  Few-shot point cloud segmentation seeks to generate per-point masks for
previously unseen categories, using only a minimal set of annotated point
clouds as reference. Existing prototype-based methods rely on support
prototypes to guide the segmentation of query point clouds, but they encounter
challenges when significant object variations exist between the support
prototypes and query features. In this work, we present dynamic prototype
adaptation (DPA), which explicitly learns task-specific prototypes for each
query point cloud to tackle the object variation problem. DPA achieves the
adaptation through prototype rectification, aligning vanilla prototypes from
support with the query feature distribution, and prototype-to-query attention,
extracting task-specific context from query point clouds. Furthermore, we
introduce a prototype distillation regularization term, enabling knowledge
transfer between early-stage prototypes and their deeper counterparts during
adaption. By iteratively applying these adaptations, we generate task-specific
prototypes for accurate mask predictions on query point clouds. Extensive
experiments on two popular benchmarks show that DPA surpasses state-of-the-art
methods by a significant margin, e.g., 7.43\% and 6.39\% under the 2-way 1-shot
setting on S3DIS and ScanNet, respectively. Code is available at
https://github.com/jliu4ai/DPA.
