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

Research output: Contribution to journalConference articleContributedpeer-review

Contributors

  • M. Jarzabek-Rychard - , Wroclaw University of Environmental and Life Sciences, TUD Dresden University of Technology (Author)
  • H. G. Maas - , Chair of Photogrammetry, TUD Dresden University of Technology (Author)

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

Original languageEnglish
Pages (from-to)395-400
Number of pages6
JournalInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
Volume43
Issue numberB2-2022
Publication statusPublished - 30 May 2022
Peer-reviewedYes

Conference

Title2022 24th ISPRS Congress on Imaging Today, Foreseeing Tomorrow, Commission II
Duration6 - 11 June 2022
CityNice
CountryFrance

Keywords

Keywords

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

Library keywords