  We present a method for decomposing the 3D scene flow observed from a moving
stereo rig into stationary scene elements and dynamic object motion. Our
unsupervised learning framework jointly reasons about the camera motion,
optical flow, and 3D motion of moving objects. Three cooperating networks
predict stereo matching, camera motion, and residual flow, which represents the
flow component due to object motion and not from camera motion. Based on rigid
projective geometry, the estimated stereo depth is used to guide the camera
motion estimation, and the depth and camera motion are used to guide the
residual flow estimation. We also explicitly estimate the 3D scene flow of
dynamic objects based on the residual flow and scene depth. Experiments on the
KITTI dataset demonstrate the effectiveness of our approach and show that our
method outperforms other state-of-the-art algorithms on the optical flow and
visual odometry tasks.
