论文标题
工业设施的几何数字孪生：工业形状的检索
Geometric Digital Twinning of Industrial Facilities: Retrieval of Industrial Shapes
论文作者
论文摘要
本文设计，实现和基准测试了一种新型的形状检索方法，可以准确地将现有工业设施的单个标记点簇（实例）与各自的CAD模型匹配。它采用了图像和点云深度学习网络的组合来对其几何相似的CAD模型进行分类和匹配。它扩展了我们先前关于几何数字双胞胎生成的研究，从点云数据，这是一个乏味的手动过程。通过我们的联合网络进行的实验表明，它可以以85.2 \％精度可靠地检索CAD模型。拟议的研究是一个基本框架，可实现几何数字双胞胎（GDT）管道，并将实际的几何配置纳入数字双胞胎。
This paper devises, implements and benchmarks a novel shape retrieval method that can accurately match individual labelled point clusters (instances) of existing industrial facilities with their respective CAD models. It employs a combination of image and point cloud deep learning networks to classify and match instances to their geometrically similar CAD model. It extends our previous research on geometric digital twin generation from point cloud data, which currently is a tedious, manual process. Experiments with our joint network reveal that it can reliably retrieve CAD models at 85.2\% accuracy. The proposed research is a fundamental framework to enable the geometric Digital Twin (gDT) pipeline and incorporate the real geometric configuration into the Digital Twin.
