  We present a boundary-aware domain adaptation model for LiDAR scan full-scene
semantic segmentation (LiDARNet). Our model can extract both the domain private
features and the domain shared features with a two-branch structure. We
embedded Gated-SCNN into the segmentor component of LiDARNet to learn boundary
information while learning to predict full-scene semantic segmentation labels.
Moreover, we further reduce the domain gap by inducing the model to learn a
mapping between two domains using the domain shared and private features.
Additionally, we introduce a new dataset (SemanticUSL\footnote{The access
address of
SemanticUSL:\url{https://unmannedlab.github.io/research/SemanticUSL}}) for
domain adaptation for LiDAR point cloud semantic segmentation. The dataset has
the same data format and ontology as SemanticKITTI. We conducted experiments on
real-world datasets SemanticKITTI, SemanticPOSS, and SemanticUSL, which have
differences in channel distributions, reflectivity distributions, diversity of
scenes, and sensors setup. Using our approach, we can get a single
projection-based LiDAR full-scene semantic segmentation model working on both
domains. Our model can keep almost the same performance on the source domain
after adaptation and get an 8\%-22\% mIoU performance increase in the target
domain.
