Score-Based Multibeam Point Cloud Denoising

TL;DR

This paper introduces a score-based neural network approach for denoising multibeam echo-sounder point clouds, improving noise removal efficiency and integration into existing bathymetric workflows.

Contribution

It adapts a 3D point cloud denoising method to MBES data, demonstrating superior performance over classical techniques.

Findings

Outperforms classical denoising methods on real MBES data

Can be integrated into existing bathymetric workflows

Code and models are publicly available

Abstract

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.