  With the rise of large-scale models trained on broad data, in-context
learning has become a new learning paradigm that has demonstrated significant
potential in natural language processing and computer vision tasks. Meanwhile,
in-context learning is still largely unexplored in the 3D point cloud domain.
Although masked modeling has been successfully applied for in-context learning
in 2D vision, directly extending it to 3D point clouds remains a formidable
challenge. In the case of point clouds, the tokens themselves are the point
cloud positions (coordinates) that are masked during inference. Moreover,
position embedding in previous works may inadvertently introduce information
leakage. To address these challenges, we introduce a novel framework, named
Point-In-Context, designed especially for in-context learning in 3D point
clouds, where both inputs and outputs are modeled as coordinates for each task.
Additionally, we propose the Joint Sampling module, carefully designed to work
in tandem with the general point sampling operator, effectively resolving the
aforementioned technical issues. We conduct extensive experiments to validate
the versatility and adaptability of our proposed methods in handling a wide
range of tasks.

Computer Vision and Pattern Recognition