Accurate semantic segmentation of railway point clouds is crucial for railway infrastructure modelling. However, existing fully-supervised methods are heavily dependent on labeled datasets, while label-efficient methods typically struggle to generate representative annotations. To address these challenges, a weakly supervised point cloud semantic segmentation method based on active learning is proposed, significantly reducing labeling requirements while maintaining high segmentation accuracy. During the labeled pool updating phase, a strategy combining high-loss regions and high-uncertainty points is employed to actively select representative samples for annotation. To enhance the model's capacity for capturing complex railway structures, geometric features are embedded into the network encoder. Additionally, a class prototype dictionary is constructed, and dynamically weighted pseudo-labels are generated to maximize the utilization of limited supervisory information during training. Experimental results on three diverse railway datasets demonstrate that the method achieves superior segmentation accuracy with fewer labels compared to both popular weakly and fully supervised methods.

中文翻译：

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面向有限标签的铁路点云主动学习驱动的语义分割


铁路点云的准确语义分割对于铁路基础设施建模至关重要。然而，现有的完全监督方法严重依赖于标记的数据集，而标签高效的方法通常难以生成具有代表性的注释。为了应对这些挑战，该文提出了一种基于主动学习的弱监督点云语义分割方法，在保持高分割精度的同时，显著降低了标注要求。在标记池更新阶段，采用结合高损耗区域和高不确定性点的策略来主动选择具有代表性的样本进行注释。为了增强模型捕获复杂铁路结构的能力，几何特征被嵌入到网络编码器中。此外，构建了一个类原型字典，并生成动态加权的伪标签，以最大限度地利用训练过程中有限的监督信息。在三个不同的铁路数据集上的实验结果表明，与流行的弱监督和完全监督方法相比，该方法以更少的标签实现了卓越的分割精度。