Skip to yearly menu bar Skip to main content

Poster Teaser
Workshop: Graph Representation Learning and Beyond (GRL+)

(#54 / Sess. 2) SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks

Fabian Fuchs


We introduce the SE(3)-Transformer, a variant of the self-attention module for 3D point clouds, 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, leading to fewer trainable parameters and thus decreased sample complexity (i.e. we need less training data). The SE(3)-Transformer leverages the benefits of self-attention to operate on large point clouds with varying number of points while guaranteeing SE(3)-equivariance for robustness. We achieve competitive performance on two real-world datasets, ScanObjectNN and QM9.

Teaser video |

Chat is not available.