压缩感知点云数据压缩

doi:10.3969/j.issn.0255-8297.2014.05.004

应用科学学报 ›› 2014, Vol. 32 ›› Issue (5): 458-462.doi: 10.3969/j.issn.0255-8297.2014.05.004

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压缩感知点云数据压缩

张习民1,2,3, 余小清1,2，万旺根1,2，张娟1,2

1. 上海大学通信与信息工程学院，上海200444
2. 上海大学智慧城市研究院，上海200444
3. 河南教育学院物理与电子工程学院，郑州450046

收稿日期:2014-02-28 修回日期:2014-07-10 出版日期:2014-09-23 发布日期:2014-07-10
作者简介:张习民，博士生，研究方向：计算机图形学，E-mail：zxmzzha@163.com；万旺根，教授，博导，研究方向：计算机图形学与虚拟现实技术，E-mail：wanwg@staff.shu.edu.cn
基金资助:
国家自然科学基金（No.61373084）；上海市教委基金（No.14YZ011）；河南省科技攻关项目基金(No.132102210548)资助

Point Cloud Compression Based on Compressed Sensing

ZHANG Xi-min1,2,3, YU Xiao-qing1,2, WAN Wang-gen1,2, ZHANG Juan1,2

1. School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China
2. Institute of Smart City, Shanghai University, Shanghai 200444, China
3. Institute of Physics and Electronic Engineering, Henan Institute of Education, Zhengzhou 450046, China

Received:2014-02-28 Revised:2014-07-10 Online:2014-09-23 Published:2014-07-10

摘要/Abstract

摘要： 在研究FARO 激光扫描点云数据特点的基础上，将压缩感知理论和算法应用于3D 点云数据的压缩. 通过对点云数据降维处理，直接对坐标数据进行采样压缩，应用正交匹配追踪算法进行恢复，并对恢复数据采用统计滤波的方法滤除边缘噪点. 仿真结果表明：该方法能有效地实现点云数据的压缩，且有较好的鲁棒性.

关键词: 点云, 压缩感知, 坐标压缩, 统计滤波

Abstract: This paper applies an algorithm for compression of 3D point cloud based on a study of scanning theory of the FARO laser scanner. The raw point cloud is processed via dimension reduction. The coordinate data are sampled and compressed, and then recovered using the orthogonal matching pursuit (OMP)algorithm.The recovered data are then processed with a statistic filter to remove edge point noise. Simulation results show that the presented method can compress point clouds with good robustness.

Key words: point cloud, compressed sensing, coordinate compression, statistic filter

中图分类号:

TN919.81

张习民1,2,3, 余小清1,2，万旺根1,2，张娟1,2. 压缩感知点云数据压缩[J]. 应用科学学报, 2014, 32(5): 458-462.

ZHANG Xi-min1,2,3, YU Xiao-qing1,2, WAN Wang-gen1,2, ZHANG Juan1,2. Point Cloud Compression Based on Compressed Sensing[J]. Journal of Applied Sciences, 2014, 32(5): 458-462.

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压缩感知点云数据压缩

Point Cloud Compression Based on Compressed Sensing

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