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Description
  • We propose a novel method for the multi-view reconstruction problem. Surfaces which do not have direct support in the input 3D point cloud and hence need not be photo-consistent but represent real parts of the scene (e.g. low-textured walls, windows, cars) are important for achieving complete reconstructions. We augmented the existing Labatut CGF 2009 method with the ability to cope with these difficult surfaces just by changing the t-edge weights in the construction of surfaces by a minimal s-t cut. Our method uses Visual-Hull to reconstruct the difficult surfaces which are not sampled densely enough by the input 3D point cloud. We demonstrate importance of these surfaces on several real-world data sets. We compare our improvement to our implementation of the Labatut CGF 2009 method and show that our method can considerably better reconstruct difficult surfaces while preserving thin structures and details in the same quality and computational time.
  • We propose a novel method for the multi-view reconstruction problem. Surfaces which do not have direct support in the input 3D point cloud and hence need not be photo-consistent but represent real parts of the scene (e.g. low-textured walls, windows, cars) are important for achieving complete reconstructions. We augmented the existing Labatut CGF 2009 method with the ability to cope with these difficult surfaces just by changing the t-edge weights in the construction of surfaces by a minimal s-t cut. Our method uses Visual-Hull to reconstruct the difficult surfaces which are not sampled densely enough by the input 3D point cloud. We demonstrate importance of these surfaces on several real-world data sets. We compare our improvement to our implementation of the Labatut CGF 2009 method and show that our method can considerably better reconstruct difficult surfaces while preserving thin structures and details in the same quality and computational time. (en)
Title
  • Multi-View Reconstruction Preserving Weakly-Supported Surfaces
  • Multi-View Reconstruction Preserving Weakly-Supported Surfaces (en)
skos:prefLabel
  • Multi-View Reconstruction Preserving Weakly-Supported Surfaces
  • Multi-View Reconstruction Preserving Weakly-Supported Surfaces (en)
skos:notation
  • RIV/68407700:21230/11:00187133!RIV12-MSM-21230___
http://linked.open...avai/predkladatel
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • S, Z(MSM6840770038)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 214353
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21230/11:00187133
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Multi-View 3D Reconstruction; Multi-View Stereo (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [B1CEC68E4D2D]
http://linked.open...v/mistoKonaniAkce
  • Colorado Springs
http://linked.open...i/riv/mistoVydani
  • Los Alamitos
http://linked.open...i/riv/nazevZdroje
  • CVPR 2011: Proceedings of the 2011 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Jančošek, Michal
  • Pajdla, Tomáš
http://linked.open...vavai/riv/typAkce
http://linked.open...ain/vavai/riv/wos
  • 000295615803051
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
issn
  • 1063-6919
number of pages
http://bibframe.org/vocab/doi
  • 10.1109/CVPR.2011.5995693
http://purl.org/ne...btex#hasPublisher
  • IEEE Computer Society Press
https://schema.org/isbn
  • 978-1-4577-0393-5
http://localhost/t...ganizacniJednotka
  • 21230
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