  Recent point cloud understanding research suffers from performance drops on
unseen data, due to the distribution shifts across different domains. While
recent studies use Domain Generalization (DG) techniques to mitigate this by
learning domain-invariant features, most are designed for a single task and
neglect the potential of testing data. Despite In-Context Learning (ICL)
showcasing multi-task learning capability, it usually relies on high-quality
context-rich data and considers a single dataset, and has rarely been studied
in point cloud understanding. In this paper, we introduce a novel, practical,
multi-domain multi-task setting, handling multiple domains and multiple tasks
within one unified model for domain generalized point cloud understanding. To
this end, we propose Domain Generalized Point-In-Context Learning (DG-PIC) that
boosts the generalizability across various tasks and domains at testing time.
In particular, we develop dual-level source prototype estimation that considers
both global-level shape contextual and local-level geometrical structures for
representing source domains and a dual-level test-time feature shifting
mechanism that leverages both macro-level domain semantic information and
micro-level patch positional relationships to pull the target data closer to
the source ones during the testing. Our DG-PIC does not require any model
updates during the testing and can handle unseen domains and multiple tasks,
\textit{i.e.,} point cloud reconstruction, denoising, and registration, within
one unified model. We also introduce a benchmark for this new setting.
Comprehensive experiments demonstrate that DG-PIC outperforms state-of-the-art
techniques significantly.
