3D Point Cloud Registration Based on Refined ICP Algorithm

Abstract

Three-dimension (3D) digitization of human face is of great value for medical plastic surgery, video making, virtual reality, and so on. Subject to the restriction of 3D scanning equipment, a single angle of scanning measurement only obtains partial information of a face. In this paper, a multi-Kinect measurement system was proposed for the scanning of human face from multiple angles simultaneously. Firstly, different sides of human face point clouds scanned by three Kinect sensors were registered by an ICP algorithm to form a full human face point cloud. Then, a bilateral filtering algorithm was refined to remove noise in the point cloud. Finally, a point feature histogram and a refined shared nearest neighbor cluster algorithm were applied to improve the iterative speed and efficiency of the ICP algorithm. Experiment shows that the refined point cloud registration algorithm is efficient for 3D digitization of full human face.