IC classifier: a classifier for 3D industrial components based on geometric prior using GNN
This addresses classification challenges for 3D industrial components, offering a domain-specific solution that appears incremental in its approach.
The paper tackles the problem of classifying 3D industrial components by proposing IC-classifier, a framework that uses geometric priors and GNNs to analyze local and global structures from point clouds, achieving competitive performance against state-of-the-art models.
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.