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Feature-preserving mesh denoising based on guided normal filtering

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Abstract

In order to robustly perform tasks based on 3D data model, we propose a feature-preserving mesh denoising algorithm based on the face classification. In the proposed algorithm, the sharp features which play a key role in 3D models are kept unchanged while denoising. The multiscale tensor voting is used to classify the faces into two classes where one is called as feature faces and another as non-feature faces. Feature faces is usually distributed in the neighbourhood of shape edges. Thus these feature faces are key faces in perceptual quality. For processing the faces more efficiently, we propose a search algorithm to find those faces which are close to the feature face and are of similar geometrical properties and then use them to guide the filtering process. The remaining faces are processed by an iteratively joint bilateral filtering. Finally, vertex position is updated according to the filtered face normals. the effectiveness of proposed approach is validated through extensive experiments. Experimental results show the performance is better than the existing methods.

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Acknowledgements

This work is partially funded by the Major State Basic Research Development Program of China (973 Program 2015CB351804), the Science and Technology Commission of China No.17-H863-03-ZT-003-010-01 and the Natural Science Foundation of China under Grant No. 61572155 and 61672188.

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Authors and Affiliations

  1. School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China

    Shaohui Liu, Renjie Wang & Feng Jiang

  2. Department of Media Software, Sungkyul University, Sungkyul, Korea

    Seungmin Rho

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  1. Shaohui Liu
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  2. Seungmin Rho
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  3. Renjie Wang
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  4. Feng Jiang
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Correspondence to Shaohui Liu.

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Liu, S., Rho, S., Wang, R. et al. Feature-preserving mesh denoising based on guided normal filtering. Multimed Tools Appl 77, 23009–23021 (2018). https://doi.org/10.1007/s11042-018-5735-9

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  • DOI: https://doi.org/10.1007/s11042-018-5735-9

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