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.

本文利用 3D 点云中表面稀疏采样的特征，引入稀疏体素完成网络(SVCN) 进行前景表面的完成。同时，我们使用局部对抗学习构建 3D 表面的先验知识。结果表明，在处理不同 LiDAR 传感器数据的跨域语义分割问题时，我们提出的方法比现有方法提高了 8.2%-36.6% 的分类性能。

基于域自适应的 LiDAR 点云语义分割完成与标注