  In this paper, we introduce a new approach for retrieval and classification
of 3D models that directly performs in the Computer-Aided Design (CAD) format
without any conversion to other representations like point clouds or meshes,
thus avoiding any loss of information. Among the various CAD formats, we
consider the widely used STEP extension, which represents a standard for
product manufacturing information. This particular format represents a 3D model
as a set of primitive elements such as surfaces and vertices linked together.
In our approach, we exploit the linked structure of STEP files to create a
graph in which the nodes are the primitive elements and the arcs are the
connections between them. We then use Graph Neural Networks (GNNs) to solve the
problem of model classification. Finally, we created two datasets of 3D models
in native CAD format, respectively, by collecting data from the Traceparts
model library and from the Configurators software modeling company. We used
these datasets to test and compare our approach with respect to
state-of-the-art methods that consider other 3D formats. Our code is available
at https://github.com/divanoLetto/3D_STEP_Classification
