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
this https URL

通过动态原型适应方法解决了少样本点云分割中的物体变异问题，并且在两个常用数据集上显著超越了最先进方法。

用蒸馏进行动态原型适应的小样本点云分割

Dynamic Prototype Adaptation with Distillation for Few-shot Point Cloud  Segmentation

Existing fully-supervised point cloud segmentation methods suffer in the
dynamic testing environment with emerging new classes. Few-shot point cloud
segmentation algorithms address this problem by learning to adapt to new
classes at the sacrifice of segmentation accuracy for the base classes, which
severely impedes its practicality. This largely motivates us to present the
first attempt at a more practical paradigm of generalized few-shot point cloud
segmentation, which requires the model to generalize to new categories with
only a few support point clouds and simultaneously retain the capability to
segment base classes. We propose the geometric words to represent geometric
components shared between the base and novel classes, and incorporate them into
a novel geometric-aware semantic representation to facilitate better
generalization to the new classes without forgetting the old ones. Moreover, we
introduce geometric prototypes to guide the segmentation with geometric prior
knowledge. Extensive experiments on S3DIS and ScanNet consistently illustrate
the superior performance of our method over baseline methods. Our code is
available at: this https URL

在动态测试环境中，现有的全监督点云分割方法无法应对新出现的类别。为解决这一问题，少样本点云分割算法通过学习适应新类别，但会牺牲对基类别的分割准确性，从而严重影响其实用性。因此，我们首次尝试了更实用的广义少样本点云分割范式，要求模型能够同时推广到用仅有少量支持点云表示的新类别，并保持对基类别的分割能力。我们提出了几何单词来表示基类别和新类别之间共享的几何组件，并将其融入新型几何感知语义表示中，以更好地推广到新类别而不会遗忘旧类别。此外，我们引入几何原型来利用几何先验知识来引导分割。在 S3DIS 和 ScanNet 上进行的大量实验证明了我们方法在基准方法上的卓越性能。