Localization of 2D Cameras in a Known Environment using Direct 2D-3D Registration
Identifieur interne : 000231 ( France/Analysis ); précédent : 000230; suivant : 000232Localization of 2D Cameras in a Known Environment using Direct 2D-3D Registration
Auteurs : Danda Pani Paudel [France] ; Cédric Demonceaux [France] ; Adlane Habed [France] ; Pascal Vasseur [France]Source :
Abstract
In this paper we propose a robust and direct 2D-to- 3D registration method for localizing 2D cameras in a known 3D environment. Although the 3D environment is known, localizing the cameras remains a challenging problem that is particularly undermined by the unknown 2D-3D correspondences, outliers, scale ambiguities and occlusions. Once the cameras are localized, the Structure-from-Motion reconstruction obtained from image correspondences is refined by means of a constrained nonlinear optimization that benefits from the knowledge of the scene. We also propose a common optimization framework for both localization and refinement steps in which projection errors in one view are minimized while preserving the existing relationships between images. The problem of occlusion and that of missing scene parts are handled by employing a scale histogram while the effect of data inaccuracies is minimized using an M-estimator- based technique.
Url:
Affiliations:
- France
- Alsace (région administrative), Grand Est, Haute-Normandie, Région Bourgogne, Région Normandie
- Dijon, Le Havre, Rouen, Strasbourg
- Université de Bourgogne, Université de Bourgogne Franche-Comté, Université de Rouen, Université de Strasbourg, Université du Havre
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Hal:hal-01017155Le document en format XML
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Although the 3D environment is known, localizing the cameras remains a challenging problem that is particularly undermined by the unknown 2D-3D correspondences, outliers, scale ambiguities and occlusions. Once the cameras are localized, the Structure-from-Motion reconstruction obtained from image correspondences is refined by means of a constrained nonlinear optimization that benefits from the knowledge of the scene. We also propose a common optimization framework for both localization and refinement steps in which projection errors in one view are minimized while preserving the existing relationships between images. The problem of occlusion and that of missing scene parts are handled by employing a scale histogram while the effect of data inaccuracies is minimized using an M-estimator- based technique.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Alsace (région administrative)</li><li>Grand Est</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>Strasbourg</li></settlement><orgName><li>Université de Bourgogne</li><li>Université de Bourgogne Franche-Comté</li><li>Université de Rouen</li><li>Université de Strasbourg</li><li>Université du Havre</li></orgName></list><tree><country name="France"><region name="Région Bourgogne"><name sortKey="Paudel, Danda Pani" sort="Paudel, Danda Pani" uniqKey="Paudel D" first="Danda Pani" last="Paudel">Danda Pani Paudel</name></region><name sortKey="Demonceaux, Cedric" sort="Demonceaux, Cedric" uniqKey="Demonceaux C" first="Cédric" last="Demonceaux">Cédric Demonceaux</name><name sortKey="Habed, Adlane" sort="Habed, Adlane" uniqKey="Habed A" first="Adlane" last="Habed">Adlane Habed</name><name sortKey="Vasseur, Pascal" sort="Vasseur, Pascal" uniqKey="Vasseur P" first="Pascal" last="Vasseur">Pascal Vasseur</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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