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.

本文提出了一种基于点云数据和自监督学习的动态估计框架，旨在高效精确地估计自动驾驶汽车与其他动态交通参与者的相对运动状态，并在实验中表现出与有监督方法相当的竞争力。