用得分模型提升海底测绘点云去噪，效果优于传统方法。
Score-Based Multibeam Point Cloud Denoising
- 基于得分的点云去噪网络，专为多波束数据设计。
- 在真实数据上实现比传统方法更高的去噪准确率。
- 可直接接入现有海底测绘流程，适合测绘与海洋研究者。
多波束测深仪（MBES）是海底地形测绘的主流传感器。近年来，成本更低的MBES设备和全球测绘计划推动了数据量的指数级增长。然而，原始MBES数据中包含1%-25%的噪声，需借助如联合不确定性与测深估计器（CUBE）等工具进行半自动过滤。本文借鉴三维点云领域的方法，提出一种基于得分的点云去噪网络，用于检测并去除MBES中的异常点。我们在真实MBES调查数据上训练并评估该网络，结果表明其性能优于经典方法，且可无缝集成至现有标准工作流。为促进后续研究，代码与预训练模型已公开发布。
原文摘要 · Abstract (English)
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.
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