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Description
  • Není k dispozici (cs)
  • A technique for building consistent 3D reconstructions from many wide base-line views based on fitting a low rank matrix to a matrix with missing data is presented. Rank-four submatrices of minimal, or slightly larger, size are sampled and spans of their columns are combined to constrain a basis of the fitted matrix. The error minimized is expressed in terms of the original subspaces which leads to a better resistance to noise compared to previous methods. More than 90% of the missing data can be handled while finding an acceptable solution efficiently. Applications to 3D reconstruction using both affine and perspective camera models are shown. For the perspective model, a new linear method based on logarithms of positive depths from cheirality is introduced to make the depths consistent with an overdetermined set of epipolar geometries. Results are shown for scenes and sequences of various types. Many images in open and closed sequences in narrow and wide base-line setups are reconstr
  • A technique for building consistent 3D reconstructions from many wide base-line views based on fitting a low rank matrix to a matrix with missing data is presented. Rank-four submatrices of minimal, or slightly larger, size are sampled and spans of their columns are combined to constrain a basis of the fitted matrix. The error minimized is expressed in terms of the original subspaces which leads to a better resistance to noise compared to previous methods. More than 90% of the missing data can be handled while finding an acceptable solution efficiently. Applications to 3D reconstruction using both affine and perspective camera models are shown. For the perspective model, a new linear method based on logarithms of positive depths from cheirality is introduced to make the depths consistent with an overdetermined set of epipolar geometries. Results are shown for scenes and sequences of various types. Many images in open and closed sequences in narrow and wide base-line setups are reconstr (en)
Title
  • 3D Reconstruction by Fitting Low-rank Matrices with Missing Data
  • Není k dispozici (cs)
  • 3D Reconstruction by Fitting Low-rank Matrices with Missing Data (en)
skos:prefLabel
  • 3D Reconstruction by Fitting Low-rank Matrices with Missing Data
  • Není k dispozici (cs)
  • 3D Reconstruction by Fitting Low-rank Matrices with Missing Data (en)
skos:notation
  • RIV/68407700:21230/05:03109933!RIV06-AV0-21230___
http://linked.open.../vavai/riv/strany
  • 198 ; 205
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
  • 552732
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21230/05:03109933
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • factorization; missing data; structure from motion; wide base-line stereo (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [14179E482ED8]
http://linked.open...v/mistoKonaniAkce
  • San Diego
http://linked.open...i/riv/mistoVydani
  • Los Alamitos
http://linked.open...i/riv/nazevZdroje
  • Proceedings of Conference on Computer Vision and Pattern Recognition (CVPR)
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
  • Pajdla, Tomáš
  • Martinec, Daniel
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://purl.org/ne...btex#hasPublisher
  • IEEE Computer Society Press
https://schema.org/isbn
  • 0-7695-2372-2
http://localhost/t...ganizacniJednotka
  • 21230
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