A structured-light system with a binary defocusing technique has the potential to have more extensive application due to its high speeds, gamma-calibration-free nature, and lack of rigid synchronization requirements between the camera and projector. However, the existing calibration methods fail to achieve high accuracy for a structured-light system with an out-of-focus projector. This paper proposes a method that can accurately calibrate a structured-light system even when the projector is not in focus, making it possible for high-accuracy and high-speed measurement with the binary defocusing method. Experiments demonstrate that our calibration approach performs consistently under different defocusing degrees, and a root-mean-square error of about 73 μm can be achieved with a calibration volume of $150(\mathrm{H})\mathrm{mm}\times 250(\mathrm{W})\mathrm{mm}\times 200(\mathrm{D})\mathrm{mm}$.

High-accuracy and high-speed three-dimensional (3D) fringe projection profilometry (FPP) has been widely applied in many fields. Recently, researchers discovered that deep learning can significantly improve fringe analysis. However, deep learning requires numerous objects to be scanned for training data. In this paper, we propose to build the digital twin of an FPP system and perform virtual scanning using computer graphics, which can significantly save cost and labor. The proposed method extracts 3D geometry directly from a single-shot fringe image, and real-world experiments have demonstrated the success of the virtually trained model. Our virtual scanning method can automatically generate 7,200 fringe images and 800 corresponding 3D scenes within 1.5 hours.

This research presents a novel method to calibrate a microscopic structured light system using a camera with a telecentric lens. The pin-hole projector calibration follows the standard pin-hole camera calibration procedures. With the calibrated projector, the 3D coordinates of those feature points used for projector calibration are then estimated through iterative Levenberg-Marquardt optimization. Those 3D feature points are further used to calibrate the camera with a telecentric lens. We will describe the mathematical model of a telecentric lens, and demonstrate that the proposed calibration framework can achieve very high accuracy: approximately 10 μm with a volume of approximately 10(H) mm × 8(W) mm × 5(D) mm.