摘要
Abstract
Due to the unique geometric shapes and complex surface structures of irregular buildings,surface recognition during reconstruction often leads to deviations,which can affect the accuracy of the reconstructed model.To address this issue,this paper proposed a method for the surface reconstruction of irregular buildings based on an improved iterative closest point(ICP)algorithm and an atrous spatial pyramid pooling(ASPP)algorithm.The improved ICP algorithm performed point cloud registration,aligning point cloud data collected from different locations into a common coordinate system,eliminating positional deviations,and enhancing registration accuracy.The Cascaded ASPP algorithm was used to design a lightweight semantic segmentation model that aggregates multi-scale information,ensuring comprehensive coverage of the visible area.The random sample consensus(RANSAC)algorithm was utilized to automatically process the recognition data of irregular buildings.Combined with the least squares method,it was used for the three-dimensional(3D)fitting of wall surfaces to obtain high-precision surface reconstruction parameters,ultimately achieving the surface reconstruction of irregular buildings.Test results show that the reconstruction closely matches the true geometric form,with reconstruction deviations on various walls of the irregular building surface remaining below 1.5.关键词
点云配准/迭代最近点(ICP)算法/级联检测分割(ASPP)算法/随机抽样一致性(RANSAC)算法/异形建筑表面Key words
point cloud registration/iterative closest point(ICP)algorithm/atrous spatial pyramid pooling(ASPP)algorithm/random sample consensus(RANSAC)algorithm/irregular building surface分类
测绘与仪器
