? 基于梯度域的点云数据几何处理框架
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Journal of Computer Science and Technology 2018, Vol. 33 Issue (4) :863-872    DOI: 10.1007/s11390-018-1861-0
Special Issue on Software Engineering for High-Confidence Systems << Previous Articles | >>
基于梯度域的点云数据几何处理框架
Hong-Xing Qin, Member, CCF, Jin-Long He, Meng-Hui Wang, Yu Dai, Zhi-Yong Ran*
College of Computer Science and Technology, Chongqing University of Posts and Telecommunications Chongqing 400065, China
A Gradient-Domain Based Geometry Processing Framework for Point Clouds
Hong-Xing Qin, Member, CCF, Jin-Long He, Meng-Hui Wang, Yu Dai, Zhi-Yong Ran*
College of Computer Science and Technology, Chongqing University of Posts and Telecommunications Chongqing 400065, China

摘要
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摘要 随着点云数据的使用越来越广泛,我们提出了应用无网格局部Petrol-Galelkin方法求解Screened泊松方程的点云数据几何滤波框架。曲面的光滑或增强通过一个梯度因子参数控制。各项异性滤波通过自适应的黎曼度量支持。与其它点云曲面处理的偏微分方程方法相比,我们的方法既不需要建立局部或全局网格,也不需要全局参数化。它仅仅依赖于局部切空间和定义其上的局部插值曲面。实验结果表明了我们方法的有效性。
关键词点云数据处理   偏微分方程   无网格方法   梯度域     
Abstract: The use of point clouds is becoming increasingly popular. We present a general framework for performing geometry filtering on point-based surface through applying the meshless local Petrol-Galelkin (MLPG) to obtain the solution of a screened Poisson equation. The enhancement or smoothing of surfaces is controlled by a gradient scale parameter. Anisotropic filtering is supported by the adapted Riemannian metric. Contrary to the other approaches of partial differential equation for point-based surface, the proposed approach neither needs to construct local or global triangular meshes, nor needs global parameterization. It is only based on the local tangent space and local interpolated surfaces. Experiments demonstrate the efficiency of our approach.
Keywordspoint clouds processing   partial differential equation   meshless method   gradient-domain     
Received 2017-07-16;
本文基金:

This work is partly supported by the National Natural Science Foundation of China under Grant Nos. 61772097 and U1401252, and Scientific and Technological Research Program of Chongqing Municipal Education Commission of China under Grant No. KJ1400429.

通讯作者: Zhi-Yong Ran,E-mail:ranzy@cqupt.edu.cn     Email: ranzy@cqupt.edu.cn
About author: Hong-Xing Qin is a professor at Chongqing University of Posts and Telecommunications, Chongqing. He received his Ph.D. degree in pattern recognition from Shanghai Jiao Tong University, Shanghai, in 2008. He worked as a postdoctoral researcher at Rutgers, the State University of New Jersey, from 2008 to 2009. His research interests include computer graphics, digital geometry processing, medical image processing, and visualization.
引用本文:   
Hong-Xing Qin, Jin-Long He, Meng-Hui Wang, Yu Dai, Zhi-Yong Ran.基于梯度域的点云数据几何处理框架[J]  Journal of Computer Science and Technology , 2018,V33(4): 863-872
Hong-Xing Qin, Jin-Long He, Meng-Hui Wang, Yu Dai, Zhi-Yong Ran.A Gradient-Domain Based Geometry Processing Framework for Point Clouds[J]  Journal of Computer Science and Technology, 2018,V33(4): 863-872
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