Building of 3D models for single objects so as for whole environments, represents a continuosly and quickly evolving new research field of the computer science. The arising interest in this field is strictly related with the increasing availability of more powerful CPUs, overall in terms of PCs, which made possible, for the first time, to effectively manage complex 3D models off the research centers, as well. A new set of issues has to be addressed in the 3D modeling of real objects. A lot of data are needed about the object surface or volume, which have then to be aggregated, regardless the data format and the acquisition device used, in order to get the final model. Actually, the data registration step requires a human operator, which were able to provide a first rough alignement between acquired data. This approach is often time-consuming, increases the final cost of the 3D model and represents the major limit to the wide spreading of real object models. Alternatively more sofisticated range data acquisition devices can be used, such a range sensor mounted on a robotic arm with six degree of freedom, but anyway it is a very expensive modeling system.
In the light of topics previously exposed, a fully automatic range data registration system has been developed. This system is able to execute all the steps needed for 3D modeling of real objects in automatic way or at least minimizing as more as possible the human intervention, without any other information but the range data only.
In this paper, the subsystem for alignement of range data pairs is presented. The work draws the idea from A. E. Johnson[1], which proposed an innovative solution for the recognition of similarities between 3D surfaces, introducing the spin-image concept. The advantage of this approach rely on high computational robustness and effectiveness, which allows to employ standard market-level CPUs. On the ground of the spin-image concept, a full data registration system was developed, in which the overlapping areas of two adjacent data set are automatically recognized, thus allowing to correctly align the two whole data sets
