Three-dimensional (3D) shape measurement system with binary defocusing technique can perform high-speed and flexible measurements if binary fringe patterns are defocused by projector properly. However, the actual defocusing degree is difficult to set, and the fringe period is difficult to determine accordingly. In this study, we present a square-binary defocusing parameter selection framework. First, we analyze the fringe formation process mathematically. The defocusing degree is quantified and manipulated by using the focusing distance of projector, which is calibrated by point spread function measurement. To optimize parameter selection, single-point sinusoidal error is modeled as the objective function for the evaluation of the defocusing effect. We verify the correctness by using different parameter combinations and object measurements in our experiments. The appropriate defocusing parameters can be easily obtained according to the analysis of practical system setup, which improves the quality and robustness of the system.

We present a compressive parallel single-pixel imaging (cPSI) method, which applies compressive sensing in the context of PSI, to achieve highly efficient light transport coefficients capture and 3D reconstruction in the presence of strong interreflections. A characteristic-based sampling strategy is introduced that has sampling frequencies with high energy and high probability. The characteristic-based sampling strategy is compared with various state-of-the-art sampling strategies, including the square, circular, uniform random, and distance-based sampling strategies. Experimental results demonstrate that the characteristic-based sampling strategy exhibits the best performance, and cPSI can obtain highly accurate 3D shape data in the presence of strong interreflections with high efficiency.

Three-dimensional (3D) shape measurement with fringe projection technique and vertical scanning setup can alleviate the problem of shadow and occlusion. However, the shape-from-defocus based method suffers from limited sensitivity and low signal-to-noise ratio (SNR), whereas the projection-triangulation based is sensitive to the zero-phase detection. In this paper, we propose paraxial 3D shape measurement using parallel single-pixel imaging (PSI). The depth is encoded in the radial distance to the projector optical center, which is determined by the projection of light transport coefficients (LTCs). The third-order polynomial fitting is used for depth mapping and calibration. Experiments on 5 objects with different materials and textures are conducted, and standards are measured to test the accuracy. The results verified that the proposed method can achieve robust, dense reconstruction with depth accuracy at 20 μm while the root-mean-square error (RMSE) of plane fitting up to 43 μm.

Riveted workpieces are widely used in manufacturing; however, current inspection sensors are mainly limited in nondestructive testing and obtaining the high-accuracy dimension automatically is difficult. We developed a 3-D sensor for rivet inspection using fringe projection profilometry (FPP) with texture constraint. We used multi-intensity high dynamic range (HDR) FPP method to address the varying reflectance of the metal surface then utilized an additional constraint calculated from the fused HDR texture to compensate for the artifacts caused by phase mixture around the stepwise edge. By combining the 2-D contours and 3-D FPP data, rivets can be easily segmented, and the edge points can be further refined for diameter measurement. We tested the performance on a sample of riveted aluminum frame and evaluated the accuracy using standard objects. Experiments show that denser 3-D data of a riveted metal workpiece can be acquired with high accuracy. Compared with the traditional FPP method, the diameter measurement accuracy can be improved by 50%.

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