  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.
