RECONSTRUCTION of RECTANGULAR WINDOWS in MULTI-LOOKING OBLIQUE VIEW ALS DATA

S. Tuttas, U. Stilla

Research output: Contribution to journalConference articlepeer-review

5 Scopus citations

Abstract

With multi-looking oblique view airborne laser scanning (ALS) it is possible to create point clouds with a nearly complete 3D coverage of a larger area. This allows, in contrast to nadir view ALS, the extraction of façade information. This paper concentrates on the reconstruction of windows. Because of the limited point density, received from oblique view ALS, the approach aims at the reconstruction of rectangular windows from sparse point clouds (<10 points/ m2). In a pre-processing step window centres are determined. For that indoor points, which lie behind the façades planes, are detected. The following reconstruction process consists of two main steps. First the window centres are used to create a hypothesis for the window outline by searching for a rectangle with maximum size, which includes the window centre but no points of the point cloud. In the second step these outlines are represented by probability density functions to model the uncertainty of the edges. All edges of one type, i.e. left, right, upper or lower edge, are combined by multiplication of their functions. Subsequently these functions are used to allocate the final edge positions to each window. The windows can be reconstructed with a width and height error of a few decimetres, what corresponds to the typical point distance in the point cloud, as far as the window centres are provided in a sufficient quality. The approach performs better the more equal windows are arranged in a façade row or column.

Original languageEnglish
Pages (from-to)317-322
Number of pages6
JournalISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Volume1
DOIs
StatePublished - 13 Jul 2012
Event22nd Congress of the International Society for Photogrammetry and Remote Sensing: Imaging a Sustainable Future, ISPRS 2012 - Melbourne, Australia
Duration: 25 Aug 20121 Sep 2012

Keywords

  • Building
  • LIDAR
  • Point Cloud
  • Reconstruction
  • Urban

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