Techniques for Automated Classification and Segregation of Mobile Mapping 3D Point Clouds

We present an approach for the automated classification and segregation of initially unordered and unstructured large 3D point clouds from mobile mapping scans. It derives disjoint point sub-clouds belonging to general surface categories such as ground, building, and vegetation. It provides a semantics-based classification by identifying typical assets in road-like environments such as vehicles and post-like structures, e. g., road signs or lamps, which are relevant for many applications using mobile scans. We present an innovative processing pipeline that allows for a semantic class detection for all points of a 3D point cloud in an automated process based solely on topology information. Our approach uses adaptive segmentation techniques as well as characteristic per-point attributes of the surface and the local point neighborhood. The techniques can be efficiently implemented and can handle large city-wide scans with billions of points, while still being easily adaptable to specific application domains and needs. The techniques can be used as base functional components in applications and systems for, e. g., asset detection, road inspection, cadastre validation, and support the automation of corresponding tasks. We have evaluated our techniques in a prototypical implementation on three datasets with different characteristics and show their practicability for these representative use cases.