Efficient preconditioning of laplacian matrices for computer graphics

Dilip Krishnan, Raanan Fattal, Richard Szeliski

Research output: Contribution to journalArticlepeer-review

93 Scopus citations

Abstract

We present a new multi-level preconditioning scheme for discrete Poisson equations that arise in various computer graphics applications such as colorization, edge-preserving decomposition for twodimensional images, and geodesic distances and diffusion on threedimensional meshes. Our approach interleaves the selection of fineand coarse-level variables with the removal of weak connections between potential fine-level variables (sparsification) and the compensation for these changes by strengthening nearby connections. By applying these operations before each elimination step and repeating the procedure recursively on the resulting smaller systems, we obtain a highly efficient multi-level preconditioning scheme with linear time and memory requirements. Our experiments demonstrate that our new scheme outperforms or is comparable with other state-of-the-art methods, both in terms of operation count and wallclock time. This speedup is achieved by the new method's ability to reduce the condition number of irregular Laplacian matrices as well as homogeneous systems. It can therefore be used for a wide variety of computational photography problems, as well as several 3D mesh processing tasks, without the need to carefully match the algorithm to the problem characteristics.

Original languageAmerican English
Article number142
JournalACM Transactions on Graphics
Volume32
Issue number4
DOIs
StatePublished - Jul 2013

Keywords

  • Computational photography
  • Laplacians
  • Matrix preconditioning
  • Mesh processing
  • Multigrid

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