Towards Robust Few-shot Point Cloud Semantic Segmentation
Yating Xu Na Zhao Gim Hee Lee
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.