Roto-translated Local Coordinate Frames For Interacting Dynamical Systems

The paper introduces local coordinate frames for each node in geometric graphs to enforce roto‑translation (Galilean) invariance in modeling interacting dynamical systems. By aligning each node’s frame with its local geometry, the method enables anisotropic filtering in graph neural networks and improves learning of highly non‑linear, time‑dependent behaviors. Experiments on traffic scenes, 3D motion capture, and colliding particles show that this approach outperforms recent state‑of‑the‑art methods.

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