Fully automatic registration of image sets on approximate geometry (Articolo in rivista)

Type
Label
  • Fully automatic registration of image sets on approximate geometry (Articolo in rivista) (literal)
Anno
  • 2013-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1007/s11263-012-0552-5 (literal)
Alternative label
  • Corsini M., Dellepiane M., Ganovelli F., Gherardi R., Fusiello A., Scopigno R. (2013)
    Fully automatic registration of image sets on approximate geometry
    in International Journal of Computer Vision
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Corsini M., Dellepiane M., Ganovelli F., Gherardi R., Fusiello A., Scopigno R. (literal)
Pagina inizio
  • 91 (literal)
Pagina fine
  • 101 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • [Online First 10 August 2012] - Tipo Progetto EU_FP7 - Virtual Museum Transnational Network Acronimo V-MUST.NET Grant agreement 270404 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://link.springer.com/article/10.1007%2Fs11263-012-0552-5 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 102 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 1 (literal)
Note
  • PuMa (literal)
  • ISI Web of Science (WOS) (literal)
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; Toshiba Cambridge Research Laboratory, UK; Università di Verona, Verona, Italy; CNR-ISTI, Pisa, Italy; (literal)
Titolo
  • Fully automatic registration of image sets on approximate geometry (literal)
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. (literal)
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