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<p><it>Abstract</it>—This research deals with the problem of range image registration for the purpose of building surface models of three-dimensional objects. The registration task involves finding the translation and rotation parameters which properly align overlapping views of the object so as to reconstruct from these <it>partial</it> surfaces, an <it>integrated</it> surface representation of the object.</p><p>The approach taken is to express the registration task as an optimization problem. We define a function which measures the quality of the alignment between the partial surfaces contained in two range images as produced by a set of motion parameters. This function computes a sum of Euclidean distances between a set of control points on one of the surfaces to corresponding points on the other. The strength of this approach resides in the method used to determine point correspondences across range images. It is based on reversing the rangefinder calibration process, resulting in a set of equations which can be used to directly compute the location of a point in a range image corresponding to an arbitrary point in three-dimensional space.</p><p>A stochastic optimization technique, very fast simulated reannealing (VFSR), is used to minimize the cost function.</p><p>Dual-view registration experiments yielded excellent results in very reasonable computational time. A multiview registration experiment was also performed, but a large processing time was required. A complete surface model of a typical 3D object was then constructed from the integration of its multiple partial views. The effectiveness with which registration of range images can be accomplished makes this method attractive for many practical applications where surface models of 3D objects must be constructed.</p>
Range, multiview, 3D, image registration, simulated annealing, surface models, suface integration, rangefinder calibration.

M. D. Levine and G. Blais, "Registering Multiview Range Data to Create 3D Computer Objects," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 17, no. , pp. 820-824, 1995.
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