Current State and Trends of Point Cloud Segmentation in Construction Research

Samuel A. Prieto, Eyob T. Mengiste, Uday Menon, Borja García de Soto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The construction industry is witnessing a transformative shift with the integration of advanced technologies, especially in the topic of 3D segmentation. This study underscores the current state and challenges of 3D segmentation, with special emphasis on construction research, and provides an insightful understanding of the latest research developments and trends. The study also looks at the performance metrics of the most relevant techniques, as well as the main limitations and research gaps, highlighting the need for further research in highly-performing techniques based on Deep Learning for point cloud segmentation in construction applications.

Original languageEnglish (US)
Title of host publicationProceedings of the 41st International Symposium on Automation and Robotics in Construction, ISARC 2024
PublisherInternational Association for Automation and Robotics in Construction (IAARC)
Pages972-979
Number of pages8
ISBN (Electronic)9780645832211
DOIs
StatePublished - 2024
Event41st International Symposium on Automation and Robotics in Construction, ISARC 2024 - Lille, France
Duration: Jun 3 2024Jun 5 2024

Publication series

NameProceedings of the International Symposium on Automation and Robotics in Construction
ISSN (Electronic)2413-5844

Conference

Conference41st International Symposium on Automation and Robotics in Construction, ISARC 2024
Country/TerritoryFrance
CityLille
Period6/3/246/5/24

Keywords

  • 3D Segmentation
  • Construction Industry
  • Deep Learning
  • Point Cloud
  • Systematic Literature Review

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Civil and Structural Engineering
  • Building and Construction
  • Safety, Risk, Reliability and Quality
  • Computer Science Applications
  • Artificial Intelligence

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