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International Journal of Computer Vision

Erschienen in:

01.03.2013

Fully Automatic Registration of Image Sets on Approximate Geometry

verfasst von: M. Corsini, M. Dellepiane, F. Ganovelli, R. Gherardi, A. Fusiello, R. Scopigno

Erschienen in: International Journal of Computer Vision | Ausgabe 1-3/2013

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Abstract

The photorealistic acquisition of 3D objects often requires color information from digital photography to be mapped on the acquired geometry, in order to obtain a textured 3D model. This paper presents a novel fully automatic 2D/3D global registration pipeline consisting of several stages that simultaneously register the input image set on the corresponding 3D object. The first stage exploits Structure From Motion (SFM) on the image set in order to generate a sparse point cloud. During the second stage, this point cloud is aligned to the 3D object using an extension of the 4 Point Congruent Set (4PCS) algorithm for the alignment of range maps. The extension accounts for models with different scales and unknown regions of overlap. In the last processing stage a global refinement algorithm based on mutual information optimizes the color projection of the aligned photos on the 3D object, in order to obtain high quality textures. The proposed registration pipeline is general, capable of dealing with small and big objects of any shape, and robust. We present results from six real cases, evaluating the quality of the final colors mapped onto the 3D object. A comparison with a ground truth dataset is also presented.

Vorheriger Artikel A Theory of Minimal 3D Point to 3D Plane Registration and Its Generalization

Nächster Artikel Online Detection of Repeated Structures in Point Clouds of Urban Scenes for Compression and Registration

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Titel: Fully Automatic Registration of Image Sets on Approximate Geometry
verfasst von: M. Corsini
M. Dellepiane
F. Ganovelli
R. Gherardi
A. Fusiello
R. Scopigno
Publikationsdatum: 01.03.2013
Verlag: Springer US
Erschienen in: International Journal of Computer Vision / Ausgabe 1-3/2013
Print ISSN: 0920-5691
Elektronische ISSN: 1573-1405
DOI: https://doi.org/10.1007/s11263-012-0552-5

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