Extracting and visualizing structural features in environmental point cloud lidar data sets

Abstract

We present a user-assisted approach to extracting and visualizing structural features from point clouds obtained by terrestrial and airborne laser scanning devices. We apply a multi-scale approach to express the membership of local point environments to corresponding geometric shape classes in terms of probability. This information is filtered and combined to establish feature graphs which can be visualized in combination with the color-encoded feature and structural probability estimates of the measured raw point data. Our method can be used, for example, for exploring geological point data scanned from multiple viewpoints.