Automated extraction of geometric primitives with solid lines from unstructured point clouds for creating digital buildings models

Minju Kim, Dongmin Lee, Taehoon Kim, Sangmin Oh, Hunhee Cho

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

Abstract

Point clouds produced by laser scanners are an invaluable source of data for reconstructing multi-dimensional digital models that reflect the as-is conditions of built facilities. However, previous studies aimed to reconstruct models by overlaying the dataset on top of ground-truth reference models to manually adjust the accuracy of the output. Therefore, this paper describes the extraction of geometric primitives with solid lines—the simplest form of objectified data that computer-aided design systems can handle—from unorganized data points and creation of digital models of built facilities in a form of floor plan. The geometric primitives are extracted from 3D points by hybridizing machine learning algorithms, which are mean-shift clustering, non-convex hull, and random sample and consensus (RANSAC). This paper provides a solution for creating a new form of as-built model with high accuracy and robustness from scratch without the involvement of ground-truth solutions or manual adjustments.

Original languageEnglish
Article number104642
JournalAutomation in Construction
Volume145
DOIs
Publication statusPublished - 2023 Jan

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT, MSIT) (No. NRF-2021R1A5A1032433 ).

Publisher Copyright:
© 2022 Elsevier B.V.

Keywords

  • As-built model creation
  • Built facilities
  • From-points-to-lines
  • Geometric primitives
  • Laser scanner
  • Outline extraction
  • Point cloud

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Civil and Structural Engineering
  • Building and Construction

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