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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F09%3A00163723%21RIV10-MSM-21230___
rdf:type
n10:Vysledek skos:Concept
dcterms:description
We present a structure-from-motion (SfM) pipeline for visual 3D modeling of a large city area using 360 deg. field of view Google Street View images. The core of the pipeline combines the state of the art techniques such as SURF feature detection, tentative matching by an approximate nearest neighbour search, relative camera motion estimation by solving 5-pt minimal camera pose problem, and sparse bundle adjustment. The robust and stable camera poses estimated by PROSAC with soft voting and by scale selection using a visual cone test bring high quality initial structure for bundle adjustment. Furthermore, searching for trajectory loops based on co-occurring visual words and closing them by adding new constraints for the bundle adjustment enforce the global consistency of camera poses and 3D structure in the sequence. We present a large-scale reconstruction computed from 4,799 images of the Google Street View Pittsburgh Research Data Set. We present a structure-from-motion (SfM) pipeline for visual 3D modeling of a large city area using 360 deg. field of view Google Street View images. The core of the pipeline combines the state of the art techniques such as SURF feature detection, tentative matching by an approximate nearest neighbour search, relative camera motion estimation by solving 5-pt minimal camera pose problem, and sparse bundle adjustment. The robust and stable camera poses estimated by PROSAC with soft voting and by scale selection using a visual cone test bring high quality initial structure for bundle adjustment. Furthermore, searching for trajectory loops based on co-occurring visual words and closing them by adding new constraints for the bundle adjustment enforce the global consistency of camera poses and 3D structure in the sequence. We present a large-scale reconstruction computed from 4,799 images of the Google Street View Pittsburgh Research Data Set.
dcterms:title
From Google Street View to 3D City Models From Google Street View to 3D City Models
skos:prefLabel
From Google Street View to 3D City Models From Google Street View to 3D City Models
skos:notation
RIV/68407700:21230/09:00163723!RIV10-MSM-21230___
n3:aktivita
n14:Z
n3:aktivity
Z(MSM6840770038)
n3:dodaniDat
n9:2010
n3:domaciTvurceVysledku
n12:6245269 n12:5043476 n12:1027832
n3:druhVysledku
n4:D
n3:duvernostUdaju
n19:S
n3:entitaPredkladatele
n21:predkladatel
n3:idSjednocenehoVysledku
315633
n3:idVysledku
RIV/68407700:21230/09:00163723
n3:jazykVysledku
n7:eng
n3:klicovaSlova
Structure from Motion; Omnidirectional Vision
n3:klicoveSlovo
n16:Structure%20from%20Motion n16:Omnidirectional%20Vision
n3:kontrolniKodProRIV
[8F98BEB07FB4]
n3:mistoKonaniAkce
Kyoto
n3:mistoVydani
Los Alamitos
n3:nazevZdroje
OMNIVIS '09: 9th IEEE Workshop on Omnidirectional Vision, Camera Networks and Non-classical Cameras
n3:obor
n11:JD
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:rokUplatneniVysledku
n9:2009
n3:tvurceVysledku
Pajdla, Tomáš Torii, Akihiko Havlena, Michal
n3:typAkce
n17:WRD
n3:zahajeniAkce
2009-10-04+02:00
n3:zamer
n20:MSM6840770038
s:numberOfPages
8
n18:hasPublisher
IEEE Computer Society Press
n15:isbn
978-1-4244-4441-0
n6:organizacniJednotka
21230