  Methods tackling multi-object tracking need to estimate the number of targets
in the sensing area as well as to estimate their continuous state. While the
majority of existing methods focus on data association, precise state (3D pose)
estimation is often only coarsely estimated by approximating targets with
centroids or (3D) bounding boxes. However, in automotive scenarios, motion
perception of surrounding agents is critical and inaccuracies in the vehicle
close-range can have catastrophic consequences. In this work, we focus on
precise 3D track state estimation and propose a learning-based approach for
object-centric relative motion estimation of partially observed objects.
Instead of approximating targets with their centroids, our approach is capable
of utilizing noisy 3D point segments of objects to estimate their motion. To
that end, we propose a simple, yet effective and efficient network, \method,
that learns to align point clouds. Our evaluation on two different datasets
demonstrates that our method outperforms computationally expensive, global 3D
registration methods while being significantly more efficient. We make our
data, code, and models available at
https://www.vision.rwth-aachen.de/page/alignnet.
