摘要
在已有经典KD树算法基础上,提出一种利用数组实现的、压缩式存储的KD树算法,该算法的优点为可以在保证搜索结果正确的前提下,极大减少算法运行时所需的内存空间,搜索效率与经典KD树算法基本相当。通过多个点云数据的实测,验证了该算法的正确性及效率。
Based on known KD Tree method, represent an EMS memory-saved KD Tree algorithm makes use of array and adopts compressing storage. With the proposed algorithm, the system can effectively get the right K-Nearest Neighbor algorithm for the specified position in a very large point set while greatly reducing requirements for EMS memory at runtime. Finally, validate the correctness and effectiveness of the algorithm through several point clouds tests.
出处
《兵工自动化》
2008年第7期23-25,共3页
Ordnance Industry Automation
关键词
KD树
点云
K最近邻查询
内存节省
KD tree
Point cloud
K-Nearest neighbor lookup
Space saving
