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A Gradient-Domain Based Geometry Processing Framework for Point Clouds

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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.

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Authors and Affiliations

  1. College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Hong-Xing Qin, Jin-Long He, Meng-Hui Wang, Yu Dai & Zhi-Yong Ran

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  1. Hong-Xing Qin
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  2. Jin-Long He
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  3. Meng-Hui Wang
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  4. Yu Dai
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  5. Zhi-Yong Ran
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Correspondence to Zhi-Yong Ran.

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Qin, HX., He, JL., Wang, MH. et al. A Gradient-Domain Based Geometry Processing Framework for Point Clouds. J. Comput. Sci. Technol. 33, 863–872 (2018). https://doi.org/10.1007/s11390-018-1861-0

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  • DOI: https://doi.org/10.1007/s11390-018-1861-0

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