用旋转声呐数据重建复杂溶洞三维结构，解决导航漂移与数据稀疏难题。
Towards 3D karst underwater scene reconstruction from rotating sonar data
- 采用连续时间SLAM校正轨迹漂移，提升定位精度
- 设计两阶段深度学习模型，从稀疏噪声数据中重建表面
- 生成可交互的3D网格，助力地下水文分析
溶洞含水层提供关键淡水资源，但其复杂的地下几何结构难以理解且存在重大风险。由于水下探测的声呐数据稀疏且噪声大，同时导航估计存在漂移，传统3D重建方法受限。本文提出一种从声呐剖面仪数据重建水下溶洞通道的流程：结合连续时间SLAM方法校正轨迹漂移，并采用新颖的两阶段深度学习方法实现表面重建，生成可用于水文地质分析的沉浸式、可导航3D网格。
原文摘要 · Abstract (English)
Karst aquifers provide critical freshwater resources but pose significant hazards due to their complex and poorly understood subsurface geometry. Mapping these environments is challenging because sonar data from underwater exploration is sparse and noisy, while navigation estimates suffer from drift limiting standard 3D reconstruction methods. We present a pipeline for reconstructing underwater karst conduits from a sonar profiler. We combine a continuous-time SLAM approach to correct trajectory drift with a novel two-stage deep learning method for surface reconstruction, producing an immersive and navigable 3D mesh for hydrogeological analysis.
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