摘要
提出一种基于散乱点云的邻域采样点数目加权的聚类简化算法,此算法以曲面变化度和聚类中采样点的数目加权共同进行阈值控制,能够在简化过程中更偏向于将包含采样点数比较多且有一定曲率的聚类进行划分,得到更合理的简化效果。
This paper proposes a clustering simplification algorithm,which has a weight of neighborhood point samples. This algorithm treats the surface variation and the quantity of point samples in the neighborhood as a threshold. So the user can control the threshold to be apt to split the clusters which have more point samples and a certain curvature,and get a more suitable simplification.
出处
《计算机与现代化》
2010年第6期3-5,共3页
Computer and Modernization
基金
湖南师范大学自然科学青年基金资助项目(60908)
关键词
散乱点云
曲面变化度
聚类
邻域
unorganized point cloud
surface variation
clustering
neighborhood
