Review for NeurIPS paper: SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks

Weaknesses: 1) The major weakness is that the paper can be seen as a simple extension of the Tensor Field Networks (TFN) [1] by adding attention layers, so novelty is somewhat limited. I do realize that some adaptations are necessary, e.g., using TFN layers to produce query/keys/values and showing that the attention weights are invariant, so there is value in the contributions. It's my understanding that the original TFN [1] was impractical for the point clouds sizes typically used for ModelNet and ShapeNet (a few thousand points). The proposed method includes a faster spherical harmonics implementation; is that part the bottleneck of the TFN? How big is the speed-up?