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 probabilistic formulation enables explicit uncertainty modeling, providing robust and interpretable mask predictions. Extensive experiments on the widely used ScanNet and S3DIS benchmarks show that our UPL achieves consistent state-of-the-art performance under different settings while providing reliable uncertainty estimation. The code is available at this https URL.
Abstract (translated)
少样本3D语义分割旨在仅使用少量标注的支撑样本，为查询点云生成准确的语义掩码。现有基于原型的方法通常从支撑集中构建紧凑且确定性的原型，以指导查询分割。然而，这种刚性表示无法捕捉由稀缺监督引入的内在不确定性，这通常导致鲁棒性下降和泛化能力有限。在本研究中，我们提出了UPL（不确定性感知原型学习），一种旨在将不确定性建模整合到少样本3D分割原型学习中的概率方法。我们的框架引入了两个关键组件。首先，UPL引入了一个双流原型优化模块，通过联合利用支撑和查询样本中的有限信息来丰富原型表示。其次，我们将原型学习公式化为变分推断问题，将类原型视为潜在变量。这种概率公式化实现了明确的不确定性建模，提供了鲁棒且可解释的掩码预测。在广泛使用的ScanNet和S3DIS基准测试上的大量实验表明，我们的UPL在不同设置下均实现了持续的最先进性能，同时提供了可靠的不确定性估计。代码可在以下网址获取：https URL。
URL
https://arxiv.org/abs/2603.19757
