  Segmentation of lidar data is a task that provides rich, point-wise
information about the environment of robots or autonomous vehicles. Currently
best performing neural networks for lidar segmentation are fine-tuned to
specific datasets. Switching the lidar sensor without retraining on a big set
of annotated data from the new sensor creates a domain shift, which causes the
network performance to drop drastically. In this work we propose a new method
for lidar domain adaption, in which we use annotated panoptic lidar datasets
and recreate the recorded scenes in the structure of a different lidar sensor.
We narrow the domain gap to the target data by recreating panoptic data from
one domain in another and mixing the generated data with parts of (pseudo)
labeled target domain data. Our method improves the nuScenes to SemanticKITTI
unsupervised domain adaptation performance by 15.2 mean Intersection over Union
points (mIoU) and by 48.3 mIoU in our semi-supervised approach. We demonstrate
a similar improvement for the SemanticKITTI to nuScenes domain adaptation by
21.8 mIoU and 51.5 mIoU, respectively. We compare our method with two state of
the art approaches for semantic lidar segmentation domain adaptation with a
significant improvement for unsupervised and semi-supervised domain adaptation.
Furthermore we successfully apply our proposed method to two entirely unlabeled
datasets of two state of the art lidar sensors Velodyne Alpha Prime and
InnovizTwo, and train well performing semantic segmentation networks for both.
