Heng Liu, Ph.D.

Postdoctoral Researcher

heng.liu@unibe.ch

AlgoHex Publications

• 

  Locally Meshable Frame Fields

  ACM Transactions on Graphics (SIGGRAPH 2023)
  Heng Liu, David Bommes

  • Project page
  • Abstract
  • BibTeX

  The main robustness issue of state-of-the-art frame field based hexahedral mesh generation algorithms originates from non-meshable topological configurations, which do not admit the construction of an integer-grid map but frequently occur in smooth frame fields. In this article, we investigate the topology of frame fields and derive conditions on their meshability, which are the basis for a novel algorithm to automatically turn a given non-meshable frame field into a similar but locally meshable one. Despite local meshability is only a necessary but not sufficient condition for the stronger requirement of meshability, our algorithm increases the 2% success rate of generating valid integer-grid maps with state-of-the-art methods to 58%, when compared on the challenging HexMe dataset [Beaufort et al. 2022]. The source code of our implementation and the data of our experiments are available at https://lib.algohex.eu.

  @article{Liu:2023:locallymeshable,
    author = {Liu, Heng and Bommes, David},
    title = {Locally Meshable Frame Fields},
    journal = {ACM Transactions on Graphics},
    volume = {42},
    number = {4},
    year = {2023},
    publisher = {ACM},
    address = {New York, NY, USA},
    doi={10.1145/3592457}
  }

• 

  Hex Me If You Can

  Computer Graphics Forum (SGP 2022)
  Pierre-Alexandre Beaufort, Maxence Reberol, Denis Kalmykov, Heng Liu, Franck Ledoux, David Bommes

  • Project page
  • Abstract
  • BibTeX

  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.

  @article {10.1111:cgf.14608,
  journal = {Computer Graphics Forum},
  title = {{Hex Me If You Can}},
  author = {Beaufort, Pierre-Alexandre and Reberol, Maxence and Kalmykov, Denis and Liu, Heng and Ledoux, Franck and Bommes, David},
  year = {2022},
  publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
  ISSN = {1467-8659},
  DOI = {10.1111/cgf.14608}
  }