Multi-camera systems have been widely adopted due to their high resistance to interference and balanced spatial precision. However, existing research lacks a systematic framework for evaluating critical configuration parameters, such as the number of cameras, their angular distribution, and baseline settings. To address this gap, this Letter proposes a measurement precision model based on error analysis theory, enabling efficient estimation of system performance to guide both hardware selection and spatial arrangement. Experimental results validate that the proposed model provides a fast, reliable, and practical reference for the design and optimization of multi-camera configurations.

Camera calibration is crucial to the application of binocular measurement. However, many of the calibrations are carried out manually, so shaking of the calibration plate cannot be avoided. When the cameras are not synchronized or the exposure time is inconsistent, the image acquisition time of different cameras does not correspond strictly, which will introduce inevitable calibration errors. In this paper, two consecutive frames are collected instead of one frame, and the calibration data are corrected by the image acquisition time value returned by the camera. Experiments show that the proposed method can greatly reduce the re-projection error of calibration, and achieve higher calibration accuracy and less image quantity dependence.

Digital image correlation (DIC) has been widely used in both experimental mechanics and engineering fields. The matching algorithm of the DIC method usually requires surfaces containing a random speckle pattern as a deformation information carrier. The speckle pattern plays an irreplaceable role in DIC, which has led to extensive research on it. However, most previous research had always focused on the fabrication and computational performance of the speckle, ignoring the value of intentionally defining the meaning of speckle in design. In this study, we describe a novel, to the best of our knowledge, speckle pattern named semantic speckle. It is a digital speckle composed of several different speckle patterns with similar characteristics. Based on the deep-learning method and matching algorithm, the central location of the semantic part in the overall speckle image can be obtained automatically. Through the intentional definition of the semantic part, it can be possible to calibrate the camera parameters and correct the external parameters of the DIC systems.

The spatial resolution and measurement accuracy of the digital image correlation (DIC) method are constrained by camera resolution. This limitation is primarily determined by hardware costs. However, in current stereo DIC measurements, only the gray level or its gradient from two images is used for integer-pixel matching and sub-pixel optimization. It implicitly treats the two images from different viewpoints as independent entities before correlating them. However, the inherent structural information has not been fully utilized. This previously overlooked structural information provides a novel approach to enhancing the accuracy of DIC by leveraging the inherent correlations between stereo image pairs. The realization of binocular super-resolution typically requires a relatively small parallax. Moreover, the DIC method can achieve image window pairing with small parallax through pre-matching. This implies that binocular super-resolution and Stereo-DIC can complement each other by sharing information. In this paper, the DIC method is employed for whole-pixel image matching, while the binocular super-resolution method, based on deep learning, is applied to process the matched image pairs. Building on previous experiments, extensive datasets containing diverse experimental scenes and various speckle patterns were compiled and utilized. Furthermore, the DIC method can establish training datasets with minimal parallax through integer-pixel matching, thereby achieving highly effective super-resolution results. Experimental results demonstrate that super-resolution images with a higher signal-to-noise ratio can be obtained. Additionally, it effectively provides more image details, which enhance the calculation accuracy and resolution of DIC.

In fringe projection profilometry, high-order harmonics information of distorted fringe will lead to errors in the phase estimation. In order to solve this problem, a point-wise phase estimation method based on a neural network (PWPE-NN) is proposed in this paper. The complex nonlinear mapping relationship between the gray values and the phase under non-sinusoidal distortion is constructed by using the simple neural network model. It establishes a novel implicit expression for phase solution without complicated measurement operations. Compared with the previous method of combining local image information, it can accurately calculate each phase value by point. The comparison results show that the traditional method is with periodic phase errors, while the proposed method can effectively eliminate phase errors caused by non-sinusoidal phase shifting.