Although extensive research has been conducted on 3D point cloud segmentation, effectively adapting generic models to novel categories remains a formidable challenge. This paper proposes a novel approach to improve point cloud few-shot segmentation (PC-FSS) models. Unlike existing PC-FSS methods that directly utilize categorical information from support prototypes to recognize novel classes in query samples, our method identifies two critical aspects that substantially enhance model performance by reducing contextual gaps between support prototypes and query features. Specifically, we (1) adapt support background prototypes to match query context while removing extraneous cues that may obscure foreground and background in query samples, and (2) holistically rectify support prototypes under the guidance of query features to emulate the latter having no semantic gap to the query targets. Our proposed designs are agnostic to the feature extractor, rendering them readily applicable to any prototype-based methods. The experimental results on S3DIS and ScanNet demonstrate notable practical benefits, as our approach achieves significant improvements while still maintaining high efficiency. The code for our approach is available at https://github.com/AaronNZH/Boosting-Few-shot-3D-Point-Cloud-Segmentation-via-Query-Guided-Enhancement

翻译：尽管三维点云分割已得到广泛研究，但如何将通用模型有效适配至新类别仍是一项严峻挑战。本文提出一种创新方法以改进点云小样本分割（PC-FSS）模型。现有PC-FSS方法直接利用支持原型中的类别信息识别查询样本中的新类别，而本文方法则通过减少支持原型与查询特征之间的语境差距，揭示了两个显著提升模型性能的关键因素。具体而言，我们（1）对支持背景原型进行适配以匹配查询语境，同时移除可能模糊查询样本中前景与背景的冗余线索；（2）在查询特征引导下对支持原型进行整体矫正，使其与查询目标之间不存在语义差距。所提设计方案与特征提取器无关，可便捷应用于任何基于原型的方法。在S3DIS与ScanNet数据集上的实验结果表明，本方法在保持高效率的同时实现了显著性能提升。本方法代码已开源至https://github.com/AaronNZH/Boosting-Few-shot-3D-Point-Cloud-Segmentation-via-Query-Guided-Enhancement