From Google Street View to 3D City Models

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F09%3A00163723" target="_blank" >RIV/68407700:21230/09:00163723 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

From Google Street View to 3D City Models

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

From Google Street View to 3D City Models

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

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Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2009

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

OMNIVIS '09: 9th IEEE Workshop on Omnidirectional Vision, Camera Networks and Non-classical Cameras

ISBN

978-1-4244-4441-0

ISSN

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e-ISSN

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Počet stran výsledku

8

Strana od-do

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Název nakladatele

IEEE Computer Society Press

Místo vydání

Los Alamitos

Místo konání akce

Kyoto

Datum konání akce

4. 10. 2009

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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