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Orthogonalized Fourier Polynomials for Signal Approximation and Transfer
Author(s) -
Maggioli F.,
Melzi S.,
Ovsjanikov M.,
Bronstein M. M.,
Rodolà E.
Publication year - 2021
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.142645
Subject(s) - pointwise , orthonormal basis , initialization , algorithm , discretization , representation (politics) , computer science , fourier transform , fourier series , mathematics , mathematical analysis , physics , quantum mechanics , politics , political science , law , programming language
We propose a novel approach for the approximation and transfer of signals across 3D shapes. The proposed solution is based on taking pointwise polynomials of the Fourier‐like Laplacian eigenbasis, which provides a compact and expressive representation for general signals defined on the surface. Key to our approach is the construction of a new orthonormal basis upon the set of these linearly dependent polynomials. We analyze the properties of this representation, and further provide a complete analysis of the involved parameters. Our technique results in accurate approximation and transfer of various families of signals between near‐isometric and non‐isometric shapes, even under poor initialization. Our experiments, showcased on a selection of downstream tasks such as filtering and detail transfer, show that our method is more robust to discretization artifacts, deformation and noise as compared to alternative approaches.