Towards Robust Few-shot Point Cloud Semantic Segmentation

TL;DR

This paper enhances the robustness of few-shot point cloud segmentation by introducing methods to effectively separate clean data from noise and suppress noisy samples, significantly improving performance under noisy support set conditions.

Contribution

It proposes a novel Component-level Clean Noise Separation (CCNS) and Multi-scale Degree-based Noise Suppression (MDNS) scheme for robust few-shot point cloud segmentation.

Findings

Significant performance improvements under noisy conditions.

Effective separation of clean and noisy support samples.

Robustness demonstrated on benchmark datasets.

Abstract

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.