Identification and classification of objects in 3D point clouds based on a semantic concept

Our real world is increasingly subject to digitization processes producing huge unstructured

3D data sets. In order to extract objects contained in these data sets subsequent analysis

steps are necessary. The most efficient but also the most expensive and time-consuming

analysis is based on manual editing, which allows integrating human knowledge and

intelligence. Computer based methods are less effective, as they mainly use implicit

knowledge allowing to parametrize algorithms, which are part of a defined processing

chain. We want to overcome these limitations through a more general and more flexible

integration of any kind of useful knowledge into the processing. This paper presents an

approach fully driven by semantic technologies and uses expert knowledge. This expert

knowledge is modeled into an ontology and describes objects, data, and algorithms. This

ontology guides an iterative reasoning process using the semantic technologies (e.g. OWL2,

SPARQL, an engine reasoner) to provide a prime flexibility. This process firstly identifies

relevant algorithms to parametrize and combine them. Secondly, it interprets the result

provided by algorithms to enrich the knowledge base. Thirdly, a semantic reasoning uses

added information to classify objects and determine, if needed, other relevant algorithms.

This process is thus, dynamically and iteratively adapted to the results of executed

algorithms. Its efficiency is presented through the result comparison of its results according

to other possible approaches.