Cross-view Domain Generalization via Geometric Consistency for LiDAR Semantic Segmentation
This addresses a critical challenge for real-world LiDAR applications by enabling models to generalize across diverse acquisition viewpoints, though it is incremental as it builds on existing domain generalization methods.
The paper tackles the problem of domain-generalized LiDAR semantic segmentation in cross-view scenarios, where models trained on source data struggle due to viewpoint differences, and proposes CVGC, a framework that enforces geometric consistency across augmented views, achieving state-of-the-art performance on six datasets.
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