Sparse Representation for 3D Shape Estimation : A Convex Relaxation Approach
We investigate the problem of estimating the 3D shape of an object defined by a set of 3D landmarks, given their 2D correspondences in a single image. A successful approach to alleviating the reconstruction ambiguity is the 3D deformable shape model and a sparse representation is often used to captu...
Veröffentlicht in: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 39(2017), 8 vom: 15. Aug., Seite 1648-1661 |
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Weitere Verfasser: | , , |
Format: | Online-Aufsatz |
Sprache: | English |
Veröffentlicht: |
2017
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Zugriff auf das übergeordnete Werk: | IEEE transactions on pattern analysis and machine intelligence |
Schlagworte: | Journal Article Research Support, U.S. Gov't, Non-P.H.S. |
Zusammenfassung: | We investigate the problem of estimating the 3D shape of an object defined by a set of 3D landmarks, given their 2D correspondences in a single image. A successful approach to alleviating the reconstruction ambiguity is the 3D deformable shape model and a sparse representation is often used to capture complex shape variability. But the model inference is still challenging due to the nonconvexity in the joint optimization of shape and viewpoint. In contrast to prior work that relies on an alternating scheme whose solution depends on initialization, we propose a convex approach to addressing this challenge and develop an efficient algorithm to solve the proposed convex program. We further propose a robust model to handle gross errors in the 2D correspondences. We demonstrate the exact recovery property of the proposed method, the advantage compared to several nonconvex baselines and the applicability to recover 3D human poses and car models from single images |
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Beschreibung: | Date Completed 08.11.2018 Date Revised 08.11.2018 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
ISSN: | 1939-3539 |
DOI: | 10.1109/TPAMI.2016.2605097 |