散乱点云的快速增量网格重建算法  

Incremental Mesh Reconstruction from Unorganized Points

作　　者：高向敏[1] 庞明勇[1] 

机构地区：[1]南京师范大学,南京210097

出　　处：《小型微型计算机系统》2011年第10期2096-2100,共5页Journal of Chinese Computer Systems

基　　金：国家自然科学基金项目(60873175)资助;安徽省高校省级自然科学研究项目(KJ2010B423)资助;江苏省高校自然科学基础研究项目(07KJD460108)资助;南京师范大学高层次人才科研启动基金项目资助

摘　　要：散乱数据的网格重建是数字几何处理的基础性技术之一.本文提出一种快速增量式散乱点云网格重建算法,运用波前(Wave Front)方法渐进地由点云数据生成物体表面的网格模型.该算法以一个"种子"三角形初始化搜索队列,以逐渐生成的新边为搜索元素,借助Kd-树空间划分技术和搜索约束条件,快速完成优化点的评估及三角面片重建,可在保证网格质量的同时,过滤部分对重建效果意义不大的点.实验表明,该算法能够高效、可靠地生成具有不同几何复杂度的原始曲面二维流形三角网格逼近,适用于海量数据点的网格重建.Mesh reconstruction of unorganized points is one of the basic technologies in digital geometry processing.In this paper,we present a fast incremental algorithm for mesh reconstruction of unoranized points.Recurring to Kd-Tree space decomposition,searching constraint and optimum vertex estimation,our algorithm uses an initialized triangle as searching seed and gradually generated-border edges as searching elements to gradually reconstruct model surface from point clouds.The algorihm also can adaptively filter some points,which are redundant to the reconstruction,according to a user-specfied threshold.The algorithm performs well for the scattered point clouds with various density.The experimental results show that the algorithm is efficient and can work well for models with arbitrary geometric complexity.

关 键 词：散乱点云 增量方法 网格重建 三角剖分 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]