PCIM: Deep Learning-Based Point Cloud Information Modeling Framework

Park, Jisoo

Title:

PCIM: Deep Learning-Based Point Cloud Information Modeling Framework

dc.contributor.advisor	Cho, Yong Kwon
dc.contributor.advisor	Gentry, T. Russell
dc.contributor.advisor	Marks, Eric
dc.contributor.advisor	Yeo, Woon-Hong
dc.contributor.advisor	Turkan, Yelda
dc.contributor.author	Park, Jisoo
dc.contributor.department	Civil and Environmental Engineering
dc.date.accessioned	2021-01-11T17:12:47Z
dc.date.available	2021-01-11T17:12:47Z
dc.date.created	2020-12
dc.date.issued	2020-12-02
dc.date.submitted	December 2020
dc.date.updated	2021-01-11T17:12:47Z
dc.description.abstract	Although building information modeling (BIM) has been widely used in entire construction projects for data exchange between stakeholders, it sometimes rarely represents the current state of the construction sites because most of the information models are built at the design phase. For this reason, several studies have implemented a process to generate as-built BIM by leveraging laser-scanned point cloud data, referred to as Scan-to-BIM. However, the conventional Scan-to-BIM process is usually performed manually or semi-manually, which is time-consuming and labor-intensive. Moreover, captured reality and measurements such as actual dimensions, shapes, colors, and damages of the target subjects that the original point cloud retains can be disregarded during the solid modeling process in Scan-to-BIM. To address this issue, this research proposes an object-oriented information modeling framework for point cloud data, named point cloud information modeling (PCIM). The main objective of this dissertation is to develop artificial intelligence (AI)-driven information modeling framework for point cloud data. To this end, this study 1) presents methods of classifying construction objects and their properties and 2) proposes a data schema to represent the classified information with an object-based hierarchy structure. At this time, the scope of this research is limited to building construction area, but the data schema can be extended to other jobs such as mechanical, electrical, and plumbing (MEP) engineering. The findings of this research may rebound to the benefit of stakeholders of construction projects considering that point clouds play an essential role in the construction management phase. Since this research will provide an automated information modeling solution for point cloud data, stakeholders that apply the proposed approach will save time to generate an as-is construction site model. Moreover, this research may fill the gaps in current studies on object classification in 3D by leveraging extended input channels such as laser intensity and material index. As this paper presents the concept of PCIM, various follow-up studies are expected to be additionally derived.
dc.description.degree	Ph.D.
dc.format.mimetype	application/pdf
dc.identifier.uri	http://hdl.handle.net/1853/64163
dc.language.iso	en_US
dc.publisher	Georgia Institute of Technology
dc.subject	PCIM
dc.subject	Point Cloud
dc.subject	BIM
dc.subject	IFC
dc.subject	Deep learning
dc.subject	Machine learning
dc.title	PCIM: Deep Learning-Based Point Cloud Information Modeling Framework
dc.type	Text
dc.type.genre	Dissertation
dspace.entity.type	Publication
local.contributor.advisor	Gentry, T. Russell
local.contributor.advisor	Yeo, Woon-Hong
local.contributor.advisor	Cho, Yong Kwon
local.contributor.corporatename	School of Civil and Environmental Engineering
local.contributor.corporatename	College of Engineering
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thesis.degree.level	Doctoral