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On the Stability of Functional Maps and Shape Difference Operators
Author(s) -
Huang R.,
Chazal F.,
Ovsjanikov M.
Publication year - 2018
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.13238
Subject(s) - robustness (evolution) , mathematics , eigenfunction , operator (biology) , stability (learning theory) , polygon mesh , point cloud , representation (politics) , computer science , geometry , artificial intelligence , eigenvalues and eigenvectors , physics , biochemistry , chemistry , repressor , quantum mechanics , machine learning , politics , transcription factor , political science , law , gene
In this paper, we provide stability guarantees for two frameworks that are based on the notion of functional maps—the framework of shape difference operators and the one of analyzing and visualizing the deformations between shapes. We consider two types of perturbations in our analysis: one is on the input shapes and the other is on the change in scale . In theory, we formulate and justify the robustness that has been observed in practical implementations of those frameworks. Inspired by our theoretical results, we propose a pipeline for constructing shape difference operators on point clouds and show numerically that the results are robust and informative. In particular, we show that both the shape difference operators and the derived areas of highest distortion are stable with respect to changes in shape representation and change of scale. Remarkably, this is in contrast with the well‐known instability of the eigenfunctions of the Laplace–Beltrami operator computed on point clouds compared to those obtained on triangle meshes.