  We propose a new algorithm for curve skeleton computation which differs from
previous algorithms by being based on the notion of local separators. The main
benefits of this approach are that it is able to capture relatively fine
details and that it works robustly on a range of shape representations.
Specifically, our method works on shape representations that can be construed
as a spatially embedded graphs. Such representations include meshes, volumetric
shapes, and graphs computed from point clouds. We describe a simple pipeline
where geometric data is initially converted to a graph, optionally simplified,
local separators are computed and selected, and finally a skeleton is
constructed. We test our pipeline on polygonal meshes, volumetric shapes, and
point clouds. Finally, we compare our results to other methods for
skeletonization according to performance and quality.
