  Tracking of objects in 3D is a fundamental task in computer vision that finds
use in a wide range of applications such as autonomous driving, robotics or
augmented reality. Most recent approaches for 3D multi object tracking (MOT)
from LIDAR use object dynamics together with a set of handcrafted features to
match detections of objects. However, manually designing such features and
heuristics is cumbersome and often leads to suboptimal performance. In this
work, we instead strive towards a unified and learning based approach to the 3D
MOT problem. We design a graph structure to jointly process detection and track
states in an online manner. To this end, we employ a Neural Message Passing
network for data association that is fully trainable. Our approach provides a
natural way for track initialization and handling of false positive detections,
while significantly improving track stability. We show the merit of the
proposed approach on the publicly available nuScenes dataset by achieving
state-of-the-art performance of 65.6% AMOTA and 58% fewer ID-switches.
