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High-performance simplification of triangular surfaces using a GPU

Due to advances in high-performance computing technologies, computer graphics techniques—especially those related to mesh simplification—have been noticeably improved. These techniques, which have a strong impact on many applications, such as geometric modeling and visualization, have been well stud...

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Detalles Bibliográficos
Autores principales: Mousa, Mohamed H., Hussein, Mohamed K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8341488/
https://www.ncbi.nlm.nih.gov/pubmed/34352031
http://dx.doi.org/10.1371/journal.pone.0255832
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author Mousa, Mohamed H.
Hussein, Mohamed K.
author_facet Mousa, Mohamed H.
Hussein, Mohamed K.
author_sort Mousa, Mohamed H.
collection PubMed
description Due to advances in high-performance computing technologies, computer graphics techniques—especially those related to mesh simplification—have been noticeably improved. These techniques, which have a strong impact on many applications, such as geometric modeling and visualization, have been well studied for more than two decades. Recent advances in GPUs have led to significant improvements in terms of speed and interactivity. In this paper, we present a mesh simplification algorithm that benefits from the parallel framework provided by recent GPUs. We customize the halfedge data structure for adaption with the dynamic memory restrictions of CUDA. The proposed algorithm is fully parallelized by employing a lock-free skip priority queue and a set of disjoint regions of the mesh. The proposed technique accelerates the simplification process while preserving the topological properties of the mesh. Some results and comparisons are provided to verify the efficiency of the proposed algorithm.
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spelling pubmed-83414882021-08-06 High-performance simplification of triangular surfaces using a GPU Mousa, Mohamed H. Hussein, Mohamed K. PLoS One Research Article Due to advances in high-performance computing technologies, computer graphics techniques—especially those related to mesh simplification—have been noticeably improved. These techniques, which have a strong impact on many applications, such as geometric modeling and visualization, have been well studied for more than two decades. Recent advances in GPUs have led to significant improvements in terms of speed and interactivity. In this paper, we present a mesh simplification algorithm that benefits from the parallel framework provided by recent GPUs. We customize the halfedge data structure for adaption with the dynamic memory restrictions of CUDA. The proposed algorithm is fully parallelized by employing a lock-free skip priority queue and a set of disjoint regions of the mesh. The proposed technique accelerates the simplification process while preserving the topological properties of the mesh. Some results and comparisons are provided to verify the efficiency of the proposed algorithm. Public Library of Science 2021-08-05 /pmc/articles/PMC8341488/ /pubmed/34352031 http://dx.doi.org/10.1371/journal.pone.0255832 Text en © 2021 Mousa, Hussein https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Mousa, Mohamed H.
Hussein, Mohamed K.
High-performance simplification of triangular surfaces using a GPU
title High-performance simplification of triangular surfaces using a GPU
title_full High-performance simplification of triangular surfaces using a GPU
title_fullStr High-performance simplification of triangular surfaces using a GPU
title_full_unstemmed High-performance simplification of triangular surfaces using a GPU
title_short High-performance simplification of triangular surfaces using a GPU
title_sort high-performance simplification of triangular surfaces using a gpu
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8341488/
https://www.ncbi.nlm.nih.gov/pubmed/34352031
http://dx.doi.org/10.1371/journal.pone.0255832
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