UNCERTAINTY MODELING FOR POINT CLOUD-BASED AUTOMATIC INDOOR SCENE RECONSTRUCTION BY STRICT ERROR PROPAGATION ANALYSIS

Publikation: Beitrag in FachzeitschriftKonferenzartikelBeigetragenBegutachtung

Beitragende

  • M. Jarzabek-Rychard - , Wroclaw University of Environmental and Life Sciences, Technische Universität Dresden (Autor:in)
  • H. G. Maas - , Professur für Photogrammetrie, Technische Universität Dresden (Autor:in)

Abstract

Accurate digital representation of indoor facilities is a key component for the generation of building twins. 3D indoor scenes are often reconstructed from 3D point clouds obtained by various measurement techniques, which usually show different accuracy characteristics. During the reconstruction process, the uncertainties of data and intermediate products propagate into the accuracy of the vectorized model. Although point clouds-based 3D building modeling has been a hot topic of research for at least two decades, a thorough analysis of error propagation for this problem from a geodetic point of view is still underrepresented. In this contribution, we propose an analytical approach to estimate the uncertainty of 3D modeling results using the analytic approach based on first-order Taylor-series expansion. A general model for the input data is established and the uncertainty expressions of all computed products are symbolically derived. We estimate the uncertainty of 3D data fitting, followed by the derivation of vectorized building parameters and their covariance matrices. The results of the theoretical approaches are tested on real data presenting an indoor scene. The practical example is illustrated, thoroughly analysed, and quantified.

Details

OriginalspracheEnglisch
Seiten (von - bis)395-400
Seitenumfang6
FachzeitschriftInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
Jahrgang43
AusgabenummerB2-2022
PublikationsstatusVeröffentlicht - 30 Mai 2022
Peer-Review-StatusJa

Konferenz

Titel2022 24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow, Commission II
Dauer6 - 11 Juni 2022
StadtNice
LandFrankreich

Schlagworte

Schlagwörter

  • Building reconstruction, Error propagation, Indoor 3D models, Taylor series, Uncertainty modeling

Bibliotheksschlagworte