A review of recent range image registration methods with accuracy evaluation <s> Control points <s> The problem of creating a complete model of a physical object is studied. Although this may be possible using intensity images, the authors use range images which directly provide access to three-dimensional information. The first problem that needs to be solved is to find the transformation between the different views. Previous approaches have either assumed this transformation to be known (which is extremely difficult for a complete model) or computed it with feature matching (which is not accurate enough for integration. The authors propose an approach that works on range data directly and registers successive views with enough overlapping area to get an accurate transformation between views. This is performed by minimizing a functional that does not require point-to-point matches. Details are given of the registration method and modeling procedure, and they are illustrated on range images of complex objects. > <s> BIB001 </s> A review of recent range image registration methods with accuracy evaluation <s> Control points <s> The authors describe a general-purpose, representation-independent method for the accurate and computationally efficient registration of 3-D shapes including free-form 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 mean-square 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 mean-square 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. > <s> BIB002 </s> A review of recent range image registration methods with accuracy evaluation <s> Control points <s> Range imaging offers an inexpensive and accurate means for digitizing the shape of three-dimensional objects. Because most objects self occlude, no single range image suffices to describe the entire object. We present a method for combining a collection of range images into a single polygonal mesh that completely describes an object to the extent that it is visible from the outside. The steps in our method are: 1) align the meshes with each other using a modified iterated closest-point algorithm, 2) zipper together adjacent meshes to form a continuous surface that correctly captures the topology of the object, and 3) compute local weighted averages of surface positions on all meshes to form a consensus surface geometry. Our system differs from previous approaches in that it is incremental; scans are acquired and combined one at a time. This approach allows us to acquire and combine large numbers of scans with minimal storage overhead. Our largest models contain up to 360,000 triangles. All the steps needed to digitize an object that requires up to 10 range scans can be performed using our system with five minutes of user interaction and a few hours of compute time. We show two models created using our method with range data from a commercial rangefinder that employs laser stripe technology. <s> BIB003 </s> A review of recent range image registration methods with accuracy evaluation <s> Control points <s> Registration and integration of measured data of real objects are becoming important in 3D modeling for computer graphics and computer-aided design. We propose a new algorithm of registration and integration of multiple range images for producing a geometric object surface model. The registration algorithm determines a set of rigid motion parameters that register a range image to a given mesh-based geometric model. The algorithm is an integration of the iterative closest point algorithm with the least median of squares estimator. After registration, points in the input range are classified into inliers and outliers according to registration errors between each data point and the model. The outliers are appended to the surface model to be used by registration with the following range images. The parts classified as inlier by at least one registration result is segmented out to be integrated. This process consisting of registration and integration is iterated until all views are integrated. We successfully experimented with the proposed method on real range image sequences taken by a rangefinder. The method does not need any preliminary processes. <s> BIB004 </s> A review of recent range image registration methods with accuracy evaluation <s> Control points <s> We propose a method to construct a 3-D geometric shape model by registering and integrating multiple range images under a unified framework. We use 3-D points around the object surface as the correspondence key, because the closest data point from an arbitrary 3-D point should be matched if the data are registered correctly with some exceptional cases. We assume that the input shapes are roughly registered in advance, and then we solve the problem by iterative loops that consist of establishing correspondence by searching closest data points, integration of corresponded data points, and registration of each data to the integrated shape. The error function is defined by the weighted variances of the signed distance field from the surface. The weighting values are determined to trim out wrong correspondences typically caused by different sampling and measurement error. We do not need to consider the cumulative pairwise registration error because each data is registered to the integrated shape. The integrated shape information is directly used to generate a surface model. We tested the method on synthetic and real range images. <s> BIB005
Although some authors use all points in the registration step BIB002 BIB001 , others use Uniform subsampling BIB003 BIB005 , Random Sampling BIB004 or Normal Sampling  . In the common case, the better solution is the Normal Sampling for two reasons. As a sampling is applied, the time required is smaller. Furthermore, as points from significant parts of the surfaces are more important compared to parts of uniform surfaces, usually better results are obtained, specially in cases where the surfaces are not very shaped.