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 https://github.com/Pixie8888/R3DFSSeg.

翻译：少样本点云语义分割旨在训练模型仅通过少量支持集样本快速适应未见类别。然而，在许多实际场景中，支持集中无噪声的假设容易被打破。本文聚焦于提升少样本点云分割在测试阶段受噪声支持集不利影响时的鲁棒性。为此，我们首先提出组件级干净噪声分离（CCNS）表示学习，通过学习判别性特征表示将目标类别的干净样本与噪声样本分离。基于CCNS有效分离的干净与噪声支持样本，我们进一步提出多尺度程度化噪声抑制（MDNS）方案，用于清除支持集中的噪声样本。我们在两个基准数据集上针对多种噪声设定进行了广泛实验，结果表明CCNS与MDNS的组合显著提升了性能。我们的代码已开源至 https://github.com/Pixie8888/R3DFSSeg。