Abstract
A non-local denoising (NLD) algorithm for point-sampled surfaces (PSSs) is presented based on similarities, including geometry intensity and features of sample points. By using the trilateral filtering operator, the differential signal of each sample point is determined and called “geometry intensity”. Based on covariance analysis, a regular grid of geometry intensity of a sample point is constructed, and the geometry-intensity similarity of two points is measured according to their grids. Based on mean shift clustering, the PSSs are clustered in terms of the local geometry-features similarity. The smoothed geometry intensity, i.e., offset distance, of the sample point is estimated according to the two similarities. Using the resulting intensity, the noise component from PSSs is finally removed by adjusting the position of each sample point along its own normal direction. Experimental results demonstrate that the algorithm is robust and can produce a more accurate denoising result while having better feature preservation.
Similar content being viewed by others
References
Amenta, N., Kil, Y.J., 2004. Defining point set surfaces. ACM Trans. on Graph., 23(3):264–270. [doi:10.1145/1015706.1015713]
Buades, A., Coll, B., Morel, J.M., 2005. A Non-Local Algorithm for Image Denoising. Proc. IEEE Computer Society Int. Conf. on Computer Vision and Pattern Recognition, p.60–65. [doi:10.1109/CVPR.2005.38]
Carr, J.C., Beatson, R.K., Cherrie, J.B., Mitchell, T.J., Fright, W.R., McCallum, B.C., Evans, T.R., 2001. Reconstruction and Representation of 3D Objects with Radial Basis Functions. Proc. ACM SIGGRAPH, p.67–76. [doi:10.1145/383259.383266]
Choudhury, P., Tumblin, J., 2003. The Trilateral Filter for High Contrast Images and Meshes. Int. Conf. on Computer Graphics and Interactive Techniques, p.186–196. [doi:10.1145/1198555.1198565]
Clarenz, U., Rumpf, M., Telea, A., 2004. Fairing of Point Based Surfaces. Proc. Computer Graphics International, p.600–603. [doi:10.1109/CGI.2004.1309272]
Comaniciu, D., Meer, P., 2002. Mean shift: a robust approach toward feature space analysis. IEEE Trans. on Pattern Anal. Machine Intell., 24(5):603–619. [doi:10.1109/34.1000236]
Daniels II, J., Ha, L.K., Ochotta, T., Silva, C.T., 2007. Robust Smooth Feature Extraction from Point Clouds. Proc. Shape Modeling International, p.123–136. [doi:10.1109/SMI.2007.32]
Dey, T.K., Sun, J., 2005. An Adaptive MLS Surface for Reconstruction with Guarantees. Proc. Symp. on Geometry Processing, p.43–52.
Fleishman, S., Drori, I., Cohen-Or, D., 2003. Bilateral mesh denoising. ACM Trans. on Graph., 22(3):950–953. [doi:10.1145/882262.882368]
Georgescu, B., Shimshoni, I., Meer, P., 2003. Mean Shift Based Clustering in High Dimensions: A Texture Classification Example. ICCV, p.456–463.
Hoppe, H., DeRose, T., Duchamp, T., McDonald, J., Stuetzle, W., 1992. Surface Reconstruction from Unorganized Points. Proc. 19th Annual Conf. on Computer Graphics and Interactive Techniques, p.71–78. [doi:10.1145/133994.134011]
Hu, G.F., Peng, Q.S., Forrest, A.R., 2006. Mean shift denoising of point-sampled surfaces. The Visual Computer, 22(3):147–157. [doi:10.1007/s00371-006-0372-0]
Jenke, P., Wand, M., Bokeloh, M., Schilling, A., Strasser, W., 2006. Bayesian point cloud reconstruction. Computer Graphics Forum, 25(3):379–388. [doi:10.1111/j.1467-8659.2006.00957.x]
Lange, C., Polthier, K., 2005. Anisotropic smoothing of point sets. Computer Aided Geometric Design, 22(7):680–692. [doi:10.1016/j.cagd.2005.06.010]
Lipman, Y., Cohen-Or, D., Levin, D., 2006. Error Bounds and Optimal Neighborhoods for MLS Approximation. Proc. Eurographics, p.71–80. [doi:10.2312/SGP/SGP06/071-080]
Mederos, B., Velho, L., de Figueiredo, L.H., 2003. Robust Smoothing of Noisy Point Clouds. Proc. SIAM Conf. on Geometric Design and Computing, p.1–13.
Pauly, M., Gross, M., 2001. Spectral Processing of Point-Sampled Geometry. Proc. ACM SIGGRAPH, p.379–386. [doi:10.1145/383259.383301]
Pauly, M., Gross, M., Kobbelt, L.P., 2002a. Efficient Simplification of Point-Sampled Surfaces. Proc. IEEE Visualization, p.163–170.
Pauly, M., Kobbelt, L.P., Gross, M., 2002b. Multiresolution Modeling of Point-Sampled Geometry. Technical Report, CS #379, ETH, Zurich.
Pauly, M., Keiser, R., Kobblet, L.P., Gross, M., 2003. Shape modeling with point-sampled geometry. ACM Trans. on Graph., 22(3):641–650. [doi:10.1145/882262.882319]
Pauly, M., Mitra, N.J., Guibas, L.J., 2004. Uncertainty and Variability in Point Cloud Surface Data. Proc. Eurographics Symp. on Point-Based Graphics, p.77–84.
Samozino, M., Alexa, M., Alliez, P., Yvinec, M., 2006. Reconstruction with Voronoi Centered Radial Basis Functions. Proc. Eurographics, p.51–60. [doi:10.2312/SGP/SGP06/051-060]
Schall, O., Belyaev, A., Seidel, H.P., 2005. Robust Filtering of Noisy Scattered Point Data. Eurographics Symp. on Point-Based Graphics, p.71–77. [doi:10.2312/SPBG/SPBG05/071-077]
Shamir, A., Shapira, L., Cohen-Or, D., 2006. Mesh analysis using geodesic mean-shift. The Visual Computer, 22(2):99–108. [doi:10.1007/s00371-006-0370-2]
Weyrich, T., Pauly, M., Keiser, R., Heinzle, S., Scandella, S., Gross, M., 2004. Post-processing of Scanned 3D Surface Data. Proc. Eurographics, p.85–94.
Xiao, C.X., Miao, Y.W., Liu, S., Peng, Q.S., 2006. A dynamic balanced flow for filtering point-sampled geometry. The Visual Computer, 22(3):210–219. [doi:10.1007/s00371-006-0377-8]
Yamauchi, H., Lee, S., Lee, Y., Ohtake, Y., Belyaev, A., Seidel, H.P., 2005. Feature Sensitive Mesh Segmentation with Mean Shift. Proc. Shape Modeling International, p.236–243. [doi:10.1109/SMI.2005.21]
Author information
Authors and Affiliations
Corresponding author
Additional information
Project supported by the Hi-Tech Research and Development Program (863) of China (Nos. 2007AA01Z311 and 2007AA04Z1A5), and the Research Fund for the Doctoral Program of Higher Education of China (No. 20060335114)
Rights and permissions
About this article
Cite this article
Wang, Rf., Chen, Wz., Zhang, Sy. et al. Similarity-based denoising of point-sampled surfaces. J. Zhejiang Univ. Sci. A 9, 807–815 (2008). https://doi.org/10.1631/jzus.A071465
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1631/jzus.A071465