  The tasks of object detection and trajectory forecasting play a crucial role
in understanding the scene for autonomous driving. These tasks are typically
executed in a cascading manner, making them prone to compounding errors.
Furthermore, there is usually a very thin interface between the two tasks,
creating a lossy information bottleneck. To address these challenges, our
approach formulates the union of the two tasks as a trajectory refinement
problem, where the first pose is the detection (current time), and the
subsequent poses are the waypoints of the multiple forecasts (future time). To
tackle this unified task, we design a refinement transformer that infers the
presence, pose, and multi-modal future behaviors of objects directly from LiDAR
point clouds and high-definition maps. We call this model DeTra, short for
object Detection and Trajectory forecasting. In our experiments, we observe
that \ourmodel{} outperforms the state-of-the-art on Argoverse 2 Sensor and
Waymo Open Dataset by a large margin, across a broad range of metrics. Last but
not least, we perform extensive ablation studies that show the value of
refinement for this task, that every proposed component contributes positively
to its performance, and that key design choices were made.
