  We develop a cylindrical shape decomposition (CSD) algorithm to decompose an
object, a union of several tubular structures, into its semantic components. We
decompose the object using its curve skeleton and restricted translational
sweeps. For that, CSD partitions the curve skeleton into maximal-length
sub-skeletons over an orientation cost, each sub-skeleton corresponds to a
semantic component. To find the intersection of the tubular components, CSD
translationally sweeps the object in decomposition intervals to identify
critical points at which the shape of the object changes substantially. CSD
cuts the object at critical points and assigns the same label to parts along
the same sub-skeleton, thereby constructing a semantic component. The proposed
method further reconstructs the acquired semantic components at the
intersection of object parts using generalized cylinders. We apply CSD for
segmenting axons in large 3D electron microscopy images and decomposing
vascular networks and synthetic objects. We show that our proposal is robust to
severe surface noise and outperforms state-of-the-art decomposition techniques
in its applications.
