and -corrected instance masks. As the size of this dataset is considerably larger and presumably more informative than MOTSChallenge, we focused our ablations on it.

Table \[tab:results-ks\] summarizes the analyses and experiments we ran on our newly generated [KITTI Synth]{}dataset, by evaluating on the official [KITTI MOTS]{}validation set. We discuss three types of ablations in what follows.

#### [KITTI Synth]{}data quality analysis.

The two topmost rows of Tab. \[tab:results-ks\] show the results when generating the validation set results solely with our proposed dataset generation pipeline, described in Section \[sec:datagen\], no learned tracking component is involved. Using the optical flow model predictions from HD$^3$ [@Yin_2019_CVPR] and their best-performing model for KITTI[^2], we obtain reasonable baseline results. When fine-tuning HD$^3$ with the optical flow training data generation process from SfM described in Section \[sec:datagen\], our synthetically extracted validation data considerably outperforms the sMOTSA scores on pedestrians and almost obtains the performance on cars for CAMOT [@Osep18ICRA], as reported in [@Voigtlaender_2019_CVPR] While this validates our tracklet generation process and the way we harvest optical flow training data, we investigate further if learning from imperfect data (tracklets are prone to break when associated via optical flow) like [KITTI Synth]{}.

#### [MOTSNet]{}tracking head ablations.

The 