Consistent roof geometry encoding for 3D building model retrieval using airborne LiDAR point clouds

Yi Chen Chen, Bo Yi Lin, Chao Hung Lin

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

A 3D building model retrieval method using airborne LiDAR point clouds as input queries is introduced. Based on the concept of data reuse, available building models in the Internet that have geometric shapes similar to a user-specified point cloud query are retrieved and reused for the purpose of data extraction and building modeling. To retrieve models efficiently, point cloud queries and building models are consistently and compactly encoded by the proposed method. The encoding focuses on the geometries of building roofs, which are the most informative part of a building in airborne LiDAR acquisitions. Spatial histograms of geometric features that describe shapes of building roofs are utilized as shape descriptor, which introduces the properties of shape distinguishability, encoding compactness, rotation invariance, and noise insensitivity. These properties facilitate the feasibility of the proposed approaches for efficient and accurate model retrieval. Analyses on LiDAR data and building model databases and the implementation of web-based retrieval system, which is available at http://pcretrieval.dgl.xyz, demonstrate the feasibility of the proposed method to retrieve polygon models using point clouds.

Original languageEnglish
Article number269
JournalISPRS International Journal of Geo-Information
Volume6
Issue number9
DOIs
Publication statusPublished - 2017 Sep

All Science Journal Classification (ASJC) codes

  • Geography, Planning and Development
  • Computers in Earth Sciences
  • Earth and Planetary Sciences (miscellaneous)

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