WAT3R：前馈水下三维重建
WAT3R: Feedforward Underwater 3D Reconstruction
- The Hong Kong University of Science and Technology（香港科技大学）
- The Chinese University of Hong Kong（香港中文大学）
- Peking University（北京大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对水下光衰减等导致三维重建难的问题，提出WAT3R前馈框架，利用退化适应及轻量级神经适应模块，单次前向传播直接输出三维点图和相机姿态，实验证明其在多类三维重建任务中优于现有方法。
AI中文摘要：
由于严重的光衰减和后向散射，可靠的前馈水下三维重建仍然具有挑战性，这会降低视觉质量并破坏视图间的特征一致性，导致多视图几何不准确。为解决此问题，我们提出WAT3R，一种直接从水下图像重建三维场景的前馈框架。通过将退化适应作为几何约束过程，WAT3R集成了轻量级神经适应模块以灵活考虑这些水下成像效果，从而提高多视图重建质量。在单次前向传播中实现，WAT3R直接并有效地从水下视频输出像素对齐的三维点图和相机姿态，实现高质量水下三维重建。在FLSea、SQUID和USOD10K数据集上进行的实验表明，我们的方法在三维重建任务上始终优于现有方法，包括多视图/单目深度估计和相机姿态估计。
英文摘要：
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.
