  We introduce the SE(3)-Transformer, a variant of the self-attention module
for 3D point clouds and graphs, which is equivariant under continuous 3D
roto-translations. Equivariance is important to ensure stable and predictable
performance in the presence of nuisance transformations of the data input. A
positive corollary of equivariance is increased weight-tying within the model.
The SE(3)-Transformer leverages the benefits of self-attention to operate on
large point clouds and graphs with varying number of points, while guaranteeing
SE(3)-equivariance for robustness. We evaluate our model on a toy N-body
particle simulation dataset, showcasing the robustness of the predictions under
rotations of the input. We further achieve competitive performance on two
real-world datasets, ScanObjectNN and QM9. In all cases, our model outperforms
a strong, non-equivariant attention baseline and an equivariant model without
attention.
