arXiv:2605.20274v1 Announce Type: cross Abstract: Hexahedral meshes are widely used in simulation pipelines, yet automatic generation remains challenging for complex CAD geometries. Polycube-based hexahedral meshing is a representative approach due to its regular, parameterization-friendly structure, but existing polycube construction methods often rely on intricate surface segmentation and local heuristics, which can produce artifacts or fail on difficult shapes. In this paper, we propose an end-to-end framework for polycube generation based on conditional diffusion models. Given an input geo
The continuous advancements in conditional diffusion models are enabling new applications in complex geometric tasks like hexahedral mesh generation, building on recent breakthroughs in generative AI.
Improved automation in hexahedral mesh generation can significantly accelerate simulation pipelines for engineering and scientific applications, impacting design and research cycles.
This research introduces a more robust and automated method for generating polycube-based hexahedral meshes, potentially reducing manual effort and improving mesh quality for complex CAD geometries.
- · AI researchers
- · Engineering simulation software developers
- · FEA/CFD engineers
- · Manufacturing and design industries
- · Manual meshing specialists (to some extent)
- · Legacy meshing software reliant on intricate manual processes
More efficient and accurate simulations across various engineering disciplines become possible.
Reduced design iteration cycles and faster product development due to quicker simulation feedback.
This could enable the simulation of more complex systems previously too difficult or time-consuming to mesh efficiently, leading to breakthroughs in areas like materials science or fluid dynamics.
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Read at arXiv cs.AI