摘要
三维点云室内平面要素的准确分割是室内模型自动化重建的基础。针对现有区域生长算法分割室内平面时边界区域点云易被错误分割的问题,提出一种边界特征约束平面分割方法。该方法首先利用欧氏聚类方法融合RGB信息对室内桌椅等不参与平面分割的部件进行聚类提取,然后对剩余点云进行平面分割,首先利用区域生长算法分割平面内部点,然后对种子点的生长过程进行监测,对处于边界区域的种子点精确识别其邻域内的边界点,以边界点为生长约束界限对平面点进行分割。采用两组不同场景的点云数据进行试验分析,试验结果表明,该分割算法能够准确地对边界区域点云进行归类,避免了点云过分割和欠分割的问题,平面分割的准确率和完整度相比于区域生长算法分别提升了3%和4%左右。本算法能够有效提升室内场景平面分割精度。
Accurate segmentation of 3D point cloud indoor plane elements is the basis of automatic reconstruction of indoor models.Aiming at the problem that the point cloud of boundary region is easily segmented incorrectly when the existing region growth algorithm is segmented indoor plane,a plane segmentation method with boundary feature constraint is proposed.This method first uses Euclidean clustering method to fuse RGB information to cluster the components that do not participate in plane segmentation,such as indoor tables and chairs,and then performs plane segmentation on the remaining point cloud.First,the region growth algorithm is used to segment the internal points in the plane,and then the growth process of seed points is monitored to accurately identify the boundary points in the neighborhood of seed points in the boundary region.The plane points are segmented by boundary points as growth constraint limits.Two sets of point cloud data in different scenes were used for experimental analysis.The test results show that the segmentation algorithm can accurately classify the point cloud in the boundary region,avoiding the problem of over-segmentation and under-segmentation of the point cloud.The accuracy and integrity of plane segmentation are improved by about 3%and 4%respectively compared with the region growth algorithm.The proposed algorithm can effectively improve the segmentation accuracy of indoor scenes.
作者
罗启雄
张春亢
罗俊
LUO Qixiong;ZHANG Chunkang;LUO Jun(Mining institute,Guizhou University,Guiyang 550025,China)
出处
《激光杂志》
CAS
北大核心
2024年第11期106-112,共7页
Laser Journal
基金
国家自然科学基金(No.41701464)
中国科学院战略性先导科技专项子课题(No.XDA2806020101)
贵州大学培养项目(No.贵大培育[2019]26号)。
关键词
室内点云场景
部件提取
平面分割
特征约束
区域生长
indoor point cloud scenario
component extraction
plane segmentation
feature constraint
regional growth
