一种基于SfM重建点云的三角网格化算法  

Triangulation algorithm based on point clouds reconstructed by SfM

作　　者：陈庭旺[1] 王庆[1] 

机构地区：[1]西北工业大学计算机学院,西安710129

出　　处：《计算机应用研究》2011年第2期794-796,800,共4页Application Research of Computers

基　　金：国家"863"计划资助项目(2007AA01Z314);国家自然科学基金资助项目(60873085);新世纪优秀人才支持计划资助项目(NCET-06-0882)

摘　　要：针对SfM重建点云的曲面建模问题,提出一种改进的区域增长网格化算法。定义k近邻影响域提高拓扑稳定性,引入二叉排序树高效地组织候选三角片,采用无向环搜索策略完成孔洞的检测,最终获得完整的三角网格面。实验结果表明,该算法相比于Possion曲面重建,在获得高的重建精度的同时显著提高了计算效率,有助于提升3D曲面重建与模型表现的性能。This paper proposed an improved region growing based triangulation algorithm for surface modeling problem from point clouds reconstructed by SfM.Defined a k-nearest neighbor influence region to improve the topological stability.It orga-nized candidate triangles efficiently by binary sort tree and accomplished holes detection by a searching strategy using undirected loop.Finally,achieved a complete triangular mesh.Experimental results show that,compared to Possion surface reconstruction,the algorithm can significantly improve the computational efficiency and acquire a high reconstructed accuracy,which helps to improve the performance of 3D surface reconstruction and model rendering.

关 键 词：三角网格化 区域增长 K近邻 影响域 二叉排序树 无向环搜索 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]