  Accurate and consistent 3D tracking from multiple cameras is a key component
in a vision-based autonomous driving system. It involves modeling 3D dynamic
objects in complex scenes across multiple cameras. This problem is inherently
challenging due to depth estimation, visual occlusions, appearance ambiguity,
etc. Moreover, objects are not consistently associated across time and cameras.
To address that, we propose an end-to-end \textbf{MU}lti-camera
\textbf{TR}acking framework called MUTR3D. In contrast to prior works, MUTR3D
does not explicitly rely on the spatial and appearance similarity of objects.
Instead, our method introduces \textit{3D track query} to model spatial and
appearance coherent track for each object that appears in multiple cameras and
multiple frames. We use camera transformations to link 3D trackers with their
observations in 2D images. Each tracker is further refined according to the
features that are obtained from camera images. MUTR3D uses a set-to-set loss to
measure the difference between the predicted tracking results and the ground
truths. Therefore, it does not require any post-processing such as non-maximum
suppression and/or bounding box association. MUTR3D outperforms
state-of-the-art methods by 5.3 AMOTA on the nuScenes dataset. Code is
available at: \url{https://github.com/a1600012888/MUTR3D}.
