摘要
本文首先简要分析了现有点云简化算法的优缺点,接着设计了一种基于kd_tree数据索引与曲率采样结合的高效简化策略,充分利用曲率采样的精度优势与kd_tree索引的速度优势,实现了基于kd_tree索引的曲率自适应点云简化算法。试验表明,该算法在减少点云数据量的同时,能够较好地保证模型中的特征点,在速度与效果上都达到了较为理想的结果。
First,the article analyzed the advantages and disadvantages of the current point cloud simplification algorithm,and then designed an efficient simplification strategy based on the index of kdtree and curvature sampling.The strategy took full advantage of the accuracy of curvature sampling and the speed advantage of kdtree,realizing the algorithm based on the kdtree Index and curvature adaptive.The experiments showed that the algorithm could reduce the point cloud data effectively,ensure the model feature points,and the speed and effectiveness achieve satisfactoryed result.
出处
《测绘科学》
CSCD
北大核心
2010年第6期67-69,共3页
Science of Surveying and Mapping
基金
国家高技术研究发展计划(863计划)重点项目(2008AA121301)
关键词
点云简化
kdtree
曲率采样
point cloud simplification
kdtree
curvature sampling