Geodesic gaussian processes for the parametric reconstruction of a free-form surface

Enrique Del Castillo, Bianca M. Colosimo, Sam Davanloo Tajbakhsh

Research output: Contribution to journalArticle

22 Citations (Scopus)

Abstract

Reconstructing a free-form surface from 3-dimensional (3D) noisy measurements is a central problem in inspection, statistical quality control, and reverse engineering. We present a new method for the statistical reconstruction of a free-form surface patch based on 3D point cloud data. The surface is represented parametrically, with each of the three Cartesian coordinates (x, y, z) a function of surface coordinates (u, v), a model form compatible with computer-aided-design (CAD) models. This model form also avoids having to choose one Euclidean coordinate (say, z) as a "response" function of the other two coordinate "locations" (say, x and y), as commonly used in previous Euclidean kriging models of manufacturing data. The (u, v) surface coordinates are computed using parameterization algorithms from the manifold learning and computer graphics literature. These are then used as locations in a spatial Gaussian process model that considers correlations between two points on the surface a function of their geodesic distance on the surface, rather than a function of their Euclidean distances over the xy plane. We show how the proposed geodesic Gaussian process (GGP) approach better reconstructs the true surface, filtering the measurement noise, than when using a standard Euclidean kriging model of the "heights", that is, z(x, y). The methodology is applied to simulated surface data and to a real dataset obtained with a noncontact laser scanner. Supplementary materials are available online.

Original languageEnglish (US)
Pages (from-to)87-99
Number of pages13
JournalTechnometrics
Volume57
Issue number1
DOIs
StatePublished - Jan 2 2015

Fingerprint

Free-form Surface
Gaussian Process
Geodesic
Euclidean
Kriging
Statistical Quality Control
Geodesic Distance
Manifold Learning
Laser Scanner
Spatial Process
Model
Reverse Engineering
Non-contact
Point Cloud
Computer-aided Design
Gaussian Model
Response Function
Euclidean Distance
Computer graphics
Cartesian

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modeling and Simulation
  • Applied Mathematics

Cite this

Del Castillo, Enrique ; Colosimo, Bianca M. ; Tajbakhsh, Sam Davanloo. / Geodesic gaussian processes for the parametric reconstruction of a free-form surface. In: Technometrics. 2015 ; Vol. 57, No. 1. pp. 87-99.
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Geodesic gaussian processes for the parametric reconstruction of a free-form surface. / Del Castillo, Enrique; Colosimo, Bianca M.; Tajbakhsh, Sam Davanloo.

In: Technometrics, Vol. 57, No. 1, 02.01.2015, p. 87-99.

Research output: Contribution to journalArticle

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