Elastic shape analysis of surfaces with second-order Sobolev metrics: a comprehensive numerical framework
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2022
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| _version_ | 1866916551185137664 |
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| author | Hartman, Emmanuel Sukurdeep, Yashil Klassen, Eric Charon, Nicolas Bauer, Martin |
| author_facet | Hartman, Emmanuel Sukurdeep, Yashil Klassen, Eric Charon, Nicolas Bauer, Martin |
| contents | This paper introduces a set of numerical methods for Riemannian shape analysis of 3D surfaces within the setting of invariant (elastic) second-order Sobolev metrics. More specifically, we address the computation of geodesics and geodesic distances between parametrized or unparametrized immersed surfaces represented as 3D meshes. Building on this, we develop tools for the statistical shape analysis of sets of surfaces, including methods for estimating Karcher means and performing tangent PCA on shape populations, and for computing parallel transport along paths of surfaces. Our proposed approach fundamentally relies on a relaxed variational formulation for the geodesic matching problem via the use of varifold fidelity terms, which enable us to enforce reparametrization independence when computing geodesics between unparametrized surfaces, while also yielding versatile algorithms that allow us to compare surfaces with varying sampling or mesh structures. Importantly, we demonstrate how our relaxed variational framework can be extended to tackle partially observed data. The different benefits of our numerical pipeline are illustrated over various examples, synthetic and real. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2204_04238 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Elastic shape analysis of surfaces with second-order Sobolev metrics: a comprehensive numerical framework Hartman, Emmanuel Sukurdeep, Yashil Klassen, Eric Charon, Nicolas Bauer, Martin Computer Vision and Pattern Recognition 68U05, 49Q10, 58D10 This paper introduces a set of numerical methods for Riemannian shape analysis of 3D surfaces within the setting of invariant (elastic) second-order Sobolev metrics. More specifically, we address the computation of geodesics and geodesic distances between parametrized or unparametrized immersed surfaces represented as 3D meshes. Building on this, we develop tools for the statistical shape analysis of sets of surfaces, including methods for estimating Karcher means and performing tangent PCA on shape populations, and for computing parallel transport along paths of surfaces. Our proposed approach fundamentally relies on a relaxed variational formulation for the geodesic matching problem via the use of varifold fidelity terms, which enable us to enforce reparametrization independence when computing geodesics between unparametrized surfaces, while also yielding versatile algorithms that allow us to compare surfaces with varying sampling or mesh structures. Importantly, we demonstrate how our relaxed variational framework can be extended to tackle partially observed data. The different benefits of our numerical pipeline are illustrated over various examples, synthetic and real. |
| title | Elastic shape analysis of surfaces with second-order Sobolev metrics: a comprehensive numerical framework |
| topic | Computer Vision and Pattern Recognition 68U05, 49Q10, 58D10 |
| url | https://arxiv.org/abs/2204.04238 |