We present a new point matching method to overcome the dense point-to-point alignment of scanned 3Dfaces. Instead of using the rigid spatial transformation in the traditional iterative closest point (ICP) algorithm, we adoptthe thin plate spline (TPS) transformation to model the deformation of different 3D faces. Because TPS is a non-rigidtransformation with good smooth property, it is suitable for formulating the complex variety of human facial morphology. A closest point searching algorithm is proposed to keep one-to-one mapping, and to get good efficiency the pointmatching method is accelerated by a KD-tree method. Having constructed the dense point-to-point correspondence of3D faces, we create 3D face morphing and animation by key-frames interpolation and obtain realistic results. Comparingwith ICP algorithm and the optical flow method, the presented point matching method can achieve good matchingaccuracy and stability. The experiment results have shown that our method is efficient for dense point objects registration.
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