用预训练模型少样本标注壳体建筑点云中的结构部件。
Multi-dataset synergistic in supervised learning to pre-label structural components in point clouds from shell construction scenes
- 用Transformer模型在少量新数据上微调，实现结构部件分割。
- 跨域迁移在大型室内数据集上表现良好，准确率提升显著。
- 适合需要快速构建建筑点云标注数据集的工程场景。
构建新训练数据集所需的大量标注工作严重阻碍了建筑领域计算机视觉与机器学习的发展。本文针对壳体建筑场景中的点云语义分割问题，探索利用标准数据集与最新Transformer模型架构。不同于聚焦建筑内部物体和家具的常见方法，本研究致力于解决建筑、工程与施工（AEC）中复杂结构部件的分割挑战。通过监督训练建立基线，并使用自建验证数据集评估跨域推理性能；同时采用迁移学习，在极少新增数据条件下最大化分割效果。结果表明，仅需少量微调，预训练的Transformer架构即可有效实现构件分割。该方法为创建更大规模训练资源时自动化标注新数据，以及高频重复对象的分割提供了可行路径。
原文摘要 · Abstract (English)
The significant effort required to annotate data for new training datasets hinders computer vision research and machine learning in the construction industry. This work explores adapting standard datasets and the latest transformer model architectures for point cloud semantic segmentation in the context of shell construction sites. Unlike common approaches focused on object segmentation of building interiors and furniture, this study addressed the challenges of segmenting complex structural components in Architecture, Engineering, and Construction (AEC). We establish a baseline through supervised training and a custom validation dataset, evaluate the cross-domain inference with large-scale indoor datasets, and utilize transfer learning to maximize segmentation performance with minimal new data. The findings indicate that with minimal fine-tuning, pre-trained transformer architectures offer an effective strategy for building component segmentation. Our results are promising for automating the annotation of new, previously unseen data when creating larger training resources and for the segmentation of frequently recurring objects.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。