Parallel Multigrid for Nonlinear Cloth Simulation

Accurate high‐resolution simulation of cloth is a highly desired computational tool in graphics applications. As single‐resolution simulation starts to reach the limit of computational power, we believe the future of cloth simulation is in multi‐resolution simulation. In this paper, we explore nonli...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Computer graphics forum Jg. 37; H. 7; S. 131 - 141
Hauptverfasser: Wang, Zhendong, Wu, Longhua, Fratarcangeli, Marco, Tang, Min, Wang, Huamin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Oxford Blackwell Publishing Ltd 01.10.2018
Schlagworte:
CCS Concepts
Cloth
Collision dynamics
Computer simulation
Computing methodologies → Physical simulation
Convergence
Finite element method
Image processing systems
Iterative methods
Newton methods
Nonlinearity
Parallel processing
Simulation
Smoothing
Software
Solvers
ISSN:0167-7055, 1467-8659, 1467-8659
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Accurate high‐resolution simulation of cloth is a highly desired computational tool in graphics applications. As single‐resolution simulation starts to reach the limit of computational power, we believe the future of cloth simulation is in multi‐resolution simulation. In this paper, we explore nonlinearity, adaptive smoothing, and parallelization under a full multigrid (FMG) framework. The foundation of this research is a novel nonlinear FMG method for unstructured meshes. To introduce nonlinearity into FMG, we propose to formulate the smoothing process at each resolution level as the computation of a search direction for the original high‐resolution nonlinear optimization problem. We prove that our nonlinear FMG is guaranteed to converge under various conditions and we investigate the improvements to its performance. We present an adaptive smoother which is used to reduce the computational cost in the regions with low residuals already. Compared to normal iterative solvers, our nonlinear FMG method provides faster convergence and better performance for both Newton's method and Projective Dynamics. Our experiment shows our method is efficient, accurate, stable against large time steps, and friendly with GPU parallelization. The performance of the method has a good scalability to the mesh resolution, and the method has good potential to be combined with multi‐resolution collision handling for real‐time simulation in the future.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0167-7055
1467-8659
1467-8659
DOI:10.1111/cgf.13554