With the success of the 3D deep learning models, various perception technologies for autonomous driving have been developed in the LiDAR domain. While these models perform well in the trained source domain, they struggle in unseen domains with a domain gap. In this paper, we propose a representation learning approach for domain generalization in LiDAR semantic segmentation, termed DGLSS++, which is designed to ensure robust performance in both the source domain and unseen domains despite training exclusively on the source domain. Our approach focuses on generalizing from a single source domain, addressing the domain shift caused by variations in LiDAR sensor configurations and scene distributions. To tackle both sparse-to-dense and dense-to-sparse generalization scenarios, we simulate unseen domains by generating sparsely and densely augmented domains. With the augmented domain, we introduce two constraints for generalizable representation learning: generalized masked sparsity invariant feature consistency (GMSIFC) and localized semantic correlation consistency (LSCC). GMSIFC aligns the internal sparse features of the source domain with those of the augmented domain at different sparsity, introducing a novel masking strategy to exclude voxel features associated with multiple inconsistent classes. For LSCC, class prototypes from spatially local regions are constrained to maintain similar correlations across all local regions, regardless of the scene or domain. In addition, we establish standardized training and evaluation protocols utilizing four real-world datasets and implement several baseline methods. Extensive experiments demonstrate our approach outperforms both UDA and DG baselines. The code is available at https://github.com/gzgzys9887/DGLSS.
Kim et al. (Thu,) studied this question.