Towards 3D karst underwater scene reconstruction from rotating sonar data
This work addresses the challenge of mapping complex underwater karst environments for hydrogeologists, but the method is tailored to a specific domain and the results are not quantified with concrete numbers.
The authors present a pipeline for 3D reconstruction of underwater karst conduits from noisy sonar data, combining continuous-time SLAM for trajectory correction and a two-stage deep learning method for surface reconstruction, producing a navigable 3D mesh for hydrogeological analysis.
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.