波浪之下的几何：稀疏视角水下3D高斯泼溅的密集先验
Geometry beneath the Waves: Dense Priors for Sparse-View Underwater 3D Gaussian Splatting
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中文总结 AI 辅助
针对水下3D重建中初始化几何限制重建质量的问题，提出利用密集先验改进稀疏视角下的3D高斯泼溅，以提升重建质量。
中文摘要 AI 辅助
水下3D重建支持从海洋生态系统监测、海底检查到水下考古、教育和沉浸式可视化等应用。3D高斯泼溅使实时逼真的新视角渲染成为可能，而水下变体则结合基于物理的图像形成模型，以将介质效应与场景辐射分离。然而，其重建质量从根本上受限于用于初始化的几何结构。
英文摘要
Underwater 3D reconstruction remains challenging under sparse views, where scattering, absorption, and suspended particles degrade feature correspondences and geometric estimation. Although feed-forward geometry foundation models offer an alternative to conventional Structure-from-Motion, their direct application underwater produces noisy and fragmented geometry that limits subsequent 3D Gaussian Splatting (3DGS). We propose a sparse-view underwater reconstruction framework that adapts feed-forward geometry to underwater degradation and exploits its dense geometric priors for view synthesis. First, we adapt VGGT using LoRA and teacher--student distillation, training on synthetically degraded underwater images while preserving clean geometric supervision. This improves robustness to underwater appearance distortions without modifying the pretrained prediction heads. Second, the predicted dense geometry initialises an intermediate 3DGS representation that generates geometry-guided pseudo-views, increasing view overlap and strengthening feature tracks for subsequent RUSplatting optimisation. Experiments on SeaThru-NeRF and Submerged3D demonstrate improved reconstruction quality under sparse-view conditions. On SeaThru-NeRF, our method improves RUSplatting from 24.37 to 27.11 dB PSNR and increases SSIM from 0.7611 to 0.8634, while achieving the best average PSNR and LPIPS on Submerged3D. These results demonstrate the potential of domain-adapted geometric priors for robust sparse-view underwater 3D reconstruction.
发表机构
- University of Bristol（布里斯托大学）
- National University of Singapore（新加坡国立大学）
- Oceanx
机构由 AI 辅助整理，请以论文原文为准。
