  Point cloud prediction is an important yet challenging task in the field of
autonomous driving. The goal is to predict future point cloud sequences that
maintain object structures while accurately representing their temporal motion.
These predicted point clouds help in other subsequent tasks like object
trajectory estimation for collision avoidance or estimating locations with the
least odometry drift. In this work, we present ATPPNet, a novel architecture
that predicts future point cloud sequences given a sequence of previous time
step point clouds obtained with LiDAR sensor. ATPPNet leverages Conv-LSTM along
with channel-wise and spatial attention dually complemented by a 3D-CNN branch
for extracting an enhanced spatio-temporal context to recover high quality
fidel predictions of future point clouds. We conduct extensive experiments on
publicly available datasets and report impressive performance outperforming the
existing methods. We also conduct a thorough ablative study of the proposed
architecture and provide an application study that highlights the potential of
our model for tasks like odometry estimation.
