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This paper presents a knowledge-based detection of objects approach using theOWL ontology language, the Semantic Web Rule Language, and 3D processingbuilt-ins aiming at combining geometrical analysis of 3D point clouds andspecialist's knowledge. Here, we share our experience regarding the creation of3D semantic facility model out of unorganized 3D point clouds. Thus, aknowledge-based detection approach of objects using the OWL ontology languageis presented. This knowledge is used to define SWRL detection rules. Inaddition, the combination of 3D processing built-ins and topological Built-Insin SWRL rules allows a more flexible and intelligent detection, and theannotation of objects contained in 3D point clouds. The created WiDOP prototypetakes a set of 3D point clouds as input, and produces as output a populatedontology corresponding to an indexed scene visualized within VRML language. Thecontext of the study is the detection of railway objects materialized withinthe Deutsche Bahn scene such as signals, technical cupboards, electric poles,etc. Thus, the resulting enriched and populated ontology, that contains theannotations of objects in the point clouds, is used to feed a GIS system or anIFC file for architecture purposes.

Knowledge Base Approach for 3D Objects Detection in Point Clouds Using 3D Processing and Specialists Knowledge