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Structured Regularization of Functional Map Computations
Author(s) -
Ren Jing,
Panine Mikhail,
Wonka Peter,
Ovsjanikov Maks
Publication year - 2019
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.13788
Subject(s) - pointwise , regularization (linguistics) , laplace operator , computation , isometry (riemannian geometry) , algorithm , range (aeronautics) , resolvent , mathematics , computer science , operator (biology) , mathematical optimization , artificial intelligence , pure mathematics , mathematical analysis , biochemistry , chemistry , materials science , repressor , transcription factor , composite material , gene
Abstract We consider the problem of non‐rigid shape matching using the functional map framework. Specifically, we analyze a commonly used approach for regularizing functional maps, which consists in penalizing the failure of the unknown map to commute with the Laplace‐Beltrami operators on the source and target shapes. We show that this approach has certain undesirable fundamental theoretical limitations, and can be undefined even for trivial maps in the smooth setting. Instead we propose a novel, theoretically well‐justified approach for regularizing functional maps, by using the notion of the resolvent of the Laplacian operator. In addition, we provide a natural one‐parameter family of regularizers, that can be easily tuned depending on the expected approximate isometry of the input shape pair. We show on a wide range of shape correspondence scenarios that our novel regularization leads to an improvement in the quality of the estimated functional, and ultimately pointwise correspondences before and after commonly‐used refinement techniques.