Single-frame structured light depth map reconstruction with absolute phase-aided supervision

In recent years, end-to-end depth map prediction from single-shot fringe modulation images in structured light 3D measurement(F2D) has drawn widespread attention, which has significantly reduced measurement times and eliminated the complex intermediate steps in the traditional method. However, F2D is a long-distance ill-conditioned prediction problem, and it is difficult for existing regression networks to achieve high-precision pixel-by-pixel prediction over long distances in space and time. For the challenge, we propose APS-UNet(Absolute Phase aided Supervision UNet), an endto- end depth maps prediction network supervised by an absolute phase branch. With the core physical process, absolute phase branch as auxiliary supervision, can decompose the one challenging long-distance prediction into two easier shortdistance prediction tasks. Moreover, in the training process, the two branches provide feedback to each other, enhancing the accuracy and robustness of depth prediction. Compared to Res-UNet, APS-UNet demonstrates a 32% decrease in mean absolute error (MAE) based on the real dataset, highlighting the effectiveness of this network.