Flow-based local optimization for image-to-geometry projection (Articolo in rivista)

Type
Label
  • Flow-based local optimization for image-to-geometry projection (Articolo in rivista) (literal)
Anno
  • 2012-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1109/TVCG.2011.75 (literal)
Alternative label
  • Dellepiane M., Marroquim R., Callieri M., Cignoni P., Scopigno R. (2012)
    Flow-based local optimization for image-to-geometry projection
    in IEEE transactions on visualization and computer graphics
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Dellepiane M., Marroquim R., Callieri M., Cignoni P., Scopigno R. (literal)
Pagina inizio
  • 463 (literal)
Pagina fine
  • 474 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • Grant agreement 231809 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5753891 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 18 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 3 (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; Department of Systems Engineering and Computer Science, Universidade Federal do Rio de Janeiro, Brasil; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; (literal)
Titolo
  • Flow-based local optimization for image-to-geometry projection (literal)
Abstract
  • The projection of a photographic dataset on a 3D model is a robust and widely applicable way to acquire appearance information of an object. The first step of this procedure is the alignment of the images on the 3D model. While any reconstruction pipeline aims at avoiding misregistration by improving camera calibrations and geometry, in practice a perfect alignment cannot always be reached. Depending on the way multiple camera images are fused on the object surface, remaining misregistrations show up either as ghosting or as discontinuities at transitions from one camera view to another. In this paper we propose a method, based on the computation of Optical Flow between overlapping images, to correct the local misalignment by determining the necessary displacement. The goal is to correct the symptoms of misregistration, instead of searching for a globally consistent mapping, which might not exist. The method scales up well with the size of the dataset (both photographic and geometric) and is quite independent of the characteristics of the 3D model (topology cleanliness, parametrization, density). The method is robust and can handle real world cases that have different characteristics: low level geometric details and images that lack enough features for global optimization or manual methods. It can be applied to different mapping strategies, such as texture or per-vertex attribute encoding. (literal)
Prodotto di
Autore CNR
Insieme di parole chiave

Incoming links:


Prodotto
Autore CNR di
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#rivistaDi
Insieme di parole chiave di
data.CNR.it