Anisotropic diffusion descriptors

D. Boscaini, J. Masci, E. Rodolà, M. M. Bronstein, D. Cremers

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)431-441
Number of pages11
JournalComputer Graphics Forum
Volume35
Issue number2
DOIs
StatePublished - 1 May 2016
Externally publishedYes

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