  Autonomous driving can benefit from motion behavior comprehension when
interacting with diverse traffic participants in highly dynamic environments.
Recently, there has been a growing interest in estimating class-agnostic motion
directly from point clouds. Current motion estimation methods usually require
vast amount of annotated training data from self-driving scenes. However,
manually labeling point clouds is notoriously difficult, error-prone and
time-consuming. In this paper, we seek to answer the research question of
whether the abundant unlabeled data collections can be utilized for accurate
and efficient motion learning. To this end, we propose a learning framework
that leverages free supervisory signals from point clouds and paired camera
images to estimate motion purely via self-supervision. Our model involves a
point cloud based structural consistency augmented with probabilistic motion
masking as well as a cross-sensor motion regularization to realize the desired
self-supervision. Experiments reveal that our approach performs competitively
to supervised methods, and achieves the state-of-the-art result when combining
our self-supervised model with supervised fine-tuning.
