基于压缩感知的点云数据压缩方法研究

项目名称： 基于压缩感知的点云数据压缩方法研究

项目编号： No.61300065

项目类型： 青年科学基金项目

立项/批准年度： 2014

项目学科： 自动化技术、计算机技术

项目作者： 张勇

作者单位： 北京工业大学

项目金额： 23万元

中文摘要： 点云数据具有明显的稀疏性,而基于压缩感知的信息编码方案因其具有协同采样编码、多空间稀疏表示和灵活解码等诸多优势为高效的稀疏信号的压缩开辟了一条新途径。 本项目拟从探索新的点云数据压缩编码方法入手，在信号稀疏表示理论的指导下，探索压缩感知的点云数据编码机理，研究基于压缩感知的点云数据编解码解决方案。在压缩感知编码方面，研究基于聚类的点云数据规格化方法,使其有序、可稀疏化；结合现有的观测矩阵设计方法，探索基于先验知识的规格点云数据观测方法。在压缩感知解码方面，针对规格点云数据的局部自相似性，研究点云数据的稀疏表示方法；针对规格点云数据的几何特征，建立鲁棒的点云数据重建模型。基于上述研究，构建高效、低编码复杂度和灵活鲁棒的新点云数据编解码方案。

中文关键词： 点云数据；压缩感知；稀疏表示；几何图像；

英文摘要： The geometric attributes of point-cloud data are continuously changing, which is obviously sparse. The information encoding scheme based on compressed sensing opened up a new way for efficient sparse signal compression, because of its collaborative sampling with encoding, multi-space sparse, flexible decoding and other advantages. In this project, we will explore a novel method for point-cloud coding, under the guidance of the signal sparse representation theory. We plan to find solutions for the point-cloud coding based on compressed sensing. For the coding part, we will propose a point-cloud regularity method based on clustering methods,by which the point-cloud can be ordered and sparse. Combined with the existing observation matrix design method, we will explore design methods of the observation matrix based on the prior knowledge. For the decoding part, a new point-cloud sparse representation method will be proposed, according to the local self-similarity of the regular point-cloud data. Then we will propose a new method of building robust point-cloud data reconstruction model based on compressive sensing, according to the local geometric features of point-cloud. Based on the research foundation mentioned above, we will build a new, efficient, low coding complexity, flexible and robust scheme for the point-c

英文关键词： point cloud data；compressed sensing；sparse representation；geometric images；