Abstract
Geospatial data acquisition of terrains produces huge, noisy and scattered point clouds. An efficient use of the acquired data requires structured and compact data representations. Working directly in a point cloud is often not appealing. To face this challenge, approximation with tensor product B-spline surfaces is attractive. It reduces the point cloud description to relatively few coefficients compared to the volume of the original point cloud. However, this representation lacks the ability to adapt the resolution of the shape to local variations in the point cloud. The result is frequently that noise is approximated and that surfaces have unwanted oscillations.
Locally Refined (LR) B-spline surfaces were introduced to face this challenge and provide a tool for approximating Geographic Information System point clouds. In our LR B-spline based approximation algorithm, iterative least-squares approximation is combined with a Multilevel B-spline Approximation to reduce memory consumption. We apply the approach to data sets from coastal regions in Norway and the Netherlands, and compare the obtained approximation with a raster method. We further highlight the potential of LR B-spline volumes for spatio-temporal visualisation of deformation patterns.
Locally Refined (LR) B-spline surfaces were introduced to face this challenge and provide a tool for approximating Geographic Information System point clouds. In our LR B-spline based approximation algorithm, iterative least-squares approximation is combined with a Multilevel B-spline Approximation to reduce memory consumption. We apply the approach to data sets from coastal regions in Norway and the Netherlands, and compare the obtained approximation with a raster method. We further highlight the potential of LR B-spline volumes for spatio-temporal visualisation of deformation patterns.