We present an algorithm that reduces significantly the level of the registration errors between all pairs in a set of range views. This algorithm refines initial estimates of the transformation matrices obtained from either the calibrated acquisition setup or a crude manual alignment. It is an instance of a category of registration algorithms known as iterated closest-point (ICP) algorithms. The algorithm considers the network of views as a whole and minimizes the registration errors of all views simultaneously. This leads to a well-balanced network of views in which the registration errors are equally distributed, an objective not met by previously published ICP algorithms which all process the views sequentially. Experimental results show that this refinement technique improves the calibrated registrations and the quality of the integrated model for complex multi-part objects. In the case of scenes comprising man-made objects of very simple shapes, the basic algorithm faces problems common to all ICP algorithms and so must be extended.

Building integrated models of existing 3-D objects is a key requirement for both reverse engineering and object recognition systems. An automatic 3-D model builder goes through three main steps: i) surface sampling from many views, ii) registration of the sampled views, and iii) integration of the registered views. The accuracy obtained depends on the acquisition and registration errors. The latter is critical since a misalignment of the range views causes their noise distributions to be centered around different means, which makes it difficult to reduce the effect of the acquisition error by simple averaging. In this paper, we propose a general algorithm that reduces significantly the level of the registration errors between all pairs in a set of range views. This algorithm refines initial estimates of the transformation matrices obtained from the calibrated acquisition setup. It considers the network of views as a whole and minimizes the registration errors of all views simultaneously. This leads to a well-balanced network of views in which the registration errors are equally distributed. Experimental results show an improvement of both the calibrated registrations and integrated models.>