Reliable Feature-Line Driven Quad-Remeshing

We present a new algorithm for the semi-regular quadrangulation of an input surface, driven by its line features, such as sharp creases. The mehod is fully automatic, requiring no user intervention, and remarkably reliable, requiring little assumptions on the input mesh, as we demonstrate by batch processing the entire Thingi10K repository, with less than 0.5% of the attempted cases failing to produce a usable mesh.

The technique is:
- Automatic , requiring a few parameters to set
- Robust , since it is based on tracing and procedural quadrangulation.


Nico Pietroni  
University of Technology Sydney
Stefano Nuvoli  University of Cagliari
Thomas Alderighi University of Pisa
Paolo Cignoni CNR of Italy
Marco Tarini University of Milan


Contact : nico.pietroni uts.edu.au

Abstract & DOI
We present a new algorithm for the semi-regular quadrangulation of an input surface, driven by its line features, such as sharp creases. We define a perfectly feature-aligned cross-field and a coarse layout of polygonal-shaped patches where we strictly ensure that all the feature-lines are represented as patch boundaries. To be able to consistently do so, we allow non-quadrilateral patches and T-junctions in the layout; the key is the ability to constrain the layout so that it still admits a globally consistent, T-junction-free, and pure-quad internal tessellation of its patches. This requires the insertion of additional irregular-vertices inside patches, but the regularity of the final- mesh is safeguarded by optimizing for both their number and for their reciprocal alignment. In total, our method guarantees the reproduction of feature-lines by construction, while still producing good quality, isometric, pure-quad, conforming meshes, making it an ideal candidate for CAD models. Moreover, the method is fully automatic, requiring no user intervention, and remarkably reliable, requiring little assumptions on the input mesh, as we demonstrate by batch processing the entire Thingi10K repository, with less than 0.5% of the attempted cases failing to produce a usable mesh.

DOI: 10.1145/3450626.3459941 ACM Transactions on Graphics (SIGGRAPH), 2021
Dataset

We performed a larger scale test on the Thingi10k dataset and another dataset composed by 300 meshes  (click on the pictures below to access the datasets)





Video and Slides


Demo and Code
QUADWILD on Github (GPL 3.0 Licence)