University of Illinois at Urbana-Champaign

Monitoring and designing built environments with computer vision

Roberts, Dominic

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https://hdl.handle.net/2142/113302

Description

Title
Monitoring and designing built environments with computer vision
Author(s)
Roberts, Dominic
Issue Date
2021-07-12
Director of Research (if dissertation) or Advisor (if thesis)
  • Golparvar-Fard, Mani
  • Forsyth, David
Doctoral Committee Chair(s)
  • Golparvar-Fard, Mani
  • Forsyth, David
Committee Member(s)
  • Hoiem, Derek
  • Savarese, Silvio
Department of Study
Computer Science
Discipline
Computer Science
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
  • Computer Vision
  • Construction Management
  • Deep Learning
Abstract
The digitalization of the construction industry has led to workflows of modern construction projects relying on visual data. In particular, cameras are used for monitoring progress and construction resource activities, and Building Information Modelling (BIM) models are used to digitally represent building assets and document construction progress. The vast amounts of resulting images and videos, as well as the size, intricacy and complexity of BIM models, incentivize the use of computer vision to facilitate and automate said workflows. In addition, the uniqueness of construction site imagery and BIM structure present one-of-a-kind opportunities for computer vision research. In this thesis, we firstly explore the effectiveness of using established vision methods for action recognition for temporally categorizing and segmenting construction worker and excavator activities, and introduce benchmark datasets to incentivize further research in this direction. Secondly, motivated by the need for semantic understanding of scenes for progress monitoring, we explore means of encouraging the geometric regularity we expect to see in scenes of built environments in outputs of 2D semantic segmentation methods. Finally, we address practical concerns of existing generative models for hierarchically structured 3D shapes.
Graduation Semester
2021-08
Type of Resource
Thesis
Permalink
http://hdl.handle.net/2142/113302
Copyright and License Information
Copyright 2021 Dominic Roberts

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