Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SAWNet: A Spatially Aware Deep Neural Network for 3D Point Cloud
  Processing   Deep neural networks have established themselves as the state-of-the-art
methodology in almost all computer vision tasks to date. But their application
to processing data lying on non-Euclidean domains is still a very active area
of research. One such area is the analysis of point cloud data which poses a
challenge due to its lack of order. Many recent techniques have been proposed,
spearheaded by the PointNet architecture. These techniques use either global or
local information from the point clouds to extract a latent representation for
the points, which is then used for the task at hand
(classification/segmentation). In our work, we introduce a neural network layer
that combines both global and local information to produce better embeddings of
these points. We enhance our architecture with residual connections, to pass
information between the layers, which also makes the network easier to train.
We achieve state-of-the-art results on the ModelNet40 dataset with our
architecture, and our results are also highly competitive with the
state-of-the-art on the ShapeNet part segmentation dataset and the indoor scene
segmentation dataset. We plan to open source our pre-trained models on github
to encourage the research community to test our networks on their data, or
simply use them for benchmarking purposes.

20
The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
23
The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
4
The categories of this paper are: cond-mat
32
The categories of this paper are: atom-ph
3
The categories of this paper are: physics
26
The categories of this paper are: bayes-an
6
The categories of this paper are: astro-ph
13
The categories of this paper are: quant-ph
24
The categories of this paper are: dg-ga