基于局部和全局采样点云数据简化算法研究  

Study on local and global point cloud data simplification algorithm

作　　者：付玮[1,2] 吴禄慎[1] 陈华伟[1] 

机构地区：[1]南昌大学机电工程学院,江西南昌330031 [2]南昌航空大学航空制造工程学院,江西南昌330069

出　　处：《激光与红外》2015年第8期1004-1008,共5页Laser & Infrared

基　　金：国家自然基金项目(No.51065021);国家自然基金项目(No.51365037)资助

摘　　要：3D激光扫描方法获取的点云数据存在大量冗余数据,为便于重建模型,对点云数据简化技术的关键是在简化数据的同时,最大限度地保留点云数据的原有特征,对点云数据简化技术进行了研究,提出了一种基于局部和全局点云特征相融合的简化算法,通过基于点云的网格分割的非均匀网格法来提取局部点云特征,并且通过基于空间体素化方法对点云进行全局采样,然后将二者特征融合,获取最佳的简化特征效果。实验表明,该算法能够适应各种类型曲面数据的简化要求,其点云简化最大误差为0.02812,点云简化平均误差为0.000472,并与非均匀网格算法和空间体素法做比较,其简化效率高,简化误差小。由此可见,该方法简化点云不但具有较高的简化效率,同时又很好地保留了原始数据的细节特征。There are a large number of redundant data in the point cloud acquired by 3D laser scanning method,thus, the point cloud data are needed to be simplified,while the original characteristics of point cloud data are utmostly re-served.A simplified algorithm based on local point cloud and global point cloud is proposed.The algorithm adopts non-uniform grid method based on point cloud segmentation to extract the local point cloud characteristics and adopts space voxel method to sample global point cloud.Then the two features are fused to obtain the optimal simplified characteris-tics.Experiment results show that this algorithm can meet the simplification requirements for various types of surface data.The maximum error of the point cloud simplification is 0.02812,and the average error of point cloud simplifica-tion is 0.000472.Comparing with non-uniform grid algorithm and space voxel method,this algorithm has high simplifi-cation efficiency and reserves the detail characteristics of the original data.

关 键 词：点云数据简化 体素化编码 局部点云采样 全局点云采样 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]