Mean Curvature Skeletons

Authors: 

Andrea Tagliasacchi, Ibraheem Alhashim, Matt Olson, and Hao Zhang
School of Computing Science, Simon Fraser University, Canada

Abstract:
Inspired by recent developments in contraction-based curve skeleton extraction, we formulate the skeletonization problem via mean curvature flow (MCF). While the classical application of MCF is surface fairing, we take ad- vantage of its area-minimizing characteristic to drive the curvature flow towards the extreme so as to collapse the input mesh geometry and obtain a skeletal structure. By analyzing the differential characteristics of the flow, we reveal that MCF locally increases shape anisotropy. This justifies the use of curvature motion for skeleton computation, and leads to the generation of what we call śmean curvature skeletonsť. To obtain a stable and effi- cient discretization, we regularize the surface mesh by performing local remeshing via edge splits and collapses. Simplifying mesh connectivity throughout the motion leads to more efficient computation and avoids numerical instability arising from degeneracies in the triangulation. In addition, the detection of collapsed geometry is facil- itated by working with simplified mesh connectivity and monitoring potential non-manifold edge collapses. With topology simplified throughout the flow, minimal post-processing is required to convert the collapsed geometry to a curve. Formulating skeletonization via MCF allows us to incorporate external energy terms easily, resulting in a constrained flow. We define one such energy term using the Voronoi medial skeleton and obtain a medially centred curve skeleton. We call the intermediate results of our skeletonization motion meso-skeletons; these consist of a mixture of curves and surface sheets as appropriate to the local 3D geometry they capture.

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Acknowledgements:
We thank the anonymous reviewers for their feedback, as well as Ramsay Dyer and Misha Kazh- dan for many invaluable discussions. This work is supported in part by an NSERC grant (No. 611370) and an NSERC Alexander Graham Bell Canada Graduate Scholarship.