  We propose the Temporal Point Cloud Networks (TPCN), a novel and flexible
framework with joint spatial and temporal learning for trajectory prediction.
Unlike existing approaches that rasterize agents and map information as 2D
images or operate in a graph representation, our approach extends ideas from
point cloud learning with dynamic temporal learning to capture both spatial and
temporal information by splitting trajectory prediction into both spatial and
temporal dimensions. In the spatial dimension, agents can be viewed as an
unordered point set, and thus it is straightforward to apply point cloud
learning techniques to model agents' locations. While the spatial dimension
does not take kinematic and motion information into account, we further propose
dynamic temporal learning to model agents' motion over time. Experiments on the
Argoverse motion forecasting benchmark show that our approach achieves the
state-of-the-art results.
