Deconfliction and Surface Generation from Bathymetry Data Using LR B-splines
For hydrographic and oceanographic applications, this work provides a practical solution for fusing multi-source bathymetry data into a compact, accurate surface model.
The paper presents a method for cleaning and approximating bathymetry point clouds using LR B-spline surfaces, achieving accurate sea bottom representation with significant data size reduction.
A set of bathymetry point clouds acquired by different measurement techniques at different times, having different accuracy and varying patterns of points, are approximated by an LR B-spline surface. The aim is to represent the sea bottom with good accuracy and at the same time reduce the data size considerably. In this process the point clouds must be cleaned by selecting the "best" points for surface generation. This cleaning process is called deconfliction, and we use a rough approximation of the combined point clouds as a reference surface to select a consistent set of points. The reference surface is updated with the selected points to create an accurate approximation. LR B-splines is the selected surface format due to its suitability for adaptive refinement and approximation, and its ability to represent local detail without a global increase in the data size of the surface