Scalable Multi-View Stereo

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F10%3A00166073" target="_blank" >RIV/68407700:21230/10:00166073 - 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

Scalable Multi-View Stereo

Popis výsledku v původním jazyce

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 theobserved 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 buildingreconstructions 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 Experimental

Název v anglickém jazyce

Scalable Multi-View Stereo

Popis výsledku anglicky

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 theobserved 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 buildingreconstructions 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 Experimental

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

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2010

Kód důvěrnosti údajů

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

Název statě ve sborníku

Workshop 2010

ISBN

978-80-01-04513-8

ISSN

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e-ISSN

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Počet stran výsledku

2

Strana od-do

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Název nakladatele

České vysoké učení technické v Praze

Místo vydání

Praha

Místo konání akce

Praha

Datum konání akce

22. 2. 2010

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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