  A robust single-shot 3D shape reconstruction technique integrating the fringe
projection profilometry (FPP) technique with the deep convolutional neural
networks (CNNs) is proposed in this letter. The input of the proposed technique
is a single FPP image, and the training and validation data sets are prepared
by using the conventional multi-frequency FPP technique. Unlike the
conventional 3D shape reconstruction methods which involve complex algorithms
and intensive computation, the proposed approach uses an end-to-end network
architecture to directly carry out the transformation of a 2D images to its
corresponding 3D shape. Experiments have been conducted to demonstrate the
validity and robustness of the proposed technique. It is capable of satisfying
various 3D shape reconstruction demands in scientific research and engineering
applications.
