  We tackle the problem of estimating optical flow from a monocular camera in
the context of autonomous driving. We build on the observation that the scene
is typically composed of a static background, as well as a relatively small
number of traffic participants which move rigidly in 3D. We propose to estimate
the traffic participants using instance-level segmentation. For each traffic
participant, we use the epipolar constraints that govern each independent
motion for faster and more accurate estimation. Our second contribution is a
new convolutional net that learns to perform flow matching, and is able to
estimate the uncertainty of its matches. This is a core element of our flow
estimation pipeline. We demonstrate the effectiveness of our approach in the
challenging KITTI 2015 flow benchmark, and show that our approach outperforms
published approaches by a large margin.
