Tensor-based quality prediction for building model reconstruction from lidar data and topographic map

B. C. Lin, R. J. You

Research output: Contribution to journalConference article

1 Citation (Scopus)

Abstract

A quality prediction method is proposed to evaluate the quality of the automatic reconstruction of building models. In this study, LiDAR data and topographic maps are integrated for building model reconstruction. Hence, data registration is a critical step for data fusion. To improve the efficiency of the data fusion, a robust least squares method is applied to register boundary points extracted from LiDAR data and building outlines obtained from topographic maps. After registration, a quality indicator based on the tensor analysis of residuals is derived in order to evaluate the correctness of the automatic building model reconstruction. Finally, an actual dataset demonstrates the quality of the predictions for automatic model reconstruction. The results show that our method can achieve reliable results and save both time and expense on model reconstruction.

Original languageEnglish
Pages (from-to)437-441
Number of pages5
JournalInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
Volume39
Publication statusPublished - 2012 Jan 1
Event22nd Congress of the International Society for Photogrammetry and Remote Sensing, ISPRS 2012 - Melbourne, Australia
Duration: 2012 Aug 252012 Sep 1

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Geography, Planning and Development

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