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Learning the dense bird's eye view (BEV) motion flow in a self-supervisedmanner is an emerging research for robotics and autonomous driving. Currentself-supervised methods mainly rely on point correspondences between pointclouds, which may introduce the problems of fake flow and inconsistency,hindering the model's ability to learn accurate and realistic motion. In thispaper, we introduce a novel cross-modality self-supervised training frameworkthat effectively addresses these issues by leveraging multi-modality data toobtain supervision signals. We design three innovative supervision signals topreserve the inherent properties of scene motion, including the masked Chamferdistance loss, the piecewise rigidity loss, and the temporal consistency loss.Through extensive experiments, we demonstrate that our proposed self-supervisedframework outperforms all previous self-supervision methods for the motionprediction task.

Self-Supervised Bird's Eye View Motion Prediction with Cross-Modality Signals