Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning point embedding for 3D data processing   Among 2D convolutional networks on point clouds, point-based approaches
consume point clouds of fixed size directly. By analysis of PointNet, a pioneer
in introducing deep learning into point sets, we reveal that current
point-based methods are essentially spatial relationship processing networks.
In this paper, we take a different approach. Our architecture, named PE-Net,
learns the representation of point clouds in high-dimensional space, and
encodes the unordered input points to feature vectors, which standard 2D CNNs
can be applied to. The recommended network can adapt to changes in the number
of input points which is the limit of current methods. Experiments show that in
the tasks of classification and part segmentation, PE-Net achieves the
state-of-the-art performance in multiple challenging datasets, such as ModelNet
and ShapeNetPart.

5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
0
The categories of this paper are: hep-ph
27
The categories of this paper are: comp-gas
32
The categories of this paper are: atom-ph
29
The categories of this paper are: funct-an
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ
34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
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The categories of this paper are: math
28
The categories of this paper are: alg-geom
3
The categories of this paper are: physics
26
The categories of this paper are: bayes-an