We propose a novel discrete solver for optimizing functional map-based energies, including descriptor preservation and pro-moting structural properties such as area-preservation, bijectivity and Laplacian commutativity among others. Unlike thecommonly-used continuous optimization methods, our approach enforces the functional map to be associated with a pointwisecorrespondence as a hard constraint, which provides a stronger link between optimized properties of functional and point-to-point maps. Under this hard constraint, our solver obtains functional maps with lower energy values compared to the standardcontinuous strategies. Perhaps more importantly, the recovered pointwise maps from our discrete solver preserve the optimizedfor functional properties and are thus of higher overall quality. We demonstrate the advantages of our discrete solver on arange of energies and shape categories, compared to existing techniques for promoting pointwise maps within the functionalmap framework. Finally, with this solver in hand, we introduce a novel Effective Functional Map Reﬁnement (EFMR) methodwhich achieves the state-of-the-art accuracy on the SHREC’19 benchmark.
Bibliographical noteKAUST Repository Item: Exported on 2021-08-25
Acknowledged KAUST grant number(s): CRG-2017-3426, OSR
Acknowledgements: The authors thank the anonymous reviewers for their valuable comments. Parts of this work were supported by the KAUST OSR Award No. CRG-2017-3426, the ERC Starting Grants No. 758800 (EXPROTEA) and No. 802554 (SPECGEO), and the ANR AI Chair AIGRETTE.
ASJC Scopus subject areas
- Computer Networks and Communications