Variational methods for geometry processing
Inspired by the success of variational techniques in various applications such as image processing and recent advance in sparse norm minimization, this research explores a class of variational methods with sparse norm for digital geometry processing which has to deal with noisy data...
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| Other Authors: | |
| Format: | Thesis |
| Language: | English |
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2014
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| Online Access: | https://hdl.handle.net/10356/61854 |
| _version_ | 1826118789644681216 |
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| author | Wu, Xiaoqun |
| author2 | Zheng Jianmin |
| author_facet | Zheng Jianmin Wu, Xiaoqun |
| author_sort | Wu, Xiaoqun |
| collection | NTU |
| description | Inspired by the success of variational techniques in various
applications such as image processing and recent advance in sparse norm minimization, this research explores a class of variational methods with sparse norm for digital geometry processing which has to deal with noisy data and non-smooth features. The variational model mainly contains two terms: one is to keep the intrinsic properties of the function and the other is to make the function satisfy some prior knowledge. Three types of geometry processing problems are studied under the similar variational framework. The research focuses on the specific formulation of the variational model and efficient numerical solvers. Its objective is to develop novel geometry processing algorithms that can effectively and robustly process geometric models characterized by the co-existence
of non-smooth features and noise/outliers, which is usually
difficult in prior art. |
| first_indexed | 2024-10-01T04:49:15Z |
| format | Thesis |
| id | ntu-10356/61854 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:49:15Z |
| publishDate | 2014 |
| record_format | dspace |
| spelling | ntu-10356/618542023-03-04T00:38:35Z Variational methods for geometry processing Wu, Xiaoqun Zheng Jianmin School of Computer Engineering DRNTU::Engineering::Computer science and engineering::Computing methodologies::Computer graphics Inspired by the success of variational techniques in various applications such as image processing and recent advance in sparse norm minimization, this research explores a class of variational methods with sparse norm for digital geometry processing which has to deal with noisy data and non-smooth features. The variational model mainly contains two terms: one is to keep the intrinsic properties of the function and the other is to make the function satisfy some prior knowledge. Three types of geometry processing problems are studied under the similar variational framework. The research focuses on the specific formulation of the variational model and efficient numerical solvers. Its objective is to develop novel geometry processing algorithms that can effectively and robustly process geometric models characterized by the co-existence of non-smooth features and noise/outliers, which is usually difficult in prior art. DOCTOR OF PHILOSOPHY (SCE) 2014-11-19T01:56:40Z 2014-11-19T01:56:40Z 2014 2014 Thesis Wu, X. (2014). Variational methods for geometry processing. Doctoral thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/61854 10.32657/10356/61854 en 152 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Computer science and engineering::Computing methodologies::Computer graphics Wu, Xiaoqun Variational methods for geometry processing |
| title | Variational methods for geometry processing |
| title_full | Variational methods for geometry processing |
| title_fullStr | Variational methods for geometry processing |
| title_full_unstemmed | Variational methods for geometry processing |
| title_short | Variational methods for geometry processing |
| title_sort | variational methods for geometry processing |
| topic | DRNTU::Engineering::Computer science and engineering::Computing methodologies::Computer graphics |
| url | https://hdl.handle.net/10356/61854 |
| work_keys_str_mv | AT wuxiaoqun variationalmethodsforgeometryprocessing |