摘要
针对高精度、高保真的点云数据在精简后点云数据重构网格精度降低误差增大的问题,提出了面向点云数据的复杂几何模型对象优化方法.首先通过空间八叉树法建立点云数据和网格的拓扑关系,并利用原始点云到重构网格的距离确定网格的误差,以目标精度为阈值,然后利用增点法对面片进行划分,最后根据插入点算法重新定位插入点.实验验证表明:利用该文方法对兔子和龙进行一次细分使得精简率90%兔子重构网格误差由0.81 mm提升到0.48 mm,精简率90%龙重构网格误差由0.36 mm提升到0.11 mm.
Aiming at the problem that the accuracy of the point cloud data reconstruction grid decreases and the error increases after the point cloud data is streamlined with high precision and high fidelity,a point cloud data-oriented complex geometric model object optimization method is proposed.First,the spatial octree method was used to establish the topological relationship between the point cloud data and the grid,and use the distance from the original point cloud to the reconstructed grid to determine the grid error.The target accuracy is taken as the threshold value,and the adding-point method was used to subdivide the mesh.Finally,relocate the insertion point according to the insertion point algorithm.The results indicated that this method can make the reduction rate 90%of the rabbit reconstructed mesh error from 0.81 mm to 0.48 mm,and the reduction rate 90%of the Dragon reconstructed mesh error from 0.36 mm to 0.11 mm.
作者
曹垚
王宗敏
李健
CAO Yao;WANG Zongmin;LI Jian(College of Water Conservancy and Environment Engineering, Zhengzhou University, Zhengzhou 450001, China;Zhongyuan University of Technology, Zhengzhou 450007,China)
出处
《华中师范大学学报（自然科学版）》
CAS
CSCD
北大核心
2020年第5期770-774,共5页
Journal of Central China Normal University：Natural Sciences
基金
河南省教育厅基金项目(14A420002)。
关键词
逆向工程
点云数据
网格细分
网格优化
Reverse engineering
point cloud
mesh subdivision
mesh optimization
