  The problem faced in this paper concerns the recognition of simple and
complex geometric primitives in point clouds resulting from scans of mechanical
CAD objects. A large number of points, the presence of noise, outliers, missing
or redundant parts and uneven distribution are the main problems to be
addressed to meet this need. In this article we propose a solution, based on
the Hough transform, that can recognize simple and complex geometric primitives
and is robust to noise, outliers, and missing parts. Additionally, we can
extract a series of geometric descriptors that uniquely characterize a
primitive and, based on them, aggregate the output into maximal or compound
primitives, thus reducing oversegmentation. The results presented in the paper
demonstrate the robustness of the method and its competitiveness with respect
to other solutions proposed in the literature.
