  Triangular meshes are widely used to represent three-dimensional objects. As
a result, many recent works have address the need for geometric deep learning
on 3D mesh. However, we observe that the complexities in many of these
architectures does not translate to practical performance, and simple deep
models for geometric graphs are competitive in practice. Motivated by this
observation, we minimally extend the update equations of E(n)-Equivariant Graph
Neural Networks (EGNNs) (Satorras et al., 2021) to incorporate mesh face
information, and further improve it to account for long-range interactions
through hierarchy. The resulting architecture, Equivariant Mesh Neural Network
(EMNN), outperforms other, more complicated equivariant methods on mesh tasks,
with a fast run-time and no expensive pre-processing. Our implementation is
available at https://github.com/HySonLab/EquiMesh
