Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Graph Signal Processing for Geometric Data and Beyond: Theory and
  Applications   Geometric data acquired from real-world scenes, e.g., 2D depth images, 3D
point clouds, and 4D dynamic point clouds, have found a wide range of
applications including immersive telepresence, autonomous driving,
surveillance, etc. Due to irregular sampling patterns of most geometric data,
traditional image/video processing methodologies are limited, while Graph
Signal Processing (GSP) -- a fast-developing field in the signal processing
community -- enables processing signals that reside on irregular domains and
plays a critical role in numerous applications of geometric data from low-level
processing to high-level analysis. To further advance the research in this
field, we provide the first timely and comprehensive overview of GSP
methodologies for geometric data in a unified manner by bridging the
connections between geometric data and graphs, among the various geometric data
modalities, and with spectral/nodal graph filtering techniques. We also discuss
the recently developed Graph Neural Networks (GNNs) and interpret the operation
of these networks from the perspective of GSP. We conclude with a brief
discussion of open problems and challenges.

19
The categories of this paper are: eess
11
The categories of this paper are: hep-lat
37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
23
The categories of this paper are: solv-int
26
The categories of this paper are: bayes-an
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat
17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
24
The categories of this paper are: dg-ga