Spectral methods have recently gained popularity in many domains of computer graphics and geometry processing, especially shape processing, computation of shape descriptors, distances, and correspondence. Spectral geometric structures are intrinsic and thus invariant to isometric deformations, are efficiently computed, and can be constructed on shapes in different representations. A notable drawback of these constructions, however, is that they are isotropic, i.e., insensitive to direction. In this paper, we show how to construct direction-sensitive spectral feature descriptors using anisotropic diffusion on meshes and point clouds. The core of our construction are directed local kernels acting similarly to steerable filters, which are learned in a task-specific manner. Remarkably, while being intrinsic, our descriptors allow to disambiguate reflection symmetries. We show the application of anisotropic descriptors for problems of shape correspondence on meshes and point clouds, achieving results significantly better than state-of-the-art methods.

Anisotropic diffusion descriptors / Boscaini, D.; Masci, J.; Rodolà, E.; Bronstein, M. M.; Cremers, D.. - In: COMPUTER GRAPHICS FORUM. - ISSN 0167-7055. - 35:2(2016), pp. 431-441. [10.1111/cgf.12844]

Anisotropic diffusion descriptors

Rodolà, E.;
2016

Abstract

Spectral methods have recently gained popularity in many domains of computer graphics and geometry processing, especially shape processing, computation of shape descriptors, distances, and correspondence. Spectral geometric structures are intrinsic and thus invariant to isometric deformations, are efficiently computed, and can be constructed on shapes in different representations. A notable drawback of these constructions, however, is that they are isotropic, i.e., insensitive to direction. In this paper, we show how to construct direction-sensitive spectral feature descriptors using anisotropic diffusion on meshes and point clouds. The core of our construction are directed local kernels acting similarly to steerable filters, which are learned in a task-specific manner. Remarkably, while being intrinsic, our descriptors allow to disambiguate reflection symmetries. We show the application of anisotropic descriptors for problems of shape correspondence on meshes and point clouds, achieving results significantly better than state-of-the-art methods.
2016
Computer graphics; Geometry; Optical anisotropy
01 Pubblicazione su rivista::01a Articolo in rivista
Anisotropic diffusion descriptors / Boscaini, D.; Masci, J.; Rodolà, E.; Bronstein, M. M.; Cremers, D.. - In: COMPUTER GRAPHICS FORUM. - ISSN 0167-7055. - 35:2(2016), pp. 431-441. [10.1111/cgf.12844]
File allegati a questo prodotto
File Dimensione Formato  
Rodola_Anisotropic_2016.pdf

solo gestori archivio

Tipologia: Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 13.11 MB
Formato Adobe PDF
13.11 MB Adobe PDF   Contatta l'autore

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1229421
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 106
  • ???jsp.display-item.citation.isi??? 83
social impact