激光技术2024，Vol.48Issue(5)：628-636,9.DOI:10.7510/jgjs.issn.1001-3806.2024.05.003
基于深度学习的机载点云屋顶平面提取算法
An airborne point cloud roof plane extraction algorithm based on deep learning
摘要
Abstract
In order to accurately extract the individual planes from various types of building roof point clouds,metric learning was used to learn separate high-dimensional depth features for the points on each plane,and each plane was considered as a separate instance.Then the extracted high-dimensional depth features were used to perform preliminary clustering of the plane points.The unclustered points were assigned to each plane by a combined metric of simple Euclidean distance and feature space distance.The proposed method was trained and tested on a synthetic dataset and the publicly available airborne point cloud building roof dataset RoofN3D,respectively.The results show that on the synthetic dataset,the accuracy,recall,and F1 scores of the extracted building planes are 0.990,0.998,and 0.994,respectively.On the airborne point cloud dataset RoofN3D,the accuracy,recall,and F1 scores of the extracted building planes are 0.945,0.971,and 0.957,respectively.The proposed method not only can accurately and effectively extract different building roof planes,but also the extracted plane edges are very accurate.In addition,the method can also accurately distinguish between the planar and non-planar contents of building roofs,which provides important help for further 3-D modeling of buildings.关键词
激光技术/激光雷达点云/平面分割/深度学习/建筑物屋顶Key words
laser technique/light detection and ranging point cloud/planar extraction/deep learning/building roofs分类
计算机与自动化引用本文复制引用
李婕,李青清,李礼,刘钊,沈阳,涂静敏..基于深度学习的机载点云屋顶平面提取算法[J].激光技术,2024,48(5):628-636,9.基金项目
国家自然科学基金资助项目(42101440) （42101440）
湖北工业大学博士科研启动基金资助项目(XJ2021004501) （XJ2021004501）
湖北省教育厅科学研究计划资助项目(Q20320413) （Q20320413）
