  In autonomous driving, accurately estimating the state of surrounding
obstacles is critical for safe and robust path planning. However, this
perception task is difficult, particularly for generic obstacles/objects, due
to appearance and occlusion changes. To tackle this problem, we propose an
end-to-end deep learning framework for LIDAR-based flow estimation in bird's
eye view (BeV). Our method takes consecutive point cloud pairs as input and
produces a 2-D BeV flow grid describing the dynamic state of each cell. The
experimental results show that the proposed method not only estimates 2-D BeV
flow accurately but also improves tracking performance of both dynamic and
static objects.
