Definition
Given two copies of an object surface at different locations and orientations in space, or two parts of the surface of a single object with at least some shared overlapping area, find a translation and rigid rotation which places the objects, or corresponding parts of the object, at the same location and orientation. This process is called rigid registration. In practice many approaches to rigid registration work by finding point-to-point correspondences between parts of the object surface in each dataset and use these to estimate the geometric transformation in either least-squares or weighted least-squares sense with closed-form solution. Often, registration algorithms also output the point-to-point correspondences which can be just as useful to many applications as the transformation itself. A correspondence is such a pair of points that while they are described in two different...
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OSU(MSU/WSU) range image database (2010) http://sampl.ece.ohio-state.edu/data/3DDB/RID/index.htm
Johnson A, Hebert M (1999) Using spin images for efficient object recognition in cluttered 3D scenes. IEEE Trans Pattern Anal Mach Intell 21:433–449
Gold S, Rangarajan A et al (1998) New algorithms for 2-D and 3-D point matching: pose estimation and correspondence. Pattern Recognit 31:1019–1031
Besl PJ, McKay ND (1992) A method for registration of 3-D shapes. IEEE Trans Pattern Anal Mach Intell 14(2): 239–256
Silva L, Bellon Olga RP, Boyer KL (2005) Precision range image registration using a robust surface interpenetration measure and enhanced genetic algorithms. IEEE Trans Pattern Anal Mach Intell 27:762–776
Liu Y (2005) Automatic 3d free form shape matching using the graduated assignment algorithm. Pattern Recognit 38:1615–1631
Phillips JM, Liu R, Tomasi C (2007) Outlier robust ICP for minimizing fractional RMSD. In: Proceedings of International Conference on 3D Digital Imaging and Modeling. IEEE Computer Society, Washington DC, pp 427–434
Chen Y, Medioni G (1992) Object modelling by registration of multiple range images. Image and Vision Computing, 10(3):145–155
Rusinkiewicz S, Levoy M (2001) Efficient variants of the ICP algorithm proceedings of 3rd international conference on 3-D digital imaging and modeling, pp 145–152
Bayati M, Shah D, Sharma M (2008) Max-product for maximum weight matching: convergence, correctness, and LP duality. IEEE Trans Inf Theory 54(3):1241–1251
Banno A, Masuda T, Oishi T, Ikeuchi K (2008) Flying laser range sensor for large-scale site-modeling and its applications in Bayon digital archival project. Int J Comput Vis 78:207–222
Pileicikien G, Surna A et al (2009) A new technique for the creation of a higher accuracy 3D geometrical model of the human masticatory system. Mechanika 78(4): 44–50
Surmann H, Nuchter A, Hertzberg J (2003) An autonomous mobile robot with a 3D laser range finder for 3D exploration and digitalization of indoor environments. Robot Auton Syst 45:181–198
Islam SMB, Davies R, Bennamoun M, Mian AS (2011) Efficient detection and recognition of 3D ears. Int J Comput Vis 95:52–73
Bosch F (2010) Automated recognition of 3D CAD model objects in laser scans and calculation of as-built dimensions for dimensional compliance control in construction. J Adv Eng Inform 24(1):107–118
Xiao P, Barnes N, Lieby P, Caetano T (2009) Applying sum and max product algorithms of belief propagation to 3D shape matching and registration. In: Proceedings of International Conference on Digital Image Computing: Techniques and Applications. IEEE Computer Society, Washington DC, pp 387–394
Albarelli A, Rodola E, Torsello A (2010) A game-theoretic approach to fine surface registration without initial motion estimation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, pp 430–437
Chui H, Rangarajan A (2003) A new point matching algorithm for non-rigid registration. Comput Vis Image Underst 89:114–141
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Liu, Y., Martin, R.R., Chen, L., Ren, X., Li, L. (2014). Rigid Registration. In: Ikeuchi, K. (eds) Computer Vision. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-31439-6_184
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DOI: https://doi.org/10.1007/978-0-387-31439-6_184
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