Sparse cholesky updates for interactive mesh parameterization

We present a novel linear solver for interactive parameterization tasks. Our method is based on the observation that quasi-conformal parameterizations of a triangle mesh are largely determined by boundary conditions. These boundary conditions are typically constructed interactively by users, who have to take several artistic and geometric constraints into account while introducing cuts on the geometry. Commonly, the main computational burden in these methods is solving a linear system every time new boundary conditions are imposed. The core of our solver is a novel approach to efficiently update the Cholesky factorization of the linear system to reflect new boundary conditions, thereby enabling a seamless and interactive workflow even for large meshes consisting of several millions of vertices.

[1]  Vipin Kumar,et al.  A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs , 1998, SIAM J. Sci. Comput..

[2]  Nikhil Ketkar,et al.  Convolutional Neural Networks , 2021, Deep Learning with Python.

[3]  Yaron Lipman,et al.  Spherical orbifold tutte embeddings , 2017, ACM Trans. Graph..

[4]  Jieyu Chu,et al.  A schur complement preconditioner for scalable parallel fluid simulation , 2017, TOGS.

[5]  Timothy A. Davis,et al.  Modifying a Sparse Cholesky Factorization , 1999, SIAM J. Matrix Anal. Appl..

[6]  Rohan Sawhney,et al.  Boundary First Flattening , 2017, ACM Trans. Graph..

[7]  Alex Pothen,et al.  Interactively Cutting and Constraining Vertices in Meshes Using Augmented Matrices , 2016, ACM Trans. Graph..

[8]  Marc Alexa,et al.  As-rigid-as-possible surface modeling , 2007, Symposium on Geometry Processing.

[9]  D K Smith,et al.  Numerical Optimization , 2001, J. Oper. Res. Soc..

[10]  Timothy A. Davis,et al.  Dynamic Supernodes in Sparse Cholesky Update/Downdate and Triangular Solves , 2009, TOMS.

[11]  Olga Sorkine-Hornung,et al.  On Linear Variational Surface Deformation Methods , 2008, IEEE Transactions on Visualization and Computer Graphics.

[12]  Timothy A. Davis,et al.  Multiple-Rank Modifications of a Sparse Cholesky Factorization , 2000, SIAM J. Matrix Anal. Appl..

[13]  Timothy A. Davis,et al.  Algorithm 832: UMFPACK V4.3---an unsymmetric-pattern multifrontal method , 2004, TOMS.

[14]  Timothy A. Davis,et al.  A survey of direct methods for sparse linear systems , 2016, Acta Numerica.

[15]  Daniel Cohen-Or,et al.  Geometry-aware bases for shape approximation , 2005, IEEE Transactions on Visualization and Computer Graphics.

[16]  Markus H. Gross,et al.  Interactive Virtual Materials , 2004, Graphics Interface.

[17]  Gene H. Golub,et al.  Methods for modifying matrix factorizations , 1972, Milestones in Matrix Computation.

[18]  James F. O'Brien,et al.  Updated sparse cholesky factors for corotational elastodynamics , 2012, TOGS.

[19]  Marc Alexa,et al.  Efficient Computation of Smoothed Exponential Maps , 2019, Comput. Graph. Forum.

[20]  Robert D. Falgout,et al.  The Design and Implementation of hypre, a Library of Parallel High Performance Preconditioners , 2006 .

[21]  Ersin Yumer,et al.  Convolutional neural networks on surfaces via seamless toric covers , 2017, ACM Trans. Graph..

[22]  Eftychios Sifakis,et al.  A scalable schur-complement fluids solver for heterogeneous compute platforms , 2016, ACM Trans. Graph..

[23]  Timothy A. Davis,et al.  Row Modifications of a Sparse Cholesky Factorization , 2005, SIAM J. Matrix Anal. Appl..

[24]  Timothy A. Davis,et al.  Direct methods for sparse linear systems , 2006, Fundamentals of algorithms.

[25]  YANQING CHEN,et al.  Algorithm 8 xx : CHOLMOD , supernodal sparse Cholesky factorization and update / downdate ∗ , 2006 .

[26]  Marc Alexa,et al.  Localized solutions of sparse linear systems for geometry processing , 2017, ACM Trans. Graph..