  The challenge that this paper addresses is how to efficiently minimize the
cost and manual labour for automatically generating object oriented geometric
Digital Twins (gDTs) of industrial facilities, so that the benefits provide
even more value compared to the initial investment to generate these models.
Our previous work achieved the current state-of-the-art class segmentation
performance (75% average accuracy per point and average AUC 90% in the CLOI
dataset classes) as presented in (Agapaki and Brilakis 2020) and directly
produces labelled point clusters of the most important to model objects (CLOI
classes) from laser scanned industrial data. CLOI stands for C-shapes,
L-shapes, O-shapes, I-shapes and their combinations. However, the problem of
automated segmentation of individual instances that can then be used to fit
geometric shapes remains unsolved. We argue that the use of instance
segmentation algorithms has the theoretical potential to provide the output
needed for the generation of gDTs. We solve instance segmentation in this paper
through (a) using a CLOI-Instance graph connectivity algorithm that segments
the point clusters of an object class into instances and (b) boundary
segmentation of points that improves step (a). Our method was tested on the
CLOI benchmark dataset (Agapaki et al. 2019) and segmented instances with
76.25% average precision and 70% average recall per point among all classes.
This proved that it is the first to automatically segment industrial point
cloud shapes with no prior knowledge other than the class point label and is
the bedrock for efficient gDT generation in cluttered industrial point clouds.
