  Despite its importance, unsupervised domain adaptation (UDA) on LiDAR
semantic segmentation is a task that has not received much attention from the
research community. Only recently, a completion-based 3D method has been
proposed to tackle the problem and formally set up the adaptive scenarios.
However, the proposed pipeline is complex, voxel-based and requires multi-stage
inference, which inhibits it for real-time inference. We propose a range
image-based, effective and efficient method for solving UDA on LiDAR
segmentation. The method exploits class prototypes from the source domain to
pseudo label target domain pixels, which is a research direction showing good
performance in UDA for natural image semantic segmentation. Applying such
approaches to LiDAR scans has not been considered because of the severe domain
shift and lack of pre-trained feature extractor that is unavailable in the
LiDAR segmentation setup. However, we show that proper strategies, including
reconstruction-based pre-training, enhanced prototypes, and selective pseudo
labeling based on distance to prototypes, is sufficient enough to enable the
use of prototypical approaches. We evaluate the performance of our method on
the recently proposed LiDAR segmentation UDA scenarios. Our method achieves
remarkable performance among contemporary methods.
