In view of the limitations of traditional measurement methods in the field of building information,such as complex operation,low timeliness and poor accuracy,a new way of combining three-dimensional scanning technolog...In view of the limitations of traditional measurement methods in the field of building information,such as complex operation,low timeliness and poor accuracy,a new way of combining three-dimensional scanning technology and BIM(Building Information Modeling)model was discussed.Focused on the efficient acquisition of building geometric information using the fast-developing 3D point cloud technology,an improved deep learning-based 3D point cloud recognition method was proposed.The method optimised the network structure based on RandLA-Net to adapt to the large-scale point cloud processing requirements,while the semantic and instance features of the point cloud were integrated to significantly improve the recognition accuracy and provide a precise basis for BIM model remodeling.In addition,a visual BIM model generation system was developed,which systematically transformed the point cloud recognition results into BIM component parameters,automatically constructed BIM models,and promoted the open sharing and secondary development of models.The research results not only effectively promote the automation process of converting 3D point cloud data to refined BIM models,but also provide important technical support for promoting building informatisation and accelerating the construction of smart cities,showing a wide range of application potential and practical value.展开更多
文摘针对巷道复杂场景中点云几何结构复杂的现象,以及现有基于深度学习的三维点云语义分割方法未能充分提取和利用点云局部信息、忽略全局上下文信息等问题,提出一种基于改进RandLA-Net的巷道点云语义分割算法MFAA-RandLA-Net(Multi-Scale Fusion And Attention RandLA-Net)。算法采用多尺度局部特征聚合解决局部特征提取时邻域尺度固定单一的问题,丰富模型获取的局部信息,提升模型提取复杂几何结构的能力。设计一种局部全局点注意力模块,有效捕获全局上下文,结合局部和全局特征信息,实现对特征的精细控制。在地下煤矿巷道数据集上的实验结果表明,MFAA-RandLA-Net模型总体精度为88.72%,平均交并比为65.84%,相比原RandLA-Net模型分别提高了0.6%和0.4%,实验验证了该模型在巷道复杂场景下有良好的三维点云语义分割性能。
文摘In view of the limitations of traditional measurement methods in the field of building information,such as complex operation,low timeliness and poor accuracy,a new way of combining three-dimensional scanning technology and BIM(Building Information Modeling)model was discussed.Focused on the efficient acquisition of building geometric information using the fast-developing 3D point cloud technology,an improved deep learning-based 3D point cloud recognition method was proposed.The method optimised the network structure based on RandLA-Net to adapt to the large-scale point cloud processing requirements,while the semantic and instance features of the point cloud were integrated to significantly improve the recognition accuracy and provide a precise basis for BIM model remodeling.In addition,a visual BIM model generation system was developed,which systematically transformed the point cloud recognition results into BIM component parameters,automatically constructed BIM models,and promoted the open sharing and secondary development of models.The research results not only effectively promote the automation process of converting 3D point cloud data to refined BIM models,but also provide important technical support for promoting building informatisation and accelerating the construction of smart cities,showing a wide range of application potential and practical value.
