In 3D registration of point clouds, the goal is to find an optimal transformation that aligns the input shapes, provided that they have some overlap. Existing methods suffer from performance degradation when the overlapping ratio between the neighbouring point clouds is small. So far, there is no existing method that can be adopted for aligning shapes with no overlap. In this letter, to the best of knowledge, the first method for the registration of 3D shapes without overlap, assuming that the shapes correspond to partial views of a known semi-rigid 3D prior is presented. The method is validated and compared to existing methods on FAUST, which is a known dataset used for human body reconstruction. Experimental results show that this approach can effectively align shapes without overlap. Compared to existing state-of-the-art methods, this approach avoids iterative optimization and is robust to outliers and inherent inaccuracies induced by an initial rough alignment of the shapes.