High-efficiency and high-accuracy digital image correlation for three-dimensional measurement

Abstract The computational efficiency and measurement accuracy of the digital image correlation (DIC) have become more and more important in recent years. For the three-dimensional DIC (3D-DIC), these issues are much more serious. First, there are two cameras employed which increases the computational amount several times. Second, because of the differences in view angles, the must-do stereo correspondence between the left and right images is equivalently a non-uniform deformation, and cannot be weakened by increasing the sampling frequency of digital cameras. This work mainly focuses on the efficiency and accuracy of 3D-DIC. The inverse compositional Gauss–Newton algorithm (IC-GN 2 ) with the second-order shape function is firstly proposed. Because it contains the second-order displacement gradient terms, the measurement accuracy for the non-uniform deformation thus can be improved significantly, which is typically one order higher than the first-order shape function combined with the IC-GN algorithm (IC-GN 1 ), and 2 times faster than the second-order shape function combined with the forward additive Gauss–Newton algorithm (FA-GN 2 ). Then, based on the features of the IC-GN 1 and IC-GN 2 algorithms, a high-efficiency and high-accuracy measurement strategy for 3D-DIC is proposed in the end.