MorphModes: Non-rigid Registration via Adaptive Skinning Eigenmodes
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909862222364672 |
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| author | Browne, Gabrielle Liu, Mengfei Grinspun, Eitan Benchekroun, Otman |
| author_facet | Browne, Gabrielle Liu, Mengfei Grinspun, Eitan Benchekroun, Otman |
| contents | Non-rigid registration is a crucial task with applications in medical imaging, industrial robotics, computer vision, and entertainment. Standard approaches accomplish this task using variations on the Non-Rigid Iterative Closest Point (NRICP) algorithms, which are prone to local minima and sensitive to initial conditions. We instead formulate the non-rigid registration problem as a Signed Distance Function (SDF) matching optimization problem, which provides richer shape information compared to traditional ICP methods. To avoid degenerate solutions, we propose to use a smooth Skinning Eigenmode subspace to parameterize the optimization problem. Finally, we propose an adaptive subspace optimization scheme to allow the resolution of localized deformations within the optimization. The result is a non-rigid registration algorithm that is more robust than NRICP, without the parameter sensitivity present in other SDF-matching approaches. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_18658 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MorphModes: Non-rigid Registration via Adaptive Skinning Eigenmodes Browne, Gabrielle Liu, Mengfei Grinspun, Eitan Benchekroun, Otman Graphics Non-rigid registration is a crucial task with applications in medical imaging, industrial robotics, computer vision, and entertainment. Standard approaches accomplish this task using variations on the Non-Rigid Iterative Closest Point (NRICP) algorithms, which are prone to local minima and sensitive to initial conditions. We instead formulate the non-rigid registration problem as a Signed Distance Function (SDF) matching optimization problem, which provides richer shape information compared to traditional ICP methods. To avoid degenerate solutions, we propose to use a smooth Skinning Eigenmode subspace to parameterize the optimization problem. Finally, we propose an adaptive subspace optimization scheme to allow the resolution of localized deformations within the optimization. The result is a non-rigid registration algorithm that is more robust than NRICP, without the parameter sensitivity present in other SDF-matching approaches. |
| title | MorphModes: Non-rigid Registration via Adaptive Skinning Eigenmodes |
| topic | Graphics |
| url | https://arxiv.org/abs/2510.18658 |