Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Morphing and Sampling Network for Dense Point Cloud Completion   3D point cloud completion, the task of inferring the complete geometric shape
from a partial point cloud, has been attracting attention in the community. For
acquiring high-fidelity dense point clouds and avoiding uneven distribution,
blurred details, or structural loss of existing methods' results, we propose a
novel approach to complete the partial point cloud in two stages. Specifically,
in the first stage, the approach predicts a complete but coarse-grained point
cloud with a collection of parametric surface elements. Then, in the second
stage, it merges the coarse-grained prediction with the input point cloud by a
novel sampling algorithm. Our method utilizes a joint loss function to guide
the distribution of the points. Extensive experiments verify the effectiveness
of our method and demonstrate that it outperforms the existing methods in both
the Earth Mover's Distance (EMD) and the Chamfer Distance (CD).

10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
36
The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom
21
The categories of this paper are: adap-org
2
The categories of this paper are: math
35
The categories of this paper are: mtrl-th
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The categories of this paper are: cond-mat
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The categories of this paper are: nucl-th
3
The categories of this paper are: physics
37
The categories of this paper are: supr-con
18
The categories of this paper are: econ
24
The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph