Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards Efficient Point Cloud Graph Neural Networks Through
  Architectural Simplification   In recent years graph neural network (GNN)-based approaches have become a
popular strategy for processing point cloud data, regularly achieving
state-of-the-art performance on a variety of tasks. To date, the research
community has primarily focused on improving model expressiveness, with
secondary thought given to how to design models that can run efficiently on
resource constrained mobile devices including smartphones or mixed reality
headsets. In this work we make a step towards improving the efficiency of these
models by making the observation that these GNN models are heavily limited by
the representational power of their first, feature extracting, layer. We find
that it is possible to radically simplify these models so long as the feature
extraction layer is retained with minimal degradation to model performance;
further, we discover that it is possible to improve performance overall on
ModelNet40 and S3DIS by improving the design of the feature extractor. Our
approach reduces memory consumption by 20$\times$ and latency by up to
9.9$\times$ for graph layers in models such as DGCNN; overall, we achieve
speed-ups of up to 4.5$\times$ and peak memory reductions of 72.5%.

23
The categories of this paper are: solv-int
29
The categories of this paper are: funct-an
3
The categories of this paper are: physics
21
The categories of this paper are: adap-org
37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
22
The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas
2
The categories of this paper are: math
12
The categories of this paper are: nucl-th
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg