摘要
针对三维点云数据简化过程中边界特征容易丢失问题,研究了一种点云边界特征提取方法。首先,对点的k近邻进行查找,并进行点的球拟合计算,得到拟合球的半径、点的曲率、点到球心距离。其次,通过数据点周围点的分布均匀性、自适应调节参数公式中的阈值,可以达到检测边界特征的目的。由不同模型的实验数据表明,该算法提取的边界满足了后期数据简化所需。
Aimed at preventing boundary feature loss more likely to occur in the process of 3D data reduction,this paper highlights a boundary feature extraction method of point cloud.The method involves firstly seeking the k close neighbors of the sampling point,and obtaining the ball fitting radius,the point curvature,and the distance between the point and the center of the sphere using the ball-fitting method,while getting,and then achieving the boundary feature detection,depending on the distributed uniformity of the closest neighbors of point and self-adaptive adjustment of the threshold value of the parameter equation.The experimental data from the different models verify the ability of the algorithm to give the boundary adequate for the follow-up data reduction.
出处
《黑龙江科技学院学报》
CAS
2013年第5期469-471,490,共4页
Journal of Heilongjiang Institute of Science and Technology
基金
国家自然科学基金项目(51075128)
国家科技重大专项项目(2010ZX04016-012)
博士后研究人员落户黑龙江科研启动资助金项目(LBH-Q12019)
关键词
逆向工程
点云数据
曲率
边界提取
reverse engineering
point cloud
curvature
boundary extraction
