Convex image orientation from relative orientations

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dc.identifier.uri Reich, Martin Heipke, Christian
dc.contributor.editor Halounova, L.
dc.contributor.editor Schindler, K.
dc.contributor.editor Limpouch, A.
dc.contributor.editor Pajdla, T.
dc.contributor.editor Šafář, V.
dc.contributor.editor Mayer, H.
dc.contributor.editor Oude Elberink, S.
dc.contributor.editor Mallet, C.
dc.contributor.editor Rottensteiner, F.
dc.contributor.editor Brédif, M.
dc.contributor.editor Skaloud, J.
dc.contributor.editor Stilla, U. 2017-03-02T12:47:57Z 2017-03-02T12:47:57Z 2016
dc.identifier.citation Reich, M.; Heipke, C.: Convex image orientation from relative orientations. In: XXIII ISPRS Congress, Commission III 3 (2016), Nr. 3, S. 107-114. DOI:
dc.description.abstract In this paper we propose a novel workflow for the estimation of global image orientations given relative orientations between pairs of overlapping images. Our approach is convex and independent on initial values. First, global rotations are estimated in a relaxed semidefinite program (SDP) and refined in an iterative least squares adjustment in the tangent space of SO(3). A critical aspect is the handling of outliers in the relative orientations. We present a novel heuristic graph based approach for filtering the relative rotations that outperforms state-of-the-art robust rotation averaging algorithms. In a second part we make use of point-observations, tracked over a set of overlapping images and formulate a linear homogeneous system of equations to transfer the scale information between triplets of images, using estimated global rotations and relative translation directions. The final step consists of refining the orientation parameters in a robust bundle adjustment. The proposed approach handles outliers in the homologous points and relative orientations in every step of the processing chain. We demonstrate the robustness of the procedure on synthetic data. Moreover, the performance of our approach is illustrated on real world benchmark data. eng
dc.language.iso eng
dc.publisher Göttingen : Copernicus GmbH
dc.relation.ispartof XXIIIrd ISPRS congress 2016 : Prague, Czech Republic, 12th-19th July 2016
dc.relation.ispartofseries ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; III-3
dc.rights CC BY 3.0 Unported
dc.subject image orientation eng
dc.subject pose estimation eng
dc.subject rotation averaging eng
dc.subject lie algebra eng
dc.subject structure-from-motion eng
dc.subject spatial intersection eng
dc.subject multiple-view geometry eng
dc.subject motion estimation eng
dc.subject camera eng
dc.subject.classification Konferenzschrift ger
dc.subject.ddc 550 | Geowissenschaften ger
dc.title Convex image orientation from relative orientations eng
dc.type Article
dc.type Text
dc.relation.essn 2194-9050
dc.relation.issn 2194-9034
dc.bibliographicCitation.issue 3
dc.bibliographicCitation.volume III-3
dc.bibliographicCitation.firstPage 107
dc.bibliographicCitation.lastPage 114
dc.description.version publishedVersion
tib.accessRights frei zug�nglich

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