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.

翻译：本文提出了一种名为IC-classifier（工业部件分类器）的新型框架，以解决三维工业部件的分类问题。该框架通过融合特定局部特征进行模型嵌入，着重于物体的局部与全局结构分析。具体而言，该框架利用图神经网络和基于几何属性的嵌入技术，在通过几何注意力机制分析全局结构的同时，有效探索物体的局部结构特征。此外，框架采用点云数据以规避高计算负载。通过与当前最优模型的性能对比实验，本框架展现了在该领域中的竞争潜力。