Diffeomorphic ICP Registration for Single and Multiple Point Sets
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arXiv
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| Auteur principal: | |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866910792223293440 |
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| author | Wohrer, Adrien |
| author_facet | Wohrer, Adrien |
| contents | We propose a generalization of the iterative closest point (ICP) algorithm for point set registration, in which the registration functions are non-rigid and follow the large deformation diffeomorphic metric mapping (LDDMM) framework. The algorithm is formulated as a well-posed probabilistic inference, and requires to solve a novel variation of LDDMM landmark registration with an additional term involving the Jacobian of the mapping. The algorithm can easily be generalized to construct a diffeomorphic, statistical atlas of multiple point sets. The method is successfully validated on a first set of synthetic data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_11986 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Diffeomorphic ICP Registration for Single and Multiple Point Sets Wohrer, Adrien Signal Processing We propose a generalization of the iterative closest point (ICP) algorithm for point set registration, in which the registration functions are non-rigid and follow the large deformation diffeomorphic metric mapping (LDDMM) framework. The algorithm is formulated as a well-posed probabilistic inference, and requires to solve a novel variation of LDDMM landmark registration with an additional term involving the Jacobian of the mapping. The algorithm can easily be generalized to construct a diffeomorphic, statistical atlas of multiple point sets. The method is successfully validated on a first set of synthetic data. |
| title | Diffeomorphic ICP Registration for Single and Multiple Point Sets |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2501.11986 |