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Published August 2018 | public
Journal Article

Quadrangulation through Morse-Parameterization Hybridization

Abstract

We introduce an approach to quadrilateral meshing of arbitrary triangulated surfaces that combines the theoretical guarantees of Morse-based approaches with the practical advantages of parameterization methods. We first construct, through an eigensolver followed by a few Gauss-Newton iterations, a periodic four-dimensional vector field that aligns with a user-provided frame field and/or a set of features over the input mesh. A field-aligned parameterization is then greedily computed along a spanning tree based on the Dirichlet energy of the optimal periodic vector field, from which quad elements are efficiently extracted over most of the surface. The few regions not yet covered by elements are then upsampled and the first component of the periodic vector field is used as a Morse function to extract the remaining quadrangles. This hybrid parameterization- and Morse-based quad meshing method is not only fast (the parameterization is greedily constructed, and the Morse function only needs to be upsampled in the few uncovered patches), but is guaranteed to provide a feature-aligned quad mesh with non-degenerate cells that closely matches the input frame field over an arbitrary surface. We show that our approach is much faster than Morse-based techniques since it does not require a densely tessellated input mesh, and is significantly more robust than parameterization-based techniques on models with complex features.

Additional Information

© 2018 ACM. We would like to thank the anonymous reviewers for their comments and suggestions. This work was partially supported by NSFC (No. 61522209, 61210007) and Fundamental Research Funds for the Central Universities (2018FZA5011). MD gratefully acknowledges the INRIA International Chair program, and Zhejiang University for hosting him superbly well during the final editing of this work. YT acknowledges partial support from NSF (CCF-1655422 and DMS-1721024). The 3D models in this paper and its supplemental material were provided by the AIM@SHAPE-VISIONAIR Repository, the INRIA Gamma team and the Stanford 3D Scanning Repository.

Additional details

Created:
August 19, 2023
Modified:
October 18, 2023