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.
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.
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.
Cite this article:
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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