  This paper presents our experience regarding the creation of 3D semantic
facility model out of unorganized 3D point clouds. Thus, a knowledge-based
detection approach of objects using the OWL ontology language is presented.
This knowledge is used to define SWRL detection rules. In addition, the
combination of 3D processing built-ins and topological Built-Ins in SWRL rules
aims at combining geometrical analysis of 3D point clouds and specialist's
knowledge. This combination allows more flexible and intelligent detection and
the annotation of objects contained in 3D point clouds. The created WiDOP
prototype takes a set of 3D point clouds as input, and produces an indexed
scene of colored objects visualized within VRML language as output. The context
of the study is the detection of railway objects materialized within the
Deutsche Bahn scene such as signals, technical cupboards, electric poles, etc.
Therefore, the resulting enriched and populated domain ontology, that contains
the annotations of objects in the point clouds, is used to feed a GIS system.
