摘要
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.
针对建筑信息领域传统测量方法的局限性,如操作复杂、时效性低及精度欠佳等问题,本研究探讨了三维扫描技术与BIM(Building Information Modeling)模型结合的新途径。本研究聚焦于利用高速发展的三维点云技术高效采集建筑几何信息,并提出一种改进的基于深度学习的三维点云识别方法。该方法基于RandLA-Net,优化网络结构以适应大规模点云处理需求,同时融合点云的语义与实例特征,显著提升识别精度,为BIM模型重塑提供精确基础。此外,本研究还开发了一套可视化BIM模型生成系统,系统化地将点云识别结果转化为BIM构件参数,自动构建BIM模型,促进了模型的开放共享与二次开发。研究成果不仅有效推进了三维点云数据向精细化BIM模型转换的自动化进程,还为促进建筑信息化、加速智慧城市构建提供了重要技术支持,展现出广泛的应用潜力和实践价值。
出处
《印刷与数字媒体技术研究》
CAS
北大核心
2024年第4期125-135,共11页
Printing and Digital Media Technology Study
