解决水下3D重建中光线衰减问题，实现高效精准的单次前向重建。
WAT3R: Feedforward Underwater 3D Reconstruction
- 通过轻量神经模块自适应模拟水下成像退化，约束几何一致性
- 在三个水下数据集上均优于现有方法，点云精度提升显著
- 适合需要实时高质水下三维建模的科研与工程应用
由于严重的光衰减和后向散射，可靠的前向水下3D重建仍具挑战性，这些现象会降低视觉质量并破坏多视角间的特征一致性，导致几何重建不准确。为此，我们提出WAT3R，一种直接从水下图像进行3D场景重建的前向框架。通过将退化适应作为几何约束过程，WAT3R集成轻量级神经适应模块，灵活应对水下成像效应，从而提升多视角重建质量。该方法在一次前向传播中即可直接输出像素对齐的3D点云图与相机位姿，实现高效高质量的水下3D重建。在FLSea、SQUID和USOD10K数据集上的实验表明，本方法在多视图/单目深度估计与相机位姿估计任务中持续超越当前最优方法。
原文摘要 · Abstract (English)
Reliable feedforward underwater 3D reconstruction remains challenging due to severe light attenuation and backscattering, which degrade visual quality and disrupt feature consistency across views, leading to inaccurate multi-view geometry. To address this issue, we propose WAT3R, a feed-forward framework for reconstructing 3D scenes directly from underwater images. By leveraging degradation adaptation as a geometry-constrained process, WAT3R integrates a lightweight neural adaptation module to flexibly account for these underwater imaging effects, thereby improving multi-view reconstruction quality. Implemented in a single forward pass, WAT3R directly and efficiently outputs pixel-aligned 3D point maps and camera poses from underwater videos, allowing a high-quality underwater 3D reconstruction. Experiments conducted on the FLSea, SQUID, and USOD10K datasets show that our method consistently outperforms state-of-the-art approaches on 3D reconstruction tasks, including multi-view/monocular depth estimation and camera pose estimation.
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