| dc.contributor.author | Wu, Bin | |
| dc.contributor.author | Rahman, Talal | |
| dc.contributor.author | Tai, Xue-Cheng | |
| dc.date.accessioned | 2021-03-10T12:38:28Z | |
| dc.date.available | 2021-03-10T12:38:28Z | |
| dc.date.created | 2019-01-27T16:15:07Z | |
| dc.date.issued | 2018 | |
| dc.identifier.isbn | 978-3-319-91273-8 | |
| dc.identifier.issn | 1612-3786 | |
| dc.identifier.uri | https://hdl.handle.net/11250/2732627 | |
| dc.description.abstract | A 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.iso | eng | en_US |
| dc.publisher | Springer | en_US |
| dc.relation.ispartof | Imaging, Vision and Learning Based on Optimization and PDEs | |
| dc.title | Sparse-Data Based 3D Surface Reconstruction for Cartoon and Map | en_US |
| dc.type | Chapter | en_US |
| dc.description.version | acceptedVersion | en_US |
| dc.rights.holder | Copyright Springer International Publishing AG, part of Springer Nature 2018. | en_US |
| cristin.ispublished | true | |
| cristin.fulltext | postprint | |
| cristin.qualitycode | 1 | |
| dc.identifier.doi | https://doi.org/10.1007/978-3-319-91274-5_3 | |
| dc.identifier.cristin | 1665697 | |
| dc.source.pagenumber | 47-64 | |
| dc.identifier.citation | In: 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 |
