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Linear Angle Based Parameterization


Zayer,  Rhaleb
Computer Graphics, MPI for Informatics, Max Planck Society;

Seidel,  Hans-Peter
Computer Graphics, MPI for Informatics, Max Planck Society;

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Zayer, R., Lévy, B., & Seidel, H.-P. (2007). Linear Angle Based Parameterization. In D. Fellner, & S. Spencer (Eds.), SGP 2007 (pp. 135-141). Aire-la-Ville, Switzerland: Eurographics Association. doi:10.2312/SGP/SGP07/135-141.

In the field of mesh parameterization, the impact of angular and boundary distortion on parameterization quality have brought forward the need for robust and efficient free boundary angle preserving methods. One of the most prominent approaches in this direction is the Angle Based Flattening (ABF) which directly formulates the problem as a constrained nonlinear optimization in terms of angles. Since the original formulation of the ABF, a steady research effort has been dedicated to improving its efficiency. As for any well posed numerical problem, the solution is generally an approximation of the underlying mathematical equations. The economy and accuracy of the solution are to a great extent affected by the kind of approximation used. In this work we reformulate the problem based on the notion of error of estimation. A careful manipulation of the resulting equations yields for the first time a linear version of angle based parameterization. The error induced by this linearization is quadratic in terms of the error in angles and the validity of the approximation is further supported by numerical results. Besides performance speedup, the simplicity of the current setup makes re-implementation and reproduction of our results straightforward.