A quality prediction method for building model reconstruction using LiDAR data and topographic maps

Rey Jer You, Bo Cheng Lin

Research output: Contribution to journalArticlepeer-review

25 Citations (Scopus)

Abstract

This paper integrates light detection and ranging (LiDAR) data and opographic maps and predicts the quality of 3-D building model reconstruction. In this paper, the tensor voting algorithm and a region-growing method are adopted to extract building roof planes and structural lines from LiDAR data, and a robust least squares method is applied to register LiDAR data with building outlines obtained from topographic maps. The minimal square sum of the separations of the most peripheral points to building outlines is adopted as the criterion for determining the transformation parameters in order to improve the efficiency of data fusion. After registration, a novel quality indicator of data fusion based on the tensor analysis of residuals is derived in order to evaluate the quality of the automatic reconstruction of 3-D building models. Finally, an actual LiDAR data set and its corresponding topographic map demonstrate the fusion procedure and the quality of the predictions related to automatic model reconstruction.

Original languageEnglish
Article number5766030
Pages (from-to)3471-3480
Number of pages10
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume49
Issue number9
DOIs
Publication statusPublished - 2011 Sep

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Earth and Planetary Sciences(all)

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