Knowledge-Driven Method for Object Qualification in 3D Point Cloud Data

The identification of objects in 3D point cloud data has always presented a real challenge. Such a process highly depends on human interpretation of the scene and its objects. Actual approaches are numerical based; in best cases, static models are used as a template for the detection process. By the presented work, we aim at extending the detection process by bringing the human expert knowledge about the scene, the objects, their characteristics and their relations onto the processing chain. To do, we present in this paper a knowledge-driven method for the detection of object and its qualification using OWL ontology. The knowledge contained by the ontology defines the constraints about the objects. Logic programs are used as rules to define constrains between objects. The processing of the scene is an iterative annotation process that combines 3D algorithms, geometric analysis, spatial analysis and especially specialist's knowledge. The created platform takes a set of 3D point clouds as input and produces as output a populated ontology corresponding to an indexed scene. The context of the study is the detection of railway objects materialized within the Germany Railway scene. Thus, the resulting enriched and populated ontology contains the annotations of objects in the point clouds, and can be used further on to feed a GIS system or an IFC file for architecture purposes.