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.

本文介绍了 SE (3)-Transformer，一种用于 3D 点云和图形的自我注意模块，它在连续 3D 旋转变换下具有等变性。SE (3)-Transformer 利用自我注意的好处来处理点云和图形，同时保证了 SE (3)- 等变性，从而提高了模型的稳健性和预测性能。本文的模型在多个数据集上均取得了竞争性能，超过了一个强的非等变的基准模型和一个等变的模型没有注意力。