论文标题
CAD 3D模型通过图神经网络分类：一种基于步骤格式的新方法
CAD 3D Model classification by Graph Neural Networks: A new approach based on STEP format
论文作者
论文摘要
在本文中，我们介绍了一种新的方法，用于检索和分类3D模型，该模型直接在计算机辅助设计（CAD）格式中执行，而无需转换到点云或网格等其他表示形式，从而避免了任何信息丢失。在各种CAD格式中，我们考虑了广泛使用的步骤扩展名，它代表了产品制造信息的标准。这种特殊的格式代表一个3D模型作为一组原始元素，例如将其链接在一起的表面和顶点。在我们的方法中，我们利用步骤文件的链接结构来创建一个图形，其中节点是原始元素，而弧线是它们之间的连接。然后，我们使用图形神经网络（GNN）来解决模型分类问题。最后，我们通过从TraceParts模型库和配置器软件建模公司收集数据，分别以本机CAD格式创建了两个3D模型的数据集。我们使用这些数据集来测试和比较考虑其他3D格式的最先进方法。我们的代码可从https://github.com/divanoletto/3d_step_classification获得
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
