The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B2-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 265–270, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-265-2021
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B2-2021, 265–270, 2021
https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-265-2021

  28 Jun 2021

28 Jun 2021

PHOTOGRAMMETRY AND COMPUTED TOMOGRAPHY POINT CLOUD REGISTRATION USING VIRTUAL CONTROL POINTS

K. Zhan1, D. Fritsch2, and J. F. Wagner1 K. Zhan et al.
  • 1Chair of Adaptive Structures in Aerospace Engineering, University of Stuttgart, Germany
  • 2Institute for Photogrammetry, University of Stuttgart, Germany

Keywords: Geometric Primitives, Plane Detection, Computed Tomography, RANSAC, Point Correspondence, Photogrammetry

Abstract. In this paper we propose a virtual control point based method for the registration of photogrammetry and computed tomography (CT) data. Because of the fundamentally different two data sources, conventional registration methods, such as manual control points registration or 3D local feature-based registration, are not suitable. The registration objective of our application is about 3D reconstructions of gyroscopes, which contain abundant geometric primitives to be fitted in the point clouds. In the first place, photogrammetry and CT scanning are applied, respectively, for 3D reconstructions. Secondly, our workflow implements a segmentation after obtaining the surface point cloud from the complete CT volumetric data. Then geometric primitives are fitted in this point cloud benefitting from the less complex cluster segments. In the next step, intersection operations of the parametrized primitives generates virtual points, which are utilized as control points for the transformation parameters estimation. A random sample consensus (RANSAC) method is applied to find the correspondences of both virtual control point sets using corresponding descriptors and calculates the transformation matrix as an initial alignment for further refining the registration. The workflow is invariant to pose, resolution, completeness and noise within our validation process.