Knowledge Base Approach for 3D Objects Detection in Point Clouds Using 3D Processing and Specialists Knowledge
This work addresses domain-specific object detection for railway infrastructure, offering an incremental approach by integrating existing semantic web tools with 3D processing.
The paper tackles 3D object detection in point clouds by combining geometric analysis with specialist knowledge using OWL ontologies and SWRL rules, resulting in a prototype that produces annotated ontologies for visualization and integration into GIS or IFC systems.
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.