Abstract
Few-shot 3D point cloud semantic segmentation (FS-3DSeg) aims to segment novel classes with only a few labeled samples. However, existing metric-based prototype learning methods generate prototypes solely from the support set, without considering their relevance to query data. This often results in prototype bias, where prototypes overfit support-specific characteristics and fail to generalize to the query distribution, especially in the presence of distribution shifts, which leads to degraded segmentation performance. To address this issue, we propose a novel Query-aware Hub Prototype (QHP) learning method that explicitly models semantic correlations between support and query sets. Specifically, we propose a Hub Prototype Generation (HPG) module that constructs a bipartite graph connecting query and support points, identifies frequently linked support hubs, and generates query-relevant prototypes that better capture cross-set semantics. To further mitigate the influence of bad hubs and ambiguous prototypes near class boundaries, we introduce a Prototype Distribution Optimization (PDO) module, which employs a purity-reweighted contrastive loss to refine prototype representations by pulling bad hubs and outlier prototypes closer to their corresponding class centers. Extensive experiments on S3DIS and ScanNet demonstrate that QHP achieves substantial performance gains over state-of-the-art methods, effectively narrowing the semantic gap between prototypes and query sets in FS-3DSeg.
Abstract (translated)
少量样本的三维点云语义分割（Few-shot 3D Point Cloud Semantic Segmentation，FS-3DSeg）的目标是使用仅有的几个标记样本对新类别进行分割。然而，现有的基于度量的方法生成原型时只从支持集（support set）中提取信息，不考虑这些原型与查询数据（query data）的相关性。这通常会导致原型偏差，即原型过度拟合于支持集特有的特征，并且无法推广到查询分布上，尤其是在存在分布偏移的情况下，导致分割性能下降。为了解决这一问题，我们提出了一种新的基于查询的中心枢纽原型（Query-aware Hub Prototype, QHP）学习方法，该方法明确建模了支持集和查询集之间的语义关联。 具体来说，我们提出了一个枢纽原型生成（Hub Prototype Generation, HPG）模块，它构建了一个连接查询点和支持点的二分图，识别出经常被链接的支持中心，并生成更能捕捉跨集合语义的相关查询原型。为了进一步减轻坏中心的影响以及类边界附近模糊原型的影响，我们引入了原型分布优化（Prototype Distribution Optimization, PDO）模块，该模块采用了一种纯净度重新加权的对比损失来通过将“坏”的枢纽和离群的原型拉向它们对应的类别中心来细化原型表示。 在S3DIS和ScanNet数据集上的广泛实验表明，QHP方法相对于最先进的方法取得了显著的性能提升，并且有效地缩小了FS-3DSeg中原型与查询集合之间的语义差距。
URL
https://arxiv.org/abs/2512.08253
