虚拟旅游中海量3D点云数据的细节层次索引技术研究

虚拟旅游中的3D点云数据特别庞大,批量索引成为了当今的研究热点。许多索引树存在兄弟结点空间区域重叠、不能实现细节层次索引、索引效率低等问题。为此,将点数据反射强度和细节层次技术引入R树,在改进R树的基础上提出LODR树。建树前,将点云数据进行排序、分组、去除空间重叠等预处理。树的每层设有不同反射强度阈值,把叶结点中满足阈值条件的索引记录沿父-祖父-曾祖父的家谱关系上移,并插入对应的非叶结点,利用该方法创建细节层次索引树。利用反射强度控制数据冗余,棱锥裁剪技术实现查询优化。实验结果表明,LODR树在细节层次索引、查询效率等方面具有明显优势。

Research on Detail Level Index Technology of Massive 3D Point Cloud Data in Virtual Tourism

D point cloud data in virtual tourism are particularly huge and the batch index has become a research hotspot.There are some problems in many index trees,such as spatial overlap of sibling node,not achieving level of detail index and low indexing efficiency.Therefore,point data reflection intensity and level of detail technology were introduced into R-tree,and LODR-tree was presented based on improved R-tree.Before establishing this tree,point cloud data needs to be pre-processed,such as sorting,grouping,removing spatial overlap and so on.The index records which meet the thre-shold conditions in the leaf nodes are inserted into the homologous non-leaf nodes along the parent-grandfather-great grandfather family relationship,and LOD index tree is created by this method.Data redundancy is controlled by reflected intensity,and query optimization is achieved by pyramid cutting technology.Finally,experiments show that LODR-tree has obvious advantages in LOD index and query efficiency.