  Equivariance to permutations and rigid motions is an important inductive bias
for various 3D learning problems. Recently it has been shown that the
equivariant Tensor Field Network architecture is universal -- it can
approximate any equivariant function. In this paper we suggest a much simpler
architecture, prove that it enjoys the same universality guarantees and
evaluate its performance on Modelnet40. The code to reproduce our experiments
is available at \url{https://github.com/simpleinvariance/UniversalNetwork}
