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Description
  • We present a novel algorithm for image-based surface reconstruction from a set of calibrated images. The problem is formulated in Bayesian framework, where estimates of depth and visibility in a set of selected cameras are iteratively improved. The core of the algorithm is the minimisation of overall geometric L_2 error between measured 3D points and the depth estimates. In the visibility estimation task, the algorithm aims at outlier detection and noise suppression, as both types of errors are often present in the stereo output. The geometrical formulation allows for simultaneous refinement of the external camera parameters, which is an essential step for obtaining accurate results even when the calibration is not precisely known. We show that the results obtained with our method are comparable to other state-of-the-art techniques.
  • We present a novel algorithm for image-based surface reconstruction from a set of calibrated images. The problem is formulated in Bayesian framework, where estimates of depth and visibility in a set of selected cameras are iteratively improved. The core of the algorithm is the minimisation of overall geometric L_2 error between measured 3D points and the depth estimates. In the visibility estimation task, the algorithm aims at outlier detection and noise suppression, as both types of errors are often present in the stereo output. The geometrical formulation allows for simultaneous refinement of the external camera parameters, which is an essential step for obtaining accurate results even when the calibration is not precisely known. We show that the results obtained with our method are comparable to other state-of-the-art techniques. (en)
Title
  • Depth Map Fusion with Camera Position Refinement
  • Depth Map Fusion with Camera Position Refinement (en)
skos:prefLabel
  • Depth Map Fusion with Camera Position Refinement
  • Depth Map Fusion with Camera Position Refinement (en)
skos:notation
  • RIV/68407700:21230/09:00161364!RIV10-AV0-21230___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(1ET101210406)
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
  • 309525
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21230/09:00161364
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • computer vision; surface reconstruction (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [33621DAFE0C2]
http://linked.open...v/mistoKonaniAkce
  • Eibiswald
http://linked.open...i/riv/mistoVydani
  • Wien
http://linked.open...i/riv/nazevZdroje
  • CVWW 2009: Computer Vision Winter Workshop 2009
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Tyleček, Radim
  • Šára, Radim
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://purl.org/ne...btex#hasPublisher
  • Pattern Recognition & Image Processing Group, Vienna University of Technology
https://schema.org/isbn
  • 978-3-200-01390-2
http://localhost/t...ganizacniJednotka
  • 21230
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