Partial-to-Partial Shape Matching with Geometric Consistency
<|reference_start|>Partial-to-Partial Shape Matching with Geometric Consistency: Finding correspondences between 3D shapes is an important and long-standing problem in computer vision, graphics and beyond. A prominent challenge are partial-to-partial shape matching settings, which occur when the shapes to match are only observed incompletely (e.g. from 3D scanning). Although partial-to-partial matching is a highly relevant setting in practice, it is rarely explored. Our work bridges the gap between existing (rather artificial) 3D full shape matching and partial-to-partial real-world settings by exploiting geometric consistency as a strong constraint. We demonstrate that it is indeed possible to solve this challenging problem in a variety of settings. For the first time, we achieve geometric consistency for partial-to-partial matching, which is realized by a novel integer non-linear program formalism building on triangle product spaces, along with a new pruning algorithm based on linear integer programming. Further, we generate a new inter-class dataset for partial-to-partial shape-matching. We show that our method outperforms current SOTA methods on both an established intra-class dataset and our novel inter-class dataset.<|reference_end|>
@article{ehm2024partial-to-partial,
    title={Partial-to-Partial Shape Matching with Geometric Consistency},
    author={Viktoria Ehm, Maolin Gao, Paul Roetzer, Marvin Eisenberger, Daniel
  Cremers, Florian Bernard},
    journal={arXiv preprint arXiv:2404.12209},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.12209},
    primaryClass={cs.CV}
}