[2603.19757] Uncertainty-aware Prototype Learning with Variational Inference for Few-shot Point Cloud Segmentation

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.19757 (cs) [Submitted on 20 Mar 2026] Title:Uncertainty-aware Prototype Learning with Variational Inference for Few-shot Point Cloud Segmentation Authors:Yifei Zhao, Fanyu Zhao, Yinsheng Li View a PDF of the paper titled Uncertainty-aware Prototype Learning with Variational Inference for Few-shot Point Cloud Segmentation, by Yifei Zhao and 2 other authors View PDF HTML (experimental) Abstract:Few-shot 3D semantic segmentation aims to generate accurate semantic masks for query point clouds with only a few annotated support examples. Existing prototype-based methods typically construct compact and deterministic prototypes from the support set to guide query segmentation. However, such rigid representations are unable to capture the intrinsic uncertainty introduced by scarce supervision, which often results in degraded robustness and limited generalization. In this work, we propose UPL (Uncertainty-aware Prototype Learning), a probabilistic approach designed to incorporate uncertainty modeling into prototype learning for few-shot 3D segmentation. Our framework introduces two key components. First, UPL introduces a dual-stream prototype refinement module that enriches prototype representations by jointly leveraging limited information from both support and query samples. Second, we formulate prototype learning as a variational inference problem, regarding class prototypes as latent variables. This probabilis...