The estimation of 3D surface correspondence constitutes a fundamental problem in shape matching and analysis ap- plications. In the presence of non-rigid shape deformations, the ambiguity of surface correspondence increases together with the complexity of registration algorithms. In this pa- per, we alleviate this problem by using One-Class Support Vector Machines (OCSVM) in order to normalize the pose of 3D objects. We show how OCSVM are employed in order to increase the consistency of translation and scale normal- ization under articulations, extrusions or the presence of outliers. To estimate the relative translation and scale of an object, we use the 3D distribution of points that is mod- elled by employing OCSVM to estimate the decision surface corresponding to the surface points of the object. To evalu- ate the performance, we use a dataset of 3D objects where we introduce various extrusions, articulations or outliers and demonstrate the increased robustness of the proposed methodology.