Abstract

The typical goal of surface remeshing consists in finding a mesh that is (1) geometrically faithful to the original geometry, (2) as coarse as possible to obtain a low-complexity representation and (3) free of bad elements that would hamper the desired application. In this paper, we design an algorithm to address all three optimization goals simultaneously. The user specifies desired bounds on approximation error δ, minimal interior angle θ and maximum mesh complexity N (number of vertices). Since such a desired mesh might not even exist, our optimization framework treats only the approximation error bound δ as a hard constraint and the other two criteria as optimization goals. More specifically, we iteratively perform carefully prioritized local operators, whenever they do not violate the approximation error bound and improve the mesh otherwise. Our optimization framework greedily searches for the coarsest mesh with minimal interior angle above θ and approximation error bounded by δ. Fast runtime is enabled by a local approximation error estimation, while implicit feature preservation is obtained by specifically designed vertex relocation operators. Experiments show that our approach delivers high-quality meshes with implicitly preserved features and better balances between geometric fidelity, mesh complexity and element quality than the state-of-the-art.

Comparison:

Downloads

Paper[preprint], Supplemental Materials[pdf], Slides[pptx], Slides for SGP 2017[pptx], Source Code[get via email], Binary Code[bin], Data[zip], BibTex[bib].

Acknowledgements

We wish to thank Mario Botsch for providing the source code and results of their method, and Pascal Frey for providing the MMGS software. This work was partially supported by the European Research Council (ERC Starting Grant Robust Geometry Processing #257474), the National Science Foundation of China (61373071, 61372168, 61620106003) and the German Research Foundation (DFG, grant GSC 111, Aachen Institute for Advanced Study in Computational Engineering Science).