  To track the 3D locations and trajectories of the other traffic participants
at any given time, modern autonomous vehicles are equipped with multiple
cameras that cover the vehicle's full surroundings. Yet, camera-based 3D object
tracking methods prioritize optimizing the single-camera setup and resort to
post-hoc fusion in a multi-camera setup. In this paper, we propose a method for
panoramic 3D object tracking, called CC-3DT, that associates and models object
trajectories both temporally and across views, and improves the overall
tracking consistency. In particular, our method fuses 3D detections from
multiple cameras before association, reducing identity switches significantly
and improving motion modeling. Our experiments on large-scale driving datasets
show that fusion before association leads to a large margin of improvement over
post-hoc fusion. We set a new state-of-the-art with 12.6% improvement in
average multi-object tracking accuracy (AMOTA) among all camera-based methods
on the competitive NuScenes 3D tracking benchmark, outperforming previously
published methods by 6.5% in AMOTA with the same 3D detector.
