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.

通过引入新颖的跨模态自监督训练框架，本文解决了点云方法中的假流和不一致性问题，提出了三种创新的监督信号来保留场景动作的内在属性，包括遮罩 Chamfer 距离损失、分段刚性损失和时间一致性损失，通过广泛实验证明，我们提出的自监督框架在动作预测任务中优于所有先前的自监督方法。