As the digital projector develops, fringe projection profilometry has been widely used in the fast 3D measurement. However, the field of view of traditional 3D measurement systems is commonly in decimeters, which limits the 3D reconstruction accuracy to tens of microns. If we want to improve the accuracy further, we have to minimize the field of view and meanwhile increase the fringe density in space. For this purpose, we developed two kinds of systems based on a stereo-microscope and telecentric lenses, respectively. We also studied the corresponding calibration frameworks and developed fast 3D measurement methods with both Fourier transform and phase- shifting algorithms for real-time 3D reconstruction of micro-scale objects.
In fringe projection profilometry, using denser fringes can improve the measurement accuracy. In real-time measurement situations, the number of the fringe pattern is limited to reduce motion-induced errors, which, however, poses more difficulties for the absolute phase recovery from dense fringes. In this paper, we propose a stereo phase matching method that takes advantage of the high-accuracy of denser fringes and the high-efficiency of using only two different frequencies of fringes. The phase map is divided into several sub-areas and in each sub-area, the phase is unwrapped independently. The correct matched pixel is easily selected from the distributed candidates in different sub-area with the help of geometry constraints.
Three-dimensional (3D) registration or matching is a crucial step in 3D model reconstruction. In this work, we develop a real-time 3D point cloud registration technology. Firstly, in order to achieve real-time 3D data acquisition, the stereo phase unwrapping method is utilized to eliminate the ambiguity of the wrapped phase, assisted with the depth constraint strategy without projecting any additional patterns or embedding any auxiliary signals. Then we implement SLAM-based coarse registration and ICP-based fine registration to match the point cloud data after the rapid identification of two-dimensional (2D) feature points. In order to improve the efficiency of 3D registration, the relative motion of the measured object at each coarse registration is quantified, through which only one fine registration is performed after several coarse registrations. The experiment shows that, the complex model can be registered in real time to reconstruct its whole 3D model with our method.