Deconfliction and Surface Generation from Bathymetry Data Using LR B-splines

TL;DR

This paper presents a method to generate accurate, compact surface models of the sea bottom from diverse bathymetry point clouds using LR B-splines, involving a deconfliction process to select optimal data points.

Contribution

It introduces a novel approach combining deconfliction and adaptive LR B-spline surface fitting for efficient bathymetry data representation.

Findings

Effective data size reduction while maintaining accuracy

Robust surface approximation from heterogeneous point clouds

Improved surface detail representation with local refinement

Abstract

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