A controlled empirical study with a unified architecture finds SE(3)-equivariant position-orientation convolutions outperform less constrained models on geometry-aligned tasks, and pose-based symmetry breaking gives consistent gains.
Bekkers et al., 2024, Appx
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Probing Equivariance and Symmetry Breaking in Convolutional Networks
A controlled empirical study with a unified architecture finds SE(3)-equivariant position-orientation convolutions outperform less constrained models on geometry-aligned tasks, and pose-based symmetry breaking gives consistent gains.