An Automatic Non-rigid Point Matching Method for Dense 3D Face Scans
Yongli Hu, Mingquan Zhou, Zhongke Wu · 2009
In this paper, we present an automatic point matching method to overcome the dense point alignment of 3D face scans. We adopt TPS (thin plate spline) transformation to model the deformation of different 3D faces, because TPS is a type of non-rigid transformation with good smooth property and suitable for formulating the complexities of human facial morphology. Generally, TPS is derived from the interpolation between two sets of aligned controlling points. To obtain fully automatic point matching method, a random point selecting method is proposed to get the controlling points of 3D faces. Integrating the point generating method with an iterative closest point searching strategy, we achieve a point-to-point alignment solution for dense 3D face scans. The point matching tests on BJUT 3D face database reveal that the TPS based method has flexible shape deformation ability. Comparing with the hand-selecting controlling point method our method has better point matching accuracy and stability. It is proved that the presented method is efficient for dense point registration with non-rigid deformation.