Superpoints in RANSAC planes: A new approach for ground surface extraction exemplified on point classification and context-aware reconstruction

In point clouds obtained from airborne data, the ground points have traditionally been identified as local minima of the altitude. Subsequently, the 2.5D digital terrain models have been computed by approximation of a smooth surfaces from the ground points. But how can we handle purely 3D surfaces of cultural heritage monuments covered by vegetation or Alpine overhangs, where trees are not necessarily growing in bottom-to-top direction? We suggest a new approach based on a combination of superpoints and RANSAC implemented as a filtering procedure, which allows efficient handling of large, challenging point clouds without necessity of training data. If training data is available, covariance-based features, point histogram features, and dataset-dependent features as well as combinations thereof are applied to classify points. Results achieved with a Random Forest classifier and non-local optimization using Markov Random Fields are analyzed for two challenging datasets: an airborne laser scan and a photogrammetrically reconstructed point cloud. As an application, surface reconstruction from the thus cleaned point sets is demonstrated.