A streaming framework for seamless detailed photo blending on massive point clouds (Contributo in atti di convegno)

Type
Label
  • A streaming framework for seamless detailed photo blending on massive point clouds (Contributo in atti di convegno) (literal)
Anno
  • 2011-01-01T00:00:00+01:00 (literal)
Alternative label
  • Pintus R., Gobbetti E., Callieri M. (2011)
    A streaming framework for seamless detailed photo blending on massive point clouds
    in Eurographics 2011: the 32nd annual conference of the European Association for Computer Graphics, EG 2011, Llandudno, UK, 11-15 aprile 2011
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Pintus R., Gobbetti E., Callieri M. (literal)
Pagina inizio
  • 25 (literal)
Pagina fine
  • 32 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • Collana o serie: Eurographics Area Papers. - Area di valutazione 15f - Scienze e tecnologie per la valutazione e la valorizzazione dei beni culturali (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://www.crs4.it/vic/cgi-bin/bib-page.cgi?id=%27Pintus:2011:SFD%27 (literal)
Rivista
Note
  • Scopu (literal)
  • PuMa (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • CNR-ISTI, Pisa, Italy (literal)
Titolo
  • A streaming framework for seamless detailed photo blending on massive point clouds (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#curatoriVolume
  • Visualization and Medical Graphics group at the School of Computer Science, Bangor University (literal)
Abstract
  • We present an efficient scalable streaming technique for mapping highly detailed color information on extremely dense point clouds. Our method does not require meshing or extensive processing of the input model, works on a coarsely spatially-reordered point stream and can adaptively refine point cloud geometry on the basis of image content. Seamless multi-band image blending is obtained by using GPU accelerated screen-space operators, which solve point set visibility, compute a per-pixel view-dependent weight and ensure a smooth weighting function over each input image. The proposed approach works independently on each image in a memory coherent manner, and can be easily extended to include further image quality estimators. The effectiveness of the method is demonstrated on a series of massive real-world point datasets. (literal)
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