  We present a system for learning motion of independently moving objects from
stereo videos. The only human annotation used in our system are 2D object
bounding boxes which introduce the notion of objects to our system. Unlike
prior learning based work which has focused on predicting dense pixel-wise
optical flow field and/or a depth map for each image, we propose to predict
object instance specific 3D scene flow maps and instance masks from which we
are able to derive the motion direction and speed for each object instance. Our
network takes the 3D geometry of the problem into account which allows it to
correlate the input images. We present experiments evaluating the accuracy of
our 3D flow vectors, as well as depth maps and projected 2D optical flow where
our jointly learned system outperforms earlier approaches trained for each task
independently.
