摘要
三维激光扫描点云为文物模型重建提供了新的数据支持,但扫描所得海量点云包含大量冗余数据,给建模带来很大不便。针对扫描点云过密、冗余数据较多的问题,提出了一种基于自适应分层的文物点云数据压缩算法。算法的基本思想是:首先通过基于倒角距离变换的自适应分层方法对原始点云进行自适应分层;然后使用弦高差值作为特征点的判别依据来删除冗余数据,采用改进的弦高差法对每层点云进行压缩,保留对模型特征贡献较大的特征点。实验结果表明,通过形状误差控制分层厚度,能在平缓部位减少层数以提高效率的同时不至于使复杂部位因分层过厚而损失重要特征,改进的弦高差法在保留大曲率特征的同时不至于使平缓部位出现孔洞,从而保证了模型重建的精度。
Point clouds with 3D laser scanning facilitate the reconstruction of cultural relics models with whole new data,which comes down with due redundancy and thus can make a great hindrance to the modelling.According to the problems such as dense scanning,lengthy data and adaptive slicing,this paper proposed a compression algorithm for cultural relics point clouds.The algorithm’s rationale went as follows:first underwent the chamfer distance transformation-based adaptive slicing process for a given point cloud.Then it removed redundant data,abiding by the chordal deviation value as the discrimination basis,and reserved the feature points that contributed substantially to the modelling by using improved chordal deviation method to compress the point cloud of each layer whereby applied.The experiment shows that by controlling layer thickness via shape errors,which can reduce and improve the layers to slice and efficiency in the smooth parts without defeaturing the complex parts despite the yet thinned thickness.Or,while the improved chordal deviation method retains the large curvature characteristics,the smooth parts will truly ensure the reconstruction accuracy of the modelling without apertures.
作者
裴书玉
杜宁
王莉
张春亢
Pei Shuyu;Du Ning;Wang Li;Zhang Chunkang(College of Mining,Guizhou University,Guiyang 550025,China)
出处
《计算机应用研究》
CSCD
北大核心
2018年第11期3500-3503,3507,共5页
Application Research of Computers
基金
贵州省科技计划资助项目(黔科合基础[2017]1026)
贵州大学研究生创新基金资助项目(研理工2017085)
关键词
自适应分层
倒角距离变换
改进的弦高差法
点云数据压缩
adaptive slice
chamfer distance transformation
improved chordal deviation method
point cloud data compression