面向文物稠密点云模型的压缩复原框架

Compression Restoration Framework for Dense Point Cloud Model of Cultural Relics

针对激光扫描仪获取的三维文物稠密点云模型在数据存储、远程传输与处理等环节导致的资源过度消耗问题,提出了一种基于贪婪算法的快速压缩与恢复框架。首先,将点云模型视为三维离散几何信号,用基于哈希函数的八叉树方法构建稠密点云的邻域约束关系。然后,计算点云邻接矩阵并构建离散拉普拉斯基对原信号进行稀疏表示,通过随机高斯矩阵对原信号进行随机采样,以完成信号压缩。最后,引入L_(0)正则化算子,采用四种经典的贪婪算法进行快速求解。用兵马俑头部点云模型和唐三彩胡人俑三维文物点云模型进行仿真测试,结果表明,本框架能完成对稠密点云模型的有效压缩和模型的快速重建。

The three-dimensional dense point cloud model of cultural relics obtained by laser scanners can easily lead to excessive consumption of resources in data storage,remote transmission,and processing.To solve this problem,the paper proposes a fast compression and recovery framework based on greedy algorithm.First,the point cloud model is regarded as a three-dimensional discrete geometric signal,and the octree method based on Hash function is used to construct the neighborhood constraint relationship for the dense point cloud.Then,the point cloud adjacency matrix is calculated and a discrete Laplacian is constructed to sparse the original signal,and the original signal is randomly sampled through a random Gaussian matrix to complete signal compression.Finally,the L_(0) regularization operator and four classical greedy algorithms are introduced to solve the problem quickly.The simulation test is carried out with the point cloud model of the terracotta warriors head and the three-dimensional cultural relic point cloud model of the Tang Sancai Huren figurines.The results show that this framework can complete the effective compression of the dense point cloud model and the rapid reconstruction of the model.