Probabilistic Registration for Gaussian Process 3D shape modelling in the presence of extensive missing data
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866911070773313536 |
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| author | Valdeira, Filipa Ferreira, Ricardo Micheletti, Alessandra Soares, Cláudia |
| author_facet | Valdeira, Filipa Ferreira, Ricardo Micheletti, Alessandra Soares, Cláudia |
| contents | We propose a shape fitting/registration method based on a Gaussian Processes formulation, suitable for shapes with extensive regions of missing data. Gaussian Processes are a proven powerful tool, as they provide a unified setting for shape modelling and fitting. While the existing methods in this area prove to work well for the general case of the human head, when looking at more detailed and deformed data, with a high prevalence of missing data, such as the ears, the results are not satisfactory. In order to overcome this, we formulate the shape fitting problem as a multi-annotator Gaussian Process Regression and establish a parallel with the standard probabilistic registration. The achieved method SFGP shows better performance when dealing with extensive areas of missing data when compared to a state-of-the-art registration method and current approaches for registration with pre-existing shape models. Experiments are conducted both for a 2D small dataset with diverse transformations and a 3D dataset of ears. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2203_14113 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Probabilistic Registration for Gaussian Process 3D shape modelling in the presence of extensive missing data Valdeira, Filipa Ferreira, Ricardo Micheletti, Alessandra Soares, Cláudia Computer Vision and Pattern Recognition Applications We propose a shape fitting/registration method based on a Gaussian Processes formulation, suitable for shapes with extensive regions of missing data. Gaussian Processes are a proven powerful tool, as they provide a unified setting for shape modelling and fitting. While the existing methods in this area prove to work well for the general case of the human head, when looking at more detailed and deformed data, with a high prevalence of missing data, such as the ears, the results are not satisfactory. In order to overcome this, we formulate the shape fitting problem as a multi-annotator Gaussian Process Regression and establish a parallel with the standard probabilistic registration. The achieved method SFGP shows better performance when dealing with extensive areas of missing data when compared to a state-of-the-art registration method and current approaches for registration with pre-existing shape models. Experiments are conducted both for a 2D small dataset with diverse transformations and a 3D dataset of ears. |
| title | Probabilistic Registration for Gaussian Process 3D shape modelling in the presence of extensive missing data |
| topic | Computer Vision and Pattern Recognition Applications |
| url | https://arxiv.org/abs/2203.14113 |