A review of recent range image registration methods with accuracy evaluation <s> Ransac-based darces <s> A popular approach for 3D registration of partially-overlapping range images is the ICP (iterative closest point) method and many of its variations. The major drawback of this type of iterative approaches is that they require a good initial estimate to guarantee that the correct solution can always be found. In this paper, we propose a new method, the RANSAC-based DARCES (data-aligned rigidity-constrained exhaustive search) method, which can solve the partially-overlapping 3D registration problem efficiently and reliably without any initial estimation. Another important characteristic of our method is that it requires no local features in the 3D data set. An extra characteristic is that, for the noiseless case, the basic algorithm of our DARCES method can guarantee that the solution it finds is the true one, due to its exhaustive-search nature. Even with the nature of exhaustive search, its time complexity can be shown to be relatively low. Experiments have demonstrated that our method is efficient and reliable for registering partially-overlapping range images. <s> BIB001 </s> A review of recent range image registration methods with accuracy evaluation <s> Ransac-based darces <s> In this paper, we propose a new method, the RANSAC-based DARCES method (data-aligned rigidity-constrained exhaustive search based on random sample consensus), which can solve the partially overlapping 3D registration problem without any initial estimation. For the noiseless case, the basic algorithm of our method can guarantee that the solution it finds is the true one, and its time complexity can be shown to be relatively low. An extra characteristic is that our method can be used even for the case that there are no local features in the 3D data sets. <s> BIB002
This method is based on finding the best three point-correspondences between two range images to obtain an estimation of the Euclidean motion. Three points are the minimum required to compute the motion between both surfaces if no other information is used BIB001 . As will be commented in Section 3.8, Feldmar used only a single point but also considered the normal vector and the principal curvature to obtain enough information to compute the rigid motion  . Three points (primary, secondary and auxiliary) in the first view are characterized by the three distances between them (d ps , d pa and d sa ). Each point in the second view is hypothesized to be the correspondence of the primary point (p 0 ). Next, the secondary point is searched for among the points located at a distance d ps from p 0 . If there are not any points in that position, another primary point is tested. Otherwise, a third point in the second view that satisfies the distances defined in the triplet is searched. Once a triplet is identified, the rigid transformation between both points can be determined. This search is repeated exhaustively for every satisfied triplet between both views and a set of potential Euclidean motions is obtained. The correct transformation is the one that obtains the largest number of corresponding points between both views. A modification of this method focused on decreasing the computing time related to the search of correspondences was proposed BIB002 . The results obtained were very good because of its robustness even in the presence of outliers. However, it can only be used when the number of points in each view is relatively small. Theoretically it is a good method. However, the precision depends on the resolution of the surface and the time increases considerably with the number of points, so that it can only be used in applications where time is not critical.