High-Resolution Bathymetry by Deep-Learning Based Point Cloud Upsampling

Gridded bathymetric data are often used to understand seafloor topography; however, high-resolution data are rare. To obtain high-resolution gridded bathymetric data, the observations from which the data are derived must be densely measured. However, this process is time consuming and expensive. In this study, we propose a method to obtain dense bathymetric data from sparse observations by treating the observed data as a 3D point cloud and applying a deep-learning-based point cloud upsampling technique. The upsampled cloud points were converted into gridded form. The effectiveness of our method was verified through both quantitative and qualitative analyses.