Authors:
Aris S. Lalos
;
Gerasimos Arvanitis
;
Anastasios Dimas
and
Kostantinos Moustakas
Affiliation:
University of Patras, Greece
Keyword(s):
Graph Signal Processing, Mesh Compression, Mesh Denoising.
Related
Ontology
Subjects/Areas/Topics:
Computer Vision, Visualization and Computer Graphics
;
Geometric Computing
;
Geometry and Modeling
Abstract:
Spectral methods are widely used in geometry processing of 3D models. They rely on the projection of the
mesh geometry on the basis defined by the eigenvectors of the graph Laplacian operator, becoming computationally
prohibitive as the density of the models increases. In this paper, we propose a novel approach for
supporting fast and efficient spectral processing of dense 3D meshes, ideally suited for real time compression
and denoising scenarios. To achieve that, we apply the problem of tracking graph Laplacian eigenspaces via
orthogonal iterations, exploiting potential spectral coherences between adjacent parts. To avoid perceptual
distortions when a fixed number of eigenvectors is used for all the individual parts, we propose a flexible
solution that automatically identifies the optimal subspace size for satisfying a given reconstruction quality
constraint. Extensive simulations carried out with different 3D meshes in compression and denoising setups,
showed that the proposed sch
emes are very fast alternatives of SVD based spectral processing while achieving
at the same time similar or even better reconstruction quality. More importantly, the proposed approach can
be employed by several other state of the art denoising methods as a preprocessing step, optimizing both their
reconstruction quality and their computational complexity.
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