  We introduce Point2Skeleton, an unsupervised method to learn skeletal
representations from point clouds. Existing skeletonization methods are limited
to tubular shapes and the stringent requirement of watertight input, while our
method aims to produce more generalized skeletal representations for complex
structures and handle point clouds. Our key idea is to use the insights of the
medial axis transform (MAT) to capture the intrinsic geometric and topological
natures of the original input points. We first predict a set of skeletal points
by learning a geometric transformation, and then analyze the connectivity of
the skeletal points to form skeletal mesh structures. Extensive evaluations and
comparisons show our method has superior performance and robustness. The
learned skeletal representation will benefit several unsupervised tasks for
point clouds, such as surface reconstruction and segmentation.
