“Multi-Resolution Approach to Computing Locally Injective Maps on Meshes” by Naitsat and Zeevi

  • ©Alexander Naitsat and Yehoshua Y. Zeevi

  • ©Alexander Naitsat and Yehoshua Y. Zeevi

  • ©Alexander Naitsat and Yehoshua Y. Zeevi

  • ©Alexander Naitsat and Yehoshua Y. Zeevi


Entry Number: 87


    Multi-Resolution Approach to Computing Locally Injective Maps on Meshes




    Computing injective mappings with low distortions on meshes is an important problem for its wide range of practical applications in computer graphics, geometric modeling and physical simulations. Such tasks as surface parameterization or shape deformation are often reduced to minimizing non-convex and non-linear geometric energies defined over triangulated domains. These energies are commonly expressed in a finite element manner as a weighted sum of distortion densities D over simplexes S: E (f [x]) = Õ s ∈S w(s)D(Js ), where x is the column stack of vertex positions under piecewise affine mapping f and Js denotes the Jacobian of f on s, modulo a rigid transformation of s to its target shape. Usually, a proper minimizer of (1) has to satisfy the following constraints: det(Js ) > 0, ∀s ∈ S; (2) Ax = b , (3) where (2) enforces f to preserve orientation of each simplex, and (A,b) is a linear system of the given positional constraints. The orientation constraints are particularly important in parameterization problems, since they avoid undesirable foldover artifacts in the texture, while positional constraints are widely used in shape deformation applications, such as point-to-point deformations, deformations with fixed anchors, and more. 

    In this work we propose a multi-resolution approach to construction of injective maps with low distortions in 2D and 3D, by initializing the optimization of (1) with the solution of the same problem in a lower resolution. In certain aspects our approach extends the recently proposed Progressive Parametrization [Liu et al. 2018], by decomposing the objective map f into a number of intermediate mappings with decreasing distortions and diminishing number of simplexes. 

    Although our approach is simple and intuitive, it has not been yet properly implemented on meshes due to the following major limitations of state-of-the-art methods: i) fast optimizers of (1) have to be initialized by an injective map f 0 [Claici et al. 2017; Rabinovich et al. 2017; Shtengel et al. 2017; Zhu et al. 2018]; ii) solvers that can handle non-injective initializations are either slow and hardly scalable [Kovalsky et al. 2014], or require a user assistance (non-automatic) [Poranne et al. 2017]; iii) methods for computing injective maps are effective only in simple configurations [Aigerman and Lipman 2013; Fu and Liu 2016; Kovalsky et al. 2015]. 

    Despite the long history of multi-resolution algorithms in computer graphics, dating back to the late 90s [Lee et al. 1998], the vast majority of these methods operate over multigrid domains, spline-based models and subdivision surfaces. Due to the aforementioned difficulties in processing non-locally injective maps, multi-resolution methods have surprisingly failed, so far, to perform competitively on meshes. In our method, the non-injective initialization obstacle, often occurring during transitions between multiple resolutions, is overcome by simultaneously repairing inverted elements and minimizing distortions by means of a novel Multi-Objective Block Coordinate Descent (MBCD) algorithm [Naitsat and Zeevi 2019]. MBCD is an adaptive local-global solver that alternates between distortion energies (1) and inverted element penalties which are optimized in coordinate blocks of varying sizes.


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    Research has been supported in part by the Ollendorff Minerva Center.