  In this paper, we propose a novel self-supervised learning model for
estimating continuous ego-motion from video. Our model learns to estimate
camera motion by watching RGBD or RGB video streams and determining
translational and rotation velocities that correctly predict the appearance of
future frames. Our approach differs from other recent work on self-supervised
structure-from-motion in its use of a continuous motion formulation and
representation of rigid motion fields rather than direct prediction of camera
parameters. To make estimation robust in dynamic environments with multiple
moving objects, we introduce a simple two-component segmentation process that
isolates the rigid background environment from dynamic scene elements. We
demonstrate state-of-the-art accuracy of the self-trained model on several
benchmark ego-motion datasets and highlight the ability of the model to provide
superior rotational accuracy and handling of non-rigid scene motions.
