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  • This paper presents a scalable multi-view stereo reconstruction method which can deal with a large number of large unorganized images in affordable time and effort. The computational effort of our technique is a linear function of the surface area of the observed scene which is conveniently discretized to represent sufficient but not excessive detail. Our technique works as a filter on a limited number of images at a time and can thus process arbitrarily large data sets using limited memory. By building reconstructions gradually, we avoid unnecessary processing of data which bring little improvement. In experiments with Middlebury and Strecha's databases, we demonstrate that we achieve results comparable to the state of the art with considerably smaller effort than used by previous methods. We present a large scale experiments in which we processed 294 unorganized images of an outdoor scene and reconstruct its 3D model and 1000 images from the Google Street View Pittsburgh.
  • This paper presents a scalable multi-view stereo reconstruction method which can deal with a large number of large unorganized images in affordable time and effort. The computational effort of our technique is a linear function of the surface area of the observed scene which is conveniently discretized to represent sufficient but not excessive detail. Our technique works as a filter on a limited number of images at a time and can thus process arbitrarily large data sets using limited memory. By building reconstructions gradually, we avoid unnecessary processing of data which bring little improvement. In experiments with Middlebury and Strecha's databases, we demonstrate that we achieve results comparable to the state of the art with considerably smaller effort than used by previous methods. We present a large scale experiments in which we processed 294 unorganized images of an outdoor scene and reconstruct its 3D model and 1000 images from the Google Street View Pittsburgh. (en)
Title
  • Scalable Multi-View Stereo
  • Scalable Multi-View Stereo (en)
skos:prefLabel
  • Scalable Multi-View Stereo
  • Scalable Multi-View Stereo (en)
skos:notation
  • RIV/68407700:21230/09:00163117!RIV10-MSM-21230___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(7E09062), 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
  • 340300
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21230/09:00163117
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • computer vision; surface reconstruction (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [797B51A14412]
http://linked.open...v/mistoKonaniAkce
  • Kyoto
http://linked.open...i/riv/mistoVydani
  • Los Alamitos
http://linked.open...i/riv/nazevZdroje
  • 3DIM '09: The 2009 IEEE International Workshop on 3-D Digital Imaging and Modeling
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
  • Jančošek, Michal
  • Pajdla, Tomáš
  • Shekhovtsov, Oleksandr
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • IEEE Computer Society Press
https://schema.org/isbn
  • 978-1-4244-4441-0
http://localhost/t...ganizacniJednotka
  • 21230
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