  We present a self-supervised learning framework to estimate the individual
object motion and monocular depth from video. We model the object motion as a 6
degree-of-freedom rigid-body transformation. The instance segmentation mask is
leveraged to introduce the information of object. Compared with methods which
predict dense optical flow map to model the motion, our approach significantly
reduces the number of values to be estimated. Our system eliminates the scale
ambiguity of motion prediction through imposing a novel geometric constraint
loss term. Experiments on KITTI driving dataset demonstrate our system is
capable to capture the object motion without external annotation. Our system
outperforms previous self-supervised approaches in terms of 3D scene flow
prediction, and contribute to the disparity prediction in dynamic area.
