Deep learning techniques have the potential to provide versatile solutions for automated semantic segmentation of bridge point clouds, but previous studies were limited to small-scale bridge point clouds and focused on limited bridge component categories due to training sample scarcity. Additionally, no prior work considered the intrinsic data imbalance problem in the bridge dataset, with the points unequally distributed between the various components. This paper presents a weighted superpoint graph (WSPG) method, where bridge point clouds were firstly clustered into hundreds of semantically homogeneous superpoints that were then classified into different bridge components using PointNet and Graph Neural Networks. The WSPG method can recognize components directly from large-scale bridge point clouds and alleviate the data imbalance by leveraging a novel loss function that assigns weights according to the number of points contained in different bridge components. The effectiveness of the method was validated on both a real-world dataset with 5 categories of bridge components and a synthetic dataset with 8 categories of bridge components. Experiment results on the real-world dataset showed that the WSPG model achieved the best performance on all overall evaluation metrics of overall accuracy (OA: 99.43%), mean class accuracy (mAcc: 98.75%) and mean Intersection over Union (mIoU: 96.49%) compared to the existing cutting edge models such as PointNet, DGCNN and the original SPG. Additionally, the WSPG method also surpassed the cutting edge representatives in terms of mAcc and mIoU on the synthetic dataset, especially increasing the original SPG by 8.5% mAcc and 6.7% mIoU. The successful application of the proposed method will significantly improve upper-level tasks such as digital twining for existing bridges.

深度学习技术有可能为桥梁点云的自动语义分割提供通用的解决方案，但之前的研究仅限于小规模的桥梁点云，并且由于训练样本稀缺而专注于有限的桥梁组件类别。此外，之前的工作没有考虑桥梁数据集中固有的数据不平衡问题，即点在各个组件之间分布不均。本文提出了一种加权超点图（WSPG）方法，首先将桥点云聚类成数百个语义同质的超点，然后使用 PointNet 和图神经网络将这些超点分类为不同的桥组件。WSPG 方法可以直接从大规模桥梁点云中识别组件，并通过利用一种新颖的损失函数来缓解数据不平衡，该损失函数根据不同桥梁组件中包含的点数分配权重。该方法的有效性在具有 5 类桥梁组件的真实数据集和具有 8 类桥梁组件的合成数据集上都得到了验证。在真实世界数据集上的实验结果表明，WSPG 模型在总体准确率（OA：99.43%）、平均类准确率（mAcc：98.75%）和平均交并比（mIoU：96.49）的所有综合评价指标上均取得了最佳性能%) 与现有的尖端模型如 PointNet、DGCNN 和原始 SPG 相比。此外，WSPG 方法在合成数据集上的 mAcc 和 mIoU 方面也超过了前沿代表，特别是将原始 SPG 提高了 8.5% mAcc 和 6.7% mIoU。该方法的成功应用将显着改善现有桥梁的数字孪生等上层任务。