A Comprehensive Deep Learning-Based Outlier Removal Method for Multibeam Bathymetric Point Cloud

To address the drawbacks that current multibeam bathymetric outlier removal methods lack repeatability, often require parameter adjustment for different regions, still require a lot of manual labor, and offer limited scalability, a deep learning-based outlier removal method for multibeam bathymetric data is proposed. The method fully considers the multibeam data measurement principles and causes of outliers in multibeam bathymetric data and includes a comprehensive sample augmentation method and an outlier removal model based on a modification of a recently proposed PCPNet architecture. In our extensive evaluation, both on synthetic and real data, our method demonstrates robust outlier removal performance in a variety of marine environments without any parameter adjustment.