  Accurate motion and depth recovery is important for many robot vision tasks
including autonomous driving. Most previous studies have achieved cooperative
multi-task interaction via either pre-defined loss functions or cross-domain
prediction. This paper presents a multi-task scheme that achieves mutual
assistance by means of our Flow to Depth (F2D), Depth to Flow (D2F), and
Exponential Moving Average (EMA). F2D and D2F mechanisms enable multi-scale
information integration between optical flow and depth domain based on
differentiable shallow nets. A dual-head mechanism is used to predict optical
flow for rigid and non-rigid motion based on a divide-and-conquer manner, which
significantly improves the optical flow estimation performance. Furthermore, to
make the prediction more robust and stable, EMA is used for our multi-task
training. Experimental results on KITTI datasets show that our multi-task
scheme outperforms other multi-task schemes and provide marked improvements on
the prediction results.
