Hex Me If You Can. (6th October 2022)

• Record Type: Journal Article
• Authors: Beaufort, P.‐A.; Reberol, M.; Kalmykov, D.; Liu, H.; Ledoux, F.; Bommes, D.
• Is Part Of: Computer graphics forum. Volume 41:Number 5(2022)
• Page Start: 125
• Page End: 134
• Publication Date: 2022-10-06
• Subjects: CCS Concepts -- General and reference → Evaluation -- Computing methodologies → Mesh geometry models -- Information systems → Test collections; Computer graphics -- Periodicals; 006.605
• DOI: 10.1111/cgf.14608
• Languages: English
• ISSNs: 0167-7055

Abstract

Abstract: 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.