Single-shot 3D shape reconstruction using deep convolutional neural networks
This addresses the need for efficient 3D reconstruction in scientific and engineering applications by reducing complexity compared to conventional methods.
The paper tackles 3D shape reconstruction from a single fringe projection profilometry image by integrating it with deep convolutional neural networks, achieving an end-to-end transformation from 2D to 3D shapes with demonstrated validity and robustness.
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.