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Authors: Aris S. Lalos ; Gerasimos Arvanitis ; Anastasios Dimas and Kostantinos Moustakas

Affiliation: University of Patras, Greece

ISBN: 978-989-758-287-5

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 sche mes 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. (More)

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Paper citation in several formats:
Lalos A., Arvanitis G., Dimas A. and Moustakas K. (2018). Block based Spectral Processing of Dense 3D Meshes using Orthogonal Iterations.In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP, ISBN 978-989-758-287-5, pages 122-132. DOI: 10.5220/0006611401220132

@conference{grapp18,
author={Aris S. Lalos and Gerasimos Arvanitis and Anastasios Dimas and Kostantinos Moustakas},
title={Block based Spectral Processing of Dense 3D Meshes using Orthogonal Iterations},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP,},
year={2018},
pages={122-132},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006611401220132},
isbn={978-989-758-287-5},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 1: GRAPP,
TI - Block based Spectral Processing of Dense 3D Meshes using Orthogonal Iterations
SN - 978-989-758-287-5
AU - Lalos A.
AU - Arvanitis G.
AU - Dimas A.
AU - Moustakas K.
PY - 2018
SP - 122
EP - 132
DO - 10.5220/0006611401220132

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