Domain-generalized LiDAR semantic segmentation (LSS) seeks to train models on source-domain point clouds that generalize reliably to multiple unseen target domains, which is essential for real-world LiDAR applications. However, existing approaches assume similar acquisition views (e.g., vehicle-mounted) and struggle in cross-view scenarios, where observations differ substantially due to viewpoint-dependent structural incompleteness and non-uniform point density. Accordingly, we formulate cross-view domain generalization for LiDAR semantic segmentation and propose a novel framework, termed CVGC (Cross-View Geometric Consistency). Specifically, we introduce a cross-view geometric augmentation module that models viewpoint-induced variations in visibility and sampling density, generating multiple cross-view observations of the same scene. Subsequently, a geometric consistency module enforces consistent semantic and occupancy predictions across geometrically augmented point clouds of the same scene. Extensive experiments on six public LiDAR datasets establish the first systematic evaluation of cross-view domain generalization for LiDAR semantic segmentation, demonstrating that CVGC consistently outperforms state-of-the-art methods when generalizing from a single source domain to multiple target domains with heterogeneous acquisition viewpoints. The source code will be publicly available at https://github.com/KintomZi/CVGC-DG

翻译：域泛化激光雷达语义分割旨在利用源域点云数据训练模型，使其能够可靠地泛化至多个未见目标域，这对于实际激光雷达应用至关重要。然而，现有方法通常假设采集视角相似（如车载视角），在跨视角场景中表现不佳，因为视角差异会导致结构不完整性和点云密度不均匀等显著观测差异。为此，我们首次系统性地提出了跨视角域泛化激光雷达语义分割问题，并提出了一个新颖的框架——CVGC（跨视角几何一致性）。具体而言，我们设计了一个跨视角几何增强模块，该模块建模由视角变化引起的可见性与采样密度差异，生成同一场景的多种跨视角观测数据。随后，几何一致性模块通过强制同一场景经几何增强后的点云在语义预测与占据预测上保持一致性来实现泛化提升。我们在六个公开激光雷达数据集上进行了大量实验，首次建立了跨视角域泛化激光雷达语义分割的系统性评估基准。实验结果表明，在从单一源域泛化至具有异构采集视角的多个目标域时，CVGC始终优于现有最先进方法。源代码将在 https://github.com/KintomZi/CVGC-DG 公开。