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Found 2 papers, 2 papers with code
Fast, Expressive SE$(n)$ Equivariant Networks through Weight-Sharing in Position-Orientation Space
The theory of homogeneous spaces tells us how to do group convolutions with feature maps over the homogeneous space of positions $\mathbb{R}^3$, position and orientations $\mathbb{R}^3 {\times} S^2$, and the group $SE(3)$ itself.
Greedy Sampling for Approximate Clustering in the Presence of Outliers
One the one hand, they possess good theoretical approximation guarantees, and on the other, they are fast and easy to implement.
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