Score-Based Multibeam Point Cloud Denoising
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 21 references that are in the map. First posted on arXiv 20 Sep 2024.
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6 of 21 references.
References · 6
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,220
- 2019PointCleanNet: Learning to Denoise and Remove Outliers from Dense Point Cloudscited by 99
- 2019Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaningcited by 58
- 2020Differentiable Manifold Reconstruction for Point Cloud Denoisingcited by 52
- 2016Density-based Denoising of Point Cloudcited by 11
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