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Depth Map Estimation with Consistent Normals from Stereo Images
The total variation regularization of non-convex data terms in continuous variational models can be convexified by the so called functional lifting, which may be considered as a continuous counterpart of Ishikawa’s method for multi-label discrete variational problems. We solve the resulting convex c...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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2020
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7302560/ http://dx.doi.org/10.1007/978-3-030-50426-7_41 |
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author | Malyshev, Alexander |
author_facet | Malyshev, Alexander |
author_sort | Malyshev, Alexander |
collection | PubMed |
description | The total variation regularization of non-convex data terms in continuous variational models can be convexified by the so called functional lifting, which may be considered as a continuous counterpart of Ishikawa’s method for multi-label discrete variational problems. We solve the resulting convex continuous variational problem by the augmented Lagrangian method. Application of this method to the dense depth map estimation allows us to obtain a consistent normal field to the depth surface as a byproduct. We illustrate the method with numerical examples of the depth map estimation for rectified stereo image pairs. |
format | Online Article Text |
id | pubmed-7302560 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-73025602020-06-19 Depth Map Estimation with Consistent Normals from Stereo Images Malyshev, Alexander Computational Science – ICCS 2020 Article The total variation regularization of non-convex data terms in continuous variational models can be convexified by the so called functional lifting, which may be considered as a continuous counterpart of Ishikawa’s method for multi-label discrete variational problems. We solve the resulting convex continuous variational problem by the augmented Lagrangian method. Application of this method to the dense depth map estimation allows us to obtain a consistent normal field to the depth surface as a byproduct. We illustrate the method with numerical examples of the depth map estimation for rectified stereo image pairs. 2020-05-25 /pmc/articles/PMC7302560/ http://dx.doi.org/10.1007/978-3-030-50426-7_41 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Malyshev, Alexander Depth Map Estimation with Consistent Normals from Stereo Images |
title | Depth Map Estimation with Consistent Normals from Stereo Images |
title_full | Depth Map Estimation with Consistent Normals from Stereo Images |
title_fullStr | Depth Map Estimation with Consistent Normals from Stereo Images |
title_full_unstemmed | Depth Map Estimation with Consistent Normals from Stereo Images |
title_short | Depth Map Estimation with Consistent Normals from Stereo Images |
title_sort | depth map estimation with consistent normals from stereo images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7302560/ http://dx.doi.org/10.1007/978-3-030-50426-7_41 |
work_keys_str_mv | AT malyshevalexander depthmapestimationwithconsistentnormalsfromstereoimages |