Treffer: Spectral Geometry Processing with Manifold Harmonics

Title:
Spectral Geometry Processing with Manifold Harmonics
Contributors:
Geometry and Lighting (ALICE), INRIA Lorraine, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Lorrain de Recherche en Informatique et ses Applications (LORIA), Institut National de Recherche en Informatique et en Automatique (Inria)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)-Université Henri Poincaré - Nancy 1 (UHP)-Université Nancy 2-Institut National Polytechnique de Lorraine (INPL)-Centre National de la Recherche Scientifique (CNRS)
Source:
Computer Graphics Forum. 27(2):251-260
Publisher Information:
CCSD; Wiley, 2008.
Publication Year:
2008
Collection:
collection:CNRS
collection:INRIA
collection:INPL
collection:INRIA-LORRAINE
collection:LORIA2
collection:INRIA-NANCY-GRAND-EST
collection:TESTALAIN1
collection:UNIV-LORRAINE
collection:INRIA2
collection:LORIA
collection:INRIA-300009
collection:AM2I-UL
Original Identifier:
HAL:
Document Type:
Zeitschrift article<br />Journal articles
Language:
English
ISSN:
0167-7055
1467-8659
Relation:
info:eu-repo/semantics/altIdentifier/doi/10.1111/j.1467-8659.2008.01122.x
DOI:
10.1111/j.1467-8659.2008.01122.x
Rights:
info:eu-repo/semantics/OpenAccess
Accession Number:
edshal.inria.00331894v1
Database:
HAL

Weitere Informationen

We present an explicit method to compute a generalization of the Fourier Transform on a mesh. It is well known that the eigenfunctions of the Laplace Beltrami operator (Manifold Harmonics) define a function basis allowing for such a transform. However, computing even just a few eigenvectors is out of reach for meshes with more than a few thousand vertices, and storing these eigenvectors is prohibitive for large meshes. To overcome these limitations, we propose a band-by-band spectrum computation algorithm and an out-of-core implementation that can compute thousands of eigenvectors for meshes with up to a million vertices. We also propose a limited-memory filtering algorithm, that does not need to store the eigenvectors. Using this latter algorithm, specific frequency bands can be filtered, without needing to compute the entire spectrum. Finally, we demonstrate some applications of our method to interactive convolution geometry filtering. These technical achievements are supported by a solid yet simple theoretic framework based on Discrete Exterior Calculus (DEC). In particular, the issues of symmetry and discretization of the operator are considered with great care.