An Adaptive ICP Registration for Facial Point Data

An algorithm for finding coupling points plays an important role in the Iterative Closest Point algorithm (ICP) which is widely used in medical imaging and 3-D architecture applications. In recent researches of finding coupling points, Approximate K-D tree search algorithm (AK-D tree) is an efficient nearest neighbor search algorithm with comparable results. We proposed Adaptive Dual AK-D tree search algorithm (ADAK-D tree) for searching and synthesizing coupling points as significant control points to improve the registration accuracy in ICP registration applications. ADAK-D tree utilizes AK-D tree twice in different geometrical projection orders to reserve true nearest neighbor points used in later ICP stages. An adaptive threshold in ADAK-D tree is used to reserve sufficient coupling points for a smaller alignment error. Experimental results are shown that the registration accuracy of using ADAK-D tree is improved than of using AK-D tree and the computation time is acceptable. We also design a system GUI based on the proposed algorithm to register the facial point data which are extracted from prestore CT imaging and captured via range scan equipments or a 3-D digitizer.