High-dynamic-range fringe projection profilometry using a double-exposure branch fusion network

While the manufacturing industry demands high-precision and efficient measurement of high-reflective workpieces, the low dynamic range of images depicting such workpieces leads to the loss of fringe information. Thus, achieving accurate measurement becomes challenging. Existing three-dimensional (3D) measurement methods have inherent limitations, such as being time-consuming, exhibiting poor precision, and poor dynamics. This paper proposes a double-exposure branch fusion network (DBF-Net) for high-dynamic-range (HDR) fringe projection profilometry. First, based on the pinhole imaging principle, the 3D measurement of fringe projections and the camera-projector inverse calibration model was explored to establish the object-image relationship accurately. Then, images of two exposure levels were randomly selected as the input for DBF-Net, and a sinusoidality-preserving algorithm was embedded into the network to obtain HDR fringe images. A weighted fusion loss function was also designed, which allows for dynamically adjusting the weighting of each term to improve the generalization of the method. By combining the aforementioned components with a phase-demodulation method, high-precision 3D reconstruction for high-reflectivity workpieces was achieved. The fringe projection profilometry system was established, and 3D accuracy verification experiments were conducted. And comparative experiments focusing on three aspects were conducted: image quality, phase demodulation accuracy, and 3D reconstruction of high-reflectivity workpieces. Based on the results, the proposed method achieves a measurement accuracy of 0.06 mm, effectively resolving the point-cloud loss issue encountered when measuring high-reflectivity surfaces.