  We study an unsupervised domain adaptation problem for the semantic labeling
of 3D point clouds, with a particular focus on domain discrepancies induced by
different LiDAR sensors. Based on the observation that sparse 3D point clouds
are sampled from 3D surfaces, we take a Complete and Label approach to recover
the underlying surfaces before passing them to a segmentation network.
Specifically, we design a Sparse Voxel Completion Network (SVCN) to complete
the 3D surfaces of a sparse point cloud. Unlike semantic labels, to obtain
training pairs for SVCN requires no manual labeling. We also introduce local
adversarial learning to model the surface prior. The recovered 3D surfaces
serve as a canonical domain, from which semantic labels can transfer across
different LiDAR sensors. Experiments and ablation studies with our new
benchmark for cross-domain semantic labeling of LiDAR data show that the
proposed approach provides 8.2-36.6% better performance than previous domain
adaptation methods.
