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P.J. Besl, N.D. McKay, "A Method for Registration of 3D Shapes," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, no. 2, pp. 239256, February, 1992.  
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@article{ 10.1109/34.121791, author = {P.J. Besl and N.D. McKay}, title = {A Method for Registration of 3D Shapes}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {14}, number = {2}, issn = {01628828}, year = {1992}, pages = {239256}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.121791}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
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TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  A Method for Registration of 3D Shapes IS  2 SN  01628828 SP239 EP256 EPD  239256 A1  P.J. Besl, A1  N.D. McKay, PY  1992 KW  3D shape registration; pattern recognition; point set registration; iterative closest point; geometric entity; meansquare distance metric; convergence; geometric model; computational geometry; convergence of numerical methods; iterative methods; optimisation; pattern recognition; picture processing VL  14 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
The authors describe a generalpurpose, representationindependent method for the accurate and computationally efficient registration of 3D shapes including freeform curves and surfaces. The method handles the full six degrees of freedom and is based on the iterative closest point (ICP) algorithm, which requires only a procedure to find the closest point on a geometric entity to a given point. The ICP algorithm always converges monotonically to the nearest local minimum of a meansquare distance metric, and the rate of convergence is rapid during the first few iterations. Therefore, given an adequate set of initial rotations and translations for a particular class of objects with a certain level of 'shape complexity', one can globally minimize the meansquare distance metric over all six degrees of freedom by testing each initial registration. One important application of this method is to register sensed data from unfixtured rigid objects with an ideal geometric model, prior to shape inspection. Experimental results show the capabilities of the registration algorithm on point sets, curves, and surfaces.
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