解决激光雷达语义分割跨视角泛化难题，提升真实场景适应能力。
Cross-view Domain Generalization via Geometric Consistency for LiDAR Semantic Segmentation
- 设计跨视角几何增强模块，模拟不同视角下的点云缺失与密度差异
- 通过几何一致性约束，使模型在多视角下输出稳定一致的语义预测
- 在6个公开数据集上验证，单源训练可泛化到多种异构视角目标域
领域泛化的激光雷达语义分割（LSS）旨在利用源域点云训练出能在多个未见目标域中可靠工作的模型，对实际应用至关重要。然而现有方法假设采集视角相似（如车载），在跨视角场景中表现不佳，因视角差异导致结构不完整和点密度非均匀。为此，我们首次提出针对激光雷达语义分割的跨视角域泛化问题，并提出新框架CVGC（Cross-View Geometric Consistency）。具体而言，引入跨视角几何增强模块，建模视角引起的可见性与采样密度变化，生成同一场景的多视角观测；随后，几何一致性模块强制同一场景的几何增强点云间语义与占据预测保持一致。在六个公开激光雷达数据集上的大量实验首次系统评估了跨视角域泛化，结果表明：从单一源域训练出发，CVGC在泛化至具有异构采集视角的多个目标域时，持续优于现有最先进方法。源代码将公开于 https://github.com/KintomZi/CVGC-DG。
原文摘要 · Abstract (English)
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
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。