基于旋转声纳数据的3D喀斯特水下场景重建
Towards 3D karst underwater scene reconstruction from rotating sonar data
AI总结：
针对声纳数据稀疏噪声大、导航漂移导致3D重建困难的问题，提出结合连续时间SLAM校正轨迹与两阶段深度学习表面重建的流水线，生成可沉浸导航的3D网格。
摘要：
喀斯特含水层提供关键的淡水资源，但由于其复杂且了解不足的地下几何结构，构成重大危害。由于水下探测的声纳数据稀疏且噪声大，而导航估计存在漂移，限制了标准3D重建方法，因此绘制这些环境具有挑战性。我们提出了一种从声纳剖面仪重建水下喀斯特管道的流水线。我们将连续时间SLAM方法用于校正轨迹漂移，与一种新颖的两阶段深度学习表面重建方法相结合，生成用于水文地质分析的沉浸式可导航3D网格。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
