Abstract
Mesh denoising is important to improve the quality of the geometry surface acquired by 3D scanning devices. This paper proposes a feature-preserving denoising framework. By classifying the faces into feature and non-feature faces, we use joint bilateral filtering and partial neighborhood filtering to deal with the face normals these two kinds of faces. Experimental results show that our method outperforms the existing methods and achieves higher quality results on the geometry feature.
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Acknowledgments
This work is supported by the Major State Basic Research Development Program of China (973 Program 2015CB351804), the National Science Foundation of China under Grants 61300110 and 61672193, and by the Science Research Foundation of Daqing Normal University under Grant No. 14ZR02.
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Wang, R., Zhao, W., Liu, S., Zhao, D., Liu, C. (2018). Feature-Preserving Mesh Denoising Based on Guided Normal Filtering. In: Zeng, B., Huang, Q., El Saddik, A., Li, H., Jiang, S., Fan, X. (eds) Advances in Multimedia Information Processing – PCM 2017. PCM 2017. Lecture Notes in Computer Science(), vol 10736. Springer, Cham. https://doi.org/10.1007/978-3-319-77383-4_90
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DOI: https://doi.org/10.1007/978-3-319-77383-4_90
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