Abstract
Obtaining 3-D reconstruction directly and expediently for the real world has became a hot topic in many fields. A 3-D reconstruction method of three views based on manifold study is proposed. Firstly, the fundamental matrix is estimated by adjacent view and optimized under three views constraint. Then 3-D point cloud is reconstructed after getting the projection matrixes of views. Further more, benefitting from minimum spanning tree, outliers are almost excluded. To increase point cloud’s density, the optimized 3-D point cloud is interpolated based on Radial Basis Function. Afterwards, the dense point cloud is mapped to two dimensional plane using manifold study algorithm, and then divided into plane Delaunay triangle nets. Completing that, the topological relations of points are mapped back to 3-D space and 3-D reconstruction is realized. Many experiments show the method proposed in paper can achieve 3-D reconstruction for three views with quite good results.
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References
Armangue, X., Salvi, J.: Overall view regarding fundamental matrix estimation. Image and Vision Computing 21(2), 200–205 (2003)
Zhong, X. H.: Research on methods for estimating the fundamental matrix. Jilin University (2005) (基本矩阵计算方法的研究)
Torr, P.: Bayesian model estimation and selection for epipolar geometry and generic manifold fitting. International Journal of Computer Vision 50(1), 35–61 (2002)
Carro, A.I., Morros, R.: Promeds: An adaptive robust fundamental matrix estimation approach. In: 3DTV-Conference, The True Vision - Capture, Transmission and Display of 3D Video, pp. 1–4 (2012)
Li, Y., Velipasalar, S., Gursoy, M.C.: An improved evolutionary algorithm for fundamental matrix estimation. In: 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, pp. 226–231. IEEE Press, Krakow (2013)
Shi, X.B., Liu, F., Wang, Y., et al.: A Fundamental Matrix Estimation Algorithm Based on Point Weighting Strategy. In: 2011 International Conference on Virtual Reality and Visualization, Beijing, pp. 24–29 (2011)
Calderon, D.B., Maria, T.: An approach for estimating the fundamental matrix. In: 2011 6th Colombian Computing Congress, Manizales, pp. 1–6 (2011)
Brandt, S.: Maximum likelihood robust regression with known and unknown residual models. In: Proceedings of the Statistical Methods in Video Processing Workshop, in Conjunction with ECCV, Copenhagen, pp. 97–102 (2002)
Brandt, S.: Maximum likelihood robust regression by mixture models. J. Journal of Mathematical Imaging and Vision. 25(1), 25–48 (2006)
Lu, S., Lei, Y., Kong, W.W., et al.: Fundamental matrix estimation based on probability analysis and sampling consensus. Control and Decision 42(2), 425–430 (2012), (基于模糊核聚类的鲁棒性基础矩阵估计算法)
Fang, L.: Research on feature based 3D scene reconstruction techniques from image sequence. Huazhong University of science and technology (2007), (基于特征的图像序列三维场景重建技术研究)
Boissonnat, J.D.: Geometric structures for three-dimensional shape representation. ACM Transactions on Graphics. 3(4), 266–286 (1984)
Wang, Q., Wang, R.Q., Ba, H.J., Peng, Q.S.: A Fast Progressive Surface Reconstruction Algorithm for Unorganized Point. Journal of Software. 11(9), 1221–1227 (2000), (散乱数据点的增量快速曲面重建算法)
Hou, W.G., Ding, M.Y.: Method of Triangulating Spatial Point s Based on Manifold Stud. J. Acta Electronica Sinica 37, 2579–2583 (2009), (基于流形学习的三维空间数据网格剖分方法)
Li, L.D., Lu, D.T., Kong, X.Y., Wu, G.: Implicit Surfaces Based on Radial Basis Function Network. Journal of Computer-aided Design & Computer Graphics 18, 1142–1148 (2006), (径向基函数网络的隐式曲面方法)
Fang, L.C., Wang, G.Z.: Radial basis functions based surface reconstruction algorithm. Journal of Zhejiang University (Engineering Science) 44, 728–731 (2010), (基于径向基函数的曲面重建算法)
Tenenbaum, J.B., Silva, V.D., Langford, J.C.: A Global Geometric Framework for Nonlinear Dimensionality Reduction 290(5500), 2319–2323 (2000)
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Cong, L., Hongrui, Z., Gang, F., Xingang, P. (2014). 3-D Reconstruction of Three Views Based on Manifold Study. In: Tan, T., Ruan, Q., Wang, S., Ma, H., Huang, K. (eds) Advances in Image and Graphics Technologies. IGTA 2014. Communications in Computer and Information Science, vol 437. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45498-5_21
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DOI: https://doi.org/10.1007/978-3-662-45498-5_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-662-45497-8
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