Abstract
To efficiently process 3D unorganized point clouds with noise and significant outliers, it is important to smooth the point clouds, but still retain faithfully the original surface geometry as much as possible. In this paper, we present a simple and fast saliency-guided smoothing method of 3D point. The method consists of three stages: firstly, saliency value for each point in noisy point clouds is calculated by site entropy rate method; secondly, robust vertex normal vector is updated by neighboring noisy normal vectors with weight terms related to detected visual saliency metrics; finally, based on least-squares error criterion, vertex position is updated with the integration of vertex normal vector. Analysis and experiments show the advantages of our proposed method over similar methods in the literature on synthetic data.
This work was supported in part by Natural Science Foundation of China (No.61231018), National Science and Technology Support Program (2015BAH31F01) and Program of Introducing Talents of Discipline to University under grant B13043.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Tasse, F.P., Kosinka, J., Dodgson, N.: Cluster-based point set saliency. In: ICCV, vol. 7, pp. 163–171 (2015)
Shtrom, E., Leifman, G., Tal, A.: Saliency detection in large point sets, pp. 3591–3598 (2013)
Guo, Yu., Wang, F., Liu, P., Xin, J., Zheng, N.: Multi-scale point set saliency detection based on site entropy rate. In: Chen, E., Gong, Y., Tie, Y. (eds.) PCM 2016. LNCS, vol. 9916, pp. 366–375. Springer, Cham (2016). doi:10.1007/978-3-319-48890-5_36
Medioni, G., Tang, C.K., Lee, M.S.: Tensor voting: theory and applications. Proc. Rfia 34(8), 1482–1495 (2000)
Min, K.P., Lee, S.J., Lee, K.H.: Multi-scale tensor voting for feature extraction from unstructured point clouds. Graph. Model. 74(4), 197–208 (2012)
Wang, W., Wang, Y., Huang, Q., Gao, W.: Measuring visual saliency by site entropy rate. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2010, San Francisco, CA, Usa, 13–18 June, vol. 119, pp. 2368–2375. DBLP (2010)
Sun, X., Rosin, P.L., Martin, R.R., Langbein, F.C.: Fast and effective feature-preserving mesh denoising. IEEE Trans. Vis. Comput. Graph. 13(5), 925–938 (2007)
Meyer, M.D., Georgel, P., Whitaker, R.T.: Robust particle systems for curvature dependent sampling of implicit surfaces. In: Shape Modeling and Applications, International Conference, pp. 124–133. IEEE (2005)
Haque, S.M., Govindu, V.M.: Robust feature-preserving denoising of 3D point clouds. In: International Conference on 3d Vision, pp. 83–91. IEEE Computer Society (2016)
Ouml Ztireli, A.C., Guennebaud, G., Gross, M.: Feature preserving point set surfaces based on non - linear kernel regression. Comput. Graph. Forum 28, 493–501 (2009)
Botsch, M., Pauly, M., Kobbelt, L., Alliez, P., Lévy, B., Bischoff, S., et al.: Geometric modeling based on polygonal meshes. Proc. Acm Siggraph Course Notes 29(29), 432–441 (2007)
Fleishman, S., Drori, I., Cohen-Or, D.: Bilateral mesh denoising. ACM SIGGRAPH, pp. 950–953. ACM (2003)
Jones, T.R., Durand, F., Desbrun, M.: Non-iterative, feature-preserving mesh smoothing. ACM Trans. Graph. 22(3), 943–949 (2003)
He, L., Schaefer, S.: Mesh denoising via L0 minimization. ACM Trans. Graph. 32(4), 1–8 (2013)
Yagou, H., Ohtake, Y., Belyaev, A.: Mesh Smoothing via Mean and Median Filtering Applied to Face Normals. In: Proceedings of Geometric Modeling and Processing, pp. 124–131. IEEE (2002)
Chen, C.Y., Cheng, K.Y.: A sharpness dependent filter for mesh smoothing. Comput. Aided Geom. Des. 22(5), 376–391 (2005)
Shen, C., O’Brien, J.F., Shewchuk, J.R.: Interpolating and approximating implicit surfaces from polygon soup. ACM Trans. Graph. 23(3), 896–904 (2004)
Guennebaud, G., Gross, M.: Algebraic point set surfaces. ACM Trans. Graph. 26(3), 23 (2007)
Clarenz, U., Rumpf, M., Telea, A.: Fairing of point based surfaces. Computer Graphics International, pp. 600–603. IEEE Computer Society (2004)
Buades, A., Coll, B., Morel, J.M.: A non-local algorithm for image denoising. In: IEEE Computer Society Conference on DBLP, Computer Vision and Pattern Recognition, CVPR 2005, vol. 2, pp. 60–65 (2005)
Jensen, R.R., Paulsen, R.R.: Second International Conference on 3D Imaging, Modeling, Processing, Visualization (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Yan, F., Wang, F., Guo, Y., Jiang, P. (2017). Saliency-Guided Smoothing for 3D Point Clouds. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10361. Springer, Cham. https://doi.org/10.1007/978-3-319-63309-1_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-63309-1_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-63308-4
Online ISBN: 978-3-319-63309-1
eBook Packages: Computer ScienceComputer Science (R0)