From 3D Point Clouds To Semantic Objects An Ontology-Based Detection Approach
This work addresses the need for automated semantic object detection in railway infrastructure for domain-specific applications like GIS and architecture, representing an incremental improvement by integrating existing methods.
The paper tackles the problem of detecting and annotating objects in 3D point clouds, specifically railway objects like signals and poles, by combining geometrical analysis with expert knowledge using OWL and SWRL, resulting in an enriched ontology for use in 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. This combination allows the detection and the annotation of objects contained in point clouds. The context of the study is the detection of railway objects 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 systems or an IFC file for architecture purposes.