A Fast Geometric Multigrid Method for Curved Surfaces

Conference Paper (2023)
Author(s)

Ruben Wiersma (TU Delft - Computer Graphics and Visualisation)

Ahmad Nasikun (Universitas Gadjah Mada, TU Delft - Computer Graphics and Visualisation)

Elmar Eisemann (TU Delft - Computer Graphics and Visualisation)

K. Hildebrandt (TU Delft - Computer Graphics and Visualisation)

Research Group
Computer Graphics and Visualisation
Copyright
© 2023 R.T. Wiersma, A. Nasikun, E. Eisemann, K.A. Hildebrandt
DOI related publication
https://doi.org/10.1145/3588432.3591502
More Info
expand_more
Publication Year
2023
Language
English
Copyright
© 2023 R.T. Wiersma, A. Nasikun, E. Eisemann, K.A. Hildebrandt
Related content
Research Group
Computer Graphics and Visualisation
Pages (from-to)
1-11
ISBN (print)
979-8-4007-0159-7
ISBN (electronic)
9798400701597
Reuse Rights

Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.

Abstract

We introduce a geometric multigrid method for solving linear systems arising from variational problems on surfaces in geometry processing, Gravo MG. Our scheme uses point clouds as a reduced representation of the levels of the multigrid hierarchy to achieve a fast hierarchy construction and to extend the applicability of the method from triangle meshes to other surface representations like point clouds, nonmanifold meshes, and polygonal meshes. To build the prolongation operators, we associate each point of the hierarchy to a triangle constructed from points in the next coarser level. We obtain well-shaped candidate triangles by computing graph Voronoi diagrams centered around the coarse points and determining neighboring Voronoi cells. Our selection of triangles ensures that the connections of each point to points at adjacent coarser and finer levels are balanced in the tangential directions. As a result, we obtain sparse prolongation matrices with three entries per row and fast convergence of the solver. Code is available at https://graphics.tudelft.nl/gravo_mg.