Title: SUG: Single-dataset Unified Generalization for 3D Point Cloud Classification. Abstract: In recent years, research on zero-shot domain adaptation, namely Domain Generalization (DG), which aims to adapt a well-trained source domain model to unseen target domains without accessing any target sample, has been fast-growing in the 2D image tasks such as classification and object detection. However, its exploration on 3D point cloud data is still insufficient and challenged by more complex and uncertain cross-domain variances with irregular point data structures and 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 the source domain data to alleviate the unforeseen domain differences faced by the well-pretrained source model. Specifically, we first design a Multi-grained Sub-domain Alignment (MSA) method that 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. Extensive experiments are conducted on three common 3D point cloud benchmarks. The experimental results demonstrate that SUG framework is effective to boost the model generalization ability for unseen target domains, even outperforming the existing unsupervised domain adaptation methods that have to access extensive target domain data, where we significantly improve classification accuracy by 7.7% on ModelNet-to-ScanNet setting and 2.3% on ShapeNet-to-ScanNet setting. Our code will be available.