Self-Supervised Bird's Eye View Motion Prediction with Cross-Modality
  Signals
Shaoheng Fang Zuhong Liu Mingyu Wang Chenxin Xu Yiqi Zhong Siheng Chen
Learning the dense bird's eye view (BEV) motion flow in a self-supervised
manner is an emerging research for robotics and autonomous driving. Current
self-supervised methods mainly rely on point correspondences between point
clouds, which may introduce the problems of fake flow and inconsistency,
hindering the model's ability to learn accurate and realistic motion. In this
paper, we introduce a novel cross-modality self-supervised training framework
that effectively addresses these issues by leveraging multi-modality data to
obtain supervision signals. We design three innovative supervision signals to
preserve the inherent properties of scene motion, including the masked Chamfer
distance loss, the piecewise rigidity loss, and the temporal consistency loss.
Through extensive experiments, we demonstrate that our proposed self-supervised
framework outperforms all previous self-supervision methods for the motion
prediction task.