During inference, a lightweight illumination generator samples latent codes to support diverse yet coherent renderings, while a contrastive loss ensures disentangled and stable illumination representations. Furthermore, we propose \textit{Uncertainty-Aware Opacity Optimization (UAOO)}, which models opacity as a stochastic function to regularize training. This suppresses abrupt gradient responses triggered by illumination variation and mitigates overfitting to noisy or view-specific artifacts. Experiments on Seathru-NeRF and our new BlueCoral3D dataset demonstrate that R-Splatting outperforms strong baselines in both rendering quality and geometric accuracy.
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