  In this paper we address the challenging problem of domain adaptation in
LiDAR semantic segmentation. We consider the setting where we have a
fully-labeled data set from source domain and a target domain with a few
labeled and many unlabeled examples. We propose a domain adaption framework
that mitigates the issue of domain shift and produces appealing performance on
the target domain. To this end, we develop a GAN-based image-to-image
translation engine that has generators with alternating connections, and couple
it with a state-of-the-art LiDAR semantic segmentation network. Our framework
is hybrid in nature in the sense that our model learning is composed of
self-supervision, semi-supervision and unsupervised learning. Extensive
experiments on benchmark LiDAR semantic segmentation data sets demonstrate that
our method achieves superior performance in comparison to strong baselines and
prior arts.
