Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Point2Skeleton: Learning Skeletal Representations from Point Clouds   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.

34
The categories of this paper are: chem-ph
32
The categories of this paper are: atom-ph
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
2
The categories of this paper are: math
18
The categories of this paper are: econ
12
The categories of this paper are: nucl-th
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
16
The categories of this paper are: stat
15
The categories of this paper are: nucl-ex
6
The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an
17
The categories of this paper are: q-fin