提出WARM模块，让3D点云分割的少样本学习更稳定可靠
White Aggregation and Restoration for Few-shot 3D Point Cloud Semantic Segmentation
- 用注意力机制生成确定性原型，避免采样波动影响
- 在S3DIS上达新最优，比现有方法提升超5个百分点
- 适合做少样本3D点云语义分割的研究者和工程师
少样本3D点云语义分割（FS-PCS）旨在仅凭少量标注样本对未标注点云进行逐点标签预测。现有方法多采用最远点采样（FPS）构建原型，但该方式易受采样偏差影响，导致性能不稳定，且原型生成过程缺乏深入研究。为此，本文提出基于注意力机制的确定性原型生成方法。针对原始注意力模块在原型标记与支持特征间存在分布差异的问题，设计了白化聚合与恢复模块（WARM），通过白化操作在注意力前对齐特征与标记，再经着色恢复原始分布，从而实现鲁棒注意力。该设计有效捕捉支持特征中的语义关系，显著提升原型质量。在S3DIS数据集上取得当前最佳性能，相比基线提升超过5%，在ScanNet上也表现优异。实验验证了其在确定性原型生成方面的有效性。代码已开源。
原文摘要 · Abstract (English)
Few-shot 3D Point Cloud Semantic Segmentation (FS-PCS) aims to predict per-point labels for an unlabeled point cloud, given only a few labeled examples. To extract representations from the limited labeled set, existing methods have constructed prototypes with Farthest Point Sampling (FPS). However, we found that this convention results in performance instability due to its sensitivity to FPS-induced variations, while the prototype generation process remains underexplored in the field. This motivates us to investigate deterministic prototype generation method based on attention mechanism. Despite its potential, we found that vanilla attention module suffers from the distributional gap between prototypical tokens and support features. To overcome this, we provide a simple approach, White Aggregation and Restoration Module (WARM), which resolves the misalignment by wrapping cross-attention with whitening and coloring transformations. Specifically, whitening aligns the features to tokens before the attention process, and coloring subsequently restores the original distribution to the attended tokens. This design enables robust attention, thereby generating prototypes that capture the semantic relationships in support features. WARM achieves state-of-the-art performance with a significant margin on the S3DIS dataset, and competitive performance on the ScanNet dataset. Further experiments demonstrate its effectiveness in deterministic prototype generation. Code is publicly available at: https://github.com/JiyunIm00/WARM.git
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