Point-In-Context: Understanding Point Cloud via In-Context Learning

The rise of large-scale models has catalyzed in-context learning as a powerful approach for multitasking, particularly in natural language and image processing. However, its application to 3D point cloud tasks has been largely unexplored. In this paper, we introduce Point-In-Context (PIC), a pioneering framework for 3D point cloud understanding that leverages in-context learning with a standard transformer architecture. PIC uniquely enables the execution of multiple tasks after a single, unified training phase, eliminating the need for fine-tuning. To extend masked point modeling to 3D in-context learning, we introduce a Joint Sampling module, a simple yet effective technique that emphasizes the mapping relationship between input and target. PIC treats both inputs and targets as coordinate-based, addressing the segmentation challenge by associating label points with pre-defined XYZ coordinates for each category. However, relying on such fixed label-coordinate assignments limits the model's ability to generalize to unseen domains. To address this limitation, we further propose two innovative training strategies: In-Context Labeling and In-Context Enhancing. These strategies are integrated into PIC++, which enhances dynamic in-context labeling and model training. Besides its multitask capability, PIC++ demonstrates generalization across part segmentation datasets by employing dynamic in-context labels and regular in-context pairs. Remarkably, PIC++, trained once without fine-tuning, can generalize effectively to unseen datasets and perform novel part segmentation through customized prompts. Overall, PIC is a general framework that seamlessly integrates additional tasks or datasets through a unified data format via in-context learning. Extensive experiments substantiate PIC's versatility and adaptability in handling diverse tasks and segmenting multiple datasets simultaneously.