用摄像头实现海洋波浪三维重建，速度快精度高。
Neural reconstruction of 3D ocean wave hydrodynamics from camera sensing
- 基于注意力金字塔结构，融合多尺度时序波浪特征。
- 中心区域波高预测达毫米级，频偏低于0.01赫兹。
- 适合长期观测、遮挡严重场景下的波浪动力学研究。
精确的三维波浪自由表面及其速度场重建对理解海洋物理至关重要。针对长期海洋波浪观测中密集视觉重建计算成本高、持续视觉遮挡带来的挑战，我们提出一种波浪自由表面视觉重建神经网络，采用注意力增强的金字塔架构，适配波浪运动的多尺度与时间连续特性。结合物理约束，从动态自由表面边界实现时序分辨的非线性三维速度场重建。真实海况实验表明，中心区域波高预测可达毫米级，主频误差低于0.01赫兹，高频频谱幂律估计精确，非线性速度场重建保真度高，且可在1.35秒内完成两百万点的密集重建。基于双目视觉数据集，该模型优于传统视觉重建方法，并在遮挡条件下保持强泛化能力，得益于其全局多尺度注意力与波传播动力学的隐式编码。
原文摘要 · Abstract (English)
Precise three-dimensional (3D) reconstruction of wave free surfaces and associated velocity fields is essential for developing a comprehensive understanding of ocean physics. To address the high computational cost of dense visual reconstruction in long-term ocean wave observation tasks and the challenges introduced by persistent visual occlusions, we propose an wave free surface visual reconstruction neural network, which is designed as an attention-augmented pyramid architecture tailored to the multi-scale and temporally continuous characteristics of wave motions. Using physics-based constraints, we perform time-resolved reconstruction of nonlinear 3D velocity fields from the evolving free-surface boundary. Experiments under real-sea conditions demonstrate millimetre-level wave elevation prediction in the central region, dominant-frequency errors below 0.01 Hz, precise estimation of high-frequency spectral power laws, and high-fidelity 3D reconstruction of nonlinear velocity fields, while enabling dense reconstruction of two million points in only 1.35 s. Built on a stereo-vision dataset, the model outperforms conventional visual reconstruction approaches and maintains strong generalization in occluded conditions, owing to its global multi-scale attention and its learned encoding of wave propagation dynamics.
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