Octree-based, automatic building façade generation from LiDAR data

Linh Truong-Hong, Debra Laefer

Research output: Contribution to journalArticle

Abstract

This paper introduces a new, octree-based algorithm to assist in the automated conversion of laser scanning point cloud data into solid models appropriate for computational analysis. The focus of the work is for typical, urban, vernacular structures to assist in better damage prediction prior to tunnelling. The proposed FaçadeVoxel algorithm automatically detects boundaries of building façades and their openings. Next, it checks and automatically fills unintentional occlusions. The proposed method produced robust and efficient reconstructions of building models from various data densities. When compared to measured drawings, the reconstructed building models were in good agreement, with only 1% relative errors in overall dimensions and 3% errors in openings. In addition, the proposed algorithm was significantly faster than other automatic approaches without compromising accuracy.

Original languageEnglish (US)
Pages (from-to)46-61
Number of pages16
JournalCAD Computer Aided Design
Volume53
DOIs
StatePublished - 2014

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Keywords

  • Computational modelling
  • Finite element modelling
  • Geometric modelling
  • Light Detection and Ranging (LiDAR)
  • Masonry buildings
  • Terrestrial laser scanning

ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design
  • Industrial and Manufacturing Engineering

Cite this

Octree-based, automatic building façade generation from LiDAR data. / Truong-Hong, Linh; Laefer, Debra.

In: CAD Computer Aided Design, Vol. 53, 2014, p. 46-61.

Research output: Contribution to journalArticle

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abstract = "This paper introduces a new, octree-based algorithm to assist in the automated conversion of laser scanning point cloud data into solid models appropriate for computational analysis. The focus of the work is for typical, urban, vernacular structures to assist in better damage prediction prior to tunnelling. The proposed Fa{\cc}adeVoxel algorithm automatically detects boundaries of building fa{\cc}ades and their openings. Next, it checks and automatically fills unintentional occlusions. The proposed method produced robust and efficient reconstructions of building models from various data densities. When compared to measured drawings, the reconstructed building models were in good agreement, with only 1{\%} relative errors in overall dimensions and 3{\%} errors in openings. In addition, the proposed algorithm was significantly faster than other automatic approaches without compromising accuracy.",
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