基于空洞检测及规则约束的立面点云门窗检测方法  

Detection method of facade door and window from point clouds based on holes and regular constraints

作　　者：莫玉晓 徐景中[1,2] MO Yuxiao;XU Jingzhong(School of Remote Sensing and Information Engineering,Wuhan University,Wuhan 430079,China;Hubei Luojia Laboratory,Wuhan 430079,China)

机构地区：[1]武汉大学遥感信息工程学院,武汉430079 [2]湖北珞珈实验室,武汉430079

出　　处：《激光杂志》2025年第1期61-67,共7页Laser Journal

基　　金：国家自然科学基金(No.41671450);珞珈实验室研究团队基金(No.230700010)。

摘　　要：针对建筑物立面门窗检测的难点,提出了一种基于空洞检测及规则约束的建筑物立面点云门窗检测方法。该方法在布料模拟滤波的基础上,通过随机抽样一致性算法进行平面分割,以提取立面点云并进行坐标变换;然后通过点云取反操作获得包含门窗信息的虚拟点云,并采用基于点云的数学形态学方法获取门窗点簇;最后根据已获取的门窗构建规则,并通过规则恢复遮挡导致的门窗缺失问题,得到最终的立面门窗检测结果。通过多组建筑物点云门窗检测实验,能有效检测出建筑物立面门窗,并克服遮挡对门窗检测的影响,检测结果的精确率、召回率、F1值分别达到93%、97%、95%以上,表明了提出的算法具有良好的性能。Addressing the challenges in facade door and window detection,a point cloud-based method utilizing void detection and rule constraints has been proposed.This method involves cloth simulation filtering followed by plane segmentation using the Random Sample Consensus(RANSAC)algorithm to extract facade point clouds and perform coordinate transformations.A virtual point cloud containing door and window information is then generated through point cloud inversion,and door and window clusters are identified using morphological methods based on point clouds.Finally,rules are applied to restore doors and windows missing due to occlusions,yielding the final detection results.Experiments on multiple building point clouds demonstrate that the proposed method effectively detects doors and windows on building facades,overcoming the impacts of occlusions.The detection results achieved precision,recall,and F1 scores of over 93%,97%,and 95%respectively,indicating the excellent performance of the proposed algorithm.

关 键 词：门窗检测 随机抽样一致性算法 空洞分析 数学形态学 

分 类 号：TN911[电子电信—通信与信息系统]