In this paper, we introduce a novel method for reconstructing surface normals and depth of dynamic objects in water. Past shape recovery methods have leveraged various visual cues for estimating shape (e.g., depth) or surface normals. Methods that estimate both compute one from the other. We show that these two geometric surface properties can be simultaneously recovered for each pixel when the object is observed underwater. Our key idea is to leverage multi-wavelength near-infrared light absorption along different underwater light paths in conjunction with surface shading. Our method can handle both Lambertian and non-Lambertian surfaces. We derive a principled theory for this surface normals and shape from water method and a practical calibration method for determining its imaging parameters values. By construction, the method can be implemented as a one-shot imaging system. We prototype both an off-line and a video-rate imaging system and demonstrate the effectiveness of the method on a number of real-world static and dynamic objects. The results show that the method can recover intricate surface features that are otherwise inaccessible.

中文翻译：

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表面法线和水的形状。

在本文中，我们介绍了一种重建水中动态物体表面法线和深度的新方法。过去的形状恢复方法利用各种视觉线索来估计形状（例如深度）或表面法线。估计两者的方法从另一个计算一个。我们表明，当在水下观察物体时，可以同时恢复每个像素的这两个几何表面属性。我们的主要想法是利用沿不同水下光路的多波长近红外光吸收以及表面着色。我们的方法可以处理朗伯曲面和非朗伯曲面。我们从水法和确定其成像参数值的实用校准方法中推导出该表面法线和形状的原理性理论。通过施工，该方法可以实现为一次性成像系统。我们对离线和视频速率成像系统进行了原型设计，并展示了该方法在许多真实世界静态和动态对象上的有效性。结果表明，该方法可以恢复原本无法访问的复杂表面特征。