Three-dimensional shape measurement plays an important role in various fields. As a way of three-dimensional measurement, fringe projection profilometry (FPP) is widely used because of its non-contact, simple structure, and high stability. One of the key challenges affecting measurement accuracy is the gamma effect. With the development of FPP technology, multi-color channels are gradually applied to the measurement, and the response of different colors in the projector-camera system (pro-cam system) is not exactly the same. Therefore, more accurate gamma correction for different color channels is required. To solve this problem, a model of joint gamma correction for different color channels is proposed. In this model, the light is subdivided into three channels: red; green; and blue (RGB). In the pro-cam system, the different responses of different colors and the influence of background light intensity on gamma correction are comprehensively considered, and some error compensation is made for color crosstalk. Compared with the traditional gamma correction methods, the gamma correction method proposed in this paper is more accurate and has a larger effective working range after correction. This method is particularly beneficial in scenarios where multiple color channels are used for measurement, as it more accurately reflects the true measurement results for each channel. The effectiveness and accuracy of the method are validated through experiments.

Three-dimensional (3D) reconstruction of high-dynamic-range (HDR) surfaces plays an important role in the fields of computer vision and image processing. Traditional 3D measurement methods often face the risk of information loss when dealing with surfaces that have HDR characteristics. To address this issue, this paper proposes a simple 3D reconstruction method, which combines the features of non-overexposed regions in polarized and unpolarized images to improve the reconstruction quality of HDR surface objects. The optimum fringe regions are extracted from images with different polarization angles, and the non-overexposed regions in normally captured unpolarized images typically contain complete fringe information and are less affected by specular highlights. The optimal fringe information from different polarized image groups is gradually used to replace the incorrect fringe information in the unpolarized image, resulting in a complete set of fringe data. Experimental results show that the proposed method requires only 24~36 images and simple phase fusion to achieve successful 3D reconstruction. It can effectively mitigate the negative impact of overexposed regions on absolute phase calculation and 3D reconstruction when reconstructing objects with strongly reflective surfaces.

Fringe projection profilometry has been widely adopted in various fields due to its non-contact nature, high accuracy, high speed, and full-field measurement capability. However, when measuring objects with highly reflective surfaces, saturation often occurs due to the limited dynamic range of the camera. To effectively address this issue, this paper proposes a novel adaptive fringe projection method. First, an intensity transfer model is established, which uses uniform grayscale images to compute surface reflectance coefficients and accurately determines the optimal projection intensity in the camera coordinate system. Subsequently, low-intensity orthogonal fringe patterns are employed to compute a smoothed absolute phase in saturated regions, establishing a coordinate mapping. The mapped pixel intensities are diffused into their neighborhoods, and the minimum value is taken in overlapping areas to generate an optimal projection intensity template in the projector coordinate system. Finally, adaptive fringe patterns are generated based on this template. Experimental results demonstrate that the proposed method achieves high-precision and high-completeness 3D measurement for objects with highly reflective surfaces.