Abstract
In many cases it is reasonable to augment general unsupervised learning by additional algorithmic structures. Kohonens self-organzing map is a typical example for such kinds of approaches. Here a 2D mesh is superimposed on pure unsupervised learning to extract topological relationships from the training data. In this work, we propose generalizing the idea of application-focused modification of ideal, unsupervised learning by the development of the smart growing cells (SGC) based on Fritzke’s growing cells structures (GCS). We substantiate this idea by presenting an algorithm which solves the well-known problem of surface reconstruction based on 3D point clouds and which outperforms the most classical approaches concerning quality and robustness.
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References
Boissonnat, J.-D.: Geometric structures for three-dimensional shape representation. ACM Trans. Graph. 3(4), 266–286 (1984)
Edelsbrunner, H., Mcke, E.P.: Three-dimensional alpha shapes (1994)
Fritzke, B.: Growing cell structures - a self-organizing network for unsupervised and supervised learning. Neural Networks 7, 1441–1460 (1993)
Fritzke, B.: A growing neural gas network learns topologies. In: Tesauro, G., Touretzky, D.S., Leen, T.K. (eds.) Advances in Neural Information Processing Systems, vol. 7, pp. 625–632. MIT Press, Cambridge (1995)
Hoffmann, M., Vrady, L.: Free-form surfaces for scattered data by neural networks. Journal for Geometry and Graphics 2, 1–6 (1998)
Hoppe, H., DeRose, T., Duchamp, T., McDonald, J.A., Stuetzle, W.: Surface reconstruction from unorganized points. In: Thomas, J.J. (ed.) SIGGRAPH, pp. 71–78. ACM (1992)
Huang, Q.-X., Adams, B., Wand, M.: Bayesian surface reconstruction via iterative scan alignment to an optimized prototype. In: SGP 2007: Proceedings of the fifth Eurographics Symposium on Geometry Processing, pp. 213–223. Aire-la-Ville, Switzerland (2007); Eurographics Association
Ivrissimtzis, I.P., Jeong, W.-K., Seidel, H.-P.: Using growing cell structures for surface reconstruction. In: SMI 2003: Proceedings of the Shape Modeling International 2003, p. 78. IEEE Computer Society, Washington (2003)
Ivrissimtzis, I., Jeong, W.-K., Seidel, H.-P.: Neural meshes: Statistical learning methods in surface reconstruction. Technical Report MPI-I-2003-4-007, Max-Planck-Institut fr Informatik, Saarbrücken (April 2003)
Ivrissimtzis, I., Lee, Y., Lee, S., Jeong, W.-K., Seidel, H.-P.: Neural mesh ensembles. In: 3DPVT 2004: Proceedings of the 3D Data Processing, Visualization, and Transmission, 2nd International Symposium, pp. 308–315. IEEE Computer Society, Washington (2004)
Ivrissimtzis, I.P., Jeong, W.-K., Lee, S., Lee, Y., Seidel, H.-P.: Neural meshes: surface reconstruction with a learning algorithm. Research Report MPI-I-2004-4-005. Max-Planck-Institut für Informatik, Stuhlsatzenhausweg 85, 66123 Saarbrücken, Germany (October 2004)
Kazhdan, M., Bolitho, M., Hoppe, H.: Poisson surface reconstruction. In: Proceedings of the Fourth Eurographics Symposium on Geometry Processing, SGP 2006, pp. 61–70. Aire-la-Ville, Switzerland (2006); Eurographics Association
Kohonen, T.: Self-Organized Formation of Topologically Correct Feature Maps. Biological Cybernetics 43, 59–69 (1982)
Kolluri, R., Shewchuk, J.R., O’Brien, J.F.: Spectral surface reconstruction from noisy point clouds. In: SGP 2004: Proceedings of the 2004 Eurographics/ACM SIGGRAPH Symposium on Geometry Processing, pp. 11–21. ACM, New York (2004)
MacQueen, J.B.: Some methods for classification and analysis of multivariate observations, pp. 281–297 (1967)
Storvik, G.: Bayesian surface reconstruction from noisy images. In: Interface 1996 (1996)
Taubin, G.: A signal processing approach to fair surface design. In: SIGGRAPH, pp. 351–358 (1995)
Vrady, L., Hoffmann, M., Kovcs, E.: Improved free-form modelling of scattered data by dynamic neural networks. Journal for Geometry and Graphics 3, 177–183 (1999)
Yoon, M., Lee, Y., Lee, S., Ivrissimtzis, I., Seidel, H.-P.: Surface and normal ensembles for surface reconstruction. Comput. Aided. Des. 39(5), 408–420 (2007)
Yu, Y.: Surface reconstruction from unorganized points using self-organizing neural networks. In: IEEE Visualization 1999, Conference Proceedings, pp. 61–64 (1999)
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Annuth, H., Bohn, CA. (2012). Smart Growing Cells: Supervising Unsupervised Learning. In: Madani, K., Dourado Correia, A., Rosa, A., Filipe, J. (eds) Computational Intelligence. IJCCI 2010. Studies in Computational Intelligence, vol 399. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27534-0_27
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DOI: https://doi.org/10.1007/978-3-642-27534-0_27
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