Computer Vision Methods for Registration: Mixing 3D Knowledge & 2D Correspondences for Accurate Image Composition
Identifieur interne : 001755 ( Hal/Corpus ); précédent : 001754; suivant : 001756Computer Vision Methods for Registration: Mixing 3D Knowledge & 2D Correspondences for Accurate Image Composition
Auteurs : Gilles Simon ; Vincent Lepetit ; Marie-Odile BergerSource :
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Abstract
We focus in this paper on the problem of adding computer-generated objects in video sequences. A two-stage robust statistical method is used for computing the pose from model-image correspondences of tracked curves. This method is able to give a correct estimate of the pose even when tracking errors occur. However, if we want to add virtual objects in a scene area which does not contain (or contains few) model features, the reprojection error in this area is likely to be large. In order to improve the accuracy of the viewpoint, we use 2D keypoints that can be easily matched in two consecutive images. As the relationship between two matched points is a function of the camera motion, the viewpoint can be improved by minimizing a cost function which encompasses the reprojection error as well as the matching error between two frames. The reliability of the system is shown on an encrustation of a virtual car in a sequence of the Stanislas square.
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A two-stage robust statistical method is used for computing the pose from model-image correspondences of tracked curves. This method is able to give a correct estimate of the pose even when tracking errors occur. However, if we want to add virtual objects in a scene area which does not contain (or contains few) model features, the reprojection error in this area is likely to be large. In order to improve the accuracy of the viewpoint, we use 2D keypoints that can be easily matched in two consecutive images. As the relationship between two matched points is a function of the camera motion, the viewpoint can be improved by minimizing a cost function which encompasses the reprojection error as well as the matching error between two frames. The reliability of the system is shown on an encrustation of a virtual car in a sequence of the Stanislas square.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">Computer Vision Methods for Registration: Mixing 3D Knowledge & 2D Correspondences for Accurate Image Composition</title><author role="aut"><persName><forename type="first">Gilles</forename><surname>Simon</surname></persName><email></email><idno type="halauthor">129832</idno><orgName ref="#struct-364475"></orgName><affiliation ref="#struct-2350"></affiliation></author><author role="aut"><persName><forename type="first">Vincent</forename><surname>Lepetit</surname></persName><email></email><idno type="halauthor">125897</idno><affiliation ref="#struct-2350"></affiliation></author><author role="aut"><persName><forename type="first">Marie-Odile</forename><surname>Berger</surname></persName><email>berger@loria.fr</email><idno type="halauthor">61390</idno><affiliation ref="#struct-2350"></affiliation></author><editor role="depositor"><persName><forename>Publications</forename><surname>Loria</surname></persName><email>publications@loria.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2006-09-26 08:19:56</date><date type="whenModified">2016-05-19 01:09:28</date><date type="whenReleased">2006-09-28 15:22:44</date><date type="whenProduced">1998</date><date type="whenEndEmbargoed">2015-02-10</date><ref type="file" target="https://hal.inria.fr/inria-00098719/document"><date notBefore="2015-02-10"></date></ref><ref type="file" n="1" target="https://hal.inria.fr/inria-00098719/file/98-R-335.pdf"><date notBefore="2015-02-10"></date></ref></edition><respStmt><resp>contributor</resp><name key="108626"><persName><forename>Publications</forename><surname>Loria</surname></persName><email>publications@loria.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">inria-00098719</idno><idno type="halUri">https://hal.inria.fr/inria-00098719</idno><idno type="halBibtex">simon:inria-00098719</idno><idno type="halRefHtml">International Workshop on Augmented Reality, 1998, San francisco, USA, 15 p, 1998</idno><idno type="halRef">International Workshop on Augmented Reality, 1998, San francisco, USA, 15 p, 1998</idno></publicationStmt><seriesStmt><idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno><idno type="stamp" n="INPL">Institut National Polytechnique de Lorraine</idno><idno type="stamp" n="LORIA2">Publications du LORIA</idno><idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno><idno type="stamp" n="INRIA-NANCY-GRAND-EST">INRIA Nancy - Grand Est</idno><idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno><idno type="stamp" n="INRIA-LORRAINE">INRIA Nancy - Grand Est</idno><idno type="stamp" n="LABO-LORIA-SET" p="LORIA">LABO-LORIA-SET</idno></seriesStmt><notesStmt><note type="commentary">Colloque avec actes et comité de lecture.</note><note type="audience" n="1">Not set</note><note type="invited" n="0">No</note><note type="popular" n="0">No</note><note type="peer" n="1">Yes</note><note type="proceedings" n="1">Yes</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Computer Vision Methods for Registration: Mixing 3D Knowledge & 2D Correspondences for Accurate Image Composition</title><author role="aut"><persName><forename type="first">Gilles</forename><surname>Simon</surname></persName><idno type="halAuthorId">129832</idno><orgName ref="#struct-364475"></orgName><affiliation ref="#struct-2350"></affiliation></author><author role="aut"><persName><forename type="first">Vincent</forename><surname>Lepetit</surname></persName><idno type="halAuthorId">125897</idno><affiliation ref="#struct-2350"></affiliation></author><author role="aut"><persName><forename type="first">Marie-Odile</forename><surname>Berger</surname></persName><email>berger@loria.fr</email><idno type="halAuthorId">61390</idno><affiliation ref="#struct-2350"></affiliation></author></analytic><monogr><idno type="localRef">98-R-335 || simon98d</idno><meeting><title>International Workshop on Augmented Reality</title><date type="start">1998</date><settlement>San francisco, USA</settlement></meeting><imprint><biblScope unit="pp">15 p</biblScope><date type="datePub">1998</date></imprint></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><keywords scheme="author"><term xml:lang="fr">réalite augmentée</term><term xml:lang="fr">viewpoint computation</term><term xml:lang="fr">augmented reality</term><term xml:lang="fr">point de vue</term></keywords><classCode scheme="halDomain" n="info.info-oh">Computer Science [cs]/Other [cs.OH]</classCode><classCode scheme="halTypology" n="COMM">Conference papers</classCode></textClass><abstract xml:lang="en">We focus in this paper on the problem of adding computer-generated objects in video sequences. A two-stage robust statistical method is used for computing the pose from model-image correspondences of tracked curves. This method is able to give a correct estimate of the pose even when tracking errors occur. However, if we want to add virtual objects in a scene area which does not contain (or contains few) model features, the reprojection error in this area is likely to be large. In order to improve the accuracy of the viewpoint, we use 2D keypoints that can be easily matched in two consecutive images. As the relationship between two matched points is a function of the camera motion, the viewpoint can be improved by minimizing a cost function which encompasses the reprojection error as well as the matching error between two frames. The reliability of the system is shown on an encrustation of a virtual car in a sequence of the Stanislas square.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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