Globally Optimal Line Clustering and Vanishing Point Estimation in Manhattan World
Identifieur interne : 000515 ( France/Analysis ); précédent : 000514; suivant : 000516Globally Optimal Line Clustering and Vanishing Point Estimation in Manhattan World
Auteurs : Jean-Charles Bazin [Suisse] ; Yongduek Seo [Corée du Sud] ; Cédric Demonceaux [France] ; Pascal Vasseur [France] ; Katsushi Ikeuchi [Japon] ; Inso Kweon [Corée du Sud] ; Marc Pollefeys [États-Unis]Source :
Abstract
The projection of world parallel lines in an image intersect at a single point called the vanishing point (VP). VPs are a key ingredient for various vision tasks including rotation estimation and 3D reconstruction. Urban environments generally exhibit some dominant orthogonal VPs. Given a set of lines extracted from a calibrated image, this paper aims to (1) determine the line clustering, i.e. find which line belongs to which VP, and (2) estimate the associated orthogonal VPs. None of the existing methods is fully satisfactory because of the inherent difficulties of the problem, such as the local minima and the chicken-and-egg aspect. In this paper, we present a new algorithm that solves the problem in a mathematically guaranteed globally optimal manner and can inherently enforce the VP orthogonality. Specifically, we formulate the task as a consensus set maximization problem over the rotation search space, and further solve it efficiently by a branch-and-bound procedure based on the Interval Analysis theory. Our algorithm has been validated successfully on sets of challenging real images as well as synthetic data sets.
Url:
Affiliations:
- Corée du Sud, France, Japon, Suisse, États-Unis
- Canton de Zurich, Haute-Normandie, Région Bourgogne, Région Normandie
- Dijon, Le Havre, Rouen, Zurich
- Université de Bourgogne, Université de Bourgogne Franche-Comté, Université de Rouen, Université du Havre, École polytechnique fédérale de Zurich
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Hal:hal-00697707Le document en format XML
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type="url">http://rcv.kaist.ac.kr/v2/</ref></desc><listRelation><relation active="#struct-301570" type="direct"></relation></listRelation><tutelles><tutelle active="#struct-301570" type="direct"><org type="institution" xml:id="struct-301570" status="VALID"><orgName>Korea Advanced Institute of Science and Technology</orgName><orgName type="acronym">KAIST</orgName><date type="start">2016-10-19</date><desc><address><country key="KR"></country></address></desc></org></tutelle></tutelles></hal:affiliation><country>Corée du Sud</country></affiliation></author><author><name sortKey="Pollefeys, Marc" sort="Pollefeys, Marc" uniqKey="Pollefeys M" first="Marc" last="Pollefeys">Marc Pollefeys</name><affiliation wicri:level="1"><hal:affiliation type="institution" xml:id="struct-44261" status="VALID"><orgName>The University of North Carolina at Chapel Hill</orgName><desc><address><addrLine>250 East Franklin Street, Chapel Hill, NC,</addrLine><country key="US"></country></address><ref 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VPs are a key ingredient for various vision tasks including rotation estimation and 3D reconstruction. Urban environments generally exhibit some dominant orthogonal VPs. Given a set of lines extracted from a calibrated image, this paper aims to (1) determine the line clustering, i.e. find which line belongs to which VP, and (2) estimate the associated orthogonal VPs. None of the existing methods is fully satisfactory because of the inherent difficulties of the problem, such as the local minima and the chicken-and-egg aspect. In this paper, we present a new algorithm that solves the problem in a mathematically guaranteed globally optimal manner and can inherently enforce the VP orthogonality. Specifically, we formulate the task as a consensus set maximization problem over the rotation search space, and further solve it efficiently by a branch-and-bound procedure based on the Interval Analysis theory. Our algorithm has been validated successfully on sets of challenging real images as well as synthetic data sets.</div></front></TEI><affiliations><list><country><li>Corée du Sud</li><li>France</li><li>Japon</li><li>Suisse</li><li>États-Unis</li></country><region><li>Canton de Zurich</li><li>Haute-Normandie</li><li>Région Bourgogne</li><li>Région Normandie</li></region><settlement><li>Dijon</li><li>Le Havre</li><li>Rouen</li><li>Zurich</li></settlement><orgName><li>Université de Bourgogne</li><li>Université de Bourgogne Franche-Comté</li><li>Université de Rouen</li><li>Université du Havre</li><li>École polytechnique fédérale de Zurich</li></orgName></list><tree><country name="Suisse"><region name="Canton de Zurich"><name sortKey="Bazin, Jean Charles" sort="Bazin, Jean Charles" uniqKey="Bazin J" first="Jean-Charles" last="Bazin">Jean-Charles Bazin</name></region></country><country name="Corée du Sud"><noRegion><name sortKey="Seo, Yongduek" sort="Seo, Yongduek" uniqKey="Seo Y" first="Yongduek" last="Seo">Yongduek Seo</name></noRegion><name sortKey="Kweon, Inso" sort="Kweon, Inso" uniqKey="Kweon I" first="Inso" last="Kweon">Inso Kweon</name></country><country name="France"><region name="Région Bourgogne"><name sortKey="Demonceaux, Cedric" sort="Demonceaux, Cedric" uniqKey="Demonceaux C" first="Cédric" last="Demonceaux">Cédric Demonceaux</name></region><name sortKey="Vasseur, Pascal" sort="Vasseur, Pascal" uniqKey="Vasseur P" first="Pascal" last="Vasseur">Pascal Vasseur</name></country><country name="Japon"><noRegion><name sortKey="Ikeuchi, Katsushi" sort="Ikeuchi, Katsushi" uniqKey="Ikeuchi K" first="Katsushi" last="Ikeuchi">Katsushi Ikeuchi</name></noRegion></country><country name="États-Unis"><noRegion><name sortKey="Pollefeys, Marc" sort="Pollefeys, Marc" uniqKey="Pollefeys M" first="Marc" last="Pollefeys">Marc Pollefeys</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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