  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%.
