Knowledge Base Approach for 3D Objects Detection in Point Clouds Using
  3D Processing and Specialists Knowledge
  This paper presents a knowledge-based detection of objects approach using the
OWL ontology language, the Semantic Web Rule Language, and 3D processing
built-ins aiming at combining geometrical analysis of 3D point clouds and
specialist's knowledge. Here, we share 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 allows a 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 as output a populated
ontology corresponding to an indexed scene visualized within VRML language. 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. Thus, the resulting enriched and populated ontology, that contains the
annotations of objects in the point clouds, is used to feed a GIS system or an
IFC file for architecture purposes.
