  Object detection and motion parameters estimation are crucial tasks for
self-driving vehicle safe navigation in a complex urban environment. In this
work we propose a novel real-time approach of temporal context aggregation for
motion detection and motion parameters estimation based on 3D point cloud
sequence. We introduce an ego-motion compensation layer to achieve real-time
inference with performance comparable to a naive odometric transform of the
original point cloud sequence. Not only is the proposed architecture capable of
estimating the motion of common road participants like vehicles or pedestrians
but also generalizes to other object categories which are not present in
training data. We also conduct an in-deep analysis of different temporal
context aggregation strategies such as recurrent cells and 3D convolutions.
Finally, we provide comparison results of our state-of-the-art model with
existing solutions on KITTI Scene Flow dataset.
