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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F09%3A00163117%21RIV10-MSM-21230___
rdf:type
skos:Concept n10:Vysledek
dcterms:description
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.
dcterms:title
Scalable Multi-View Stereo Scalable Multi-View Stereo
skos:prefLabel
Scalable Multi-View Stereo Scalable Multi-View Stereo
skos:notation
RIV/68407700:21230/09:00163117!RIV10-MSM-21230___
n3:aktivita
n7:P n7:Z
n3:aktivity
P(7E09062), Z(MSM6840770038)
n3:dodaniDat
n6:2010
n3:domaciTvurceVysledku
n4:8527504 n4:6245269 n4:9335927
n3:druhVysledku
n22:D
n3:duvernostUdaju
n12:S
n3:entitaPredkladatele
n18:predkladatel
n3:idSjednocenehoVysledku
340300
n3:idVysledku
RIV/68407700:21230/09:00163117
n3:jazykVysledku
n19:eng
n3:klicovaSlova
computer vision; surface reconstruction
n3:klicoveSlovo
n9:computer%20vision n9:surface%20reconstruction
n3:kontrolniKodProRIV
[797B51A14412]
n3:mistoKonaniAkce
Kyoto
n3:mistoVydani
Los Alamitos
n3:nazevZdroje
3DIM '09: The 2009 IEEE International Workshop on 3-D Digital Imaging and Modeling
n3:obor
n8:JD
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:projekt
n15:7E09062
n3:rokUplatneniVysledku
n6:2009
n3:tvurceVysledku
Jančošek, Michal Shekhovtsov, Oleksandr Pajdla, Tomáš
n3:typAkce
n17:WRD
n3:zahajeniAkce
2009-10-03+02:00
n3:zamer
n14:MSM6840770038
s:numberOfPages
9
n16:hasPublisher
IEEE Computer Society Press
n21:isbn
978-1-4244-4441-0
n11:organizacniJednotka
21230