In process plants, 3D computer-aided design (CAD) plant models are frequently generated via reverse engineering using point cloud data. Generating a 3D model from scan data consists of point cloud collection, preprocessing, and modeling. The process of 3D modeling to create a plant 3D CAD model from a registered point cloud consists of grouping similar point clouds into several segmented point clouds, identifying the components represented by each segment, selecting catalogs for the components, and placing them into 3D design space. The core of a 3D modeling process is to identify components represented by the segmented point clouds. This study proposes a deep learning-based method to retrieve catalogs for piping components to support the reconstruction of a plant 3D CAD model from point clouds. A prototype catalog retrieval system is implemented based on the proposed method, and the retrieval system is evaluated experimentally using point clouds obtained from a process plant. The results demonstrate that, in terms of accuracy, the proposed method outperformed a conventional shape descriptor-based retrieval method.

在加工厂中，经常使用点云数据通过逆向工程生成3D计算机辅助设计（CAD）工厂模型。根据扫描数据生成3D模型包括点云收集，预处理和建模。从已注册的点云创建工厂3D CAD模型的3D建模过程包括将相似的点云分组为几个分段的点云，识别每个分段所代表的组件，选择组件的目录并将它们放置在3D设计空间中。3D建模过程的核心是识别由分段点云表示的组件。这项研究提出了一种基于深度学习的方法，用于检索管道组件的目录，以支持从点云重建工厂3D CAD模型。基于提出的方法实现了原型目录检索系统，并使用从加工厂获得的点云对检索系统进行了实验评估。结果表明，在准确性方面，该方法优于传统的基于形状描述符的检索方法。