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struction. For such shapes, the object-space skele-
tonization methods mentioned in Sec. 2 should be
used.
5 CONCLUSIONS
We have presented a new method for computing
curve-skeletons as unstructured point clouds. Our
method extends the view-based curve-skeleton ex-
traction of Livesu et al. in several directions: (1)
Using salience-based skeletons to guarantee preser-
vation of terminal skeleton branches, (2) using depth
information to reduce the number of false-positives in
the 3D skeleton reconstruction, and (3) sharpening the
obtained point-cloud representation to better approx-
imate the 1D singularity locus of the curve skeleton.
We trade off speed for accuracy, by generating more
conservative skeleton samples and using more view-
points. However, by using a GPU implementation,
we achieve the same speed as the original method,
but deliver a much cleaner and sharper 3D skeleton
point-cloud approximation. Overall, our method can
be used either as a front-end for reconstructing line-
based representations of 3D curve skeletons, or for di-
rectly rendering such skeletons as unstructured point
clouds.
Future work can improve the point matching ac-
curacy, for example by using optical flow models
or exploiting geometric variability properties of 2D
skeletons. Separately, implementing the 3D geodesic-
based curve-skeleton detector of (Dey and Sun, 2006)
by using the 2D collapsed boundary metric ρ (Eqn. 5)
is a promising way for recovering highly accurate
curve skeletons in this view-based framework.
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