Score-Based Multibeam Point Cloud Denoising
This work addresses noise filtering in MBES data for bathymetry mapping, offering a method that can be integrated into existing workflows, but it is incremental as it adapts an existing technique to a new domain.
The authors tackled the problem of noise in multibeam echo-sounder (MBES) bathymetry data, which contains 1-25% noise, by adapting a score-based point cloud denoising network for outlier detection and denoising, and found it outperformed classical methods.
Multibeam echo-sounder (MBES) is the de-facto sensor for bathymetry mapping. In recent years, cheaper MBES sensors and global mapping initiatives have led to exponential growth of available data. However, raw MBES data contains 1-25% of noise that requires semi-automatic filtering using tools such as Combined Uncertainty and Bathymetric Estimator (CUBE). In this work, we draw inspirations from the 3D point cloud community and adapted a score-based point cloud denoising network for MBES outlier detection and denoising. We trained and evaluated this network on real MBES survey data. The proposed method was found to outperform classical methods, and can be readily integrated into existing MBES standard workflow. To facilitate future research, the code and pretrained model are available online.