The curvature interpolation method for surface reconstruction for geospatial point cloud data

Surface reconstruction for scattered data is an ill-posed problem and most computational algorithms become overly expensive as the number of sample points increases. This article studies an effective partial differential equation (PDE)-based algorithm, called the curvature interpolation method with iterative refinement (IR-CIM). The new method iteratively utilizes curvature-related information which is estimated from an intermediate surface of the nonuniform data and plays a role of driving force for the reconstruction of a reliable image surface. The IR-CIM is applied for digital elevation modelling for geospatial point cloud data of overlapping strip scans acquired by light detection and ranging (LiDAR) technology. This article also introduces an effective initialization strategy for large areas of missing data and a robust method for the elimination of the Moir? effect over strip overlaps. The resulting algorithm converges to a piecewise smooth image, with little dependence on sample rates, outperforming inverse-distance weighting methods in both efficiency and accuracy.