Although Domain Generalization (DG) problem has been fast-growing in the 2D image tasks, its exploration on 3D point cloud data is still insufficient and challenged by more complex and uncertain cross-domain variances with uneven inter-class modality distribution. In this paper, different from previous 2D DG works, we focus on the 3D DG problem and propose a Single-dataset Unified Generalization (SUG) framework that only leverages a single source dataset to alleviate the unforeseen domain differences faced by a well-trained source model. Specifically, we first design a Multi-grained Sub-domain Alignment (MSA) method, which can constrain the learned representations to be domain-agnostic and discriminative, by performing a multi-grained feature alignment process between the splitted sub-domains from the single source dataset. Then, a Sample-level Domain-aware Attention (SDA) strategy is presented, which can selectively enhance easy-to-adapt samples from different sub-domains according to the sample-level inter-domain distance to avoid the negative transfer. Experiments demonstrate that our SUG can boost the generalization ability for unseen target domains, even outperforming the existing unsupervised domain adaptation methods that have to access extensive target domain data. Our code is available at https://github.com/SiyuanHuang95/SUG.

翻译：尽管域泛化（Domain Generalization, DG）问题在二维图像任务中已快速发展，但在三维点云数据上的探索仍显不足，且面临跨域差异更复杂、不确定性更强以及类间模态分布不均等挑战。本文不同于以往二维DG工作，聚焦三维DG问题，提出一种仅利用单源数据集即可缓解已训练源模型所面临的未知域差异的单数据集统一泛化（Single-dataset Unified Generalization, SUG）框架。具体而言，我们首先设计了一种多粒度子域对齐（Multi-grained Sub-domain Alignment, MSA）方法，通过对单源数据集中划分的子域执行多粒度特征对齐过程，约束学习到的表示具有域无关性和判别性。其次，提出样本级域感知注意力（Sample-level Domain-aware Attention, SDA）策略，该策略可根据样本级域间距离，选择性地增强来自不同子域中易适应样本的权重，从而避免负迁移。实验表明，我们的SUG能够提升对未见目标域的泛化能力，甚至优于需访问大量目标域数据的现有无监督域适应方法。代码已开源至https://github.com/SiyuanHuang95/SUG。