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A Robust Feature-aware Sparse Mesh Representation


Fuentes Perez, Lizeth J; Romero Calla, Luciano A; Montenegro, Anselmo A; Mura, Claudio; Pajarola, R (2020). A Robust Feature-aware Sparse Mesh Representation. In: Proceedings of Pacific Graphics Short Papers, Wellington New Zealand, 1 October 2020 - 1 October 2020, 25-30.

Abstract

The sparse representation of signals defined on Euclidean domains has been successfully applied in signal processing. Bringing the power of sparse representations to non-regular domains is still a challenge, but promising approaches have started emerging recently. In this paper, we investigate the problem of sparsely representing discrete surfaces and propose a new representation that is capable of providing tools for solving different geometry processing problems. The sparse discrete surface representation is obtained by combining innovative approaches into an integrated method. First, to deal with irregular mesh domains, we devised a new way to subdivide discrete meshes into a set of patches using a feature-aware seed sampling. Second, we achieve good surface approximation with over-fitting control by combining the power of a continuous global dictionary representation with a modified Orthogonal Marching Pursuit. The discrete surface approximation results produced were able to preserve the shape features while being robust to over-fitting. Our results show that the method is quite promising for applications like surface re-sampling and mesh compression.

Abstract

The sparse representation of signals defined on Euclidean domains has been successfully applied in signal processing. Bringing the power of sparse representations to non-regular domains is still a challenge, but promising approaches have started emerging recently. In this paper, we investigate the problem of sparsely representing discrete surfaces and propose a new representation that is capable of providing tools for solving different geometry processing problems. The sparse discrete surface representation is obtained by combining innovative approaches into an integrated method. First, to deal with irregular mesh domains, we devised a new way to subdivide discrete meshes into a set of patches using a feature-aware seed sampling. Second, we achieve good surface approximation with over-fitting control by combining the power of a continuous global dictionary representation with a modified Orthogonal Marching Pursuit. The discrete surface approximation results produced were able to preserve the shape features while being robust to over-fitting. Our results show that the method is quite promising for applications like surface re-sampling and mesh compression.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Uncontrolled Keywords:graphics, geometry processing
Language:English
Event End Date:1 October 2020
Deposited On:15 Dec 2020 12:15
Last Modified:07 Jan 2021 07:24
Publisher:Pacific Graphics
OA Status:Green
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.2312/pg.20201226
Other Identification Number:merlin-id:20181

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