  Smart City applications such as intelligent traffic routing or accident
prevention rely on computer vision methods for exact vehicle localization and
tracking. Due to the scarcity of accurately labeled data, detecting and
tracking vehicles in 3D from multiple cameras proves challenging to explore. We
present a massive synthetic dataset for multiple vehicle tracking and
segmentation in multiple overlapping and non-overlapping camera views. Unlike
existing datasets, which only provide tracking ground truth for 2D bounding
boxes, our dataset additionally contains perfect labels for 3D bounding boxes
in camera- and world coordinates, depth estimation, and instance, semantic and
panoptic segmentation. The dataset consists of 17 hours of labeled video
material, recorded from 340 cameras in 64 diverse day, rain, dawn, and night
scenes, making it the most extensive dataset for multi-target multi-camera
tracking so far. We provide baselines for detection, vehicle re-identification,
and single- and multi-camera tracking. Code and data are publicly available.
