Abstract
A coarse registration method using Mixed Integer Linear Programming (MILP) is described that finds global optimal registration parameter values that are independent of the values of invariant features. We formulate the range image registration problem using MILP. Our algorithm using MILP formulation finds the best balanced optimal registration for robustly aligning two range images with the best balanced accuracy. It adjusts the error tolerance automatically in accordance with the accuracy of the given range image data. Experimental results show that this method of coarse registration is highly effective.
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References
Besl, P.J., McKay, N.D.: A Method for Registration of 3-D Shapes. IEEE Trans. on PAMI 14(2), 239–256 (1992)
Campbell, R.J., Flynn, P.J.: A Survey of Free-Form Object Representation and Recognition Techniques. CVIU 81, 166–210 (2001)
Chen, C.C., Stamos, I.: Range Image Registration Based on Circular Features. In: Proc. 3DPVT, pp. 447–454 (2006)
Chua, C.S., Jarvis, R.: 3D Free-Form Surface Registration and Object Recognition. IJCV 17(1), 77–99 (1996)
He, W., Ma, W., Zha, H.: Automatic Registration of Range Images Based on Correspondence of Complete Plane Patches. In: Proc. 3DIM, pp. 470–475 (2005)
Higuchi, K., Hebert, M., Ikeuchi, K.: Building 3-D Models from Unregistered Range Images. GMIP 57(4), 315–333 (1995)
Johnson, A.E., Hebert, M.: Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes. IEEE Trans. on PAMI 21(5), 433–449 (1999)
Johnson, E.L., Nemhauser, G.L., Savelsbergh, M.W.P.: Progress in Linear Programming-Based Algorithms for Integer Programming: An Exposition. INFORMS Journal on Computing 12(1), 2–23 (2000)
Koenderink, J.J.: Solid Shape. MIT Press, Cambridge (1990)
Šára, R., Okatahi, I.S., Sugimoto, A.: Globally Convergent Range Image Registration by Graph Kernel Algorithm. In: Proc. 3DIM, pp. 377–384 (2005)
Rusinkiewicz, S., Levoy, M.: Efficient Variants of the ICP Algorithm. In: Proc. 3DIM, pp. 145–152 (2001)
Stein, F., Medioni, G.: Structural indexing: Efficient 3-D object recognition. IEEE Trans. on PAMI 14(2), 125–145 (1992)
Umeyama, S.: Least-Square Estimation of Transformation Parameters Between Two Point Patterns. IEEE Trans. on PAMI 13(4), 376–380 (1991)
Stanford 3D Scanning Repository, http://www-graphics.stanford.edu/data/3Dscanrep/
The Ohio State University Range Image Repository, http://sampl.ece.ohio-state.edu/data/3DDB/RID/minolta/
Georgia Institute of Technology Large Geometric Models Archive, http://www-static.cc.gatech.edu/projects/large_models/
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© 2007 Springer-Verlag Berlin Heidelberg
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Sakakubara, S., Kounoike, Y., Shinano, Y., Shimizu, I. (2007). Automatic Range Image Registration Using Mixed Integer Linear Programming. In: Yagi, Y., Kang, S.B., Kweon, I.S., Zha, H. (eds) Computer Vision – ACCV 2007. ACCV 2007. Lecture Notes in Computer Science, vol 4844. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76390-1_42
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DOI: https://doi.org/10.1007/978-3-540-76390-1_42
Publisher Name: Springer, Berlin, Heidelberg
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