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dc.contributor.authorWu, Bin
dc.contributor.authorRahman, Talal
dc.contributor.authorTai, Xue-Cheng
dc.date.accessioned2021-03-10T12:38:28Z
dc.date.available2021-03-10T12:38:28Z
dc.date.created2019-01-27T16:15:07Z
dc.date.issued2018
dc.identifier.isbn978-3-319-91273-8
dc.identifier.issn1612-3786
dc.identifier.urihttps://hdl.handle.net/11250/2732627
dc.description.abstractA model combining the first-order and the second-order variational regularizations for the purpose of 3D surface reconstruction based on 2D sparse data is proposed. The model includes a hybrid fidelity constraint which allows the initial conditions to be switched flexibly between vectors and elevations. A numerical algorithm based on the augmented Lagrangian method is also proposed. The numerical experiments are presented, showing its excellent performance both in designing cartoon characters, as well as in recovering oriented three dimensional maps from contours or points with elevation information.en_US
dc.language.isoengen_US
dc.publisherSpringeren_US
dc.relation.ispartofImaging, Vision and Learning Based on Optimization and PDEs
dc.titleSparse-Data Based 3D Surface Reconstruction for Cartoon and Mapen_US
dc.typeConference objecten_US
dc.description.versionacceptedVersionen_US
dc.rights.holderCopyright Springer International Publishing AG, part of Springer Nature 2018.en_US
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1
dc.identifier.doihttps://doi.org/10.1007/978-3-319-91274-5_3
dc.identifier.cristin1665697
dc.identifier.citationIn: In: Tai XC., Bae E., Lysaker M. (eds) Imaging, Vision and Learning Based on Optimization and PDEs. IVLOPDE 2016. Mathematics and Visualization: 47-64.en_US


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