High-precision point cloud registration method based on volume image correlation

Abstract

Abstract With the rapid development of binocular reconstruction, fringe projection profilometry, and time of flight, 3D imaging technology has been widely applied in the field of 3D measurement. However, due to limited measurement range and self-occlusion, point cloud registration methods are often used to obtain larger or more complete 3D contours. Although many scholars have proposed various point cloud registration methods, the accuracy and efficiency of point cloud registration still need to be further improved, especially for point clouds with different density or non-rigid transformation. Image registration technology based on image correlation has been developed for many years and has achieved great success in fields such as computer vision, photomechanics, and photogrammetry. Therefore, a simple and direct idea in this paper is to transform the point cloud registration problem into volume image correlation problem. By this, an efficient image registration method based on fast Fourier transform and an inverse compositional Gaussian Newton optimization method that only needs to calculate the Hessian matrix once can be introduced into the point cloud registration field, which can greatly improve the speed and accuracy of point cloud registration. Comparative experiments have shown that our method has doubled the accuracy and efficiency compared to the iterative closest point (ICP) method, and its practicality has also been verified in impeller reconstruction experiments.