一种大规模散乱数据自适应压缩与曲面重建方法  

A New Approach of Adaptive Compression and Mesh Generation for Large Scale Scattered Data

作　　者：王晓明[1] 刘吉晓[1] 

机构地区：[1]山东交通学院数理系,山东济南250023

出　　处：《工程图学学报》2010年第2期92-96,共5页Journal of Engineering Graphics

摘　　要：针对大规模散乱数据点云,提出了一种基于曲率与距离的三角网格抽样方法。算法既能保证所生成网格曲面中每个三角片具有较好的形状,又能较鲜明地刻画曲面的细节特征。同时还能将原先规模较大的点云压缩到事先可控的数量上,是一种简单高效的自适应压缩和曲面生成方法。A triangular mesh sampling method based on the curvature and distance is proposed for the large scale scattered data.By this approach,the large scale scattered data can be compressed to a reasonable expected scale,and the triangular mesh generated by the compressed data can clearly describe the details of surface features.Every triangle patch of the generated mesh also has good shape.The experiments show that the method is efficient and easy to apply.

关 键 词：计算机应用 曲面重建 数据压缩 散乱数据 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]