  In this paper, we focus on a less explored, but more realistic and complex
problem of domain adaptation in LiDAR semantic segmentation. There is a
significant drop in performance of an existing segmentation model when training
(source domain) and testing (target domain) data originate from different LiDAR
sensors. To overcome this shortcoming, we propose an unsupervised domain
adaptation framework that leverages unlabeled target domain data for
self-supervision, coupled with an unpaired mask transfer strategy to mitigate
the impact of domain shifts. Furthermore, we introduce the gated adapter module
with a small number of parameters into the network to account for target
domain-specific information. Experiments adapting from both real-to-real and
synthetic-to-real LiDAR semantic segmentation benchmarks demonstrate the
significant improvement over prior arts.
