Surface matching is a common task in computer graphics and computer vision. In this paper, we introduce a novel algorithm that aligns scanned point-based sufaces to the related 3 0 model without assumptions about their relative position and orientation. Our method estimates the transformation that matches two surfaces from a pair of corresponding points using their suface positions, nonnals and principal curvature directions. This approximate alignment is used as the input of ZCP algorithm to rejne the registration of the suflace to the model. The algorithm is easy to implement and proved to be reliable even with coarse point clouds. Furthermore, with the help of multi-scale feature points, we can improve the reliability of our surface matching algorithm.