Inhaltszusammenfassung

Due to the increasing availability of large unstructured point clouds obtained from laser scanning and/or photogrammetric data, there is a growing demand for automatic processing methods. Given the complexity of the underlying problems, several new methods try to use semantic knowledge in particular for supporting object detection and classification. In this paper, we present a novel approach which makes use of advanced algorithms to benefit from intelligent knowledge management strategies fo...Due to the increasing availability of large unstructured point clouds obtained from laser scanning and/or photogrammetric data, there is a growing demand for automatic processing methods. Given the complexity of the underlying problems, several new methods try to use semantic knowledge in particular for supporting object detection and classification. In this paper, we present a novel approach which makes use of advanced algorithms to benefit from intelligent knowledge management strategies for the processing of 3D point clouds and for object classification in scanned scenes. In particular, our method extends the use of semantic knowledge to all stages of the processing, including the guidance of the 3D processing algorithms. The complete solution consists of a multistage iterative concept based on three factors: the modelled knowledge, the package of algorithms, and the classification engine. Two case studies illustrating our approach are presented in this paper. The studies were carried out on scans of the waiting area of an airport and along the tracks of a railway. In both cases the goal was to detect and identify objects within a defined area. With our results we demonstrate the applicability of our approach. » weiterlesen» einklappen

Autoren

Klassifikation

DFG Fachgebiet:
Informatik

DDC Sachgruppe:
Informatik