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In this paper, we propose an approach to address the problem of classifying3D industrial components by introducing a novel framework named IC-classifier(Industrial Component classifier). Our framework is designed to focus on theobject's local and global structures, emphasizing the former by incorporatingspecific local features for embedding the model. By utilizing graphical neuralnetworks and embedding derived from geometric properties, IC-classifierfacilitates the exploration of the local structures of the object while usinggeometric attention for the analysis of global structures. Furthermore, theframework uses point clouds to circumvent the heavy computation workload. Theproposed framework's performance is benchmarked against state-of-the-artmodels, demonstrating its potential to compete in the field.

IC classifier: a classifier for 3D industrial components based on geometric prior using GNN