通过类别级几何学习提升3D点云分割的跨域泛化能力
Domain-aware Category-level Geometry Learning Segmentation for 3D Point Clouds
- 引入类别级几何嵌入，捕捉每类物体的细粒度几何特征
- 在多个数据集上达到当前最优分割精度，优于主流域泛化方法
- 适合需要在未知环境部署3D分割模型的研究与应用
3D分割中的域泛化是模型部署到未见环境的关键挑战。现有方法通过增强点云数据分布缓解域偏移，但忽略了类别级别的分布与对齐。本文提出一种类别级几何学习框架，以挖掘域不变的几何特征，实现域泛化的3D语义分割。具体地，提出类别级几何嵌入（CGE），感知点云特征的细粒度几何属性，构建每类物体的几何特性，并将几何嵌入与语义学习耦合。其次，提出几何一致性学习（GCL），模拟潜在的3D分布并对齐类别级几何嵌入，使模型聚焦于几何不变信息，提升泛化性能。实验验证了该方法的有效性，在多个基准数据集上达到与最先进域泛化方法相当甚至更优的分割精度。
原文摘要 · Abstract (English)
Domain generalization in 3D segmentation is a critical challenge in deploying models to unseen environments. Current methods mitigate the domain shift by augmenting the data distribution of point clouds. However, the model learns global geometric patterns in point clouds while ignoring the category-level distribution and alignment. In this paper, a category-level geometry learning framework is proposed to explore the domain-invariant geometric features for domain generalized 3D semantic segmentation. Specifically, Category-level Geometry Embedding (CGE) is proposed to perceive the fine-grained geometric properties of point cloud features, which constructs the geometric properties of each class and couples geometric embedding to semantic learning. Secondly, Geometric Consistent Learning (GCL) is proposed to simulate the latent 3D distribution and align the category-level geometric embeddings, allowing the model to focus on the geometric invariant information to improve generalization. Experimental results verify the effectiveness of the proposed method, which has very competitive segmentation accuracy compared with the state-of-the-art domain generalized point cloud methods.
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