An Identify-Then-Eliminate-Based Pixel Correspondence Establishment Method for Fringe Projection Profilometry Against Interreflection

Widely applied fringe projection profilometry (FPP) faces significant challenges when measuring objects with strong interreflections. The existence of interreflections results in the overlap of the same-frequency fringes, which severely affects the establishment of the pixel correspondence. Existing methods typically quantify interreflection errors pixel by pixel, which is unnecessary for pixels free from interreflections. In this article, an identify-then-eliminate-based method to establish pixel correspondence is proposed against surface interreflections. Different from current methods, it first identifies severely affected pixels and then eliminates interreflection errors in a targeted manner. Specifically, in the pixel identification stage, the dual-frequency phase-shifting (DFPS) method is utilized, and an error model is constructed to accurately identify pixels with notable interreflection errors. Then, in the error elimination stage, reverse-sequence frequency-phase joint encoding (RFE) patterns are designed to facilitate the separation of interreflections. A single-pixel encoding neural network is proposed to obtain accurate pixel correspondence with captured two-stage data. Experiments on industrial parts with strong surface interreflections illustrate that compared to existing methods, our method obtains high-precision, continuous, and realistic point cloud data. Additionally, our method also maintains the efficiency of measuring objects with strong interreflections.