  We introduce SE3-Nets, which are deep neural networks designed to model and
learn rigid body motion from raw point cloud data. Based only on sequences of
depth images along with action vectors and point wise data associations,
SE3-Nets learn to segment effected object parts and predict their motion
resulting from the applied force. Rather than learning point wise flow vectors,
SE3-Nets predict SE3 transformations for different parts of the scene. Using
simulated depth data of a table top scene and a robot manipulator, we show that
the structure underlying SE3-Nets enables them to generate a far more
consistent prediction of object motion than traditional flow based networks.
Additional experiments with a depth camera observing a Baxter robot pushing
objects on a table show that SE3-Nets also work well on real data.
