An Improved Refinement and Decimation Method for Adaptive Terrain Surface Approximation

Abstract

An improved method for adaptively constructing a terrain surface representation from a set of data points is presented . Refinement and decimation steps are repeatedly applied triangular meshes, incrementally determining a better distribution of the data points, while a specified arror tolerance is preserved. Even though not asymptomatically optimal or monotically convergent, it produces approximations that are, experimentally, significantly better than those generated by straightforward greedy insertion algorithms. A new local error metric is used ti select points to be inserted into the triangulation, based on the maximum vertical error weighted by the standard deviation calculated in a neighborhood of the candidate point. Controversely, a measure of angle between surface normals is used to determine whether a vertex should be removed from the triangulation. The method has been implemented and tested on both synthetic test cases and real terrain data sets.