Automated Object Segmentation in Existing Industrial Facilities
type: CreativeWork
description: Shape segmentation from point cloud data is a core step of the digital twinning process for industrial facilities. However, this process is labour-intensive with 90% of the cost being spent on converting point cloud data to a model. This counteracts the perceived value of the resulting model in managing and retrofitting the facilities and motivates the use of automation to reduce this cost. In the US alone, unplanned factory shutdowns due to maintenance cost $50 billion per year. Better documenting the existing conditions can significantly circumvent irreversible damages and decrease the frequency of shutdowns, thus boosting the productivity of industrial assets. This explains why there is a huge market demand for less labour-intensive industrial documentation. Shape segmentation in the literature has so far mostly focused on cylinders, with state-of- the-art methods achieving 60-70% precision and recall for cylinder detection. Such performance is promising, but far from drastically eliminating the manual labour cost, as all other shapes have to be segmented manually. Yet the search space is massive; industrial facilities contain thousands of object types, making automated detection an impossible problem. Hence, there is a direct need to prioritise the most tedious to model objects. The objective of this PhD research is to devise, implement and benchmark a novel framework 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. This is addressed by first identifying the most important shapes to be modelled and then developing algorithms to efficiently detect those shapes. The former is achieved by answering the following three general research questions: a) what are the most frequent industrial object types?, b) what is the time to model the most frequent object types in state-of-the-art commercial software? and c) what is the performance…
abstract: Shape segmentation from point cloud data is a core step of the digital twinning process for industrial facilities. However, this process is labour-intensive with 90% of the cost being spent on converting point cloud data to a model. This counteracts the perceived value of the resulting model in managing and retrofitting the facilities and motivates the use of automation to reduce this cost. In the US alone, unplanned factory shutdowns due to maintenance cost $50 billion per year. Better documenting the existing conditions can significantly circumvent irreversible damages and decrease the frequency of shutdowns, thus boosting the productivity of industrial assets. This explains why there is a huge market demand for less labour-intensive industrial documentation. Shape segmentation in the literature has so far mostly focused on cylinders, with state-of- the-art methods achieving 60-70% precision and recall for cylinder detection. Such performance is promising, but far from drastically eliminating the manual labour cost, as all other shapes have to be segmented manually. Yet the search space is massive; industrial facilities contain thousands of object types, making automated detection an impossible problem. Hence, there is a direct need to prioritise the most tedious to model objects. The objective of this PhD research is to devise, implement and benchmark a novel framework 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. This is addressed by first identifying the most important shapes to be modelled and then developing algorithms to efficiently detect those shapes. The former is achieved by answering the following three general research questions: a) what are the most frequent industrial object types?, b) what is the time to model the most frequent object types in state-of-the-art commercial software? and c) what is the performance…
author: Agapaki, Evangelia
language: en
keywords: Digital Twin, Industrial Factory, Point Cloud Data, Deep Learning, Class Segmentation, Instance Segmentation
license: https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/ff6897d0-ab2a-4bd9-8e58-05518b13f067/download
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
date published: 2020-07-18
date modified: 2026-07-22T22:23:59Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.52102