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基于改进曲线收缩流方法的点云张开孔洞的虚拟修补 被引量:6

Opening-Hole Repairingin Point Cloud Based on Improved Curve Contraction Flows
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摘要 针对现有方法修补点云张开孔洞效果不佳的问题,提出了一种基于改进的曲线收缩流虚拟修补点云张开孔洞的方法。在计算出孔洞边界点的表面法向量和切向量的基础上,根据特征线补全边界缺失部分,组成全部边界;然后得到各边界点切向量垂直平面与边界的交叉点,并与对应边界点构成交叉向量;利用内法向量和交叉向量的加权和计算边界点生长方向,根据原始点云的密度确定生长步长,从而进行修补;最后,利用拉普拉斯平滑算法光顺网格,使网格均匀化,得到最终修补效果。实验结果表明,与基曲面网格添加法、波前法和几何特征修补法对比,基于改进的曲线收缩流方法在高斯曲率分布相似性方面提高26.8%。 A novel method of opening-hole virtual repairing in point cloud is proposed based on modified curve contraction flow, which aims to improve the effect of existing means. Firstly, on the basis of calculating the surface normal vector and tangent vector of the hole boundary point, the whole boundary is formed according to the feature line. Secondly, the cross-point of each boundary point is obtained by the intersection of the vertical plane of the tangent vector and the boundary, and a cross-vector is formed with the corresponding boundary point. The growth direction of the boundary point is calculated by using the weighted sum of the inward vector and the cross-vector, and the growth step is determined according to the density of the original point cloud so as to be repaired. Finally, the Laplace smoothing algorithm is utilized to smooth and uniform mesh for obtaining final repairing result. The experimental results show that the similarity of Gaussian curvature distribution of our approach improves by 26.8%, compared with the base surface mesh addition method, wave front method and geometric feature repaired method.
作者 张琦 蔺素珍 白佳璐 钟家让 Zhang Qi;Lin Suzhen;Bai Jialu;Zhong Jiarang(School of Computer and Control Engineering,North University of China,Taiyuan,Shanxi 030051,China;Center for Cultural Relic Protection,Shanxi Museum,Taiyuan,Shanxi 030024,China)
出处 《激光与光电子学进展》 CSCD 北大核心 2018年第9期119-125,共7页 Laser & Optoelectronics Progress
基金 山西省重点研发计划(指南)资助项目(201603D321128) 山西省应用基础研究项目(201701D121062)
关键词 图像处理 修补点云张开孔洞 改进曲线收缩流 拉普拉斯平滑 高斯曲率分布 image processing repair the point cloud opening-holes improved curve contraction flow Laplace smoothing Gaussian curvature distribution
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