Limit Shapes – A Tool for Understanding Shape Differences and Variability in 3D Model Collections

Autor: Leonidas J. Guibas, Maks Ovsjanikov, Ruqi Huang, Panos Achlioptas
Přispěvatelé: Laboratoire d'informatique de l'École polytechnique [Palaiseau] (LIX), Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X), Stanford University
Jazyk: angličtina
Předmět:
Zdroj: Symposium of Geometry Processing 2019
Symposium of Geometry Processing 2019, Jul 2019, Milan, Italy. pp.187-202, ⟨10.1111/cgf.13799⟩
Computer Graphics Forum
ISSN: 1467-8659
0167-7055
DOI: 10.1111/cgf.13799
Popis: International audience; We propose a novel construction for extracting a central or limit shape in a shape collection, connected via a functional map network. Our approach is based on enriching the latent space induced by a functional map network with an additional natural metric structure. We call this shape-like dual object the limit shape and show that its construction avoids many of the biases introduced by selecting a fixed base shape or template. We also show that shape differences between real shapes and the limit shape can be computed and characterize the unique properties of each shape in a collection-leading to a compact and rich shape representation. We demonstrate the utility of this representation in a range of shape analysis tasks, including improving functional maps in difficult situations through the mediation of limit shapes, understanding and visualizing the variability within and across different shape classes, and several others. In this way, our analysis sheds light on the missing geometric structure in previously used latent functional spaces, demonstrates how these can be addressed and finally enables a compact and meaningful shape representation useful in a variety of practical applications.
Databáze: OpenAIRE
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje