Hexahedral meshing based on 'SRF' approach.
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README.md

Hex Machina

Python implementation of the SRF approach to hexahedral meshing. (Work in Progress)

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TODO

  • The final volume parametrization step is not working (linear mixed-integer CG).
  • Edge collapse adjustments need to be implemented.
  • Move numerical optimizations to CUDA.
  • Back-propagation to compute gradients in L-BFGS, hopefully improve runtime.
  • Maybe run additional smoothing on surface cross-field.

Algorithm

Inputs : Triangle mesh as .stl (binary)

Given an input triangle mesh, a tetrahedral mesh is generated (using TetGen) and its boundary surface is extracted. The vertex-based curvatures and normals of the boundary surface are computed, as well as other topological information. A 3D frame is initialized at the centroid of each tetrahedron. The frame field smoothness is optimized by minimizing a non-linear energy function, solved using the efficient L-BFGS method. Moreover, some case-by-case adjustments are made to ensure the field is singularity-restricted. After the optimization, a volume parametrization is fit to the framefield as an atlas of linear maps defined on the vertices, using a CG (Conjuate Gradient) method. The hexahedral mesh is extracted based on the integer isosurface intersections.

Outputs: Saved to disk as .vtk files, which can be viewed in Paraview. This includes tetrahedral mesh, curvature cross-field, optimized 3D frame field, singular graph and the hexahedral mesh.

State of the Art

A non-exhaustive list of papers that inspired this implementation.

Volume parametrization

Boundary parametrization

Dependencies

Python 3, and set up other dependencies with:

pip install requirements.txt