From 3D Point Clouds To Semantic Objects An Ontology-Based Detection Approach

TL;DR

This paper introduces an ontology-based method for detecting and annotating railway objects in 3D point clouds by combining geometric analysis with domain knowledge, facilitating integration with GIS and architectural systems.

Contribution

It presents a novel knowledge-based detection approach using OWL and SWRL to combine geometric analysis with domain expertise for object detection in 3D point clouds.

Findings

Effective detection and annotation of railway objects in point clouds

Enriched ontology can be integrated into GIS and architectural workflows

Combines geometric analysis with domain knowledge for improved detection

Abstract

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.