**8. Conclusion and discussion**

By this chapter, we tried to contribute on the ongoing enhancement of the Semantic Web technologies through focusing on the possibility of integrating 3D processing and spatial topological components within its framework. It makes an attempt to cross the boundary of using semantics within the 3D processing researches to provide interoperability and takes it a step forward in using the underlying knowledge technology to provide 3D processing and topological analysis through knowledge.

The presented contribution raises the issue of object detection and recognition in 3D point clouds within the laser scanner by using available knowledge on the target domain, the processing algorithms and the 3D spatial topological relations.

The WiDOP framework is primarily designed to facilitate the object detection and recognition in 3D point clouds. It being based on Semantic Web technologies and has ontology in its core. The top level ontology provides the base for functionalities of the application. This prior knowledge modeled within an ontology structure. SWRL rules are used to control the 3D processing execution, the topological qualification and finally to annotate the detected elements in order to enrich the ontology and to drive the detection of new objects.

The designed prototype takes 3D point clouds of a facility, and produce fully annotated scene within a VRML model file. The suggested solution for this challenging problem has proven its efficiency through real tests within the Deutsche Bahn scene. The creation of processing and topological Built-Ins has presented a robust solution to resolve our problematic and to prove the ability of the semantic web language to intervene in any domain and create the difference.

The benefits of the emerging Semantic Web technology through its knowledge tools are quite visible to the convention technologies that rely heavily on database systems. More precisely, the benefits that have been experienced during the design and development of the WiDOP platform is quite strong. The flexibility nature of ontology based system allows integrating new components at any time of development and even implementations.

Future work will include the integration of new knowledge intervening during the annotation process. It will also include the update of the general platform architecture, by ensuring more interaction between the scene knowledge and the 3D processing. Add to that, it will include a more robust identification and annotation process of objects based on each object characteristics add to the integration of new 3D parameter knowledge's that can intervene within the detection and annotation process to make the process more flexible and intelligent.
