Deep Learning Based Single-Shot Profilometry by Three-Channel Binary-Defocused Projection

Abstract

Fringe projection profilometry (FPP), a widely used 3D reconstruction method, often encounters a dilemma between speed and accuracy for dynamic measurement. This paper proposes a deep-learning based single-shot 3D reconstruction method, which considers both speed and accuracy. We utilize the individual red, green, and blue channels of the projector to successively project three binary patterns in a defocused manner. At the same time, the camera exposures during this process and finally captures a single image. During image processing, we combine the task of the wrapped phase and absolute phase prediction, which enables an end-to-end high-precision estimation of the absolute phase from a single fringe pattern through a single network. Experiments on various scenes, encompassing both static and dynamic objects, substantiate our method’s high-quality 3D reconstruction capability from only a single shot, surpassing the performance of previous approaches.