[KITTI MOTS]{}training data, our [KITTI Synth]{}-based results improve substantially on cars, again confirming the quality of our automatically extracted dataset.

The bottommost part of this table shows the performance when training on the full [KITTI Synth]{}dataset and evaluating on [KITTI MOTS]{}validation, using different pre-training settings. The first is pre-trained on ImageNet and the second on both, ImageNet and Mapillary Vistas. While there is only little gap between them on the car class, the performance on pedestrians rises by over 5% when pre-training on Vistas. The most encouraging finding is that using our solely automatically extracted [KITTI Synth]{}dataset we obtain significantly improved scores (+1.6% on cars, +7.4% on pedestrians) in comparison to the previous state-of-the-art method from [@Voigtlaender_2019_CVPR], trained on manually curated data.

#### [MOTSNet]{}on [KITTI MOTS]{}.

In Tab. \[tab:results-km\] we directly compare against previously top-performing works including TrackR-CNN [@Voigtlaender_2019_CVPR] and references from therein, CAMOT [@Osep18ICRA], CIWT [@osep2017combined], and BeyondPixels [@sharma2018beyond]. It is worth noting that the last three approaches were reported in [@Voigtlaender_2019_CVPR] and partially built on. We provide all relevant MOTS measures together with separate average AP numbers to estimate the quality of bounding box detections or instance segmentation approaches, respectively. For our [MOTSNet]{}we again show results under different 