CLOI：一个用于生成工业设施几何数字孪生的自动基准框架
CLOI: An Automated Benchmark Framework For Generating Geometric Digital Twins Of Industrial Facilities
摘要：
本文设计，实现和基准测试一种新的框架，名为CLOI，可以准确地生成个人标记的点集群的最重要的形状，现有的工业设施，以最小的手工努力，在一个通用的点级格式。CLOI采用深度学习和几何方法的组合来将点分割成类和个体实例。目前商业软件中从点云数据生成几何数字孪生是一个繁琐的手工过程。我们的CLOI框架的实验表明，该方法可以可靠地分割复杂和不完整的点云的工业设施，产生82%的类分割精度。与目前的做法相比，拟议的框架可以实现平均节省30%的时间。CLOI是第一个为工业工厂最重要的对象实现几何数字孪生的框架。为进一步研究生成建筑环境的语义丰富的数字孪生提供了基础。
英文摘要：
This paper devises, implements and benchmarks a novel framework, named CLOI, that can accurately generate individual labelled point clusters of the most important shapes of existing industrial facilities with minimal manual effort in a generic point-level format. CLOI employs a combination of deep learning and geometric methods to segment the points into classes and individual instances. The current geometric digital twin generation from point cloud data in commercial software is a tedious, manual process. Experiments with our CLOI framework reveal that the method can reliably segment complex and incomplete point clouds of industrial facilities, yielding 82% class segmentation accuracy. Compared to the current state-of-practice, the proposed framework can realize estimated time-savings of 30% on average. CLOI is the first framework of its kind to have achieved geometric digital twinning for the most important objects of industrial factories. It provides the foundation for further research on the generation of semantically enriched digital twins of the built environment.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
