Reconstructing building mass models from UAV images

Minglei Li, Liangliang Nan, Neil Smith, Peter Wonka

Research output: Contribution to journalArticle

32 Scopus citations

Abstract

Abstract We present an automatic reconstruction pipeline for large scale urban scenes from aerial images captured by a camera mounted on an unmanned aerial vehicle. Using state-of-the-art Structure from Motion and Multi-View Stereo algorithms, we first generate a dense point cloud from the aerial images. Based on the statistical analysis of the footprint grid of the buildings, the point cloud is classified into different categories (i.e., buildings, ground, trees, and others). Roof structures are extracted for each individual building using Markov random field optimization. Then, a contour refinement algorithm based on pivot point detection is utilized to refine the contour of patches. Finally, polygonal mesh models are extracted from the refined contours. Experiments on various scenes as well as comparisons with state-of-the-art reconstruction methods demonstrate the effectiveness and robustness of the proposed method.

Original languageEnglish (US)
Article number2616
Pages (from-to)84-93
Number of pages10
JournalComputers and Graphics (Pergamon)
Volume54
DOIs
StatePublished - Feb 1 2016

Keywords

  • Aerial images
  • Graph cut
  • Markov random field
  • Point cloud
  • Urban reconstruction

ASJC Scopus subject areas

  • Engineering(all)
  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design

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