  Scene understanding is a pivotal task for autonomous vehicles to safely
navigate in the environment. Recent advances in deep learning enable accurate
semantic reconstruction of the surroundings from LiDAR data. However, these
models encounter a large domain gap while deploying them on vehicles equipped
with different LiDAR setups which drastically decreases their performance.
Fine-tuning the model for every new setup is infeasible due to the expensive
and cumbersome process of recording and manually labeling new data.
Unsupervised Domain Adaptation (UDA) techniques are thus essential to fill this
domain gap and retain the performance of models on new sensor setups without
the need for additional data labeling. In this paper, we propose AdaptLPS, a
novel UDA approach for LiDAR panoptic segmentation that leverages task-specific
knowledge and accounts for variation in the number of scan lines, mounting
position, intensity distribution, and environmental conditions. We tackle the
UDA task by employing two complementary domain adaptation strategies,
data-based and model-based. While data-based adaptations reduce the domain gap
by processing the raw LiDAR scans to resemble the scans in the target domain,
model-based techniques guide the network in extracting features that are
representative for both domains. Extensive evaluations on three pairs of
real-world autonomous driving datasets demonstrate that AdaptLPS outperforms
existing UDA approaches by up to 6.41 pp in terms of the PQ score.
