Few-shot point cloud semantic segmentation aims to train a model to quickly
adapt to new unseen classes with only a handful of support set samples.
However, the noise-free assumption in the support set can be easily violated in
many practical real-world settings. In this paper, we focus on improving the
robustness of few-shot point cloud segmentation under the detrimental influence
of noisy support sets during testing time. To this end, we first propose a
Component-level Clean Noise Separation (CCNS) representation learning to learn
discriminative feature representations that separates the clean samples of the
target classes from the noisy samples. Leveraging the well separated clean and
noisy support samples from our CCNS, we further propose a Multi-scale
Degree-based Noise Suppression (MDNS) scheme to remove the noisy shots from the
support set. We conduct extensive experiments on various noise settings on two
benchmark datasets. Our results show that the combination of CCNS and MDNS
significantly improves the performance. Our code is available at
this https URL

通过在训练过程中学习用于将目标类的干净样本与噪声样本分离的判别性特征表示的组件级清理噪声分离（CCNS）表示学习，以及借助 CCNS 中清晰而分离的干净和噪声支持样本，提出了一种多尺度基于度数的噪声抑制（MDNS）方案，用于从支持集中去除噪声，从而显著提高了少样本点云语义分割的鲁棒性。