  Appearance-based detectors achieve remarkable performance on common scenes,
but tend to fail for scenarios lack of training data. Geometric motion
segmentation algorithms, however, generalize to novel scenes, but have yet to
achieve comparable performance to appearance-based ones, due to noisy motion
estimations and degenerate motion configurations. To combine the best of both
worlds, we propose a modular network, whose architecture is motivated by a
geometric analysis of what independent object motions can be recovered from an
egomotion field. It takes two consecutive frames as input and predicts
segmentation masks for the background and multiple rigidly moving objects,
which are then parameterized by 3D rigid transformations. Our method achieves
state-of-the-art performance for rigid motion segmentation on KITTI and Sintel.
The inferred rigid motions lead to a significant improvement for depth and
scene flow estimation. At the time of submission, our method ranked 1st on
KITTI scene flow leaderboard, out-performing the best published method (scene
flow error: 4.89% vs 6.31%).
