The reconstruction of 3D objects based on point clouds data presents a major task in many application field since it consumes time and require human interactions to yield a promising result. Robust and quick methods for complete object extraction or identification are still an ongoing research topic and suffer from the complex structure of the data, which cannot be sufficiently modeled by purely numerical strategies. Our work aims at defining a new way of automatically and intelligently processing of 3D point clouds from a 3D laser scanner. This processing is based on the combination of 3D processing technologies and Semantic Web technologies. Therefore, the intention of our approach is to take the human cognitive strategy as an example, and to simulate this process based on available knowledge for the objects of interest. First, this process introduces a semantic structure for the object description. Second, the semantics guides the algorithms to detect and recognize objects, which will yield a higher effectiveness. Hence, our research proposes an approach which uses knowledge to select and guide the 3D processing algorithms on the 3D point clouds.