Accelerated Phase Deviation Elimination for Measuring Moving Object Shape with Phase-Shifting-Profilometry

Abstract

Eliminating the phase deviation caused by object motion plays a vital role to obtain the precise phase map to recover the object shape with phase-shifting-profilometry. Pixel-by-pixel phase retrieval using the least-squares algorithm has been widely employed to eliminate the phase deviation caused by moving object. However, pixel-level operation can only eliminate phase deviation within a limited range, and will bring high computational burden. In this paper, we propose an image-level phase compensation method with stochastic gradient descent (SGD) algorithm to accelerate the phase deviation elimination. Since the iteration calculation is implemented at the image-level, the proposed method can accelerate the convergence significantly. Furthermore, since the proposed algorithm is able to correct the phase deviation within (−π,π), the algorithm can tolerate a greater motion range. In addition to simulation experiments, we consider 2-D motion of the object, and conduct a series of comparative experiments to validate the effectiveness of the proposed method in a larger motion range.

3D reconstruction of moving object by double sampling based on phase shifting profilometry

“…In recent years, 3D measurement of moving objects has attracted widespread attention [4]. Lei Lu et al [5][6][7] proposed a method for tracking object motion and compensating for motion errors. The motion of the object is tracked by computer vision technology, the rotation and translation vector describing the motion of the object is obtained, and finally the motion error is compensated according to the size of the motion.…”