  We propose several cost functions for registration of shapes encoded with
Euclidean and/or non-Euclidean information (unit vectors). Our framework is
assessed for estimation of both rigid and non-rigid transformations between the
target and model shapes corresponding to 2D contours and 3D surfaces. The
experimental results obtained confirm that using the combination of a point's
position and unit normal vector in a cost function can enhance the registration
results compared to state of the art methods.
