Sparse Randomized Approximation of Normal Cycles
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911714731098112 |
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| author | Paul, Allen Campbell, Neill Shardlow, Tony |
| author_facet | Paul, Allen Campbell, Neill Shardlow, Tony |
| contents | We extend our work for compression of currents and varifolds to a compression algorithm for the embedded normal cycles representation of shape, restricted to the constant normal kernel case, using the Nystrom approximation in Reproducing Kernel Hilbert Spaces (RKHS) and ridge leverage score (RLS) sampling. Our method comes with theoretical guarantees on the compression error decay, and the approximations are shown to be effective for downstream tasks such as nonlinear shape registration in the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework, even for very high compression ratios. The performance of our algorithm is demonstrated on large-scale shape data from modern geometry processing datasets and is shown to accelerate downstream registration tasks significantly. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_01057 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sparse Randomized Approximation of Normal Cycles Paul, Allen Campbell, Neill Shardlow, Tony Numerical Analysis We extend our work for compression of currents and varifolds to a compression algorithm for the embedded normal cycles representation of shape, restricted to the constant normal kernel case, using the Nystrom approximation in Reproducing Kernel Hilbert Spaces (RKHS) and ridge leverage score (RLS) sampling. Our method comes with theoretical guarantees on the compression error decay, and the approximations are shown to be effective for downstream tasks such as nonlinear shape registration in the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework, even for very high compression ratios. The performance of our algorithm is demonstrated on large-scale shape data from modern geometry processing datasets and is shown to accelerate downstream registration tasks significantly. |
| title | Sparse Randomized Approximation of Normal Cycles |
| topic | Numerical Analysis |
| url | https://arxiv.org/abs/2503.01057 |