  We present the first event-based learning approach for motion segmentation in
indoor scenes and the first event-based dataset - EV-IMO - which includes
accurate pixel-wise motion masks, egomotion and ground truth depth. Our
approach is based on an efficient implementation of the SfM learning pipeline
using a low parameter neural network architecture on event data. In addition to
camera egomotion and a dense depth map, the network estimates pixel-wise
independently moving object segmentation and computes per-object 3D
translational velocities for moving objects. We also train a shallow network
with just 40k parameters, which is able to compute depth and egomotion.
  Our EV-IMO dataset features 32 minutes of indoor recording with up to 3 fast
moving objects simultaneously in the camera field of view. The objects and the
camera are tracked by the VICON motion capture system. By 3D scanning the room
and the objects, accurate depth map ground truth and pixel-wise object masks
are obtained, which are reliable even in poor lighting conditions and during
fast motion. We then train and evaluate our learning pipeline on EV-IMO and
demonstrate that our approach far surpasses its rivals and is well suited for
scene constrained robotics applications.
