Registration method of point clouds using improved digital image correlation coefficient

Three-dimensional (3-D) modeling is widely applied in the fields of measurement engineering and manufacturing. Registration of point clouds is a crucial step in 3-D model reconstruction. We present a registration method using improved digital image correlation (DIC) coefficient. First, key points are selected using principal component analysis and further extracted using the k-means clustering algorithm. Second, a grayscale image for each key point is generated as a feature descriptor using inverse distance weighted interpolation. Then, the presented method determines key point correspondences by improving the DIC coefficient. Finally, we apply the iterative closest point algorithm to fine registration. Experiment results show that the proposed method is accurate, time efficient, and it has a good antinoise performance. (C) 2018 Society of Photo-Optical Instrumentation Engineers (SPIE).