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 2,048 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: this https URL

该研究论文通过引入标准化的少样本点云语义分割设置和基于相关性优化的新模型 (COSeg)，解决了目前该领域面临的前景泄漏、稀疏点分布等问题，在流行数据集上的实验证明了 COSeg 相对于现有方法的优越性。