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Hex Me If You Can

HexMe consists of 189 tetrahedral meshes with tagged features and a workflow to generate them. The primary purpose of HexMe meshes is to enable consistent and practically meaningful evaluation of hexahedral meshing algorithms and related techniques, specifically regarding the correct meshing of spec...

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Autores principales: Beaufort, P.‐A., Reberol, M., Kalmykov, D., Liu, H., Ledoux, F., Bommes, D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9828569/
https://www.ncbi.nlm.nih.gov/pubmed/36636106
http://dx.doi.org/10.1111/cgf.14608
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author Beaufort, P.‐A.
Reberol, M.
Kalmykov, D.
Liu, H.
Ledoux, F.
Bommes, D.
author_facet Beaufort, P.‐A.
Reberol, M.
Kalmykov, D.
Liu, H.
Ledoux, F.
Bommes, D.
author_sort Beaufort, P.‐A.
collection PubMed
description HexMe consists of 189 tetrahedral meshes with tagged features and a workflow to generate them. The primary purpose of HexMe meshes is to enable consistent and practically meaningful evaluation of hexahedral meshing algorithms and related techniques, specifically regarding the correct meshing of specified feature points, curves, and surfaces. The tetrahedral meshes have been generated with Gmsh, starting from 63 computer‐aided design (CAD) models from various databases. To highlight and label the diverse and challenging aspects of hexahedral mesh generation, the CAD models are classified into three categories: simple, nasty, and industrial. For each CAD model, we provide three kinds of tetrahedral meshes (uniform, curvature‐adapted, and box‐embedded). The mesh generation pipeline is defined with the help of Snakemake, a modern workflow management system, which allows us to specify a fully automated, extensible, and sustainable workflow. It is possible to download the whole dataset or select individual meshes by browsing the online catalog. The HexMe dataset is built with evolution in mind and prepared for future developments. A public GitHub repository hosts the HexMe workflow, where external contributions and future releases are possible and encouraged. We demonstrate the value of HexMe by exploring the robustness limitations of state‐of‐the‐art frame‐field‐based hexahedral meshing algorithm. Only for 19 of 189 tagged tetrahedral inputs all feature entities are meshed correctly, while the average success rates are 70.9% / 48.5% / 34.6% for feature points/curves/surfaces.
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spelling pubmed-98285692023-01-10 Hex Me If You Can Beaufort, P.‐A. Reberol, M. Kalmykov, D. Liu, H. Ledoux, F. Bommes, D. Comput Graph Forum Tools and Data HexMe consists of 189 tetrahedral meshes with tagged features and a workflow to generate them. The primary purpose of HexMe meshes is to enable consistent and practically meaningful evaluation of hexahedral meshing algorithms and related techniques, specifically regarding the correct meshing of specified feature points, curves, and surfaces. The tetrahedral meshes have been generated with Gmsh, starting from 63 computer‐aided design (CAD) models from various databases. To highlight and label the diverse and challenging aspects of hexahedral mesh generation, the CAD models are classified into three categories: simple, nasty, and industrial. For each CAD model, we provide three kinds of tetrahedral meshes (uniform, curvature‐adapted, and box‐embedded). The mesh generation pipeline is defined with the help of Snakemake, a modern workflow management system, which allows us to specify a fully automated, extensible, and sustainable workflow. It is possible to download the whole dataset or select individual meshes by browsing the online catalog. The HexMe dataset is built with evolution in mind and prepared for future developments. A public GitHub repository hosts the HexMe workflow, where external contributions and future releases are possible and encouraged. We demonstrate the value of HexMe by exploring the robustness limitations of state‐of‐the‐art frame‐field‐based hexahedral meshing algorithm. Only for 19 of 189 tagged tetrahedral inputs all feature entities are meshed correctly, while the average success rates are 70.9% / 48.5% / 34.6% for feature points/curves/surfaces. John Wiley and Sons Inc. 2022-10-06 2022-08 /pmc/articles/PMC9828569/ /pubmed/36636106 http://dx.doi.org/10.1111/cgf.14608 Text en © 2022 The Author(s). Computer Graphics Forum published by Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Tools and Data
Beaufort, P.‐A.
Reberol, M.
Kalmykov, D.
Liu, H.
Ledoux, F.
Bommes, D.
Hex Me If You Can
title Hex Me If You Can
title_full Hex Me If You Can
title_fullStr Hex Me If You Can
title_full_unstemmed Hex Me If You Can
title_short Hex Me If You Can
title_sort hex me if you can
topic Tools and Data
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9828569/
https://www.ncbi.nlm.nih.gov/pubmed/36636106
http://dx.doi.org/10.1111/cgf.14608
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