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We study an unsupervised domain adaptation problem for the semantic labelingof 3D point clouds, with a particular focus on domain discrepancies induced bydifferent LiDAR sensors. Based on the observation that sparse 3D point cloudsare sampled from 3D surfaces, we take a Complete and Label approach to recoverthe underlying surfaces before passing them to a segmentation network.Specifically, we design a Sparse Voxel Completion Network (SVCN) to completethe 3D surfaces of a sparse point cloud. Unlike semantic labels, to obtaintraining pairs for SVCN requires no manual labeling. We also introduce localadversarial learning to model the surface prior. The recovered 3D surfacesserve as a canonical domain, from which semantic labels can transfer acrossdifferent LiDAR sensors. Experiments and ablation studies with our newbenchmark for cross-domain semantic labeling of LiDAR data show that theproposed approach provides 8.2-36.6% better performance than previous domainadaptation methods.

Complete & Label: A Domain Adaptation Approach to Semantic Segmentation of LiDAR Point Clouds