  Learning depth and ego-motion from unlabeled videos via self-supervision from
epipolar projection can improve the robustness and accuracy of the 3D
perception and localization of vision-based robots. However, the rigid
projection computed by ego-motion cannot represent all scene points, such as
points on moving objects, leading to false guidance in these regions. To
address this problem, we propose an Attentional Separation-and-Aggregation
Network (ASANet), which can learn to distinguish and extract the scene's static
and dynamic characteristics via the attention mechanism. We further propose a
novel MotionNet with an ASANet as the encoder, followed by two separate
decoders, to estimate the camera's ego-motion and the scene's dynamic motion
field. Then, we introduce an auto-selecting approach to detect the moving
objects for dynamic-aware learning automatically. Empirical experiments
demonstrate that our method can achieve the state-of-the-art performance on the
KITTI benchmark.
