具有不确定性意识的原型学习与变分推断用于少样本点云分割
Uncertainty-aware Prototype Learning with Variational Inference for Few-shot Point Cloud Segmentation
AI总结：
本文提出UPL方法，通过引入双流原型细化模块和变分推断框架，提升少样本点云分割的鲁棒性和不确定性建模能力，在ScanNet和S3DIS基准上取得最佳性能。
摘要：
少样本3D语义分割旨在仅使用少量标注支持示例为查询点云生成准确的语义掩膜。现有基于原型的方法通常从支持集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少监督引入的内在不确定性，常导致鲁棒性下降和泛化能力有限。本文提出UPL（不确定性意识原型学习），一种旨在将不确定性建模融入少样本3D分割原型学习的概率方法。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细化模块，通过联合利用支持和查询样本的有限信息来丰富原型表示。其次，我们将原型学习 formulation 为变分推断问题，将类原型视为潜在变量。这种概率 formulation 允许显式不确定性建模，提供稳健且可解释的掩膜预测。在广泛使用的ScanNet和S3DIS基准上进行的大量实验表明，我们的UPL在不同设置下均取得一致的最佳性能，同时提供可靠的不确定性估计。代码可在https://fdueblab-upl.github.io/上获得。
英文摘要：
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 https://fdueblab-upl.github.io/.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
