论文标题
用于调查尺度的自洽性测深的神经形状阴影来自侧扫
Neural Shape-from-Shading for Survey-Scale Self-Consistent Bathymetry from Sidescan
论文作者
论文摘要
侧can声纳是一种小型且具有成本效益的传感解决方案，可以轻松地安装在大多数船上。从历史上看，它一直用于生成高清图像，专家可能用来识别海底或水柱上的目标。虽然已提出溶液仅从侧扫或与Multibeam结合使用的情况下，但影响有限。这部分是由于仅限于单侧扫描线的结果。在本文中，我们提出了一种现代化的，可售的解决方案，以从许多侧扫线中创建高质量的测量规模测深。通过纳入对同一位置的多个观察结果，可以改善结果，因为估计值相互加强。我们的方法基于正弦表示网络，这是神经表示学习的最新进步。我们通过从大型侧扫调查中产生测深，证明了该方法的可伸缩性。通过与高精度多电流传感器收集的数据进行比较，可以证明所得的质量。
Sidescan sonar is a small and cost-effective sensing solution that can be easily mounted on most vessels. Historically, it has been used to produce high-definition images that experts may use to identify targets on the seafloor or in the water column. While solutions have been proposed to produce bathymetry solely from sidescan, or in conjunction with multibeam, they have had limited impact. This is partly a result of mostly being limited to single sidescan lines. In this paper, we propose a modern, salable solution to create high quality survey-scale bathymetry from many sidescan lines. By incorporating multiple observations of the same place, results can be improved as the estimates reinforce each other. Our method is based on sinusoidal representation networks, a recent advance in neural representation learning. We demonstrate the scalability of the approach by producing bathymetry from a large sidescan survey. The resulting quality is demonstrated by comparing to data collected with a high-precision multibeam sensor.
