Abstract
The most challenging problem in mesh denoising is to distinguish features from noise. Based on the robust guided normal estimation and alternate vertex updating strategy, we investigate a new feature-preserving mesh denoising method. To accurately capture local structures around features, we propose a corner-aware neighborhood (CAN) scheme. By combining both overall normal distribution of all faces in a CAN and individual normal influence of the interested face, we give a new consistency measuring method, which greatly improves the reliability of the estimated guided normals. As the noise level lowers, we take as guidance the previous filtered normals, which coincides with the emerging rolling guidance idea. In the vertex updating process, we classify vertices according to filtered normals at each iteration and reposition vertices of distinct types alternately with individual regularization constraints. Experiments on a variety of synthetic and real data indicate that our method adapts to various noise, both Gaussian and impulsive, no matter in the normal direction or in a random direction, with few triangles flipped.
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Acknowledgements
The authors would like to appreciate Wang-yu ZHANG for providing executable programs. The models used in this paper are courtesy of the AIM Shape Repository, the Stanford 3D Scanning Repository, and Laser Design.
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Project supported by the National Natural Science Foundation of China (Nos. 61402224 and 61222206), the Natural Science Foundation of Jiangsu Province, China (No. BK2014833), and the Natural Science Foundation of Suzhou University of Science and Technology, China (No. XKZ201611)
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Li, T., Wang, J., Liu, H. et al. Efficient mesh denoising via robust normal filtering and alternate vertex updating. Frontiers Inf Technol Electronic Eng 18, 1828–1842 (2017). https://doi.org/10.1631/FITEE.1601229
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DOI: https://doi.org/10.1631/FITEE.1601229