4.7 Article

Level-of-Detail Quad Meshing

Journal

ACM TRANSACTIONS ON GRAPHICS
Volume 33, Issue 6, Pages -

Publisher

ASSOC COMPUTING MACHINERY
DOI: 10.1145/2661229.2661240

Keywords

guiding fields; quad meshing

Funding

  1. European Research Council [340884, 257474]
  2. DFG Cluster of Excellence on Ultra High-Speed Mobile Information and Communication (UMIC), German Research Foundation [DFG EXC 89]
  3. Aachen Institute for Advanced Study in Computational Engineering Science (AICES)

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The most effective and popular tools for obtaining feature aligned quad meshes from triangular input meshes are based on cross field guided parametrization. These methods are incarnations of a conceptual three-step pipeline: (1) cross field computation, (2) fieldguided surface parametrization, (3) quad mesh extraction. While in most meshing scenarios the user prescribes a desired target quad size or edge length, this information is typically taken into account from step 2 onwards only, but not in the cross field computation step. This turns into a problem in the presence of small scale geometric or topological features or noise in the input mesh: closely placed singularities are induced in the cross field, which are not properly reproducible by vertices in a quad mesh with the prescribed edge length, causing severe distortions or even failure of the meshing algorithm. We reformulate the construction of cross fields as well as field-guided parametrizations in a scale-aware manner which effectively suppresses densely spaced features and noise of geometric as well as topological kind. Dominant large-scale features are adequately preserved in the output by relying on the unaltered input mesh as the computational domain.

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