Employing variance component estimation for point cloud based geometric surface representation by B-splines
Elisabeth Ötsch, Corinna Harmening, Hans Neuner
TL;DR
This paper explores using variance component estimation to assess uncertainties in geometric surface modeling from point clouds using B-splines.
Contribution
The novelty lies in applying BIQUE-based VCE to separate measurement and model uncertainties in B-spline surface approximations.
Findings
Variance components are separable when model uncertainty exceeds measurement uncertainty.
Only points affected by model deviations should be included for accurate estimation.
Simulated data confirms the feasibility of the proposed method for uncertainty separation.
Abstract
When it comes to the significance assessment of interepochal displacements, great reliance on the stochastic information related to the point clouds introduced in the estimation of the geometric representation of the analysed surface is present. Possible uncertainty sources in point clouds are instrument-, environment- or object sided. Further, the chosen mathematical model for point cloud approximation may introduce an uncertainty budget ascribed as model uncertainty. The present contribution employs variance component estimation (VCE) in the course of geometric point cloud approximation with tensor product B-spline surfaces. A method using the BIQUE-estimation of the variance components is used. It enables considering overlapping variance components. Here, those are related to measurement and model uncertainties. The aim of the article is to investigate whether a realistic estimation…
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Taxonomy
Topics3D Shape Modeling and Analysis · 3D Surveying and Cultural Heritage · Remote Sensing and LiDAR Applications