Toward the Automatic Generation of a Semantic VRML Model from Unorganized 3D Point Clouds
This work addresses the need for automated semantic modeling in railway infrastructure management, though it appears incremental as it builds on existing knowledge-based and rule-based approaches.
The paper tackles the problem of automatically generating semantic 3D models from unorganized point clouds, specifically for railway objects like signals and poles, resulting in a prototype that produces indexed, colored VRML outputs for use in GIS systems.
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.