Rethinking Few-shot 3D Point Cloud Semantic Segmentation
Authors: Zhaochong An, Guolei Sun, Yun Liu, Fayao Liu, Zongwei Wu, Dan Wang, Luc Van Gool, Serge Belongie

This paper revisits few-shot 3D point cloud semantic segmentation (FS-PCS) with a focus on two significant issues in the state-of-the-art: foreground leakage and sparse point distribution. The former arises from non-uniform point sampling allowing models to distinguish the density disparities between foreground and background for easier segmentation. The latter results from sampling only 2048 points limiting semantic information and deviating from the real-world practice. To address these issues we introduce a standardized FS-PCS setting upon which a new benchmark is built. Moreover we propose a novel FS-PCS model. While previous methods are based on feature optimization by mainly refining support features to enhance prototypes our method is based on correlation optimization referred to as Correlation Optimization Segmentation (COSeg). Specifically we compute Class-specific Multi-prototypical Correlation (CMC) for each query point representing its correlations to category prototypes. Then we propose the Hyper Correlation Augmentation (HCA) module to enhance CMC. Furthermore tackling the inherent property of few-shot training to incur base susceptibility for models we propose to learn non-parametric prototypes for the base classes during training. The learned base prototypes are used to calibrate correlations for the background class through a Base Prototypes Calibration (BPC) module. Experiments on popular datasets demonstrate the superiority of COSeg over existing methods. The code is available at github.com/ZhaochongAn/COSeg.