Spectral Descriptors for 3D Deformable Shape Matching : A Comparative Survey

A large number of 3D spectral descriptors have been proposed in the literature, which act as an essential component for 3D deformable shape matching and related applications. An outstanding descriptor should have desirable natures including high-level descriptive capacity, cheap storage, and robustn...

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Veröffentlicht in:IEEE transactions on visualization and computer graphics. - 1996. - PP(2024) vom: 21. Feb.
1. Verfasser: Liu, Shengjun (VerfasserIn)
Weitere Verfasser: Wang, Haibo, Yan, Dong-Ming, Li, Qinsong, Luo, Feifan, Teng, Zi, Liu, Xinru
Format: Online-Aufsatz
Sprache:English
Veröffentlicht: 2024
Zugriff auf das übergeordnete Werk:IEEE transactions on visualization and computer graphics
Schlagworte:Journal Article
Beschreibung
Zusammenfassung:A large number of 3D spectral descriptors have been proposed in the literature, which act as an essential component for 3D deformable shape matching and related applications. An outstanding descriptor should have desirable natures including high-level descriptive capacity, cheap storage, and robustness to a set of nuisances. It is, however, unclear which descriptors are more suitable for a particular application. This paper fills the gap by comprehensively evaluating nine state-of-the-art spectral descriptors on ten popular deformable shape datasets as well as perturbations such as mesh discretization, geometric noise, scale transformation, non-isometric setting, partiality, and topological noise. Our evaluated terms for a spectral descriptor cover four major concerns, i.e., distinctiveness, robustness, compactness, and computational efficiency. In the end, we present a summary of the overall performance and several interesting findings that can serve as guidance for the following researchers to construct a new spectral descriptor and choose an appropriate spectral feature in a particular application
Beschreibung:Date Revised 23.02.2024
published: Print-Electronic
Citation Status Publisher
ISSN:1941-0506
DOI:10.1109/TVCG.2024.3368083