  In this paper, we propose an approach to address the problem of classifying
3D industrial components by introducing a novel framework named IC-classifier
(Industrial Component classifier). Our framework is designed to focus on the
object's local and global structures, emphasizing the former by incorporating
specific local features for embedding the model. By utilizing graphical neural
networks and embedding derived from geometric properties, IC-classifier
facilitates the exploration of the local structures of the object while using
geometric attention for the analysis of global structures. Furthermore, the
framework uses point clouds to circumvent the heavy computation workload. The
proposed framework's performance is benchmarked against state-of-the-art
models, demonstrating its potential to compete in the field.
