基于改进RandLA-Net的建筑构件点云提取方法  

Building Component Point Cloud Extraction Method Based on Improved RandLA-Net

作　　者：李昊宇 廖维张[1,2] LI Hao-yu;LIAO Wei-zhang(Beijing Higher Education Engineering Research Center for Engineering Structures and New Materials,Beijing University of Architecture,Beijing 100044,China;Beijing High Precision Innovation Center for Future Urban Design,Beijing Architecture University,Beijing 100044,China)

机构地区：[1]北京建筑大学工程结构与新材料北京市高等学校工程研究中心,北京100044 [2]北京建筑大学北京未来城市设计高精尖创新中心,北京100044

出　　处：《科学技术与工程》2025年第6期2461-2468,共8页Science Technology and Engineering

基　　金：国家自然科学基金重点项目(52130809);北京建筑大学校级教研重点项目(Y2106)。

摘　　要：点云数据在建筑逆向建模、三维重建乃至施工进程等方面均有巨大优势。采集建筑结构点云时通常包含海量点云,并且如梁、柱等构件的点云数据至关重要。现有的三维点云语义分割方法对大规模点云进行处理时存在局部特征提取不充分、识别精度有待提升等问题,提出了一种改进RandLA-Net深度学习网络的建筑关键构件的大规模点云语义分割方法,通过在局部空间编码部分增加坐标注意力模块提高分割结果的鲁棒性;构建了通道注意力模块增强模型的特征判断能力;引入了焦点损失函数训练网络,有效解决了建筑点云场景内类别不平衡的问题,实现了对建筑结构点云数据的快速处理和对建筑关键构件的有效提取。最后通过实验进行性能对比分析。试验结果表明,改进模型对大规模点云进行语义分割相较于传统RandLA-Net模型在整体准确率和局部构件提取准确率上均有提升,证实了本文方法具有更强的性能和应用价值。The significant advantages of point cloud data are presented in domains such as architectural reverse modeling,3D reconstruction,and construction progress monitoring.Vast amounts of data are typically involved in the collection of point clouds for architectural structures,with the point clouds of components like beams and columns being particularly crucial.The challenges faced by current semantic segmentation methods for 3D point clouds when processing large-scale data include insufficient extraction of local features and suboptimal recognition accuracy.An enhanced approach for the semantic segmentation of large-scale point clouds of key architectural components using the RandLA-Net deep learning network was proposed.In this regard,the robustness of segmentation results was improved by incorporating a coordinate attention module in the local spatial encoding section.Furthermore,an extended channel attention module has been developed to strengthen the model s capability in feature discernment,and a focal loss function has been introduced to effectively train the network,while addressing class imbalance issues within architectural point cloud scenes.Consequently,the efficient processing of architectural structure point cloud data and the extraction of key components are enabled.The performance comparisons and analyses conducted through experiments demonstrate that the original RandLA-Net model is outperformed by our model in terms of overall accuracy and component extraction precision in semantic segmentation of large-scale point clouds,thereby confirming the enhanced performance and practical value of the proposed method.

关 键 词：三维点云 建筑工程点云 深度学习 点云语义分割 注意力机制 

分 类 号：TU17[建筑科学—建筑理论]